diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
index b290e09..e2a410d 100644
--- a/.devcontainer/devcontainer.json
+++ b/.devcontainer/devcontainer.json
@@ -1,4 +1,5 @@
 {
+    "$schema": "https://raw.githubusercontent.com/devcontainers/spec/main/schemas/devContainer.schema.json",
     "name": "nfcore",
     "image": "nfcore/gitpod:latest",
     "remoteUser": "gitpod",
diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md
deleted file mode 100644
index 3a5f6ae..0000000
--- a/.github/CONTRIBUTING.md
+++ /dev/null
@@ -1,125 +0,0 @@
-# `nf-core/dmscore`: Contributing Guidelines
-
-Hi there!
-Many thanks for taking an interest in improving nf-core/dmscore.
-
-We try to manage the required tasks for nf-core/dmscore using GitHub issues, you probably came to this page when creating one.
-Please use the pre-filled template to save time.
-
-However, don't be put off by this template - other more general issues and suggestions are welcome!
-Contributions to the code are even more welcome ;)
-
-> [!NOTE]
-> If you need help using or modifying nf-core/dmscore then the best place to ask is on the nf-core Slack [#dmscore](https://nfcore.slack.com/channels/dmscore) channel ([join our Slack here](https://nf-co.re/join/slack)).
-
-## Contribution workflow
-
-If you'd like to write some code for nf-core/dmscore, the standard workflow is as follows:
-
-1. Check that there isn't already an issue about your idea in the [nf-core/dmscore issues](https://github.com/nf-core/dmscore/issues) to avoid duplicating work. If there isn't one already, please create one so that others know you're working on this
-2. [Fork](https://help.github.com/en/github/getting-started-with-github/fork-a-repo) the [nf-core/dmscore repository](https://github.com/nf-core/dmscore) to your GitHub account
-3. Make the necessary changes / additions within your forked repository following [Pipeline conventions](#pipeline-contribution-conventions)
-4. Use `nf-core pipelines schema build` and add any new parameters to the pipeline JSON schema (requires [nf-core tools](https://github.com/nf-core/tools) >= 1.10).
-5. Submit a Pull Request against the `dev` branch and wait for the code to be reviewed and merged
-
-If you're not used to this workflow with git, you can start with some [docs from GitHub](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests) or even their [excellent `git` resources](https://try.github.io/).
-
-## Tests
-
-You have the option to test your changes locally by running the pipeline. For receiving warnings about process selectors and other `debug` information, it is recommended to use the debug profile. Execute all the tests with the following command:
-
-```bash
-nf-test test --profile debug,test,docker --verbose
-```
-
-When you create a pull request with changes, [GitHub Actions](https://github.com/features/actions) will run automatic tests.
-Typically, pull-requests are only fully reviewed when these tests are passing, though of course we can help out before then.
-
-There are typically two types of tests that run:
-
-### Lint tests
-
-`nf-core` has a [set of guidelines](https://nf-co.re/developers/guidelines) which all pipelines must adhere to.
-To enforce these and ensure that all pipelines stay in sync, we have developed a helper tool which runs checks on the pipeline code. This is in the [nf-core/tools repository](https://github.com/nf-core/tools) and once installed can be run locally with the `nf-core pipelines lint <pipeline-directory>` command.
-
-If any failures or warnings are encountered, please follow the listed URL for more documentation.
-
-### Pipeline tests
-
-Each `nf-core` pipeline should be set up with a minimal set of test-data.
-`GitHub Actions` then runs the pipeline on this data to ensure that it exits successfully.
-If there are any failures then the automated tests fail.
-These tests are run both with the latest available version of `Nextflow` and also the minimum required version that is stated in the pipeline code.
-
-## Patch
-
-:warning: Only in the unlikely and regretful event of a release happening with a bug.
-
-- On your own fork, make a new branch `patch` based on `upstream/main` or `upstream/master`.
-- Fix the bug, and bump version (X.Y.Z+1).
-- Open a pull-request from `patch` to `main`/`master` with the changes.
-
-## Getting help
-
-For further information/help, please consult the [nf-core/dmscore documentation](https://nf-co.re/dmscore/usage) and don't hesitate to get in touch on the nf-core Slack [#dmscore](https://nfcore.slack.com/channels/dmscore) channel ([join our Slack here](https://nf-co.re/join/slack)).
-
-## Pipeline contribution conventions
-
-To make the `nf-core/dmscore` code and processing logic more understandable for new contributors and to ensure quality, we semi-standardise the way the code and other contributions are written.
-
-### Adding a new step
-
-If you wish to contribute a new step, please use the following coding standards:
-
-1. Define the corresponding input channel into your new process from the expected previous process channel.
-2. Write the process block (see below).
-3. Define the output channel if needed (see below).
-4. Add any new parameters to `nextflow.config` with a default (see below).
-5. Add any new parameters to `nextflow_schema.json` with help text (via the `nf-core pipelines schema build` tool).
-6. Add sanity checks and validation for all relevant parameters.
-7. Perform local tests to validate that the new code works as expected.
-8. If applicable, add a new test command in `.github/workflow/ci.yml`.
-9. Update MultiQC config `assets/multiqc_config.yml` so relevant suffixes, file name clean up and module plots are in the appropriate order. If applicable, add a [MultiQC](https://https://multiqc.info/) module.
-10. Add a description of the output files and if relevant any appropriate images from the MultiQC report to `docs/output.md`.
-
-### Default values
-
-Parameters should be initialised / defined with default values within the `params` scope in `nextflow.config`.
-
-Once there, use `nf-core pipelines schema build` to add to `nextflow_schema.json`.
-
-### Default processes resource requirements
-
-Sensible defaults for process resource requirements (CPUs / memory / time) for a process should be defined in `conf/base.config`. These should generally be specified generic with `withLabel:` selectors so they can be shared across multiple processes/steps of the pipeline. A nf-core standard set of labels that should be followed where possible can be seen in the [nf-core pipeline template](https://github.com/nf-core/tools/blob/main/nf_core/pipeline-template/conf/base.config), which has the default process as a single core-process, and then different levels of multi-core configurations for increasingly large memory requirements defined with standardised labels.
-
-The process resources can be passed on to the tool dynamically within the process with the `${task.cpus}` and `${task.memory}` variables in the `script:` block.
-
-### Naming schemes
-
-Please use the following naming schemes, to make it easy to understand what is going where.
-
-- initial process channel: `ch_output_from_<process>`
-- intermediate and terminal channels: `ch_<previousprocess>_for_<nextprocess>`
-
-### Nextflow version bumping
-
-If you are using a new feature from core Nextflow, you may bump the minimum required version of nextflow in the pipeline with: `nf-core pipelines bump-version --nextflow . [min-nf-version]`
-
-### Images and figures
-
-For overview images and other documents we follow the nf-core [style guidelines and examples](https://nf-co.re/developers/design_guidelines).
-
-## GitHub Codespaces
-
-This repo includes a devcontainer configuration which will create a GitHub Codespaces for Nextflow development! This is an online developer environment that runs in your browser, complete with VSCode and a terminal.
-
-To get started:
-
-- Open the repo in [Codespaces](https://github.com/nf-core/dmscore/codespaces)
-- Tools installed
-  - nf-core
-  - Nextflow
-
-Devcontainer specs:
-
-- [DevContainer config](.devcontainer/devcontainer.json)
diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
index 58a5523..78f6876 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -8,7 +8,7 @@ body:
         Before you post this issue, please check the documentation:
 
         - [nf-core website: troubleshooting](https://nf-co.re/usage/troubleshooting)
-        - [nf-core/dmscore pipeline documentation](https://nf-co.re/dmscore/usage)
+        - [nf-core/deepmutscan pipeline documentation](https://nf-co.re/deepmutscan/usage)
   - type: textarea
     id: description
     attributes:
@@ -46,4 +46,4 @@ body:
         * Executor _(eg. slurm, local, awsbatch)_
         * Container engine: _(e.g. Docker, Singularity, Conda, Podman, Shifter, Charliecloud, or Apptainer)_
         * OS _(eg. CentOS Linux, macOS, Linux Mint)_
-        * Version of nf-core/dmscore _(eg. 1.1, 1.5, 1.8.2)_
+        * Version of nf-core/deepmutscan _(eg. 1.1, 1.5, 1.8.2)_
diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml
index 08f0c49..572e0cf 100644
--- a/.github/ISSUE_TEMPLATE/config.yml
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -2,6 +2,6 @@ contact_links:
   - name: Join nf-core
     url: https://nf-co.re/join
     about: Please join the nf-core community here
-  - name: "Slack #dmscore channel"
-    url: https://nfcore.slack.com/channels/dmscore
-    about: Discussion about the nf-core/dmscore pipeline
+  - name: "Slack #deepmutscan channel"
+    url: https://nfcore.slack.com/channels/deepmutscan
+    about: Discussion about the nf-core/deepmutscan pipeline
diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml
index a08dd5f..0731019 100644
--- a/.github/ISSUE_TEMPLATE/feature_request.yml
+++ b/.github/ISSUE_TEMPLATE/feature_request.yml
@@ -1,5 +1,5 @@
 name: Feature request
-description: Suggest an idea for the nf-core/dmscore pipeline
+description: Suggest an idea for the nf-core/deepmutscan pipeline
 labels: enhancement
 body:
   - type: textarea
diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
index 0d6f133..1e78c19 100644
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -1,22 +1,22 @@
 <!--
-# nf-core/dmscore pull request
+# nf-core/deepmutscan pull request
 
-Many thanks for contributing to nf-core/dmscore!
+Many thanks for contributing to nf-core/deepmutscan!
 
 Please fill in the appropriate checklist below (delete whatever is not relevant).
 These are the most common things requested on pull requests (PRs).
 
 Remember that PRs should be made against the dev branch, unless you're preparing a pipeline release.
 
-Learn more about contributing: [CONTRIBUTING.md](https://github.com/nf-core/dmscore/tree/master/.github/CONTRIBUTING.md)
+Learn more about contributing: [CONTRIBUTING.md](https://github.com/nf-core/deepmutscan/tree/master/docs/CONTRIBUTING.md)
 -->
 
 ## PR checklist
 
 - [ ] This comment contains a description of changes (with reason).
 - [ ] If you've fixed a bug or added code that should be tested, add tests!
-- [ ] If you've added a new tool - have you followed the pipeline conventions in the [contribution docs](https://github.com/nf-core/dmscore/tree/master/.github/CONTRIBUTING.md)
-- [ ] If necessary, also make a PR on the nf-core/dmscore _branch_ on the [nf-core/test-datasets](https://github.com/nf-core/test-datasets) repository.
+- [ ] If you've added a new tool - have you followed the pipeline conventions in the [contribution docs](https://github.com/nf-core/deepmutscan/tree/master/docs/CONTRIBUTING.md)
+- [ ] If necessary, also make a PR on the nf-core/deepmutscan _branch_ on the [nf-core/test-datasets](https://github.com/nf-core/test-datasets) repository.
 - [ ] Make sure your code lints (`nf-core pipelines lint`).
 - [ ] Ensure the test suite passes (`nextflow run . -profile test,docker --outdir <OUTDIR>`).
 - [ ] Check for unexpected warnings in debug mode (`nextflow run . -profile debug,test,docker --outdir <OUTDIR>`).
diff --git a/.github/actions/get-shards/action.yml b/.github/actions/get-shards/action.yml
new file mode 100644
index 0000000..e2833ee
--- /dev/null
+++ b/.github/actions/get-shards/action.yml
@@ -0,0 +1,69 @@
+name: "Get number of shards"
+description: "Get the number of nf-test shards for the current CI job"
+inputs:
+  max_shards:
+    description: "Maximum number of shards allowed"
+    required: true
+  paths:
+    description: "Component paths to test"
+    required: false
+  tags:
+    description: "Tags to pass as argument for nf-test --tag parameter"
+    required: false
+outputs:
+  shard:
+    description: "Array of shard numbers"
+    value: ${{ steps.shards.outputs.shard }}
+  total_shards:
+    description: "Total number of shards"
+    value: ${{ steps.shards.outputs.total_shards }}
+runs:
+  using: "composite"
+  steps:
+    - name: Install nf-test
+      uses: nf-core/setup-nf-test@4069fbbaabe94c08faba4ad261bfa88225ba133f # v2
+      with:
+        version: ${{ env.NFT_VER }}
+    - name: Get number of shards
+      id: shards
+      shell: bash
+      run: |
+        # Run nf-test with dynamic parameter
+        nftest_output=$(nf-test test \
+          --profile +docker \
+          $(if [ -n "${{ inputs.tags }}" ]; then echo "--tag ${{ inputs.tags }}"; fi) \
+          --dry-run \
+          --ci \
+          --changed-since HEAD^) || {
+            echo "nf-test command failed with exit code $?"
+            echo "Full output: $nftest_output"
+            exit 1
+        }
+        echo "nf-test dry-run output: $nftest_output"
+
+        # Default values for shard and total_shards
+        shard="[]"
+        total_shards=0
+
+        # Check if there are related tests
+        if echo "$nftest_output" | grep -q 'No tests to execute'; then
+          echo "No related tests found."
+        else
+          # Extract the number of related tests
+          number_of_shards=$(echo "$nftest_output" | sed -n 's|.*Executed \([0-9]*\) tests.*|\1|p')
+          if [[ -n "$number_of_shards" && "$number_of_shards" -gt 0 ]]; then
+            shards_to_run=$(( $number_of_shards < ${{ inputs.max_shards }} ? $number_of_shards : ${{ inputs.max_shards }} ))
+            shard=$(seq 1 "$shards_to_run" | jq -R . | jq -c -s .)
+            total_shards="$shards_to_run"
+          else
+            echo "Unexpected output format. Falling back to default values."
+          fi
+        fi
+
+        # Write to GitHub Actions outputs
+        echo "shard=$shard" >> $GITHUB_OUTPUT
+        echo "total_shards=$total_shards" >> $GITHUB_OUTPUT
+
+        # Debugging output
+        echo "Final shard array: $shard"
+        echo "Total number of shards: $total_shards"
diff --git a/.github/actions/nf-test/action.yml b/.github/actions/nf-test/action.yml
new file mode 100644
index 0000000..7940f81
--- /dev/null
+++ b/.github/actions/nf-test/action.yml
@@ -0,0 +1,121 @@
+name: "nf-test Action"
+description: "Runs nf-test with common setup steps"
+inputs:
+  profile:
+    description: "Profile to use"
+    required: true
+  shard:
+    description: "Shard number for this CI job"
+    required: true
+  total_shards:
+    description: "Total number of test shards(NOT the total number of matrix jobs)"
+    required: true
+  paths:
+    description: "Test paths"
+    required: true
+  tags:
+    description: "Tags to pass as argument for nf-test --tag parameter"
+    required: false
+runs:
+  using: "composite"
+  steps:
+    # The pipeline pulls ~17 container images, three of them heavy bioconductor/R stacks, which
+    # overruns the runner disk part-way through (docker exit 125, "no space left on device").
+    # Reclaim the preinstalled toolchain first: the RunsOn default image is ubuntu24-full-x64, so the
+    # same Android/.NET/Haskell bloat as a GitHub-hosted runner is present and removable.
+    - name: Free disk space
+      uses: jlumbroso/free-disk-space@54081f138730dfa15788a46383842cd2f914a1be # v1.3.1
+      with:
+        tool-cache: false # later steps need the toolchain
+        large-packages: false # slowest sweep, smallest reliable gain
+
+    - name: Setup Nextflow
+      uses: nf-core/setup-nextflow@893c28b667aedeba26e37f296d260ccc5bc4d914 # v3
+      with:
+        version: "${{ env.NXF_VERSION }}"
+
+    - name: Set up Python
+      uses: actions/setup-python@6d4fd22b20465ccc0f7354476588e8df7c9ca0e0 # v7
+      with:
+        python-version: "3.15"
+
+    - name: Install nf-test
+      uses: nf-core/setup-nf-test@4069fbbaabe94c08faba4ad261bfa88225ba133f # v2
+      with:
+        version: "${{ env.NFT_VER }}"
+        install-pdiff: true
+
+    - name: Setup apptainer
+      if: contains(inputs.profile, 'singularity')
+      uses: eWaterCycle/setup-apptainer@95b5c781df5f72fd2271cc3cff06904a09d2a087 # v3
+
+    - name: Set up Singularity
+      if: contains(inputs.profile, 'singularity')
+      shell: bash
+      run: |
+        mkdir -p $NXF_SINGULARITY_CACHEDIR
+        mkdir -p $NXF_SINGULARITY_LIBRARYDIR
+
+    - name: Conda setup
+      if: contains(inputs.profile, 'conda')
+      uses: conda-incubator/setup-miniconda@be893c923ea9cf1cf7cd510fbdde27c7e18cbdcb # v4.1.0
+      with:
+        auto-update-conda: true
+        conda-solver: libmamba
+        channels: conda-forge
+        channel-priority: strict
+        conda-remove-defaults: true
+
+    - name: Run nf-test
+      shell: bash
+      env:
+        NFT_WORKDIR: ${{ env.NFT_WORKDIR }}
+      run: |
+        nf-test test \
+          --profile=+${{ inputs.profile }} \
+          $(if [ -n "${{ inputs.tags }}" ]; then echo "--tag ${{ inputs.tags }}"; fi) \
+          --ci \
+          --changed-since HEAD^ \
+          --verbose \
+          --tap=test.tap \
+          --shard ${{ inputs.shard }}/${{ inputs.total_shards }}
+
+          # Save the absolute path of the test.tap file to the output
+          echo "tap_file_path=$(realpath test.tap)" >> $GITHUB_OUTPUT
+
+    - name: Generate test summary
+      if: always()
+      shell: bash
+      run: |
+        # Add header if it doesn't exist (using a token file to track this)
+        if [ ! -f ".summary_header" ]; then
+          echo "# 🚀 nf-test results" >> $GITHUB_STEP_SUMMARY
+          echo "" >> $GITHUB_STEP_SUMMARY
+          echo "| Status | Test Name | Profile | Shard |" >> $GITHUB_STEP_SUMMARY
+          echo "|:------:|-----------|---------|-------|" >> $GITHUB_STEP_SUMMARY
+          touch .summary_header
+        fi
+
+        if [ -f test.tap ]; then
+          while IFS= read -r line; do
+            if [[ $line =~ ^ok ]]; then
+              test_name="${line#ok }"
+              # Remove the test number from the beginning
+              test_name="${test_name#* }"
+              echo "| ✅ | ${test_name} | ${{ inputs.profile }} | ${{ inputs.shard }}/${{ inputs.total_shards }} |" >> $GITHUB_STEP_SUMMARY
+            elif [[ $line =~ ^not\ ok ]]; then
+              test_name="${line#not ok }"
+              # Remove the test number from the beginning
+              test_name="${test_name#* }"
+              echo "| ❌ | ${test_name} | ${{ inputs.profile }} | ${{ inputs.shard }}/${{ inputs.total_shards }} |" >> $GITHUB_STEP_SUMMARY
+            fi
+          done < test.tap
+        else
+          echo "| ⚠️ | No test results found | ${{ inputs.profile }} | ${{ inputs.shard }}/${{ inputs.total_shards }} |" >> $GITHUB_STEP_SUMMARY
+        fi
+
+    - name: Clean up
+      if: always()
+      shell: bash
+      run: |
+        sudo rm -rf /home/ubuntu/tests/
diff --git a/.github/workflows/awsfulltest.yml b/.github/workflows/awsfulltest.yml
index 50287a8..32705db 100644
--- a/.github/workflows/awsfulltest.yml
+++ b/.github/workflows/awsfulltest.yml
@@ -4,66 +4,76 @@ name: nf-core AWS full size tests
 # It runs the -profile 'test_full' on AWS batch
 
 on:
-  pull_request:
-    branches:
-      - main
-      - master
   workflow_dispatch:
   pull_request_review:
     types: [submitted]
+  release:
+    types: [published]
 
 jobs:
   run-platform:
     name: Run AWS full tests
-    # run only if the PR is approved by at least 2 reviewers and against the master branch or manually triggered
-    if: github.repository == 'nf-core/dmscore' && github.event.review.state == 'approved' && github.event.pull_request.base.ref == 'master' || github.event_name == 'workflow_dispatch'
+    # run only if the PR is approved by at least 2 reviewers and against the master/main branch or manually triggered
+    if: github.repository == 'nf-core/deepmutscan' && github.event.review.state == 'approved' && (github.event.pull_request.base.ref == 'master' || github.event.pull_request.base.ref == 'main') || github.event_name == 'workflow_dispatch' || github.event_name == 'release'
     runs-on: ubuntu-latest
     steps:
-      - name: Get PR reviews
-        uses: octokit/request-action@v2.x
-        if: github.event_name != 'workflow_dispatch'
-        id: check_approvals
-        continue-on-error: true
-        with:
-          route: GET /repos/${{ github.repository }}/pulls/${{ github.event.pull_request.number }}/reviews?per_page=100
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Check for approvals
-        if: ${{ failure() && github.event_name != 'workflow_dispatch' }}
-        run: |
-          echo "No review approvals found. At least 2 approvals are required to run this action automatically."
-          exit 1
-
-      - name: Check for enough approvals (>=2)
-        id: test_variables
-        if: github.event_name != 'workflow_dispatch'
+      - name: Set revision variable
+        id: revision
         run: |
-          JSON_RESPONSE='${{ steps.check_approvals.outputs.data }}'
-          CURRENT_APPROVALS_COUNT=$(echo $JSON_RESPONSE | jq -c '[.[] | select(.state | contains("APPROVED")) ] | length')
-          test $CURRENT_APPROVALS_COUNT -ge 2 || exit 1 # At least 2 approvals are required
+          echo "revision=${{ (github.event_name == 'workflow_dispatch' || github.event_name == 'release') && github.sha || 'dev' }}" >> "$GITHUB_OUTPUT"
 
       - name: Launch workflow via Seqera Platform
-        uses: seqeralabs/action-tower-launch@v2
-        # TODO nf-core: You can customise AWS full pipeline tests as required
-        # Add full size test data (but still relatively small datasets for few samples)
-        # on the `test_full.config` test runs with only one set of parameters
+        id: launch
+        uses: seqeralabs/action-seqera-launch@f7b1a39afc8bb65f58b84150cd214b33fe416fef
         with:
-          workspace_id: ${{ secrets.TOWER_WORKSPACE_ID }}
-          access_token: ${{ secrets.TOWER_ACCESS_TOKEN }}
-          compute_env: ${{ secrets.TOWER_COMPUTE_ENV }}
-          revision: ${{ github.sha }}
-          workdir: s3://${{ secrets.AWS_S3_BUCKET }}/work/dmscore/work-${{ github.sha }}
+          workspace-id: ${{ vars.TOWER_WORKSPACE_ID }}
+          access-token: ${{ secrets.TOWER_ACCESS_TOKEN }}
+          compute-env: ${{ vars.TOWER_COMPUTE_ENV }}
+          nextflow-version: 26.10.5
+          syntax-parser: v2
+          wait: completion
+          revision: ${{ steps.revision.outputs.revision }}
+          work-dir: s3://${{ vars.AWS_S3_BUCKET }}/work/deepmutscan/work-${{ steps.revision.outputs.revision }}
+          nextflow-config: |
+            plugins {
+              id 'nf-slack@0.5.0'
+              id 'nf-schema@2.7.2'
+            }
+            slack {
+              enabled = true
+              bot {
+                token = '${{ secrets.NFSLACK_BOT_TOKEN }}'
+                channel = 'deepmutscan'
+              }
+              onStart {
+                enabled = false
+              }
+              onComplete {
+                message = ':white_check_mark: *deepmutscan/test_full* completed successfully! :tada:'
+              }
+              onError {
+                message = ':x: *deepmutscan/test_full* failed :crying_cat_face:'
+              }
+            }
           parameters: |
             {
-              "hook_url": "${{ secrets.MEGATESTS_ALERTS_SLACK_HOOK_URL }}",
-              "outdir": "s3://${{ secrets.AWS_S3_BUCKET }}/dmscore/results-${{ github.sha }}"
+              "outdir": "s3://${{ vars.AWS_S3_BUCKET }}/deepmutscan/results-${{ steps.revision.outputs.revision }}",
+              "input": "s3://${{ vars.AWS_S3_BUCKET }}/test-data/deepmutscan/PRJEB110196/GID1A_full.csv"
             }
           profiles: test_full
 
-      - uses: actions/upload-artifact@v4
+      - name: Save Seqera Platform launch result
+        if: always() && steps.launch.outputs.workflow-id != ''
+        env:
+          SEQERA_WORKFLOW_ID: ${{ steps.launch.outputs.workflow-id }}
+          SEQERA_WORKFLOW_URL: ${{ steps.launch.outputs.workflow-url }}
+          SEQERA_WORKFLOW_STATUS: ${{ steps.launch.outputs.status }}
+          SEQERA_WORKFLOW_ERROR: ${{ steps.launch.outputs.error-message }}
+        run: |
+          jq -n '{workflowId: env.SEQERA_WORKFLOW_ID, workflowUrl: env.SEQERA_WORKFLOW_URL, status: env.SEQERA_WORKFLOW_STATUS, errorMessage: env.SEQERA_WORKFLOW_ERROR}' > seqera-launch.json
+
+      - uses: actions/upload-artifact@cf430e030ddbb5b0abf93d22962f4752f3646cd9 # v7.0.2
+        if: always() && steps.launch.outputs.workflow-id != ''
         with:
-          name: Seqera Platform debug log file
-          path: |
-            seqera_platform_action_*.log
-            seqera_platform_action_*.json
+          name: Seqera Platform launch result
+          path: seqera-launch.json
diff --git a/.github/workflows/awstest.yml b/.github/workflows/awstest.yml
index 4a4b5db..635b5a5 100644
--- a/.github/workflows/awstest.yml
+++ b/.github/workflows/awstest.yml
@@ -7,27 +7,27 @@ on:
 jobs:
   run-platform:
     name: Run AWS tests
-    if: github.repository == 'nf-core/dmscore'
+    if: github.repository == 'nf-core/deepmutscan'
     runs-on: ubuntu-latest
     steps:
       # Launch workflow using Seqera Platform CLI tool action
       - name: Launch workflow via Seqera Platform
-        uses: seqeralabs/action-tower-launch@v2
+        uses: seqeralabs/action-tower-launch@51565b514bff1827cf34620de25d0055759f1fc9 # v2
         with:
-          workspace_id: ${{ secrets.TOWER_WORKSPACE_ID }}
+          workspace_id: ${{ vars.TOWER_WORKSPACE_ID }}
           access_token: ${{ secrets.TOWER_ACCESS_TOKEN }}
-          compute_env: ${{ secrets.TOWER_COMPUTE_ENV }}
+          compute_env: ${{ vars.TOWER_COMPUTE_ENV }}
           revision: ${{ github.sha }}
-          workdir: s3://${{ secrets.AWS_S3_BUCKET }}/work/dmscore/work-${{ github.sha }}
+          workdir: s3://${{ vars.AWS_S3_BUCKET }}/work/deepmutscan/work-${{ github.sha }}
           parameters: |
             {
-              "outdir": "s3://${{ secrets.AWS_S3_BUCKET }}/dmscore/results-test-${{ github.sha }}"
+              "outdir": "s3://${{ vars.AWS_S3_BUCKET }}/deepmutscan/results-test-${{ github.sha }}"
             }
           profiles: test
 
-      - uses: actions/upload-artifact@v4
+      - uses: actions/upload-artifact@cf430e030ddbb5b0abf93d22962f4752f3646cd9 # v7.0.2
         with:
           name: Seqera Platform debug log file
           path: |
-            seqera_platform_action_*.log
-            seqera_platform_action_*.json
+            tower_action_*.log
+            tower_action_*.json
diff --git a/.github/workflows/branch.yml b/.github/workflows/branch.yml
index b51378f..33cc9a8 100644
--- a/.github/workflows/branch.yml
+++ b/.github/workflows/branch.yml
@@ -56,7 +56,7 @@ jobs:
 
       - name: Upload PR comment artifact
         if: failure()
-        uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
+        uses: actions/upload-artifact@cf430e030ddbb5b0abf93d22962f4752f3646cd9 # v7.0.2
         with:
           name: pr-comment
           path: pr-comment/
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
deleted file mode 100644
index 92396ce..0000000
--- a/.github/workflows/ci.yml
+++ /dev/null
@@ -1,87 +0,0 @@
-name: nf-core CI
-# This workflow runs the pipeline with the minimal test dataset to check that it completes without any syntax errors
-on:
-  push:
-    branches:
-      - dev
-  pull_request:
-  release:
-    types: [published]
-  workflow_dispatch:
-
-env:
-  NXF_ANSI_LOG: false
-  NXF_SINGULARITY_CACHEDIR: ${{ github.workspace }}/.singularity
-  NXF_SINGULARITY_LIBRARYDIR: ${{ github.workspace }}/.singularity
-
-concurrency:
-  group: "${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}"
-  cancel-in-progress: true
-
-jobs:
-  test:
-    name: "Run pipeline with test data (${{ matrix.NXF_VER }} | ${{ matrix.test_name }} | ${{ matrix.profile }})"
-    # Only run on push if this is the nf-core dev branch (merged PRs)
-    if: "${{ github.event_name != 'push' || (github.event_name == 'push' && github.repository == 'nf-core/dmscore') }}"
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        NXF_VER:
-          - "24.04.2"
-          - "latest-everything"
-        profile:
-          - "conda"
-          - "docker"
-          - "singularity"
-        test_name:
-          - "test"
-        isMaster:
-          - ${{ github.base_ref == 'master' }}
-        # Exclude conda and singularity on dev
-        exclude:
-          - isMaster: false
-            profile: "conda"
-          - isMaster: false
-            profile: "singularity"
-    steps:
-      - name: Check out pipeline code
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4
-        with:
-          fetch-depth: 0
-
-      - name: Set up Nextflow
-        uses: nf-core/setup-nextflow@v2
-        with:
-          version: "${{ matrix.NXF_VER }}"
-
-      - name: Set up Apptainer
-        if: matrix.profile == 'singularity'
-        uses: eWaterCycle/setup-apptainer@main
-
-      - name: Set up Singularity
-        if: matrix.profile == 'singularity'
-        run: |
-          mkdir -p $NXF_SINGULARITY_CACHEDIR
-          mkdir -p $NXF_SINGULARITY_LIBRARYDIR
-
-      - name: Set up Miniconda
-        if: matrix.profile == 'conda'
-        uses: conda-incubator/setup-miniconda@a4260408e20b96e80095f42ff7f1a15b27dd94ca # v3
-        with:
-          miniconda-version: "latest"
-          auto-update-conda: true
-          conda-solver: libmamba
-          channels: conda-forge,bioconda
-
-      - name: Set up Conda
-        if: matrix.profile == 'conda'
-        run: |
-          echo $(realpath $CONDA)/condabin >> $GITHUB_PATH
-          echo $(realpath python) >> $GITHUB_PATH
-
-      - name: Clean up Disk space
-        uses: jlumbroso/free-disk-space@54081f138730dfa15788a46383842cd2f914a1be # v1.3.1
-
-      - name: "Run pipeline with test data ${{ matrix.NXF_VER }} | ${{ matrix.test_name }} | ${{ matrix.profile }}"
-        run: |
-          nextflow run ${GITHUB_WORKSPACE} -profile ${{ matrix.test_name }},${{ matrix.profile }} --outdir ./results
diff --git a/.github/workflows/clean-up.yml b/.github/workflows/clean-up.yml
index 0b6b1f2..b694012 100644
--- a/.github/workflows/clean-up.yml
+++ b/.github/workflows/clean-up.yml
@@ -10,7 +10,7 @@ jobs:
       issues: write
       pull-requests: write
     steps:
-      - uses: actions/stale@28ca1036281a5e5922ead5184a1bbf96e5fc984e # v9
+      - uses: actions/stale@4391f3da665fdf50b6810c1a66712fb9ba21aa93 # v11
         with:
           stale-issue-message: "This issue has been tagged as awaiting-changes or awaiting-feedback by an nf-core contributor. Remove stale label or add a comment otherwise this issue will be closed in 20 days."
           stale-pr-message: "This PR has been tagged as awaiting-changes or awaiting-feedback by an nf-core contributor. Remove stale label or add a comment if it is still useful."
diff --git a/.github/workflows/download_pipeline.yml b/.github/workflows/download_pipeline.yml
index ab06316..f672810 100644
--- a/.github/workflows/download_pipeline.yml
+++ b/.github/workflows/download_pipeline.yml
@@ -12,14 +12,6 @@ on:
         required: true
         default: "dev"
   pull_request:
-    types:
-      - opened
-      - edited
-      - synchronize
-    branches:
-      - main
-      - master
-  pull_request_target:
     branches:
       - main
       - master
@@ -46,26 +38,34 @@ jobs:
     runs-on: ubuntu-latest
     needs: configure
     steps:
+      - name: Check out pipeline code
+        uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7
+
       - name: Install Nextflow
-        uses: nf-core/setup-nextflow@v2
+        uses: nf-core/setup-nextflow@893c28b667aedeba26e37f296d260ccc5bc4d914 # v3
 
       - name: Disk space cleanup
-        uses: jlumbroso/free-disk-space@54081f138730dfa15788a46383842cd2f914a1be # v1.3.1
+        uses: jlumbroso/free-disk-space@ceedf095f4ec1a097402bc6bd80831f2e1a6fde6 # v2.0.0
 
-      - uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5
+      - uses: actions/setup-python@6d4fd22b20465ccc0f7354476588e8df7c9ca0e0 # v7
         with:
-          python-version: "3.12"
+          python-version: "3.15"
           architecture: "x64"
 
       - name: Setup Apptainer
-        uses: eWaterCycle/setup-apptainer@4bb22c52d4f63406c49e94c804632975787312b3 # v2.0.0
+        uses: eWaterCycle/setup-apptainer@95b5c781df5f72fd2271cc3cff06904a09d2a087 # v3
         with:
           apptainer-version: 1.3.4
 
+      - name: Read .nf-core.yml
+        id: read_yml
+        run: |
+          echo "nf_core_version=$(yq '.nf_core_version' ${{ github.workspace }}/.nf-core.yml)" >> "$GITHUB_OUTPUT"
+
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install git+https://github.com/nf-core/tools.git@dev
+          pip install nf-core==${{ steps.read_yml.outputs['nf_core_version'] }}
 
       - name: Make a cache directory for the container images
         run: |
@@ -120,6 +120,7 @@ jobs:
           echo "IMAGE_COUNT_AFTER=$image_count" >> "$GITHUB_OUTPUT"
 
       - name: Compare container image counts
+        id: count_comparison
         run: |
           if [ "${{ steps.count_initial.outputs.IMAGE_COUNT_INITIAL }}" -ne "${{ steps.count_afterwards.outputs.IMAGE_COUNT_AFTER }}" ]; then
             initial_count=${{ steps.count_initial.outputs.IMAGE_COUNT_INITIAL }}
@@ -132,3 +133,10 @@ jobs:
           else
             echo "The pipeline can be downloaded successfully!"
           fi
+
+      - name: Upload Nextflow logfile for debugging purposes
+        uses: actions/upload-artifact@cf430e030ddbb5b0abf93d22962f4752f3646cd9 # v7.0.2
+        with:
+          name: nextflow_logfile.txt
+          path: .nextflow.log*
+          include-hidden-files: true
diff --git a/.github/workflows/linting.yml b/.github/workflows/linting.yml
index a5164f3..7811abe 100644
--- a/.github/workflows/linting.yml
+++ b/.github/workflows/linting.yml
@@ -3,9 +3,6 @@ name: nf-core linting
 # It runs the `nf-core pipelines lint` and markdown lint tests to ensure
 # that the code meets the nf-core guidelines.
 on:
-  push:
-    branches:
-      - dev
   pull_request:
   release:
     types: [published]
@@ -14,46 +11,42 @@ jobs:
   pre-commit:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4
+      - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7
 
-      - name: Set up Python 3.12
-        uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5
-        with:
-          python-version: "3.12"
-
-      - name: Install pre-commit
-        run: pip install pre-commit
+      - name: Install Nextflow
+        uses: nf-core/setup-nextflow@v3
 
-      - name: Run pre-commit
-        run: pre-commit run --all-files
+      - name: Run prek
+        uses: j178/prek-action@44924fe9ef4a6f88253c4b6b1bf11c689d3ce53d # v3.1.0
 
   nf-core:
     runs-on: ubuntu-latest
     steps:
       - name: Check out pipeline code
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4
+        uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7
 
       - name: Install Nextflow
-        uses: nf-core/setup-nextflow@v2
+        uses: nf-core/setup-nextflow@v3
 
-      - uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5
+      - uses: actions/setup-python@6d4fd22b20465ccc0f7354476588e8df7c9ca0e0 # v7
         with:
-          python-version: "3.12"
+          python-version: "3.15"
           architecture: "x64"
 
+      - name: Setup uv
+        uses: astral-sh/setup-uv@07a047e608db84b3d8c06cd9a7c7dcc9c355c18e # v10.3.2
+
       - name: read .nf-core.yml
-        uses: pietrobolcato/action-read-yaml@1.1.0
+        uses: pietrobolcato/action-read-yaml@9f13718d61111b69f30ab4ac683e67a56d254e1d # 1.1.0
         id: read_yml
         with:
           config: ${{ github.workspace }}/.nf-core.yml
 
       - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install nf-core==${{ steps.read_yml.outputs['nf_core_version'] }}
+        run: uv tool install nf-core==${{ steps.read_yml.outputs['nf_core_version'] }}
 
       - name: Run nf-core pipelines lint
-        if: ${{ github.base_ref != 'master' }}
+        if: ${{ github.base_ref != 'master' && github.base_ref != 'main' }}
         env:
           GITHUB_COMMENTS_URL: ${{ github.event.pull_request.comments_url }}
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
@@ -61,7 +54,7 @@ jobs:
         run: nf-core -l lint_log.txt pipelines lint --dir ${GITHUB_WORKSPACE} --markdown lint_results.md
 
       - name: Run nf-core pipelines lint --release
-        if: ${{ github.base_ref == 'master' }}
+        if: ${{ github.base_ref == 'master' || github.base_ref == 'main' }}
         env:
           GITHUB_COMMENTS_URL: ${{ github.event.pull_request.comments_url }}
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
@@ -74,7 +67,7 @@ jobs:
 
       - name: Upload linting log file artifact
         if: ${{ always() }}
-        uses: actions/upload-artifact@b4b15b8c7c6ac21ea08fcf65892d2ee8f75cf882 # v4
+        uses: actions/upload-artifact@cf430e030ddbb5b0abf93d22962f4752f3646cd9 # v7.0.2
         with:
           name: linting-logs
           path: |
@@ -95,7 +88,7 @@ jobs:
 
       - name: Upload PR comment artifact
         if: ${{ always() }}
-        uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
+        uses: actions/upload-artifact@cf430e030ddbb5b0abf93d22962f4752f3646cd9 # v7.0.2
         with:
           name: pr-comment
           path: pr-comment/
diff --git a/.github/workflows/nf-test.yml b/.github/workflows/nf-test.yml
new file mode 100644
index 0000000..d8617b4
--- /dev/null
+++ b/.github/workflows/nf-test.yml
@@ -0,0 +1,201 @@
+name: Run nf-test
+on:
+  pull_request:
+    paths-ignore:
+      - "docs/**"
+      - "**/meta.yml"
+      - "**/*.md"
+      - "**/*.png"
+      - "**/*.svg"
+  release:
+    types: [published]
+  workflow_dispatch:
+
+# Cancel if a newer run is started
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+
+env:
+  GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+  NFT_VER: "0.9.4"
+  NFT_WORKDIR: "~"
+  NXF_ANSI_LOG: false
+  NXF_SINGULARITY_CACHEDIR: ${{ github.workspace }}/.singularity
+  NXF_SINGULARITY_LIBRARYDIR: ${{ github.workspace }}/.singularity
+
+jobs:
+  nf-test-changes:
+    name: nf-test-changes
+    runs-on: # use self-hosted runners
+      - runs-on=${{ github.run_id }}-nf-test-changes
+      - runner=4cpu-linux-x64
+    outputs:
+      shard: ${{ steps.set-shards.outputs.shard }}
+      total_shards: ${{ steps.set-shards.outputs.total_shards }}
+    steps:
+      - name: Clean Workspace # Purge the workspace in case it's running on a self-hosted runner
+        run: |
+          ls -la ./
+          rm -rf ./* || true
+          rm -rf ./.??* || true
+          ls -la ./
+      - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7
+        with:
+          fetch-depth: 0
+
+      - name: get number of shards
+        id: set-shards
+        uses: ./.github/actions/get-shards
+        env:
+          NFT_VER: ${{ env.NFT_VER }}
+        with:
+          max_shards: 7
+
+      - name: debug
+        run: |
+          echo ${{ steps.set-shards.outputs.shard }}
+          echo ${{ steps.set-shards.outputs.total_shards }}
+
+  nf-test:
+    name: "${{ matrix.profile }} | ${{ matrix.NXF_VER }} | ${{ matrix.shard }}/${{ needs.nf-test-changes.outputs.total_shards }}"
+    needs: [nf-test-changes]
+    if: ${{ needs.nf-test-changes.outputs.total_shards != '0' }}
+    runs-on: # use self-hosted runners
+      - runs-on=${{ github.run_id }}-nf-test
+      - runner=4cpu-linux-x64
+    strategy:
+      fail-fast: false
+      matrix:
+        shard: ${{ fromJson(needs.nf-test-changes.outputs.shard) }}
+        profile: [conda, docker, singularity]
+        isMain:
+          - ${{ github.base_ref == 'master' || github.base_ref == 'main' }}
+        # Exclude conda and singularity on dev
+        exclude:
+          - isMain: false
+            profile: "conda"
+          - isMain: false
+            profile: "singularity"
+        NXF_VER:
+          - "25.10.4"
+          - "latest-everything"
+    env:
+      NXF_ANSI_LOG: false
+      TOTAL_SHARDS: ${{ needs.nf-test-changes.outputs.total_shards }}
+
+    steps:
+      - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7
+        with:
+          fetch-depth: 0
+
+      - name: Run nf-test
+        id: run_nf_test
+        uses: ./.github/actions/nf-test
+        continue-on-error: ${{ matrix.NXF_VER == 'latest-everything' }}
+        env:
+          NFT_WORKDIR: ${{ env.NFT_WORKDIR }}
+          NXF_VERSION: ${{ matrix.NXF_VER }}
+        with:
+          profile: ${{ matrix.profile }}
+          shard: ${{ matrix.shard }}
+          total_shards: ${{ env.TOTAL_SHARDS }}
+
+      - name: Report test status
+        if: ${{ always() }}
+        run: |
+          if [[ "${{ steps.run_nf_test.outcome }}" == "failure" ]]; then
+            echo "::error::Test with ${{ matrix.NXF_VER }} failed"
+            # Add to workflow summary
+            echo "## ❌ Test failed: ${{ matrix.profile }} | ${{ matrix.NXF_VER }} | Shard ${{ matrix.shard }}/${{ env.TOTAL_SHARDS }}" >> $GITHUB_STEP_SUMMARY
+            if [[ "${{ matrix.NXF_VER }}" == "latest-everything" ]]; then
+              echo "::warning::Test with latest-everything failed but will not cause workflow failure. Please check if the error is expected or if it needs fixing."
+            fi
+            if [[ "${{ matrix.NXF_VER }}" != "latest-everything" ]]; then
+              exit 1
+            fi
+          fi
+
+      # continue-on-error keeps latest-everything from failing the job, so it never shows up in
+      # `needs.nf-test.result` downstream and CI stays green. Surface it via a PR comment instead;
+      # other NXF_VER failures already fail the job/CI directly, so no comment is needed for those.
+      - name: Prepare PR comment fragment
+        if: ${{ always() && steps.run_nf_test.outcome == 'failure' && matrix.NXF_VER == 'latest-everything' }}
+        run: |
+          mkdir -p pr-comment-fragment
+          echo "* ❌ \`${{ matrix.profile }}\` | \`${{ matrix.NXF_VER }}\` | Shard ${{ matrix.shard }}/${{ env.TOTAL_SHARDS }}" > pr-comment-fragment/fragment.md
+
+      - name: Upload PR comment fragment
+        if: ${{ always() && steps.run_nf_test.outcome == 'failure' && matrix.NXF_VER == 'latest-everything' }}
+        uses: actions/upload-artifact@cf430e030ddbb5b0abf93d22962f4752f3646cd9 # v7
+        with:
+          name: pr-comment-fragment-${{ strategy.job-index }}
+          path: pr-comment-fragment/
+
+  confirm-pass:
+    needs: [nf-test]
+    if: always()
+    runs-on: # use self-hosted runners
+      - runs-on=${{ github.run_id }}-confirm-pass
+      - runner=2cpu-linux-x64
+    steps:
+      - name: One or more tests failed (excluding latest-everything)
+        if: ${{ contains(needs.*.result, 'failure') }}
+        run: exit 1
+
+      - name: One or more tests cancelled
+        if: ${{ contains(needs.*.result, 'cancelled') }}
+        run: exit 1
+
+      - name: All tests ok
+        if: ${{ contains(needs.*.result, 'success') }}
+        run: exit 0
+
+      - name: debug-print
+        if: always()
+        run: |
+          echo "::group::DEBUG: `needs` Contents"
+          echo "DEBUG: toJSON(needs) = ${{ toJSON(needs) }}"
+          echo "DEBUG: toJSON(needs.*.result) = ${{ toJSON(needs.*.result) }}"
+          echo "::endgroup::"
+
+      - name: Download PR comment fragments
+        if: ${{ always() }}
+        uses: actions/download-artifact@9000827ccba6bdab643e8b6fd33ac0654aef8333 # v8.0.2
+        continue-on-error: true
+        with:
+          pattern: pr-comment-fragment-*
+          path: pr-comment-fragments
+          merge-multiple: true
+
+      # Build a comment for the shared pr-comment.yml poster to publish on the PR.
+      # Based on the fragments above (not needs.*.result) so non-blocking failures are still reported.
+      - name: Prepare PR comment
+        if: ${{ always() }}
+        env:
+          PR_NUMBER: ${{ github.event.pull_request.number }}
+          RUN_URL: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}
+        run: |
+          mkdir -p pr-comment
+          echo "$PR_NUMBER" > pr-comment/pr_number.txt
+          echo "nf-test" > pr-comment/header.txt
+          if [ -d pr-comment-fragments ] && [ -n "$(ls -A pr-comment-fragments)" ]; then
+            {
+              echo "## ❌ nf-test failed with latest Nextflow version"
+              echo ""
+              echo "> [!NOTE]"
+              echo "> Tests with Nextflow's latest version failed but it will not cause a CI workflow failure."
+              echo "> Please check if the failure is expected with newer (edge-)releases of Nextflow or if it needs fixing."
+              echo ""
+              cat pr-comment-fragments/*.md
+              echo ""
+              echo "See the [full run](${RUN_URL}) for details."
+            } > pr-comment/comment.md
+          fi
+
+      - name: Upload PR comment artifact
+        if: ${{ always() }}
+        uses: actions/upload-artifact@cf430e030ddbb5b0abf93d22962f4752f3646cd9 # v7
+        with:
+          name: pr-comment
+          path: pr-comment/
diff --git a/.github/workflows/pr-comment.yml b/.github/workflows/pr-comment.yml
index ab7b59d..093fa63 100644
--- a/.github/workflows/pr-comment.yml
+++ b/.github/workflows/pr-comment.yml
@@ -31,7 +31,7 @@ jobs:
     if: github.event.workflow_run.event == 'pull_request'
     steps:
       - name: Download PR comment artifact
-        uses: dawidd6/action-download-artifact@b6e2e70617bc3265edd6dab6c906732b2f1ae151 # v21
+        uses: dawidd6/action-download-artifact@eab87c9830c39eff17e5a6eadb20bfb4bc880477 # v27
         with:
           run_id: ${{ github.event.workflow_run.id }}
           name: pr-comment
diff --git a/.github/workflows/release-announcements.yml b/.github/workflows/release-announcements.yml
index 450b1d5..4974f44 100644
--- a/.github/workflows/release-announcements.yml
+++ b/.github/workflows/release-announcements.yml
@@ -14,7 +14,11 @@ jobs:
         run: |
           echo "topics=$(curl -s https://nf-co.re/pipelines.json | jq -r '.remote_workflows[] | select(.full_name == "${{ github.repository }}") | .topics[]' | awk '{print "#"$0}' | tr '\n' ' ')" | sed 's/-//g' >> $GITHUB_OUTPUT
 
-      - uses: rzr/fediverse-action@master
+      - name: get description
+        id: get_description
+        run: |
+          echo "description=$(curl -s https://nf-co.re/pipelines.json | jq -r '.remote_workflows[] | select(.full_name == "${{ github.repository }}") | .description')" >> $GITHUB_OUTPUT
+      - uses: rzr/fediverse-action@66c2cbb5b1997666b0e28d597631b6a4f09a2719 # v0.0.6
         with:
           access-token: ${{ secrets.MASTODON_ACCESS_TOKEN }}
           host: "mstdn.science" # custom host if not "mastodon.social" (default)
@@ -22,48 +26,15 @@ jobs:
           # https://docs.github.com/en/developers/webhooks-and-events/webhooks/webhook-events-and-payloads#release
           message: |
             Pipeline release! ${{ github.repository }} v${{ github.event.release.tag_name }} - ${{ github.event.release.name }}!
-
+            ${{ steps.get_description.outputs.description }}
             Please see the changelog: ${{ github.event.release.html_url }}
 
             ${{ steps.get_topics.outputs.topics }} #nfcore #openscience #nextflow #bioinformatics
 
-  send-tweet:
-    runs-on: ubuntu-latest
-
-    steps:
-      - uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5
-        with:
-          python-version: "3.10"
-      - name: Install dependencies
-        run: pip install tweepy==4.14.0
-      - name: Send tweet
-        shell: python
-        run: |
-          import os
-          import tweepy
-
-          client = tweepy.Client(
-              access_token=os.getenv("TWITTER_ACCESS_TOKEN"),
-              access_token_secret=os.getenv("TWITTER_ACCESS_TOKEN_SECRET"),
-              consumer_key=os.getenv("TWITTER_CONSUMER_KEY"),
-              consumer_secret=os.getenv("TWITTER_CONSUMER_SECRET"),
-          )
-          tweet = os.getenv("TWEET")
-          client.create_tweet(text=tweet)
-        env:
-          TWEET: |
-            Pipeline release! ${{ github.repository }} v${{ github.event.release.tag_name }} - ${{ github.event.release.name }}!
-
-            Please see the changelog: ${{ github.event.release.html_url }}
-          TWITTER_CONSUMER_KEY: ${{ secrets.TWITTER_CONSUMER_KEY }}
-          TWITTER_CONSUMER_SECRET: ${{ secrets.TWITTER_CONSUMER_SECRET }}
-          TWITTER_ACCESS_TOKEN: ${{ secrets.TWITTER_ACCESS_TOKEN }}
-          TWITTER_ACCESS_TOKEN_SECRET: ${{ secrets.TWITTER_ACCESS_TOKEN_SECRET }}
-
   bsky-post:
     runs-on: ubuntu-latest
     steps:
-      - uses: zentered/bluesky-post-action@80dbe0a7697de18c15ad22f4619919ceb5ccf597 # v0.1.0
+      - uses: zentered/bluesky-post-action@5a91cc2ad10a304a4e96c16182dbe4918710bcf6 # v0.4.0
         with:
           post: |
             Pipeline release! ${{ github.repository }} v${{ github.event.release.tag_name }} - ${{ github.event.release.name }}!
diff --git a/.github/workflows/template-version-comment.yml b/.github/workflows/template-version-comment.yml
index ee102f7..775758b 100644
--- a/.github/workflows/template-version-comment.yml
+++ b/.github/workflows/template-version-comment.yml
@@ -12,7 +12,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Check out pipeline code
-        uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
+        uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7
         with:
           ref: ${{ github.event.pull_request.head.sha }}
 
@@ -54,7 +54,7 @@ jobs:
           fi
 
       - name: Upload PR comment artifact
-        uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
+        uses: actions/upload-artifact@cf430e030ddbb5b0abf93d22962f4752f3646cd9 # v7.0.2
         with:
           name: pr-comment
           path: pr-comment/
diff --git a/.gitignore b/.gitignore
index a42ce01..b55a302 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,3 +7,6 @@ testing/
 testing*
 *.pyc
 null/
+.lineage/
+.nf-test/
+.nf-test.log
diff --git a/.hooks/block_pipeline_outdir.sh b/.hooks/block_pipeline_outdir.sh
new file mode 100755
index 0000000..e9ba4f9
--- /dev/null
+++ b/.hooks/block_pipeline_outdir.sh
@@ -0,0 +1,44 @@
+#!/usr/bin/env bash
+# This hook is used to block commits if they include staged files inside a directory
+# which also contains a subdirectory called `pipeline_info`. The purpose of this is to
+# prevent users from inadvertently committing output from pipeline test runs inside the
+# development directory.
+
+set -e
+
+status=0
+seen_dirs=""
+
+while IFS= read -r file; do
+    # The offending output bundle's root is the ancestor directory that has
+    # `pipeline_info` as an immediate child, so callers can restore it in one go.
+    if [[ "$file" == pipeline_info/* ]]; then
+        top_dir="pipeline_info"
+    elif [[ "$file" == */pipeline_info/* ]]; then
+        top_dir="${file%%/pipeline_info/*}"
+    else
+        top_dir=""
+        dir=$(dirname "$file")
+        while [[ "$dir" != "." && "$dir" != "/" ]]; do
+            if [[ -d "$dir/pipeline_info" ]]; then
+                top_dir="$dir"
+                break
+            fi
+            dir=$(dirname "$dir")
+        done
+    fi
+
+    if [[ -n "$top_dir" ]]; then
+        echo "❌ Commit blocked: Please do not commit output from pipeline test runs to the pipeline code itself: $file"
+        status=1
+        case "$seen_dirs" in
+            *"|$top_dir|"*) ;;
+            *)
+                echo "Run 'git restore --staged $top_dir' to remove the whole output folder from the staging area."
+                seen_dirs="$seen_dirs|$top_dir|"
+                ;;
+        esac
+    fi
+done < <(git diff --cached --name-only)
+
+exit "$status"
diff --git a/.nf-core.yml b/.nf-core.yml
index 6fff11a..f4a2686 100644
--- a/.nf-core.yml
+++ b/.nf-core.yml
@@ -1,17 +1,24 @@
-repository_type: pipeline
-nf_core_version: 3.1.2
 lint:
   files_exist:
-    - .github/workflows/linting_comment.yml
+    - conf/igenomes.config
+    - conf/igenomes_ignored.config
+  # The two logos are intentionally the project's own artwork rather than the template's
+  # generated versions, so files_unchanged must not flag them.
   files_unchanged:
-    - .github/workflows/branch.yml
-    - .github/workflows/linting.yml
+    - assets/nf-core-deepmutscan_logo_light.png
+    - docs/images/nf-core-deepmutscan_logo_dark.png
+nf_core_version: 4.2.2
+repository_type: pipeline
 template:
+  author: Benjamin Wehnert & Maximilian R. Stammnitz
+  description: "Processing and analysis of deep mutational scanning (DMS) data from
+    shotgun and amplicon sequencing: read alignment, variant counting, sequencing-error
+    correction, library QC and fitness estimation"
+  force: false
+  is_nfcore: true
+  name: deepmutscan
   org: nf-core
-  name: dmscore
-  description: "Until now, most Deep Mutational Scanning (DMS) experiments relied on variant-specific barcoded libraries for sequencing. This method enabled DMS on large proteins and led to many great publications. Recently, efforts have increased to make use of the classic and more simple random fragmentation-based short-read sequencing (“shotgun-sequencing”). This saves time and money and due to its simpler experimental design is less prone to mistakes. dmscore handles the essential computational steps, processing the raw FASTQ files and generating a count table of variants. Along the way, it provides multiple QC metrics, enabling users to quickly evaluate the success of their experimental setup."
-  author: Benjamin Wehnert & Max Stammnitz
-  version: 1.0.0dev
-  force: true
   outdir: .
-  is_nfcore: true
+  skip_features:
+    - igenomes
+  version: 1.0.0
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 9e9f0e1..0399860 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -4,10 +4,49 @@ repos:
     hooks:
       - id: prettier
         additional_dependencies:
-          - prettier@3.2.5
-
-  - repo: https://github.com/editorconfig-checker/editorconfig-checker.python
-    rev: "3.0.3"
+          - prettier@3.9.10
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v6.0.0
     hooks:
-      - id: editorconfig-checker
-        alias: ec
+      - id: trailing-whitespace
+        args: [--markdown-linebreak-ext=md]
+        exclude: |
+          (?x)^(
+              .*ro-crate-metadata.json$|
+              modules/(?!local/).*|
+              subworkflows/(?!local/).*|
+              .*\.snap$|
+              assets/variant_effect_inspection_tool/3Dmol-min\.js.*
+          )$
+      - id: end-of-file-fixer
+        exclude: |
+          (?x)^(
+              .*ro-crate-metadata.json$|
+              modules/(?!local/).*|
+              subworkflows/(?!local/).*|
+              .*\.snap$|
+              assets/variant_effect_inspection_tool/3Dmol-min\.js.*
+          )$
+      - id: check-added-large-files
+        args: [--maxkb=5000]
+        exclude: |
+          (?x)^(
+              .*ro-crate-metadata.json$|
+              .*\.snap$|
+              lib/nfcore_external_java_deps.jar$|
+              docs/.*\.(svg|pdf)$|
+              assets/.*$
+          )$
+      - id: check-merge-conflict
+  - repo: https://github.com/seqeralabs/nf-lint-pre-commit
+    rev: v0.3.0
+    hooks:
+      - id: nextflow-lint
+        files: '\.nf$|nextflow\.config$'
+        args: ["-output", "json"]
+  - repo: local
+    hooks:
+      - id: block-pipeline-outdir
+        name: Prevent committing output from pipeline test runs to the pipeline code itself
+        entry: ./.hooks/block_pipeline_outdir.sh
+        language: script
diff --git a/.prettierignore b/.prettierignore
index edd29f0..0bd1d5e 100644
--- a/.prettierignore
+++ b/.prettierignore
@@ -1,6 +1,4 @@
 email_template.html
-adaptivecard.json
-slackreport.json
 .nextflow*
 work/
 data/
@@ -10,4 +8,8 @@ testing/
 testing*
 *.pyc
 bin/
+.nf-test/
 ro-crate-metadata.json
+modules/nf-core/
+subworkflows/nf-core/
+assets/variant_effect_inspection_tool/3Dmol-min.js
diff --git a/.prettierrc.yml b/.prettierrc.yml
index c81f9a7..07dbd8b 100644
--- a/.prettierrc.yml
+++ b/.prettierrc.yml
@@ -1 +1,6 @@
 printWidth: 120
+tabWidth: 4
+overrides:
+  - files: "*.{md,yml,yaml,html,css,scss,js,cff}"
+    options:
+      tabWidth: 2
diff --git a/.vscode/extensions.json b/.vscode/extensions.json
new file mode 100644
index 0000000..8389b2d
--- /dev/null
+++ b/.vscode/extensions.json
@@ -0,0 +1,12 @@
+{
+    // See https://github.com/nf-core/vscode-extensionpack
+    "recommendations": [
+        "codezombiech.gitignore",
+        "esbenp.prettier-vscode",
+        "jebbs.markdown-extended",
+        "mechatroner.rainbow-csv",
+        "nextflow.nextflow",
+        "onnovalkering.vscode-singularity",
+        "redhat.vscode-yaml"
+    ]
+}
diff --git a/.vscode/settings.json b/.vscode/settings.json
index a33b527..a003358 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -1,3 +1,4 @@
 {
-    "markdown.styles": ["public/vscode_markdown.css"]
+    // Render nf-core website admonitions nicely in markdown previews
+    "markdown.styles": ["https://nf-co.re/vscode_markdown.css"]
 }
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 40ea82f..9bb3a0c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,16 +1,58 @@
-# nf-core/dmscore: Changelog
+# nf-core/deepmutscan: Changelog
 
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/)
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## v1.0.0dev - [date]
+## v1.0.0 - [2026-10-13]
 
-Initial release of nf-core/dmscore, created with the [nf-core](https://nf-co.re/) template.
+Initial release of nf-core/deepmutscan, created with the [nf-core](https://nf-co.re/) template.
 
 ### `Added`
 
-### `Fixed`
+- Samplesheet input of paired-end FASTQ files per library, annotated with `sample`, `type` (`input`, `output`, `wildtype`) and `replicate`, validated at start-up (one sample per run, unique `sample`/`type`/`replicate`, matching `input` and `output` replicate numbers, one `wildtype` library for `--error_correction wildtype`), together with `--reading_frame` against the reference
+- Input FASTQ checks before any reads are processed: quick checks of the first read of every file at start-up (empty, not gzip or not FASTQ, same file or same reads twice, mates with different names, file shared between libraries), then `fq lint` on the complete files (truncated or corrupted gzip, malformed records, unequal read numbers, mates out of order); all faulty libraries are reported together
+- Raw read QC with FastQC and MultiQC
+- Read-pair merging into consensus reads with `vsearch --fastq_mergepairs`
+- Alignment of the merged reads to a gene-sized reference ORF with BWA-MEM (`--fasta`, `--reading_frame`)
+- Filtering of unmapped, secondary, supplementary, low mapping-quality (MAPQ < 30) and indel-containing alignments with samtools, then coordinate sorting and indexing; wildtype reads are retained for error correction
+- Light-weight variant counter built on the `pysam` and `polars` libraries in Python, replacing `GATK AnalyzeSaturationMutagenesis` with a column-compatible output, including a minimum base quality (`--base_qual`, default Q40) and read-edge exclusion window (`--min_flank`, default 2 bp)
+- Annotation and filtering of variant counts against the programmed mutagenesis library (`--mutagenesis_type` `nnk`, `nns`, `nnh`, `nnn`, `nnk_nns`, `nnk_nns_nnh` or `custom` with `--custom_codon_library`)
+- Single-nucleotide variant sequencing-error correction (`--error_correction`):
+  - `false_doubles` (default): error rates estimated from read-linked false double mutant codons, by maximum likelihood (`--false_doubles_method mle`, default) or empirical Bayes (`eb`), within a configurable codon window (`--false_doubles_codon_window`)
+  - `wildtype`: error profile from additional deep sequencing of the unmutated template (`type: wildtype` samplesheet rows)
+  - `none`
+- Interactive, run-level HTML error-correction report
+- DMS library QC per library: count and count-per-coverage heatmaps, sorted count distributions with LogDiff (variants without counts drawn at a floor derived from the data and counted separately; LogDiff not reported when more than 10% of variants have no counts), sliding-window coverage and count profiles (`--sliding_window_size`, `--aimed_cov`), and sequencing-depth rarefaction on a geometric depth grid (`--run_seqdepth`)
+- Optional fitness estimation (`--fitness`): merged count tables, experimental design, synonymous wildtype proxy selection, default log-ratio fitness with replicate rescaling and summary statistics, replicate correlation plots and fitness heatmap
+- Optional fitness estimation with DiMSum (`--dimsum`) and mutscan edgeR / limma (`--mutscan`)
+- Minimum number of reads a variant needs in every `input` replicate to receive a fitness estimate (`--min_counts`, default 10), applied in the same way by the default estimator, DiMSum and mutscan; variant counting, the sequencing-error correction and the library QC always use all reads
+- Pseudocount for variants that drop out of an `output` replicate (`--output_pseudocount`, default 1; 0 gives dropouts no fitness value)
+- Optional interactive 3D variant effect inspection tool built from a user-supplied wildtype structure (`--pdb`)
+- Self-contained, all-in-one run report (`deepmutscan_report.html`) embedding QC, error-correction, fitness and MultiQC results, with links to Nextflow's execution report and timeline
+- Docker and Singularity images (amd64 and arm64) and conda lock files for all modules, built from each module's `environment.yml`; DiMSum and mutscan are amd64 only for now
+- `test` profile on a 50,000 read-pair subsample of a GID1A nicking-mutagenesis GluePCA experiment, with three nf-test pipeline tests (all options; without fitness, error correction, SeqDepth and structure; stub run), and `test_full` profile on the complete dataset (ENA PRJEB110196); test data and the PDB structure are hosted on [nf-core/test-datasets](https://github.com/nf-core/test-datasets/tree/deepmutscan)
 
 ### `Dependencies`
 
-### `Deprecated`
+| Dependency    | Version               |
+| ------------- | --------------------- |
+| `fastqc`      | 0.13.0                |
+| `fq`          | 0.12.0                |
+| `multiqc`     | 1.35                  |
+| `bwa`         | 0.7.19                |
+| `samtools`    | 1.21, 1.22.1          |
+| `vsearch`     | 2.30.0                |
+| `python`      | 3.12                  |
+| `pysam`       | 0.24.0                |
+| `polars`      | 1.33.1 (lts-cpu)      |
+| `pyarrow`     | 24.0.0                |
+| `biopython`   | 1.87                  |
+| `numpy`       | 1.26.4, 2.5.1         |
+| `pandas`      | 2.2.1                 |
+| `r-base`      | 4.4.2 (DiMSum), 4.5.1 |
+| `biostrings`  | 2.74.0, 2.78.0        |
+| `r-tidyverse` | 2.0.0                 |
+| `r-ggplot2`   | 3.5.1, 4.0.2          |
+| `r-zoo`       | 1.8_15                |
+| `r-dimsum`    | 1.4                   |
+| `mutscan`     | 1.0.0                 |
diff --git a/CITATIONS.md b/CITATIONS.md
index a7e69f7..cbcebb6 100644
--- a/CITATIONS.md
+++ b/CITATIONS.md
@@ -1,22 +1,102 @@
-# nf-core/dmscore: Citations
+# nf-core/deepmutscan: Citations
 
-## [nf-core](https://pubmed.ncbi.nlm.nih.gov/32055031/)
+## [nf-core](https://www.nature.com/articles/s41587-020-0439-x)
 
-> Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Mar;38(3):276-278. doi: 10.1038/s41587-020-0439-x. PubMed PMID: 32055031.
+> Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Mar;38(3):276-278. doi: 10.1038/s41587-020-0439-x.
 
 ## [Nextflow](https://pubmed.ncbi.nlm.nih.gov/28398311/)
 
-> Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017 Apr 11;35(4):316-319. doi: 10.1038/nbt.3820. PubMed PMID: 28398311.
+> Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017 Apr 11;35(4):316-319. doi: 10.1038/nbt.3820.
 
 ## Pipeline tools
 
+- [fq](https://github.com/stjude-rust-labs/fq)
+
+  > St. Jude Rust Labs. fq: FASTQ file manipulation and validation [Online].
+
 - [FastQC](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
 
-> Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online].
+  > Andrews S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online].
+
+- [BWA-MEM](https://arxiv.org/abs/1303.3997)
+
+  > Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv. 2013;1303.3997 [q-bio.GN]. doi: 10.48550/arXiv.1303.3997.
+
+- [SAMtools](https://doi.org/10.1093/gigascience/giab008)
+
+  > Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, Li H. Twelve years of SAMtools and BCFtools. Gigascience. 2021 Feb 16;10(2):giab008. doi: 10.1093/gigascience/giab008.
+
+- [VSEARCH](https://peerj.com/articles/2584/)
+
+  > Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016 Oct 18;4:e2584. doi: 10.7717/peerj.2584.
+
+- [pysam](https://github.com/pysam-developers/pysam)
+
+  > Heger A, Jacobs K, et al. pysam: a Python module for reading, manipulating and writing genomic data sets. https://github.com/pysam-developers/pysam
+
+- [Polars](https://github.com/pola-rs/polars)
+
+  > Vink R, et al. Polars: Lightning-fast DataFrame library for Rust and Python. https://github.com/pola-rs/polars
+
+- [Biopython](https://doi.org/10.1093/bioinformatics/btp163)
+
+  > Cock PJ, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A, Friedberg I, Hamelryck T, Kauff F, Wilczynski B, de Hoon MJ. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics. 2009 Jun 1;25(11):1422-3. doi: 10.1093/bioinformatics/btp163.
+
+- [NumPy](https://www.nature.com/articles/s41586-020-2649-2)
+
+  > Harris CR, Millman KJ, van der Walt SJ, et al. Array programming with NumPy. Nature. 2020;585(7825):357-362. doi: 10.1038/s41586-020-2649-2.
+
+- [pandas](https://pandas.pydata.org/)
+
+  > McKinney W. Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference. 2010;56-61. doi: 10.25080/Majora-92bf1922-00a.
+
+- [R](https://www.r-project.org/)
+
+  > R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
+
+- [Biostrings](https://bioconductor.org/packages/Biostrings/)
+
+  > Pagès H, Aboyoun P, Gentleman R, DebRoy S. Biostrings: Efficient manipulation of biological strings. R package. doi: 10.18129/B9.bioc.Biostrings.
+
+- [ggplot2](https://ggplot2.tidyverse.org/)
+
+  > Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York; 2016. ISBN: 978-3-319-24277-4.
+
+- [tidyverse](https://www.tidyverse.org/) (dplyr, tidyr, stringr)
+
+  > Wickham H, Averick M, Bryan J, et al. Welcome to the tidyverse. Journal of Open Source Software. 2019;4(43):1686. doi: 10.21105/joss.01686.
+
+- [zoo](https://cran.r-project.org/package=zoo)
+
+  > Zeileis A, Grothendieck G. zoo: S3 Infrastructure for Regular and Irregular Time Series. Journal of Statistical Software. 2005;14(6):1-27. doi: 10.18637/jss.v014.i06.
+
+- [DiMSum](https://link.springer.com/article/10.1186/s13059-020-02091-3)
+
+  > Faure AJ, Schmiedel JM, Baeza-Centurion P, Lehner B. DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies. Genome Biol. 2020;21(1):207. doi: 10.1186/s13059-020-02091-3.
+
+- [mutscan](https://link.springer.com/article/10.1186/s13059-023-02967-0)
+
+  > Soneson C, Bendel AM, Diss G, Stadler MB. mutscan-a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data. Genome Biol. 2023;24(1):132. doi: 10.1186/s13059-023-02967-0.
+
+- [edgeR](https://doi.org/10.1093/bioinformatics/btp616)
+
+  > Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010 Jan 1;26(1):139-40. doi: 10.1093/bioinformatics/btp616.
+
+- [limma](https://doi.org/10.1093/nar/gkv007)
+
+  > Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015 Apr 20;43(7):e47. doi: 10.1093/nar/gkv007.
+
+- [3Dmol.js](https://doi.org/10.1093/bioinformatics/btu829)
+
+  > Rego N, Koes D. 3Dmol.js: molecular visualization with WebGL. Bioinformatics. 2015 Apr 15;31(8):1322-4. doi: 10.1093/bioinformatics/btu829. (Bundled in the interactive variant effect inspection tool; BSD-3-Clause.)
+
+- [AlphaFold Protein Structure Database](https://alphafold.ebi.ac.uk/) (test-profile structure only)
+
+  > Varadi M, Bertoni D, Magana P, et al. AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences. Nucleic Acids Res. 2024;52(D1):D368-D375. doi: 10.1093/nar/gkad1011.
 
-- [MultiQC](https://pubmed.ncbi.nlm.nih.gov/27312411/)
+- [MultiQC](https://doi.org/10.1093/bioinformatics/btw354)
 
-> Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016 Oct 1;32(19):3047-8. doi: 10.1093/bioinformatics/btw354. Epub 2016 Jun 16. PubMed PMID: 27312411; PubMed Central PMCID: PMC5039924.
+  > Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016 Oct 1;32(19):3047-8. doi: 10.1093/bioinformatics/btw354. Epub 2016 Jun 16.
 
 ## Software packaging/containerisation tools
 
@@ -24,18 +104,18 @@
 
   > Anaconda Software Distribution. Computer software. Vers. 2-2.4.0. Anaconda, Nov. 2016. Web.
 
-- [Bioconda](https://pubmed.ncbi.nlm.nih.gov/29967506/)
+- [Bioconda](https://www.nature.com/articles/s41592-018-0046-7)
 
-  > Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J; Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018 Jul;15(7):475-476. doi: 10.1038/s41592-018-0046-7. PubMed PMID: 29967506.
+  > Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J; Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018 Jul;15(7):475-476. doi: 10.1038/s41592-018-0046-7.
 
-- [BioContainers](https://pubmed.ncbi.nlm.nih.gov/28379341/)
+- [BioContainers](https://doi.org/10.1093/bioinformatics/btx192)
 
-  > da Veiga Leprevost F, Grüning B, Aflitos SA, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Alvarez RV, Griss J, Nesvizhskii AI, Perez-Riverol Y. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics. 2017 Aug 15;33(16):2580-2582. doi: 10.1093/bioinformatics/btx192. PubMed PMID: 28379341; PubMed Central PMCID: PMC5870671.
+  > da Veiga Leprevost F, Grüning B, Aflitos SA, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Alvarez RV, Griss J, Nesvizhskii AI, Perez-Riverol Y. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics. 2017 Aug 15;33(16):2580-2582. doi: 10.1093/bioinformatics/btx192.
 
 - [Docker](https://dl.acm.org/doi/10.5555/2600239.2600241)
 
   > Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 2014(239), 2. doi: 10.5555/2600239.2600241.
 
-- [Singularity](https://pubmed.ncbi.nlm.nih.gov/28494014/)
+- [Singularity](https://doi.org/10.1371/journal.pone.0177459)
 
-  > Kurtzer GM, Sochat V, Bauer MW. Singularity: Scientific containers for mobility of compute. PLoS One. 2017 May 11;12(5):e0177459. doi: 10.1371/journal.pone.0177459. eCollection 2017. PubMed PMID: 28494014; PubMed Central PMCID: PMC5426675.
+  > Kurtzer GM, Sochat V, Bauer MW. Singularity: Scientific containers for mobility of compute. PLoS One. 2017 May 11;12(5):e0177459. doi: 10.1371/journal.pone.0177459. eCollection 2017.
diff --git a/LICENSE b/LICENSE
index 48133aa..8119d7b 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) The nf-core/dmscore team
+Copyright (c) The nf-core/deepmutscan team
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/README.md b/README.md
index b6ae647..035cb32 100644
--- a/README.md
+++ b/README.md
@@ -1,99 +1,125 @@
 <h1>
   <picture>
-    <source media="(prefers-color-scheme: dark)" srcset="docs/images/nf-core-dmscore_logo_dark.png">
-    <img alt="nf-core/dmscore" src="docs/images/nf-core-dmscore_logo_light.png">
+    <source media="(prefers-color-scheme: dark)" srcset="docs/images/nf-core-deepmutscan_logo_dark.png">
+    <img alt="nf-core/deepmutscan" src="docs/images/nf-core-deepmutscan_logo_light.png">
   </picture>
 </h1>
 
-[![GitHub Actions CI Status](https://github.com/nf-core/dmscore/actions/workflows/ci.yml/badge.svg)](https://github.com/nf-core/dmscore/actions/workflows/ci.yml)
-[![GitHub Actions Linting Status](https://github.com/nf-core/dmscore/actions/workflows/linting.yml/badge.svg)](https://github.com/nf-core/dmscore/actions/workflows/linting.yml)[![AWS CI](https://img.shields.io/badge/CI%20tests-full%20size-FF9900?labelColor=000000&logo=Amazon%20AWS)](https://nf-co.re/dmscore/results)[![Cite with Zenodo](http://img.shields.io/badge/DOI-10.5281/zenodo.XXXXXXX-1073c8?labelColor=000000)](https://doi.org/10.5281/zenodo.XXXXXXX)
+[![GitHub Actions CI Status](https://github.com/nf-core/deepmutscan/actions/workflows/nf-test.yml/badge.svg)](https://github.com/nf-core/deepmutscan/actions/workflows/nf-test.yml)
+[![GitHub Actions Linting Status](https://github.com/nf-core/deepmutscan/actions/workflows/linting.yml/badge.svg)](https://github.com/nf-core/deepmutscan/actions/workflows/linting.yml)[![AWS CI](https://img.shields.io/badge/CI%20tests-full%20size-FF9900?labelColor=000000&logo=Amazon%20AWS)](https://nf-co.re/deepmutscan/results)[![Cite with Zenodo](http://img.shields.io/badge/DOI-10.5281/zenodo.XXXXXXX-1073c8?labelColor=000000)](https://doi.org/10.5281/zenodo.XXXXXXX)
 [![nf-test](https://img.shields.io/badge/unit_tests-nf--test-337ab7.svg)](https://www.nf-test.com)
 
-[![Nextflow](https://img.shields.io/badge/nextflow%20DSL2-%E2%89%A524.04.2-23aa62.svg)](https://www.nextflow.io/)
+[![Nextflow](https://img.shields.io/badge/version-%E2%89%A525.10.4-green?style=flat&logo=nextflow&logoColor=white&color=%230DC09D&link=https%3A%2F%2Fnextflow.io)](https://www.nextflow.io/)
+[![nf-core template version](https://img.shields.io/badge/nf--core_template-4.2.2-green?style=flat&logo=nfcore&logoColor=white&color=%2324B064&link=https%3A%2F%2Fnf-co.re)](https://github.com/nf-core/tools/releases/tag/4.2.2)
 [![run with conda](http://img.shields.io/badge/run%20with-conda-3EB049?labelColor=000000&logo=anaconda)](https://docs.conda.io/en/latest/)
 [![run with docker](https://img.shields.io/badge/run%20with-docker-0db7ed?labelColor=000000&logo=docker)](https://www.docker.com/)
 [![run with singularity](https://img.shields.io/badge/run%20with-singularity-1d355c.svg?labelColor=000000)](https://sylabs.io/docs/)
-[![Launch on Seqera Platform](https://img.shields.io/badge/Launch%20%F0%9F%9A%80-Seqera%20Platform-%234256e7)](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-core/dmscore)
+[![Launch on Seqera Platform](https://img.shields.io/badge/Launch%20%F0%9F%9A%80-Seqera%20Platform-%234256e7)](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-core/deepmutscan)
 
-[![Get help on Slack](http://img.shields.io/badge/slack-nf--core%20%23dmscore-4A154B?labelColor=000000&logo=slack)](https://nfcore.slack.com/channels/dmscore)[![Follow on Twitter](http://img.shields.io/badge/twitter-%40nf__core-1DA1F2?labelColor=000000&logo=twitter)](https://twitter.com/nf_core)[![Follow on Mastodon](https://img.shields.io/badge/mastodon-nf__core-6364ff?labelColor=FFFFFF&logo=mastodon)](https://mstdn.science/@nf_core)[![Watch on YouTube](http://img.shields.io/badge/youtube-nf--core-FF0000?labelColor=000000&logo=youtube)](https://www.youtube.com/c/nf-core)
+[![Get help on Slack](http://img.shields.io/badge/slack-nf--core%20%23deepmutscan-4A154B?labelColor=000000&logo=slack)](https://nfcore.slack.com/channels/deepmutscan)[![Follow on Twitter](http://img.shields.io/badge/twitter-%40nf__core-1DA1F2?labelColor=000000&logo=twitter)](https://twitter.com/nf_core)[![Follow on Mastodon](https://img.shields.io/badge/mastodon-nf__core-6364ff?labelColor=FFFFFF&logo=mastodon)](https://mstdn.science/@nf_core)[![Watch on YouTube](http://img.shields.io/badge/youtube-nf--core-FF0000?labelColor=000000&logo=youtube)](https://www.youtube.com/c/nf-core)
 
 ## Introduction
 
-**nf-core/dmscore** is a bioinformatics pipeline that ...
+**nf-core/deepmutscan** is a workflow designed for the analysis of deep mutational scanning (DMS) data. DMS enables researchers to experimentally measure the fitness effects of thousands of gene variants simultaneously, helping to classify disease-causing mutants in human and other species populations, and to learn the fundamental rules of protein architecture, small-molecule binding, mRNA splicing, viral evolution and many other quantifiable phenotypes.
 
-<!-- TODO nf-core:
-   Complete this sentence with a 2-3 sentence summary of what types of data the pipeline ingests, a brief overview of the
-   major pipeline sections and the types of output it produces. You're giving an overview to someone new
-   to nf-core here, in 15-20 seconds. For an example, see https://github.com/nf-core/rnaseq/blob/master/README.md#introduction
--->
+While DNA synthesis and sequencing technologies have advanced substantially, long open reading frame (ORF) targets still present a major challenge for DMS studies. Shotgun DNA sequencing of randomly fragmented variant libraries can greatly speed up the inference of long ORF mutant fitness landscapes, as it avoids the need for library barcoding or multi-tile sequencing. We have designed `nf-core/deepmutscan` to unlock shotgun sequencing-based DMS studies on long ORFs, and to simplify and standardise the bioinformatics steps involved in processing such experiments – from read alignment to QC reporting, variant count error correction and fitness landscape inference. Amplicon (tile) sequencing data can be processed in the same way.
 
-<!-- TODO nf-core: Include a figure that guides the user through the major workflow steps. Many nf-core
-     workflows use the "tube map" design for that. See https://nf-co.re/docs/contributing/design_guidelines#examples for examples.   -->
-<!-- TODO nf-core: Fill in short bullet-pointed list of the default steps in the pipeline -->1. Read QC ([`FastQC`](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/))2. Present QC for raw reads ([`MultiQC`](http://multiqc.info/))
+![nf-core/deepmutscan workflow](docs/images/pipeline.png)
 
-## Usage
+## Major features
 
-> [!NOTE]
-> If you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/usage/installation) on how to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline) with `-profile test` before running the workflow on actual data.
+- End-to-end processing of DMS libraries from shotgun (randomly fragmented) or amplicon short-read sequencing
+- Light-weight variant counter with base-quality and read-edge filters, replacing `GATK AnalyzeSaturationMutagenesis`
+- Intrinsic sequencing-error correction of single-nucleotide variant counts from read-linked false double mutants (maximum-likelihood or empirical-Bayes estimators), or from additional wildtype template sequencing
+- Read QC with [FastQC](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/), aggregated across all libraries with [MultiQC](https://multiqc.info/)
+- Library quality control: mutant count heatmaps, positional coverage and mutation-type biases, sequencing-depth rarefaction
+- Fitness estimation with a built-in log-ratio estimator, plus optional [DiMSum](https://github.com/lehner-lab/DiMSum) and [mutscan](https://github.com/fmicompbio/mutscan)
+- Support for degenerate codon libraries (NNK, NNS, NNH, NNN and combinations), e.g. from nicking mutagenesis, and for custom (position-specific) codon libraries, e.g. from Twist tiles
+- A single, self-contained HTML report per run, and an optional interactive 3D variant effect inspection tool when a wildtype structure is supplied
+- Containerisation via Docker, Singularity/Apptainer and Conda; scalability across HPC and cloud systems
 
-<!-- TODO nf-core: Describe the minimum required steps to execute the pipeline, e.g. how to prepare samplesheets.
-     Explain what rows and columns represent. For instance (please edit as appropriate):
+For more details on the individual steps, please read the [usage documentation](https://nf-co.re/deepmutscan/usage).
 
-First, prepare a samplesheet with your input data that looks as follows:
+## Pipeline summary
 
-`samplesheet.csv`:
+1. Input FASTQ checks ([`fq lint`](https://github.com/stjude-rust-labs/fq)) and raw read QC ([`FastQC`](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/))
+2. Merging of overlapping read pairs into consensus reads for base error reduction ([`vsearch --fastq_mergepairs`](https://github.com/torognes/vsearch))
+3. Alignment of the merged reads to the reference ORF ([`BWA-MEM`](https://github.com/lh3/bwa))
+4. Filtering of unmapped, secondary, supplementary, low mapping-quality and indel-containing alignments, sorting and indexing ([`samtools`](https://www.htslib.org/))
+5. Variant counting (custom counter built on [`pysam`](https://github.com/pysam-developers/pysam) and [`polars`](https://pola.rs))
+6. Annotation and filtering of variant counts against the programmed mutagenesis library
+7. Single-nucleotide variant sequencing-error correction via false double mutants or wildtype sequencing
+8. DMS library quality control and visualisation
+9. _Optional:_ fitness estimation from matched input/output samples (default estimator, [`DiMSum`](https://github.com/lehner-lab/DiMSum), [`mutscan`](https://github.com/fmicompbio/mutscan)) and interactive 3D variant effect inspection tool ([`3Dmol.js`](https://3dmol.csb.pitt.edu/))
+10. An all-in-one `deepmutscan_report.html`
 
-```csv
-sample,fastq_1,fastq_2
-CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz
-```
+## Usage
 
-Each row represents a fastq file (single-end) or a pair of fastq files (paired end).
+> [!NOTE]
+> If you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/get_started/environment_setup/overview) on how to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/get_started/run-your-first-pipeline) with `-profile test` before running the workflow on actual data.
 
--->
+First, prepare a samplesheet with your input data. Each row represents one sequencing library as a pair of FASTQ files (paired-end), annotated with the biological sample, its type in the selection experiment (`input`, `output` or `wildtype`) and the replicate number:
 
-Now, you can run the pipeline using:
+```csv title="samplesheet.csv"
+sample,type,replicate,file1,file2
+ORF1,input,1,/reads/input1_R1.fastq.gz,/reads/input1_R2.fastq.gz
+ORF1,input,2,/reads/input2_R1.fastq.gz,/reads/input2_R2.fastq.gz
+ORF1,output,1,/reads/output1_R1.fastq.gz,/reads/output1_R2.fastq.gz
+ORF1,output,2,/reads/output2_R1.fastq.gz,/reads/output2_R2.fastq.gz
+```
 
-<!-- TODO nf-core: update the following command to include all required parameters for a minimal example -->
+Secondly, provide the gene or gene region of interest as a reference FASTA file via `--fasta`, and the nucleotide coordinates of the mutagenised open reading frame within it via `--reading_frame` (1-based, inclusive, e.g. `151-450` for 100 codons starting at nucleotide 151).
 
-```bash
-nextflow run nf-core/dmscore \
+Now, you can run the pipeline using:
+
+```bash title="example_run.sh"
+nextflow run nf-core/deepmutscan \
    -profile <docker/singularity/.../institute> \
-   --input samplesheet.csv \
-   --outdir <OUTDIR>
+   --input ./samplesheet.csv \
+   --fasta ./ref.fa \
+   --reading_frame 151-450 \
+   --outdir ./results
 ```
 
+Add `--fitness` to estimate variant fitness from the input and output samples, and see the [usage documentation](https://nf-co.re/deepmutscan/usage) and the [parameter documentation](https://nf-co.re/deepmutscan/parameters) for all other options.
+
 > [!WARNING]
 > Please provide pipeline parameters via the CLI or Nextflow `-params-file` option. Custom config files including those provided by the `-c` Nextflow option can be used to provide any configuration _**except for parameters**_; see [docs](https://nf-co.re/docs/usage/getting_started/configuration#custom-configuration-files).
 
-For more details and further functionality, please refer to the [usage documentation](https://nf-co.re/dmscore/usage) and the [parameter documentation](https://nf-co.re/dmscore/parameters).
-
 ## Pipeline output
 
-To see the results of an example test run with a full size dataset refer to the [results](https://nf-co.re/dmscore/results) tab on the nf-core website pipeline page.
-For more details about the output files and reports, please refer to the
-[output documentation](https://nf-co.re/dmscore/output).
+To see the results of an example test run with a full size dataset refer to the [results](https://nf-co.re/deepmutscan/results) tab on the nf-core website pipeline page.
+For more details about the output files and reports, please refer to the [output documentation](https://nf-co.re/deepmutscan/output).
 
 ## Credits
 
-nf-core/dmscore was originally written by Benjamin Wehnert & Max Stammnitz.
+nf-core/deepmutscan was originally written by [Benjamin Wehnert](https://github.com/BenjaminWehnert1008) and [Maximilian Stammnitz](https://github.com/MaximilianStammnitz) at the [Centre for Genomic Regulation (CRG), Barcelona](https://www.crg.eu/), with the generous support of an EMBO Long-term Postdoctoral Fellowship, the Erasmus+ programme and a Marie Skłodowska-Curie grant by the European Union.
 
 We thank the following people for their extensive assistance in the development of this pipeline:
 
-<!-- TODO nf-core: If applicable, make list of people who have also contributed -->
+- [Fei Sang](https://github.com/fei-hgi) (Wellcome Sanger Institute) – original variant counting implementation
+- [Júlia Mir-Pedrol](https://github.com/mirpedrol) (CRG) – nf-core development guidance and code review
+- [Matthias Hörtenhuber](https://github.com/mashehu) (SciLifeLab) – nf-core development guidance and code review
 
 ## Contributions and Support
 
-If you would like to contribute to this pipeline, please see the [contributing guidelines](.github/CONTRIBUTING.md).
+If you would like to contribute to this pipeline, please see the [contributing guidelines](docs/CONTRIBUTING.md).
 
-For further information or help, don't hesitate to get in touch on the [Slack `#dmscore` channel](https://nfcore.slack.com/channels/dmscore) (you can join with [this invite](https://nf-co.re/join/slack)).
+For further information or help, don't hesitate to get in touch on the [Slack `#deepmutscan` channel](https://nfcore.slack.com/channels/deepmutscan) (you can join with [this invite](https://nf-co.re/join/slack)). Bug reports and feature requests are welcome as GitHub [issues](https://github.com/nf-core/deepmutscan/issues).
+
+For scientific discussions around the use of this pipeline (e.g. on experimental design or sequencing data requirements), please feel free to get in touch with us directly:
+
+- Benjamin Wehnert — wehnertbenjamin@gmail.com
+- Maximilian Stammnitz — maximilian.stammnitz@crg.eu
 
 ## Citations
 
-<!-- TODO nf-core: Add citation for pipeline after first release. Uncomment lines below and update Zenodo doi and badge at the top of this file. -->
-<!-- If you use nf-core/dmscore for your analysis, please cite it using the following doi: [10.5281/zenodo.XXXXXX](https://doi.org/10.5281/zenodo.XXXXXX) -->
+If you use `nf-core/deepmutscan` for your analysis, please cite it as follows:
+
+> Wehnert B, et al. _bioRxiv_ preprint (in preparation).
 
-<!-- TODO nf-core: Add bibliography of tools and data used in your pipeline -->
+<!-- Add the Zenodo DOI after the first release: -->
+<!-- If you use nf-core/deepmutscan for your analysis, please cite it using the following doi: [10.5281/zenodo.XXXXXX](https://doi.org/10.5281/zenodo.XXXXXX) -->
 
 An extensive list of references for the tools used by the pipeline can be found in the [`CITATIONS.md`](CITATIONS.md) file.
 
diff --git a/assets/adaptivecard.json b/assets/adaptivecard.json
deleted file mode 100644
index 62ad62a..0000000
--- a/assets/adaptivecard.json
+++ /dev/null
@@ -1,67 +0,0 @@
-{
-    "type": "message",
-    "attachments": [
-        {
-            "contentType": "application/vnd.microsoft.card.adaptive",
-            "contentUrl": null,
-            "content": {
-                "\$schema": "http://adaptivecards.io/schemas/adaptive-card.json",
-                "msteams": {
-                    "width": "Full"
-                },
-                "type": "AdaptiveCard",
-                "version": "1.2",
-                "body": [
-                    {
-                        "type": "TextBlock",
-                        "size": "Large",
-                        "weight": "Bolder",
-                        "color": "<% if (success) { %>Good<% } else { %>Attention<%} %>",
-                        "text": "nf-core/dmscore v${version} - ${runName}",
-                        "wrap": true
-                    },
-                    {
-                        "type": "TextBlock",
-                        "spacing": "None",
-                        "text": "Completed at ${dateComplete} (duration: ${duration})",
-                        "isSubtle": true,
-                        "wrap": true
-                    },
-                    {
-                        "type": "TextBlock",
-                        "text": "<% if (success) { %>Pipeline completed successfully!<% } else { %>Pipeline completed with errors. The full error message was: ${errorReport}.<% } %>",
-                        "wrap": true
-                    },
-                    {
-                        "type": "TextBlock",
-                        "text": "The command used to launch the workflow was as follows:",
-                        "wrap": true
-                    },
-                    {
-                        "type": "TextBlock",
-                        "text": "${commandLine}",
-                        "isSubtle": true,
-                        "wrap": true
-                    }
-                ],
-                "actions": [
-                    {
-                        "type": "Action.ShowCard",
-                        "title": "Pipeline Configuration",
-                        "card": {
-                            "type": "AdaptiveCard",
-                            "\$schema": "http://adaptivecards.io/schemas/adaptive-card.json",
-                            "body": [
-                                {
-                                    "type": "FactSet",
-                                    "facts": [<% out << summary.collect{ k,v -> "{\"title\": \"$k\", \"value\" : \"$v\"}"}.join(",\n") %>
-                                    ]
-                                }
-                            ]
-                        }
-                    }
-                ]
-            }
-        }
-    ]
-}
diff --git a/assets/email_template.html b/assets/email_template.html
index 8baab9e..0fec607 100644
--- a/assets/email_template.html
+++ b/assets/email_template.html
@@ -4,21 +4,21 @@
   <meta http-equiv="X-UA-Compatible" content="IE=edge">
   <meta name="viewport" content="width=device-width, initial-scale=1">
 
-  <meta name="description" content="nf-core/dmscore: Until now, most Deep Mutational Scanning (DMS) experiments relied on variant-specific barcoded libraries for sequencing. This method enabled DMS on large proteins and led to many great publications. Recently, efforts have increased to make use of the classic and more simple random fragmentation-based short-read sequencing (“shotgun-sequencing”). This saves time and money and due to its simpler experimental design is less prone to mistakes. dmscore handles the essential computational steps, processing the raw FASTQ files and generating a count table of variants. Along the way, it provides multiple QC metrics, enabling users to quickly evaluate the success of their experimental setup.">
-  <title>nf-core/dmscore Pipeline Report</title>
+  <meta name="description" content="nf-core/deepmutscan: Processing and analysis of deep mutational scanning (DMS) data from shotgun and amplicon sequencing: read alignment, variant counting, sequencing-error correction, library QC and fitness estimation">
+  <title>nf-core/deepmutscan Pipeline Report</title>
 </head>
 <body>
 <div style="font-family: Helvetica, Arial, sans-serif; padding: 30px; max-width: 800px; margin: 0 auto;">
 
 <img src="cid:nfcorepipelinelogo">
 
-<h1>nf-core/dmscore ${version}</h1>
+<h1>nf-core/deepmutscan ${version}</h1>
 <h2>Run Name: $runName</h2>
 
 <% if (!success){
     out << """
     <div style="color: #a94442; background-color: #f2dede; border-color: #ebccd1; padding: 15px; margin-bottom: 20px; border: 1px solid transparent; border-radius: 4px;">
-        <h4 style="margin-top:0; color: inherit;">nf-core/dmscore execution completed unsuccessfully!</h4>
+        <h4 style="margin-top:0; color: inherit;">nf-core/deepmutscan execution completed unsuccessfully!</h4>
         <p>The exit status of the task that caused the workflow execution to fail was: <code>$exitStatus</code>.</p>
         <p>The full error message was:</p>
         <pre style="white-space: pre-wrap; overflow: visible; margin-bottom: 0;">${errorReport}</pre>
@@ -27,7 +27,7 @@ <h4 style="margin-top:0; color: inherit;">nf-core/dmscore execution completed un
 } else {
     out << """
     <div style="color: #3c763d; background-color: #dff0d8; border-color: #d6e9c6; padding: 15px; margin-bottom: 20px; border: 1px solid transparent; border-radius: 4px;">
-        nf-core/dmscore execution completed successfully!
+        nf-core/deepmutscan execution completed successfully!
     </div>
     """
 }
@@ -44,8 +44,8 @@ <h3>Pipeline Configuration:</h3>
     </tbody>
 </table>
 
-<p>nf-core/dmscore</p>
-<p><a href="https://github.com/nf-core/dmscore">https://github.com/nf-core/dmscore</a></p>
+<p>nf-core/deepmutscan</p>
+<p><a href="https://github.com/nf-core/deepmutscan">https://github.com/nf-core/deepmutscan</a></p>
 
 </div>
 
diff --git a/assets/email_template.txt b/assets/email_template.txt
index 4247bc4..907c089 100644
--- a/assets/email_template.txt
+++ b/assets/email_template.txt
@@ -4,15 +4,15 @@
   |\\ | |__  __ /  ` /  \\ |__) |__         }  {
   | \\| |       \\__, \\__/ |  \\ |___     \\`-._,-`-,
                                         `._,._,'
-  nf-core/dmscore ${version}
+  nf-core/deepmutscan ${version}
 ----------------------------------------------------
 Run Name: $runName
 
 <% if (success){
-    out << "## nf-core/dmscore execution completed successfully! ##"
+    out << "## nf-core/deepmutscan execution completed successfully! ##"
 } else {
     out << """####################################################
-## nf-core/dmscore execution completed unsuccessfully! ##
+## nf-core/deepmutscan execution completed unsuccessfully! ##
 ####################################################
 The exit status of the task that caused the workflow execution to fail was: $exitStatus.
 The full error message was:
@@ -35,5 +35,5 @@ Pipeline Configuration:
 <% out << summary.collect{ k,v -> " - $k: $v" }.join("\n") %>
 
 --
-nf-core/dmscore
-https://github.com/nf-core/dmscore
+nf-core/deepmutscan
+https://github.com/nf-core/deepmutscan
diff --git a/assets/error_correction_report/ec_report_template.html b/assets/error_correction_report/ec_report_template.html
new file mode 100644
index 0000000..70f4708
--- /dev/null
+++ b/assets/error_correction_report/ec_report_template.html
@@ -0,0 +1,1839 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <title>nf-core/deepmutscan · sequencing-error correction · __SAMPLE__</title>
+    <style>
+      :root {
+        --bg: #ffffff;
+        --panel: #f7f8fa;
+        --ink: #1a1f2b;
+        --ink2: #5b6470;
+        --muted: #98a2b3;
+        --line: #e6e9ef;
+        --grid: #eef1f5;
+        --accent: #2f6bd8;
+        --accent-soft: #d7e2f7;
+        --pos: #2f9e6b;
+        --neg: #d1495b;
+        --ring: #ffffff;
+      }
+      @media (prefers-color-scheme: dark) {
+        :root {
+          --bg: #0f1219;
+          --panel: #161b24;
+          --ink: #e8ecf3;
+          --ink2: #a8b2c1;
+          --muted: #6b7688;
+          --line: #232a36;
+          --grid: #1c222d;
+          --accent: #5b90ee;
+          --accent-soft: #22304a;
+          --pos: #3fb883;
+          --neg: #e06377;
+          --ring: #0f1219;
+        }
+      }
+      :root[data-theme="dark"] {
+        --bg: #0f1219;
+        --panel: #161b24;
+        --ink: #e8ecf3;
+        --ink2: #a8b2c1;
+        --muted: #6b7688;
+        --line: #232a36;
+        --grid: #1c222d;
+        --accent: #5b90ee;
+        --accent-soft: #22304a;
+        --pos: #3fb883;
+        --neg: #e06377;
+        --ring: #0f1219;
+      }
+      :root[data-theme="light"] {
+        --bg: #ffffff;
+        --panel: #f7f8fa;
+        --ink: #1a1f2b;
+        --ink2: #5b6470;
+        --muted: #98a2b3;
+        --line: #e6e9ef;
+        --grid: #eef1f5;
+        --accent: #2f6bd8;
+        --accent-soft: #d7e2f7;
+        --pos: #2f9e6b;
+        --neg: #d1495b;
+        --ring: #ffffff;
+      }
+      /* Substitution-class palette. Fixed slot order (a class never changes colour
+         with the data); validated as a set for CVD separation, lightness band and
+         chroma against this report's panel surfaces, light and dark. */
+      :root {
+        --c1: #2a78d6;
+        --c2: #1baf7a;
+        --c3: #eda100;
+        --c4: #008300;
+        --c5: #4a3aa7;
+        --c6: #e34948;
+      }
+      @media (prefers-color-scheme: dark) {
+        :root {
+          --c1: #3987e5;
+          --c2: #199e70;
+          --c3: #c98500;
+          --c4: #008300;
+          --c5: #9085e9;
+          --c6: #e66767;
+        }
+      }
+      :root[data-theme="dark"] {
+        --c1: #3987e5;
+        --c2: #199e70;
+        --c3: #c98500;
+        --c4: #008300;
+        --c5: #9085e9;
+        --c6: #e66767;
+      }
+      :root[data-theme="light"] {
+        --c1: #2a78d6;
+        --c2: #1baf7a;
+        --c3: #eda100;
+        --c4: #008300;
+        --c5: #4a3aa7;
+        --c6: #e34948;
+      }
+      * {
+        box-sizing: border-box;
+      }
+      body {
+        margin: 0;
+        background: var(--bg);
+        color: var(--ink);
+        font:
+          14px/1.5 -apple-system,
+          BlinkMacSystemFont,
+          "Segoe UI",
+          Roboto,
+          Helvetica,
+          Arial,
+          sans-serif;
+        -webkit-font-smoothing: antialiased;
+      }
+      .wrap {
+        max-width: 1080px;
+        margin: 0 auto;
+        padding: 28px 22px 60px;
+      }
+      header {
+        display: flex;
+        align-items: baseline;
+        gap: 14px;
+        flex-wrap: wrap;
+        margin-bottom: 4px;
+      }
+      h1 {
+        font-size: 20px;
+        font-weight: 650;
+        margin: 0;
+        letter-spacing: -0.01em;
+      }
+      .lead {
+        color: var(--ink2);
+        font-size: 13px;
+        margin: 6px 0 0;
+        max-width: 760px;
+      }
+      .sub {
+        color: var(--ink2);
+        font-size: 13px;
+      }
+      .badge {
+        display: inline-block;
+        padding: 2px 9px;
+        border-radius: 999px;
+        background: var(--accent-soft);
+        color: var(--accent);
+        font-size: 12px;
+        font-weight: 600;
+        border: 1px solid transparent;
+      }
+      .tiles {
+        display: grid;
+        grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
+        gap: 12px;
+        margin: 20px 0 26px;
+      }
+      .tile {
+        background: var(--panel);
+        border: 1px solid var(--line);
+        border-radius: 12px;
+        padding: 14px 16px;
+      }
+      .tile .k {
+        color: var(--ink2);
+        font-size: 12px;
+        font-weight: 500;
+      }
+      .tile .v {
+        font-size: 22px;
+        font-weight: 650;
+        letter-spacing: -0.02em;
+        margin-top: 3px;
+      }
+      .tile .v small {
+        font-size: 13px;
+        font-weight: 500;
+        color: var(--muted);
+      }
+      .sechead {
+        display: flex;
+        align-items: baseline;
+        gap: 10px;
+        margin: 34px 0 2px;
+      }
+      .sechead .n {
+        font-size: 12px;
+        font-weight: 700;
+        color: var(--accent);
+        letter-spacing: 0.04em;
+      }
+      .sechead h2 {
+        font-size: 16px;
+        font-weight: 650;
+        margin: 0;
+        letter-spacing: -0.01em;
+      }
+      .secsub {
+        color: var(--ink2);
+        font-size: 12.5px;
+        margin: 3px 0 14px;
+        max-width: 820px;
+      }
+      .panel {
+        background: var(--panel);
+        border: 1px solid var(--line);
+        border-radius: 14px;
+        padding: 16px 18px;
+        margin-bottom: 18px;
+      }
+      .panel h3 {
+        font-size: 13.5px;
+        font-weight: 600;
+        margin: 0 0 2px;
+      }
+      .panel .desc {
+        color: var(--ink2);
+        font-size: 12.5px;
+        margin: 0 0 12px;
+      }
+      .grid2 {
+        display: grid;
+        grid-template-columns: 1fr 1fr;
+        gap: 18px;
+      }
+      @media (max-width: 760px) {
+        .grid2 {
+          grid-template-columns: 1fr;
+        }
+      }
+      svg {
+        display: block;
+        width: 100%;
+        height: auto;
+        overflow: visible;
+      }
+      .axis {
+        stroke: var(--line);
+        stroke-width: 1;
+      }
+      .grid {
+        stroke: var(--grid);
+        stroke-width: 1;
+      }
+      .tick {
+        fill: var(--muted);
+        font-size: 10px;
+      }
+      .albl {
+        fill: var(--ink2);
+        font-size: 11px;
+      }
+      .tt {
+        position: fixed;
+        pointer-events: none;
+        background: var(--ink);
+        color: var(--bg);
+        font-size: 12px;
+        padding: 5px 8px;
+        border-radius: 7px;
+        opacity: 0;
+        transition: opacity 0.08s;
+        white-space: nowrap;
+        z-index: 50;
+        box-shadow: 0 4px 14px rgba(0, 0, 0, 0.18);
+      }
+      .controls {
+        display: flex;
+        gap: 10px;
+        align-items: center;
+        margin-bottom: 10px;
+        flex-wrap: wrap;
+      }
+      input[type="search"] {
+        flex: 1;
+        min-width: 160px;
+        background: var(--bg);
+        border: 1px solid var(--line);
+        border-radius: 9px;
+        padding: 7px 11px;
+        color: var(--ink);
+        font-size: 13px;
+      }
+      table {
+        width: 100%;
+        border-collapse: collapse;
+        font-variant-numeric: tabular-nums;
+      }
+      .tblwrap {
+        max-height: 420px;
+        overflow: auto;
+        border: 1px solid var(--line);
+        border-radius: 10px;
+      }
+      th,
+      td {
+        padding: 7px 12px;
+        text-align: right;
+        border-bottom: 1px solid var(--line);
+        white-space: nowrap;
+      }
+      th:first-child,
+      td:first-child {
+        text-align: left;
+      }
+      thead th {
+        position: sticky;
+        top: 0;
+        background: var(--panel);
+        cursor: pointer;
+        user-select: none;
+        font-size: 12px;
+        color: var(--ink2);
+        font-weight: 600;
+        z-index: 1;
+      }
+      thead th:hover {
+        color: var(--ink);
+      }
+      th .ar {
+        color: var(--muted);
+        font-size: 10px;
+      }
+      td.pos {
+        color: var(--pos);
+      }
+      td.neg {
+        color: var(--neg);
+      }
+      tbody tr:hover {
+        background: var(--bg);
+      }
+      .foot {
+        color: var(--muted);
+        font-size: 11.5px;
+        margin-top: 26px;
+        text-align: center;
+      }
+      .sub2 {
+        font-size: 12.5px;
+        font-weight: 600;
+        color: var(--ink2);
+        margin: 0 0 8px;
+      }
+      .legend {
+        display: flex;
+        flex-wrap: wrap;
+        gap: 6px;
+        margin: 2px 0 12px;
+      }
+      .row {
+        display: flex;
+        flex-wrap: wrap;
+        gap: 6px;
+        align-items: center;
+      }
+      .sw {
+        display: inline-block;
+        width: 10px;
+        height: 10px;
+        border-radius: 3px;
+        flex: none;
+        vertical-align: middle;
+      }
+      code {
+        font-family: ui-monospace, SFMono-Regular, Menlo, monospace;
+        font-size: 12px;
+      }
+      .chip {
+        display: inline-flex;
+        align-items: center;
+        gap: 6px;
+        padding: 3px 9px 3px 7px;
+        border: 1px solid var(--line);
+        border-radius: 999px;
+        background: var(--bg);
+        color: var(--ink2);
+        font-size: 12px;
+        font-variant-numeric: tabular-nums;
+        cursor: pointer;
+        user-select: none;
+      }
+      .chip:hover {
+        color: var(--ink);
+      }
+      .chip .sw {
+        width: 10px;
+        height: 10px;
+        border-radius: 3px;
+        flex: none;
+      }
+      .chip.off {
+        opacity: 0.38;
+      }
+      .chip.off .sw {
+        background: var(--muted) !important;
+      }
+      /* single-file selector: a segmented control, exactly one active */
+      .seg {
+        display: inline-flex;
+        gap: 4px;
+        flex-wrap: wrap;
+        margin-bottom: 12px;
+      }
+      .seg .opt {
+        padding: 4px 11px;
+        border: 1px solid var(--line);
+        border-radius: 8px;
+        background: var(--bg);
+        color: var(--ink2);
+        font-size: 12.5px;
+        cursor: pointer;
+        user-select: none;
+      }
+      .seg .opt.on {
+        background: var(--accent-soft);
+        color: var(--accent);
+        border-color: transparent;
+        font-weight: 600;
+      }
+      .clbl {
+        font-size: 11px;
+        color: var(--ink2);
+        font-weight: 600;
+      }
+      /* info button + popover */
+      .ibtn {
+        display: inline-flex;
+        align-items: center;
+        justify-content: center;
+        width: 15px;
+        height: 15px;
+        border-radius: 50%;
+        border: 1px solid var(--line);
+        background: var(--bg);
+        color: var(--ink2);
+        font-size: 10px;
+        font-weight: 700;
+        line-height: 1;
+        cursor: pointer;
+        margin-left: 6px;
+        padding: 0;
+        vertical-align: 1px;
+      }
+      .ibtn:hover {
+        color: var(--ink);
+        border-color: var(--muted);
+      }
+      .ipop {
+        position: fixed;
+        z-index: 20;
+        max-width: 320px;
+        background: var(--panel);
+        color: var(--ink);
+        border: 1px solid var(--line);
+        border-radius: 9px;
+        padding: 9px 11px;
+        font-size: 12px;
+        line-height: 1.45;
+        box-shadow: 0 8px 26px rgba(0, 0, 0, 0.18);
+      }
+      .empty {
+        color: var(--muted);
+        font-size: 12.5px;
+        padding: 22px 0;
+        text-align: center;
+      }
+      /* the correlation matrix is roughly square; left to fill the column it would tower over the page,
+         so it is capped and centred */
+      #c-corr {
+        max-width: 560px;
+        margin: 0 auto;
+      }
+      /* section-3 heading + file selector pin to the top while its panels scroll past, so the reader can
+         always see (and change) which file is driving them */
+      .stick {
+        position: sticky;
+        top: 0;
+        z-index: 6;
+        background: var(--bg);
+        padding-bottom: 8px;
+        margin-bottom: 4px;
+        box-shadow: 0 6px 10px -8px rgba(0, 0, 0, 0.35);
+      }
+      .stick .sechead {
+        margin-top: 22px;
+      }
+      .stick .secsub {
+        margin-bottom: 8px;
+      }
+      /* lower-triangle scatter cells: clickable, and outlined on hover / while open to signal it */
+      #c-corr rect.scell {
+        cursor: pointer;
+      }
+      #c-corr rect.scell:hover,
+      #c-corr rect.scell.hot {
+        stroke: var(--accent);
+        stroke-width: 1.5;
+      }
+      /* floating card holding the enlarged pairwise scatter */
+      #corr-zoom {
+        position: fixed;
+        left: 50%;
+        top: 50%;
+        transform: translate(-50%, -50%);
+        z-index: 40;
+        display: none;
+        background: var(--panel);
+        border: 1px solid var(--line);
+        border-radius: 12px;
+        box-shadow: 0 12px 40px rgba(0, 0, 0, 0.35);
+        padding: 12px 14px;
+      }
+      #corr-zoom.on {
+        display: block;
+      }
+      #corr-zoom .cz-head {
+        display: flex;
+        align-items: center;
+        gap: 10px;
+        font-size: 12px;
+        color: var(--ink2);
+        margin-bottom: 6px;
+      }
+      #corr-zoom .cz-x {
+        margin-left: auto;
+        border: none;
+        background: transparent;
+        color: var(--ink2);
+        font-size: 18px;
+        line-height: 1;
+        cursor: pointer;
+      }
+    </style>
+  </head>
+  <body>
+    <div class="wrap">
+      <header>
+        <h1>Sequencing-error correction</h1>
+        <span class="sub">sample <b id="h-sample"></b></span>
+        <span class="badge" id="h-method"></span>
+      </header>
+      <p class="lead" id="h-lead"></p>
+
+      <div class="tiles" id="tiles"></div>
+
+      <!-- ============ SECTION 1 · comparison across files ============ -->
+      <div class="sechead">
+        <span class="n">1 · COMPARE</span>
+        <h2>
+          Reproducibility of the error signature
+          <button
+            class="ibtn"
+            type="button"
+            data-info="Upper triangle: Pearson r between two files of the per-variant removed error frequency (raw − corrected counts-per-coverage) over shared SNVs. Lower triangle: the same pair as a scatter — click to enlarge, hover a point for its variant."
+          >
+            i
+          </button>
+        </h2>
+      </div>
+      <p class="secsub">
+        Where errors recur. The upper triangle scores how strongly two files agree on which variants are error-prone;
+        the lower triangle shows each pair as a scatter (click to enlarge). Higher agreement means the correction
+        reflects sequencing chemistry rather than chance.
+      </p>
+      <div class="panel">
+        <div id="c-corr"></div>
+      </div>
+
+      <!-- ============ SECTION 2 · raw vs corrected, input / output ============ -->
+      <div class="sechead">
+        <span class="n">2 · SELECTION</span>
+        <h2>
+          Raw versus corrected frequency
+          <button
+            class="ibtn"
+            type="button"
+            data-info="Per-SNV raw vs corrected frequency (counts ÷ coverage); dashed y = x marks no change. The 'removed' lane holds variants floored to zero, which the log axis cannot place. Error bars span the spread across selected files."
+          >
+            i
+          </button>
+        </h2>
+      </div>
+      <p class="secsub">
+        Effect of correction on measured variants, split by input and output libraries. Toggle files; with two or more,
+        the bars span their spread.
+      </p>
+      <div class="grid2" id="scatter-groups"></div>
+
+      <!-- ============ SECTION 3 · one file at a time ============ -->
+      <!-- header + file selector stay pinned while scrolling this section, so it is always clear which
+           file the panels below are showing -->
+      <div class="stick">
+        <div class="sechead">
+          <span class="n">3 · DETAIL</span>
+          <h2>
+            One sequencing file in detail
+            <button
+              class="ibtn"
+              type="button"
+              data-info="Every panel in this section is computed from the file chosen in the selector."
+            >
+              i
+            </button>
+          </h2>
+        </div>
+        <p class="secsub">Choose one file; every panel below recomputes for it alone.</p>
+        <div class="seg" id="file-seg"></div>
+      </div>
+
+      <div class="panel" id="snv-panel">
+        <div class="legend" id="snv-legend"></div>
+        <div class="grid2">
+          <div>
+            <div class="sub2">
+              Sequencing-error frequency by substitution class
+              <button
+                class="ibtn"
+                type="button"
+                data-info="Per-SNV removed error frequency (raw − corrected counts-per-coverage ≈ per-nt error rate) in the selected file, by strand-symmetric substitution class; tick = class median."
+              >
+                i
+              </button>
+            </div>
+            <div id="c-bee"></div>
+          </div>
+          <div>
+            <div class="sub2">
+              Correction magnitude
+              <button
+                class="ibtn"
+                type="button"
+                data-info="Percentage of each SNV's counts removed in the selected file; untouched variants are omitted."
+              >
+                i
+              </button>
+            </div>
+            <div id="c-hist"></div>
+          </div>
+        </div>
+      </div>
+
+      <div class="panel">
+        <h3>
+          Where errors concentrate along the ORF
+          <button
+            class="ibtn"
+            type="button"
+            data-info="Fraction of counts removed per residue along the reading frame, selected file."
+          >
+            i
+          </button>
+        </h3>
+        <p class="desc">Fraction of counts removed at each residue position.</p>
+        <div id="c-bias"></div>
+      </div>
+
+      <div class="panel">
+        <h3>
+          Single-nucleotide substitutions by position
+          <button
+            class="ibtn"
+            type="button"
+            data-info="One row per SNV of the selected file, in reference (base_mut) coordinates; frequencies are counts ÷ coverage."
+          >
+            i
+          </button>
+        </h3>
+        <div class="controls">
+          <input
+            type="search"
+            id="snvq"
+            placeholder="Filter (e.g. K181M, 893, T&gt;A)…"
+            aria-label="Filter single-nucleotide substitutions"
+          />
+          <span class="sub" id="snvcount"></span>
+        </div>
+        <div class="tblwrap">
+          <table id="snvtbl">
+            <thead>
+              <tr>
+                <th data-k="ntpos">Position</th>
+                <th data-k="nt">Substitution</th>
+                <th data-k="pos_mut">Residue change</th>
+                <th data-k="cls">Class</th>
+                <th data-k="f_raw">Raw freq</th>
+                <th data-k="f_cor">Corrected freq</th>
+                <th data-k="pct">% change</th>
+              </tr>
+            </thead>
+            <tbody></tbody>
+          </table>
+        </div>
+      </div>
+
+      <div class="foot">nf-core/deepmutscan · sequencing-error correction report</div>
+    </div>
+
+    <div class="tt" id="tt"></div>
+    <div id="corr-zoom"></div>
+
+    <script id="ec-data" type="application/json">
+      __EC_DATA__
+    </script>
+    <script>
+      const DATA = JSON.parse(document.getElementById("ec-data").textContent);
+      const FILES = (DATA.files || []).map((f) => (typeof f === "string" ? { id: f } : f));
+      const V = DATA.variants || [];
+      const CLASSES = DATA.classes || [];
+      const V_BY_KEY = {}; // variant lookup for the correlation-matrix scatter point annotations
+      for (const v of V) V_BY_KEY[v.key] = v;
+      let R_LAST = null; // last correlation matrix (with its keyed pairs), for openScatter
+      const tt = document.getElementById("tt");
+      const fmt = (x, d = 0) =>
+        x == null || isNaN(x) ? "–" : Number(x).toLocaleString(undefined, { maximumFractionDigits: d });
+      const sci = (x) => (x == null || isNaN(x) ? "–" : Number(x).toExponential(2));
+      const esc = (s) => String(s).replace(/&/g, "&amp;").replace(/</g, "&lt;").replace(/"/g, "&quot;");
+      const showTT = (e, html) => {
+        tt.innerHTML = html;
+        tt.style.opacity = 1;
+        tt.style.left = Math.min(e.clientX + 12, innerWidth - tt.offsetWidth - 8) + "px";
+        tt.style.top = e.clientY - tt.offsetHeight - 10 + "px";
+      };
+      const hideTT = () => (tt.style.opacity = 0);
+      const SVGNS = "http://www.w3.org/2000/svg";
+      const el = (n, a = {}) => {
+        const e = document.createElementNS(SVGNS, n);
+        for (const k in a) e.setAttribute(k, a[k]);
+        return e;
+      };
+      const clsColor = (k) => `var(--c${CLASSES.findIndex((c) => c.cls === k) + 1})`;
+      const clsLabel = (k) => (CLASSES.find((c) => c.cls === k) || {}).label || k;
+
+      // One tooltip handler per chart instead of two listeners on each of the thousands of points.
+      // The tip text rides on the element as data-tip, so a single delegated mousemove serves the
+      // whole SVG — far lighter to build and to repaint than per-marker listeners.
+      function tipDelegate(svg) {
+        svg.addEventListener("mousemove", (e) => {
+          const t = e.target.getAttribute && e.target.getAttribute("data-tip");
+          if (t) showTT(e, t);
+          else hideTT();
+        });
+        svg.addEventListener("mouseleave", hideTT);
+      }
+
+      // Short, distinct file labels: strip the prefix and suffix every id shares, so a 6×6 matrix and
+      // the selectors stay readable ("input_1" rather than "GID1_input_1_pe").
+      function shortLabels(ids) {
+        if (ids.length < 2) return ids.slice();
+        let pre = ids[0],
+          suf = ids[0];
+        for (const s of ids) {
+          while (!s.startsWith(pre)) pre = pre.slice(0, -1);
+          while (!s.endsWith(suf)) suf = suf.slice(1);
+        }
+        const a = pre.length,
+          b = suf.length;
+        return ids.map((s) => {
+          const t = s.slice(a, s.length - b).replace(/^[_.-]+|[_.-]+$/g, "");
+          return t || s;
+        });
+      }
+      const SHORT = shortLabels(FILES.map((f) => f.id));
+
+      document.getElementById("h-sample").textContent = DATA.sample;
+      const estimatorLabel = { mle: "maximum-likelihood estimation", eb: "empirical-Bayes estimation" }[DATA.estimator];
+      const methodLabel =
+        DATA.method === "false_doubles"
+          ? `False-doubles correction${estimatorLabel ? ` (${estimatorLabel})` : ""}`
+          : DATA.method === "wildtype"
+            ? "Wildtype-based correction"
+            : DATA.method;
+      document.getElementById("h-method").textContent = methodLabel;
+      document.getElementById("h-lead").textContent =
+        `How ${methodLabel.toLowerCase()} estimated the sequencing-error background in single-nucleotide counts and ` +
+        `what it removed, across ${FILES.length} file${FILES.length === 1 ? "" : "s"} of sample ${DATA.sample}.`;
+
+      /* ---------- per-file, per-variant quantities ---------- */
+      // The error frequency the correction removed from a variant in one file. Equal to the estimated
+      // per-nucleotide error rate at that site (coverage cancels): counts and coverage are what the
+      // report already carries, so nothing extra has to be threaded through the pipeline for this.
+      const removed = (v, fi) => (v.f_raw[fi] != null && v.f_cor[fi] != null ? v.f_raw[fi] - v.f_cor[fi] : null);
+
+      /* ---------- axis helpers ---------- */
+      function textW(svg, s, cls) {
+        const t = el("text", { class: cls, x: -9999, y: -9999 });
+        t.textContent = s;
+        svg.appendChild(t);
+        const w = t.getComputedTextLength();
+        t.remove();
+        return w;
+      }
+      const TICK_PX = 10,
+        LBL_PX = 11;
+
+      /* =========================================================
+         SECTION 1 — cross-file correlation of the error signature
+         ========================================================= */
+      function pearson(pairs) {
+        const n = pairs.length;
+        if (n < 3) return null;
+        let sx = 0,
+          sy = 0,
+          sxx = 0,
+          syy = 0,
+          sxy = 0;
+        for (const [a, b] of pairs) {
+          sx += a;
+          sy += b;
+          sxx += a * a;
+          syy += b * b;
+          sxy += a * b;
+        }
+        const cov = sxy - (sx * sy) / n;
+        const vx = sxx - (sx * sx) / n,
+          vy = syy - (sy * sy) / n;
+        if (vx <= 0 || vy <= 0) return null;
+        return cov / Math.sqrt(vx * vy);
+      }
+      // r -> colour on a sequential ramp anchored at the panel; the matrix is about agreement, so only
+      // the 0..1 range is coloured and anything negative (essentially never seen for a real signature)
+      // shares the low end.
+      function rColor(r) {
+        if (r == null) return "var(--grid)";
+        const t = Math.max(0, Math.min(1, r));
+        return `color-mix(in srgb, var(--accent) ${Math.round(t * 100)}%, var(--bg))`;
+      }
+      function drawCorr() {
+        const host = document.getElementById("c-corr");
+        host.innerHTML = "";
+        const N = FILES.length;
+        if (N < 2) {
+          host.innerHTML =
+            '<div class="empty">A single sequencing file — cross-file reproducibility needs at least two.</div>';
+          return;
+        }
+        // per file: map variant key -> removed error frequency (SNVs only)
+        const maps = FILES.map((_, fi) => {
+          const m = new Map();
+          for (const v of V) {
+            if (!v.cls) continue;
+            const e = removed(v, fi);
+            if (e != null) m.set(v.key, e);
+          }
+          return m;
+        });
+        const R = [];
+        for (let i = 0; i < N; i++) {
+          R.push([]);
+          for (let j = 0; j < N; j++) {
+            if (i === j) {
+              R[i].push({ r: 1, n: maps[i].size });
+              continue;
+            }
+            const pairs = [];
+            const a = maps[i],
+              b = maps[j];
+            const [small, other] = a.size < b.size ? [a, b] : [b, a];
+            // Keyed points are kept only for the lower triangle (i>j), which is where the scatterplots are
+            // drawn; x = column file (j), y = row file (i), so the thumbnail and its enlargement share a
+            // frame with the matrix.
+            const keyed = i > j ? [] : null;
+            for (const [k, va] of small) {
+              const vb = other.get(k);
+              if (vb == null) continue; // r is symmetric, so lane order is irrelevant
+              pairs.push([va, vb]);
+              if (keyed) keyed.push({ key: k, x: maps[j].get(k), y: maps[i].get(k) });
+            }
+            R[i].push({ r: pearson(pairs), n: pairs.length, pairs: keyed });
+          }
+        }
+        R_LAST = R;
+        closeScatter(); // a rebuild (e.g. theme change) invalidates any open cell
+        const off = Math.max(...SHORT.map((s) => s.length)) * 6 + 14;
+        const cell = Math.max(30, Math.min(64, Math.floor((980 - off) / N)));
+        const W = off + N * cell + 12,
+          H = off + N * cell + 34;
+        const svg = el("svg", { viewBox: `0 0 ${W} ${H}` });
+        host.appendChild(svg);
+        for (let i = 0; i < N; i++) {
+          const rl = el("text", { class: "tick", x: off - 6, y: off + i * cell + cell / 2 + 3, "text-anchor": "end" });
+          rl.textContent = SHORT[i];
+          svg.appendChild(rl);
+          const cl = el("text", {
+            class: "tick",
+            x: off + i * cell + cell / 2,
+            y: off - 6,
+            "text-anchor": "start",
+            transform: `rotate(-45 ${off + i * cell + cell / 2} ${off - 6})`,
+          });
+          cl.textContent = SHORT[i];
+          svg.appendChild(cl);
+        }
+        for (let i = 0; i < N; i++)
+          for (let j = 0; j < N; j++) {
+            const c = R[i][j];
+            const x = off + j * cell,
+              y = off + i * cell;
+            if (i > j) {
+              // lower triangle: a scatter thumbnail of the two files' removed-error frequencies. Clickable
+              // (hover outline hints at it); click enlarges it (openScatter), where points annotate on hover.
+              svg.appendChild(
+                el("rect", {
+                  x: x + 1,
+                  y: y + 1,
+                  width: cell - 2,
+                  height: cell - 2,
+                  rx: 4,
+                  fill: "var(--bg)",
+                  stroke: "var(--line)",
+                  "stroke-width": 0.5,
+                }),
+              );
+              if (c.pairs && c.pairs.length >= 3) miniScatter(svg, c.pairs, x + 3, y + 3, cell - 6, rColor(c.r));
+              const hit = el("rect", {
+                x,
+                y,
+                width: cell,
+                height: cell,
+                fill: "transparent",
+                class: "scell",
+                "data-ij": i + "," + j,
+              });
+              hit.addEventListener("mousemove", (e) =>
+                showTT(
+                  e,
+                  `<b>${esc(SHORT[i])} × ${esc(SHORT[j])}</b><br>` +
+                    (c.r == null ? "too few shared variants" : `r = ${c.r.toFixed(3)} · ${fmt(c.n)} shared variants`) +
+                    (c.pairs && c.pairs.length >= 3 ? "<br><span style='opacity:.7'>click to enlarge</span>" : ""),
+                ),
+              );
+              hit.addEventListener("mouseleave", hideTT);
+              if (c.pairs && c.pairs.length >= 3) hit.addEventListener("click", () => openScatter(i, j));
+              svg.appendChild(hit);
+              continue;
+            }
+            const rect = el("rect", {
+              x: x + 1,
+              y: y + 1,
+              width: cell - 2,
+              height: cell - 2,
+              rx: 4,
+              fill: rColor(c.r),
+              stroke: "var(--line)",
+              "stroke-width": 0.5,
+            });
+            svg.appendChild(rect);
+            if (c.r != null && cell >= 34) {
+              const t = el("text", {
+                x: x + cell / 2,
+                y: y + cell / 2 + 3,
+                "text-anchor": "middle",
+                "font-size": 10,
+                "font-variant-numeric": "tabular-nums",
+                fill: c.r > 0.55 ? "var(--bg)" : "var(--ink2)",
+              });
+              t.textContent = i === j ? "—" : c.r.toFixed(2);
+              svg.appendChild(t);
+            }
+            const hit = el("rect", { x, y, width: cell, height: cell, fill: "transparent" });
+            hit.addEventListener("mousemove", (e) =>
+              showTT(
+                e,
+                `<b>${esc(SHORT[i])} × ${esc(SHORT[j])}</b><br>` +
+                  (i === j
+                    ? `${fmt(c.n)} variants`
+                    : c.r == null
+                      ? "too few shared variants"
+                      : `r = ${c.r.toFixed(3)} · ${fmt(c.n)} shared variants`),
+              ),
+            );
+            hit.addEventListener("mouseleave", hideTT);
+            svg.appendChild(hit);
+          }
+        // colour key
+        const kx = off,
+          ky = off + N * cell + 18,
+          kw = Math.min(180, N * cell);
+        for (let s = 0; s < kw; s++) {
+          svg.appendChild(el("rect", { x: kx + s, y: ky, width: 1, height: 8, fill: rColor(s / kw) }));
+        }
+        ["0", "0.5", "1"].forEach((lab, i) => {
+          const t = el("text", {
+            class: "tick",
+            x: kx + (kw * i) / 2,
+            y: ky + 20,
+            "text-anchor": i === 0 ? "start" : i === 2 ? "end" : "middle",
+          });
+          t.textContent = lab;
+          svg.appendChild(t);
+        });
+        const kl = el("text", { class: "tick", x: kx + kw + 10, y: ky + 8, "text-anchor": "start" });
+        kl.textContent = "correlation (r)";
+        svg.appendChild(kl);
+      }
+
+      // A shared log scale for a set of {x,y} error-frequency pairs (both axes use it, so y=x reads true).
+      function pairScale(pairs) {
+        const vals = pairs.flatMap((p) => [p.x, p.y]).filter((v) => v > 0);
+        const lo = vals.length ? Math.min(...vals) : 1e-6;
+        const hi = vals.length ? Math.max(...vals) : 1;
+        const l10 = (v) => Math.log10(Math.max(v, lo / 2));
+        const span = l10(hi) - l10(lo) || 1;
+        // Normalised 0..1 position, CLAMPED: a variant the correction left alone removes 0, which on a log
+        // axis falls below the floor and would otherwise plot at a negative coordinate - outside the cell.
+        // Clamping pins those to the bottom-left edge instead of letting the cloud spill past its box.
+        const nrm = (v) => Math.max(0, Math.min(1, (l10(v) - l10(lo)) / span));
+        return { lo, hi, l10, span, nrm };
+      }
+
+      // Thumbnail scatter inside a matrix cell: just the dot cloud, no axes.
+      function miniScatter(svg, pairs, x0, y0, size, col) {
+        const s = pairScale(pairs);
+        const px = (v) => x0 + s.nrm(v) * size;
+        const py = (v) => y0 + size - s.nrm(v) * size;
+        for (const p of pairs) {
+          svg.appendChild(
+            el("circle", { cx: px(p.x), cy: py(p.y), r: 0.7, fill: col || "var(--accent)", "fill-opacity": 0.5 }),
+          );
+        }
+      }
+
+      // The enlarged, hoverable scatter shown on click. Lives in a floating card (#corr-zoom).
+      let corrOpen = null; // "i,j" of the open cell, or null
+      function openScatter(i, j) {
+        const zoom = document.getElementById("corr-zoom");
+        const cell = R_LAST[i][j];
+        if (!cell || !cell.pairs) return;
+        corrOpen = i + "," + j;
+        const S = 360,
+          m = { l: 60, r: 12, t: 12, b: 46 };
+        const iw = S - m.l - m.r,
+          ih = S - m.t - m.b;
+        const sc = pairScale(cell.pairs);
+        const px = (v) => m.l + sc.nrm(v) * iw;
+        const py = (v) => m.t + ih - sc.nrm(v) * ih;
+        const svg = el("svg", { viewBox: `0 0 ${S} ${S}`, width: S, height: S });
+        tipDelegate(svg);
+        // y = x reference
+        svg.appendChild(
+          el("line", {
+            x1: px(sc.lo),
+            y1: py(sc.lo),
+            x2: px(sc.hi),
+            y2: py(sc.hi),
+            stroke: "var(--ink2)",
+            "stroke-dasharray": "4 4",
+            "stroke-width": 1,
+          }),
+        );
+        [0, 1, 2, 3].forEach((k) => {
+          const gv = Math.pow(10, sc.l10(sc.lo) + (sc.span * k) / 3);
+          svg.appendChild(el("line", { class: "grid", x1: m.l, x2: m.l + iw, y1: py(gv), y2: py(gv) }));
+          const ty = el("text", { class: "tick", x: m.l - 6, y: py(gv) + 3, "text-anchor": "end" });
+          ty.textContent = gv.toExponential(0);
+          svg.appendChild(ty);
+          const tx = el("text", { class: "tick", x: px(gv), y: m.t + ih + TICK_PX + 4, "text-anchor": "middle" });
+          tx.textContent = gv.toExponential(0);
+          svg.appendChild(tx);
+        });
+        for (const p of cell.pairs) {
+          const vv = V_BY_KEY[p.key];
+          svg.appendChild(
+            el("circle", {
+              cx: px(p.x),
+              cy: py(p.y),
+              r: 2.2,
+              fill: "var(--accent)", // stay blue like the thumbnail; class colour is not the point here
+              "fill-opacity": 0.7,
+              "data-tip": `<b>${esc(vv ? vv.pos_mut : p.key)}</b>${vv ? ` · <code>${esc(vv.nt)}</code>` : ""}<br>${esc(SHORT[j])} ${sci(p.x)}<br>${esc(SHORT[i])} ${sci(p.y)}`,
+            }),
+          );
+        }
+        const xl = el("text", { class: "albl", x: m.l + iw / 2, y: S - 6, "text-anchor": "middle" });
+        xl.textContent = SHORT[j] + " — removed error frequency";
+        svg.appendChild(xl);
+        const yl = el("text", {
+          class: "albl",
+          x: -(m.t + ih / 2),
+          y: 14,
+          transform: "rotate(-90)",
+          "text-anchor": "middle",
+        });
+        yl.textContent = SHORT[i] + " — removed error frequency";
+        svg.appendChild(yl);
+        zoom.innerHTML = `<div class="cz-head">${esc(SHORT[i])} × ${esc(SHORT[j])} · r = ${cell.r == null ? "–" : cell.r.toFixed(3)} · ${fmt(cell.pairs.length)} shared variants<button class="cz-x" type="button" aria-label="close">×</button></div>`;
+        zoom.appendChild(svg);
+        zoom.classList.add("on");
+        zoom.querySelector(".cz-x").addEventListener("click", closeScatter);
+        // reflect the open cell in the matrix
+        document
+          .querySelectorAll("#c-corr .scell")
+          .forEach((r) => r.classList.toggle("hot", r.dataset.ij === corrOpen));
+      }
+      function closeScatter() {
+        const zoom = document.getElementById("corr-zoom");
+        if (zoom) {
+          zoom.classList.remove("on");
+          zoom.innerHTML = "";
+        }
+        corrOpen = null;
+        document.querySelectorAll("#c-corr .scell.hot").forEach((r) => r.classList.remove("hot"));
+      }
+      // Dismiss: a click that is neither inside the card nor on a scatter cell (clicking another cell just
+      // re-opens via its own handler), and whenever the matrix scrolls out of view.
+      document.addEventListener("click", (e) => {
+        if (!corrOpen) return;
+        if (e.target.closest("#corr-zoom") || e.target.closest(".scell")) return;
+        closeScatter();
+      });
+
+      /* =========================================================
+         SECTION 2 — raw vs corrected, split by library type
+         ========================================================= */
+      // one independent selection set per type group
+      const groupSel = {};
+      function typeGroups() {
+        const order = ["input", "output", "quality"];
+        const present = [];
+        for (const t of order) {
+          const idx = FILES.map((f, i) => i).filter((i) => (FILES[i].type || "") === t);
+          if (idx.length) present.push({ type: t, idx });
+        }
+        // any unforeseen type still gets a panel rather than vanishing
+        const known = new Set(order);
+        const rest = FILES.map((f, i) => i).filter((i) => !known.has(FILES[i].type || ""));
+        if (rest.length) present.push({ type: FILES[rest[0]].type || "other", idx: rest });
+        return present;
+      }
+      const GROUPS = typeGroups();
+      GROUPS.forEach((g) => (groupSel[g.type] = new Set(g.idx)));
+
+      function groupTitle(t) {
+        return (
+          { input: "Input libraries", output: "Output libraries", quality: "Quality controls" }[t] || t + " libraries"
+        );
+      }
+
+      function buildGroups() {
+        const host = document.getElementById("scatter-groups");
+        host.innerHTML = "";
+        GROUPS.forEach((g) => {
+          const box = document.createElement("div");
+          box.innerHTML =
+            `<div class="sub2">${esc(groupTitle(g.type))}</div>` +
+            `<div class="row" data-grp="${esc(g.type)}">` +
+            g.idx
+              .map(
+                (i) =>
+                  `<span class="chip${groupSel[g.type].has(i) ? "" : " off"}" data-f="${i}">${esc(SHORT[i])}</span>`,
+              )
+              .join("") +
+            `</div><div id="scat-${esc(g.type)}"></div>`;
+          host.appendChild(box);
+        });
+      }
+      document.getElementById("scatter-groups").addEventListener("click", (e) => {
+        const c = e.target.closest("[data-f]");
+        if (!c) return;
+        const grp = c.closest("[data-grp]").dataset.grp;
+        const i = +c.dataset.f;
+        const set = groupSel[grp];
+        if (set.has(i)) set.delete(i);
+        else set.add(i);
+        if (!set.size) GROUPS.find((g) => g.type === grp).idx.forEach((k) => set.add(k));
+        buildGroups();
+        GROUPS.forEach((g) => drawScatter(g));
+      });
+
+      // mean and sample-SD of a per-file quantity over the selected files of a group
+      function aggSel(v, field, set) {
+        const vals = [];
+        for (const i of set) {
+          const x = v[field][i];
+          if (x != null && !isNaN(x)) vals.push(x);
+        }
+        if (!vals.length) return null;
+        const mean = vals.reduce((a, b) => a + b, 0) / vals.length;
+        let sd = null;
+        if (vals.length > 1) {
+          let ss = 0;
+          for (const x of vals) ss += (x - mean) * (x - mean);
+          sd = Math.sqrt(ss / (vals.length - 1));
+        }
+        return { mean, sd, n: vals.length };
+      }
+
+      function drawScatter(g) {
+        const host = document.getElementById("scat-" + g.type);
+        host.innerHTML = "";
+        const set = groupSel[g.type];
+        const pts = [];
+        for (const v of V) {
+          if (!v.cls) continue;
+          const fr = aggSel(v, "f_raw", set);
+          if (!fr || !(fr.mean > 0)) continue;
+          const fc = aggSel(v, "f_cor", set);
+          pts.push({ v, fr: fr.mean, frsd: fr.sd, fc: fc ? fc.mean : 0, fcsd: fc ? fc.sd : null, n: fr.n });
+        }
+        if (!pts.length) {
+          host.innerHTML = '<div class="empty">No variants for this selection.</div>';
+          return;
+        }
+        const W = 500,
+          H = 260,
+          m = { l: 0, r: 10, t: 10, b: 0 };
+        const svg = el("svg", { viewBox: `0 0 ${W} ${H}` });
+        host.appendChild(svg);
+        tipDelegate(svg);
+        const xs = pts.flatMap((p) => [p.fr, p.frsd ? p.fr + p.frsd : p.fr]);
+        const ys = pts.filter((p) => p.fc > 0).flatMap((p) => [p.fc, p.fcsd ? p.fc + p.fcsd : p.fc]);
+        const lo = Math.min(...xs, ...(ys.length ? ys : xs)),
+          hi = Math.max(...xs, ...(ys.length ? ys : xs));
+        const l10 = (v) => Math.log10(Math.max(v, lo / 2));
+        const span = l10(hi) - l10(lo) || 1;
+        const labels = [0, 1, 2, 3].map((i) => Math.pow(10, l10(lo) + (span * i) / 3).toExponential(0));
+        m.l = Math.ceil(LBL_PX + 18 + Math.max(...labels.map((s) => textW(svg, s, "tick"))) + 8);
+        m.b = TICK_PX + 8 + LBL_PX + 14;
+        const iw = W - m.l - m.r,
+          ih = H - m.t - m.b;
+        const lane = 16;
+        const x = (v) => m.l + ((l10(v) - l10(lo)) / span) * iw;
+        const y = (v) => m.t + (ih - lane) - ((l10(v) - l10(lo)) / span) * (ih - lane);
+        const yZero = m.t + ih - lane / 2;
+        [0, 1, 2, 3].forEach((i) => {
+          const gv = Math.pow(10, l10(lo) + (span * i) / 3);
+          const gy = y(gv);
+          svg.appendChild(el("line", { class: "grid", x1: m.l, x2: m.l + iw, y1: gy, y2: gy }));
+          const t = el("text", { class: "tick", x: m.l - 8, y: gy + 3, "text-anchor": "end" });
+          t.textContent = gv.toExponential(0);
+          svg.appendChild(t);
+          const tx = el("text", { class: "tick", x: x(gv), y: m.t + ih + TICK_PX + 4, "text-anchor": "middle" });
+          tx.textContent = gv.toExponential(0);
+          svg.appendChild(tx);
+        });
+        svg.appendChild(
+          el("line", { x1: m.l, x2: m.l + iw, y1: yZero, y2: yZero, stroke: "var(--line)", "stroke-dasharray": "3 3" }),
+        );
+        const lz = el("text", { class: "tick", x: m.l - 8, y: yZero + 3, "text-anchor": "end" });
+        lz.textContent = "removed";
+        svg.appendChild(lz);
+        svg.appendChild(
+          el("line", {
+            x1: x(lo),
+            y1: y(lo),
+            x2: x(hi),
+            y2: y(hi),
+            stroke: "var(--ink2)",
+            "stroke-dasharray": "4 4",
+            "stroke-width": 1,
+          }),
+        );
+        pts.forEach((p) => {
+          const px = x(p.fr),
+            py = p.fc > 0 ? y(p.fc) : yZero;
+          const col = clsColor(p.v.cls);
+          if (p.frsd != null) {
+            const x0 = Math.max(p.fr - p.frsd, lo / 2);
+            svg.appendChild(
+              el("line", {
+                x1: x(x0),
+                x2: x(p.fr + p.frsd),
+                y1: py,
+                y2: py,
+                stroke: col,
+                "stroke-width": 1,
+                "stroke-opacity": 0.35,
+              }),
+            );
+          }
+          // vertical (corrected-frequency) spread bar, mirroring the horizontal raw one
+          if (p.fc > 0 && p.fcsd != null) {
+            svg.appendChild(
+              el("line", {
+                x1: px,
+                x2: px,
+                y1: y(Math.max(p.fc - p.fcsd, lo / 2)),
+                y2: y(p.fc + p.fcsd),
+                stroke: col,
+                "stroke-width": 1,
+                "stroke-opacity": 0.35,
+              }),
+            );
+          }
+          const pct = p.fr > 0 ? ((p.fc - p.fr) / p.fr) * 100 : null;
+          const c = el("circle", {
+            cx: px,
+            cy: py,
+            r: 2,
+            fill: col,
+            "fill-opacity": 0.7,
+            "data-tip":
+              `<b>${esc(p.v.pos_mut)}</b> · <code>${esc(p.v.nt)}</code><br>raw ${sci(p.fr)}` +
+              (p.frsd != null ? ` ± ${sci(p.frsd)} (n=${p.n})` : ` (n=${p.n})`) +
+              `<br>corrected ${sci(p.fc)}` +
+              (p.fcsd != null ? ` ± ${sci(p.fcsd)}` : "") +
+              (pct == null ? "" : `<br>${fmt(pct, 1)} % change`),
+          });
+          svg.appendChild(c);
+        });
+        const yl = el("text", {
+          class: "albl",
+          x: -(m.t + ih / 2),
+          y: LBL_PX,
+          transform: "rotate(-90)",
+          "text-anchor": "middle",
+        });
+        yl.textContent = "Corrected frequency";
+        svg.appendChild(yl);
+        const xl = el("text", { class: "albl", x: m.l + iw / 2, y: H - 4, "text-anchor": "middle" });
+        xl.textContent = "Raw frequency";
+        svg.appendChild(xl);
+      }
+
+      /* =========================================================
+         SECTION 3 — single file: bias, beeswarm, magnitude, table
+         ========================================================= */
+      let curFile = 0;
+      let clsFilter = null;
+
+      function buildSeg() {
+        document.getElementById("file-seg").innerHTML = FILES.map(
+          (f, i) => `<span class="opt${i === curFile ? " on" : ""}" data-i="${i}">${esc(SHORT[i])}</span>`,
+        ).join("");
+      }
+      document.getElementById("file-seg").addEventListener("click", (e) => {
+        const o = e.target.closest("[data-i]");
+        if (!o) return;
+        curFile = +o.dataset.i;
+        buildSeg();
+        drawDetail();
+      });
+
+      function drawLegend() {
+        document.getElementById("snv-legend").innerHTML = CLASSES.map(
+          (c, i) =>
+            `<span class="chip${clsFilter && clsFilter !== c.cls ? " off" : ""}" data-c="${esc(c.cls)}">` +
+            `<span class="sw" style="background:var(--c${i + 1})"></span>${esc(c.label)}</span>`,
+        ).join("");
+      }
+      document.getElementById("snv-legend").addEventListener("click", (e) => {
+        const c = e.target.closest("[data-c]");
+        if (!c) return;
+        clsFilter = clsFilter === c.dataset.c ? null : c.dataset.c;
+        drawLegend();
+        drawBee();
+        drawTable();
+      });
+
+      /* --- positional error bias, single file --- */
+      function drawBias() {
+        const host = document.getElementById("c-bias");
+        host.innerHTML = "";
+        const by = new Map();
+        for (const v of V) {
+          if (v.pos == null) continue;
+          const r = v.raw[curFile],
+            c = v.cor[curFile];
+          if (r == null) continue;
+          const p = by.get(v.pos) || { raw: 0, cor: 0 };
+          p.raw += r;
+          p.cor += c == null ? 0 : c;
+          by.set(v.pos, p);
+        }
+        const P = [...by.entries()]
+          .map(([pos, d]) => ({ pos, frac: d.raw > 0 ? 1 - d.cor / d.raw : null }))
+          .filter((d) => d.frac != null)
+          .sort((a, b) => a.pos - b.pos);
+        if (!P.length) return (host.innerHTML = '<div class="empty">No corrected variants in this file.</div>');
+        const W = 1000,
+          H = 230,
+          m = { l: 0, r: 12, t: 10, b: 0 };
+        const svg = el("svg", { viewBox: `0 0 ${W} ${H}` });
+        host.appendChild(svg);
+        const maxP = Math.max(...P.map((d) => d.pos));
+        const maxY = Math.max(0.001, ...P.map((d) => d.frac));
+        const labels = [0, 1, 2, 3, 4].map((i) => (((maxY * i) / 4) * 100).toFixed(1) + "%");
+        m.l = Math.ceil(LBL_PX + 18 + Math.max(...labels.map((s) => textW(svg, s, "tick"))) + 8);
+        m.b = TICK_PX + 8 + LBL_PX + 8;
+        const iw = W - m.l - m.r,
+          ih = H - m.t - m.b;
+        const x = (p) => m.l + ((p - 1) / Math.max(1, maxP - 1)) * iw;
+        const y = (v) => m.t + ih - (v / maxY) * ih;
+        labels.forEach((lab, i) => {
+          const gy = y((maxY * i) / 4);
+          svg.appendChild(el("line", { class: "grid", x1: m.l, x2: m.l + iw, y1: gy, y2: gy }));
+          const t = el("text", { class: "tick", x: m.l - 8, y: gy + 3, "text-anchor": "end" });
+          t.textContent = lab;
+          svg.appendChild(t);
+        });
+        const step = Math.max(1, Math.round(maxP / 10));
+        for (let p = 1; p <= maxP; p += step) {
+          const t = el("text", { class: "tick", x: x(p), y: m.t + ih + TICK_PX + 5, "text-anchor": "middle" });
+          t.textContent = p;
+          svg.appendChild(t);
+        }
+        svg.appendChild(el("line", { class: "axis", x1: m.l, x2: m.l + iw, y1: m.t + ih, y2: m.t + ih }));
+        const yl = el("text", {
+          class: "albl",
+          x: -(m.t + ih / 2),
+          y: LBL_PX,
+          transform: "rotate(-90)",
+          "text-anchor": "middle",
+        });
+        yl.textContent = "Counts removed";
+        svg.appendChild(yl);
+        const xl = el("text", { class: "albl", x: m.l + iw / 2, y: H - 4, "text-anchor": "middle" });
+        xl.textContent = "ORF residue position";
+        svg.appendChild(xl);
+        let d = "";
+        P.forEach((p, i) => (d += (i ? " L " : "M ") + x(p.pos) + " " + y(p.frac)));
+        svg.appendChild(el("path", { d, fill: "none", stroke: "var(--c1)", "stroke-width": 1.5 }));
+        const ov = el("rect", { x: m.l, y: m.t, width: iw, height: ih, fill: "transparent" });
+        svg.appendChild(ov);
+        ov.addEventListener("mousemove", (e) => {
+          const r = svg.getBoundingClientRect(),
+            px = ((e.clientX - r.left) / r.width) * W;
+          const pp = Math.round(((px - m.l) / iw) * (maxP - 1)) + 1;
+          const d0 = P.find((z) => z.pos === pp);
+          if (!d0) return hideTT();
+          showTT(e, `<b>position ${pp}</b><br>${(d0.frac * 100).toFixed(2)} % of counts removed`);
+        });
+        ov.addEventListener("mouseleave", hideTT);
+      }
+
+      /* --- beeswarm: raw & corrected frequency per class, single file --- */
+      // A tidy beeswarm without a physics library. Points are sorted by y, then each is offset in x to
+      // the position nearest the lane centre where its marker does not overlap any already-placed one
+      // (true circle-to-circle distance, so no two dots touch). Nothing is clamped during placement, so
+      // there is never a forced overlap; if the resulting swarm is wider than the lane it is scaled back
+      // to fit, which only nudges points in the very densest classes. Items carry .yPix in, get .dx out.
+      // stable 0..1 hash of a string, for deterministic per-variant jitter
+      function jhash(s) {
+        let h = 0;
+        for (let i = 0; i < s.length; i++) h = (h * 31 + s.charCodeAt(i)) | 0;
+        return ((h >>> 0) % 1000) / 1000;
+      }
+      // Places every point at the nearest free x to its own lane centre (`p.base`), writing an ABSOLUTE
+      // x to `p.absX`. Both lanes of a class must go through one call: placing them separately lets a
+      // dense lane drift sideways into its neighbour's space and overlap it, since the natural swarm
+      // width is deliberately not clamped (compressing to fit a lane is what reintroduces overlaps).
+      function beeswarm(pts, r) {
+        const use = pts.filter((p) => p.yPix != null).sort((a, b) => a.yPix - b.yPix);
+        const placed = [];
+        const min2 = (2 * r + 0.5) * (2 * r + 0.5);
+        for (const p of use) {
+          let best = p.base;
+          for (let s = 0; s < 800; s++) {
+            const cand = p.base + (s === 0 ? 0 : (s % 2 ? 1 : -1) * Math.ceil(s / 2) * (r * 0.85));
+            const clash = placed.some((q) => {
+              const dx = q.absX - cand,
+                dy = q.yPix - p.yPix;
+              return dx * dx + dy * dy < min2;
+            });
+            if (!clash) {
+              best = cand;
+              break;
+            }
+          }
+          p.absX = best;
+          placed.push({ absX: best, yPix: p.yPix });
+        }
+        return pts;
+      }
+
+      function drawBee() {
+        const host = document.getElementById("c-bee");
+        host.innerHTML = "";
+        // One point per SNV per class = the error frequency the correction removed from that variant in
+        // the selected file (raw − corrected counts-per-coverage), i.e. the estimated per-nt error rate at
+        // that site. Untouched variants remove 0 and have no rate to place on a log axis, so only variants
+        // with a positive removed frequency are shown.
+        const perClass = CLASSES.map((c) => {
+          const pts = [];
+          for (const v of V) {
+            if (v.cls !== c.cls) continue;
+            const rmv = removed(v, curFile);
+            if (rmv != null && rmv > 0) pts.push({ v, y: rmv });
+          }
+          return { cls: c.cls, label: c.label, pts };
+        }).filter((s) => s.pts.length);
+        if (!perClass.length)
+          return (host.innerHTML = '<div class="empty">No single-nucleotide error was removed in this file.</div>');
+        const all = perClass.flatMap((s) => s.pts.map((p) => p.y));
+        const lo = Math.min(...all),
+          hi = Math.max(...all);
+        // taller than the other panels: more vertical room spreads the swarm in y, which is what keeps
+        // dense classes from having to overlap in x
+        const W = 500,
+          H = 360,
+          m = { l: 0, r: 10, t: 10, b: 0 };
+        const svg = el("svg", { viewBox: `0 0 ${W} ${H}` });
+        host.appendChild(svg);
+        tipDelegate(svg);
+        const l10 = (v) => Math.log10(Math.max(v, lo / 2));
+        const span = l10(hi) - l10(lo) || 1;
+        const labels = [0, 1, 2, 3].map((i) => Math.pow(10, l10(lo) + (span * i) / 3).toExponential(0));
+        m.l = Math.ceil(LBL_PX + 18 + Math.max(...labels.map((s) => textW(svg, s, "tick"))) + 8);
+        m.b = TICK_PX + 8 + LBL_PX + 16;
+        const iw = W - m.l - m.r,
+          ih = H - m.t - m.b;
+        const y = (v) => m.t + ih - ((l10(v) - l10(lo)) / span) * ih;
+        [0, 1, 2, 3].forEach((i) => {
+          const gv = Math.pow(10, l10(lo) + (span * i) / 3);
+          const gy = y(gv);
+          svg.appendChild(el("line", { class: "grid", x1: m.l, x2: m.l + iw, y1: gy, y2: gy }));
+          const t = el("text", { class: "tick", x: m.l - 8, y: gy + 3, "text-anchor": "end" });
+          t.textContent = gv.toExponential(0);
+          svg.appendChild(t);
+        });
+        const bw = iw / perClass.length;
+        const med = (xs) => {
+          if (!xs.length) return null;
+          const s = [...xs].sort((a, b) => a - b);
+          const h = (s.length - 1) / 2;
+          return (s[Math.floor(h)] + s[Math.ceil(h)]) / 2;
+        };
+        // Frequencies are counts/coverage, so many variants land on nearly identical values; a small
+        // deterministic vertical jitter (stable per variant, recoverable on hover) breaks those ties so
+        // the swarm spreads into neighbouring y-space rather than stacking dots in x. Every point of every
+        // class is placed in ONE pass (per-class placement would let a dense swarm bleed into a neighbour).
+        const half = bw * 0.22;
+        const rr = 1.1;
+        const items = [];
+        perClass.forEach((s, i) => {
+          const cx = m.l + bw * (i + 0.5);
+          const dim = clsFilter && clsFilter !== s.cls;
+          s.cx = cx;
+          s.op = dim ? 0.14 : 1;
+          s.col = clsColor(s.cls);
+          s.pts.forEach((p) =>
+            items.push({
+              v: p.v,
+              y: p.y,
+              base: cx,
+              col: s.col,
+              op: s.op,
+              yPix: y(p.y) + (jhash(p.v.key) - 0.5) * 16,
+            }),
+          );
+        });
+        beeswarm(items, rr);
+        items.forEach((p) => {
+          svg.appendChild(
+            el("circle", {
+              cx: p.absX,
+              cy: p.yPix,
+              r: rr,
+              fill: p.col,
+              "fill-opacity": 0.65 * p.op,
+              "data-tip": `<b>${esc(p.v.pos_mut)}</b> · <code>${esc(p.v.nt)}</code><br>error frequency ${sci(p.y)}`,
+            }),
+          );
+        });
+        perClass.forEach((s) => {
+          const mm = med(s.pts.map((p) => p.y)); // class median error frequency
+          if (mm != null)
+            svg.appendChild(
+              el("line", {
+                x1: s.cx - half,
+                x2: s.cx + half,
+                y1: y(mm),
+                y2: y(mm),
+                stroke: s.col,
+                "stroke-width": 1.5,
+                "stroke-opacity": s.op,
+              }),
+            );
+          const t = el("text", {
+            class: "tick",
+            x: s.cx,
+            y: m.t + ih + TICK_PX + 4,
+            "text-anchor": "middle",
+            "fill-opacity": s.op,
+          });
+          t.textContent = s.cls;
+          svg.appendChild(t);
+        });
+        svg.appendChild(el("line", { class: "axis", x1: m.l, x2: m.l + iw, y1: m.t + ih, y2: m.t + ih }));
+        const yl = el("text", {
+          class: "albl",
+          x: -(m.t + ih / 2),
+          y: LBL_PX,
+          transform: "rotate(-90)",
+          "text-anchor": "middle",
+        });
+        yl.textContent = "Removed error frequency";
+        svg.appendChild(yl);
+        const xl = el("text", { class: "albl", x: m.l + iw / 2, y: H - 4, "text-anchor": "middle" });
+        xl.textContent = "substitution class";
+        svg.appendChild(xl);
+      }
+
+      /* --- correction magnitude, single file --- */
+      // Percentage of each single-nucleotide variant's counts removed by correction, 0–100 %. Correction
+      // only ever subtracts and floors at zero, so this is bounded. Untouched variants sit in the 0 % bar,
+      // so the panel shows at a glance how few variants were adjusted versus left alone.
+      function drawHist() {
+        const host = document.getElementById("c-hist");
+        host.innerHTML = "";
+        const pcts = [];
+        for (const v of V) {
+          if (!v.cls) continue;
+          const r = v.raw[curFile],
+            c = v.cor[curFile];
+          if (r == null || !(r > 0) || c == null) continue;
+          const pct = ((r - c) / r) * 100; // fraction of the raw counts removed, 0..100
+          if (pct > 0) pcts.push(Math.min(100, pct)); // adjusted variants only - untouched ones are excluded
+        }
+        if (!pcts.length) return (host.innerHTML = '<div class="empty">No variants were adjusted in this file.</div>');
+        const NB = 40,
+          bw = 100 / NB;
+        const bins = new Array(NB).fill(0);
+        pcts.forEach((p) => {
+          let k = Math.floor(p / bw);
+          if (k >= NB) k = NB - 1;
+          bins[k]++;
+        });
+        const W = 500,
+          H = 300,
+          m = { l: 0, r: 12, t: 10, b: 0 };
+        const svg = el("svg", { viewBox: `0 0 ${W} ${H}` });
+        host.appendChild(svg);
+        tipDelegate(svg);
+        const maxC = Math.max(...bins);
+        const ylabels = [0, 1, 2, 3, 4].map((i) => fmt(Math.round((maxC * i) / 4)));
+        m.l = Math.ceil(LBL_PX + 18 + Math.max(...ylabels.map((s) => textW(svg, s, "tick"))) + 8);
+        m.b = TICK_PX + 8 + LBL_PX + 8;
+        const iw = W - m.l - m.r,
+          ih = H - m.t - m.b;
+        const x = (p) => m.l + (p / 100) * iw;
+        const y = (c) => m.t + ih - (c / maxC) * ih;
+        ylabels.forEach((lab, i) => {
+          const gy = y((maxC * i) / 4);
+          svg.appendChild(el("line", { class: "grid", x1: m.l, x2: m.l + iw, y1: gy, y2: gy }));
+          const t = el("text", { class: "tick", x: m.l - 8, y: gy + 3, "text-anchor": "end" });
+          t.textContent = lab;
+          svg.appendChild(t);
+        });
+        bins.forEach((c, k) => {
+          if (!c) return;
+          const x0 = x(k * bw),
+            x1 = x((k + 1) * bw);
+          svg.appendChild(
+            el("rect", {
+              x: x0 + 0.4,
+              y: y(c),
+              width: Math.max(0.8, x1 - x0 - 0.8),
+              height: m.t + ih - y(c),
+              fill: "var(--c1)",
+              "fill-opacity": 0.85,
+              "data-tip": `<b>${fmt(k * bw, 0)} … ${fmt((k + 1) * bw, 0)} %</b><br>${fmt(c)} variants`,
+            }),
+          );
+        });
+        for (let i = 0; i <= 4; i++) {
+          const p = (100 * i) / 4;
+          const t = el("text", { class: "tick", x: x(p), y: m.t + ih + TICK_PX + 5, "text-anchor": "middle" });
+          t.textContent = p + " %";
+          svg.appendChild(t);
+        }
+        svg.appendChild(el("line", { class: "axis", x1: m.l, x2: m.l + iw, y1: m.t + ih, y2: m.t + ih }));
+        const yl = el("text", {
+          class: "albl",
+          x: -(m.t + ih / 2),
+          y: LBL_PX,
+          transform: "rotate(-90)",
+          "text-anchor": "middle",
+        });
+        yl.textContent = "Variants";
+        svg.appendChild(yl);
+        const xl = el("text", { class: "albl", x: m.l + iw / 2, y: H - 4, "text-anchor": "middle" });
+        xl.textContent = "% removed";
+        svg.appendChild(xl);
+      }
+
+      /* --- SNV table, single file --- */
+      let sortK = "ntpos",
+        sortAsc = true;
+      function drawTable() {
+        const q = document.getElementById("snvq").value.trim().toLowerCase();
+        let rows = [];
+        for (const v of V) {
+          if (!v.cls) continue;
+          if (clsFilter && v.cls !== clsFilter) continue;
+          const fr = v.f_raw[curFile],
+            fc = v.f_cor[curFile];
+          if (fr == null) continue;
+          const pct = fr > 0 && fc != null ? ((fc - fr) / fr) * 100 : null;
+          rows.push({ v, f_raw: fr, f_cor: fc, pct });
+        }
+        if (q)
+          rows = rows.filter(
+            (a) =>
+              a.v.pos_mut.toLowerCase().includes(q) ||
+              a.v.nt.toLowerCase().includes(q) ||
+              String(a.v.ntpos).includes(q) ||
+              (a.v.cls || "").toLowerCase().includes(q),
+          );
+        const get = (a) =>
+          ({
+            ntpos: a.v.ntpos,
+            nt: a.v.nt,
+            pos_mut: a.v.pos_mut,
+            cls: a.v.cls,
+            f_raw: a.f_raw,
+            f_cor: a.f_cor,
+            pct: a.pct,
+          })[sortK];
+        rows.sort((p, q2) => {
+          const A = get(p),
+            B = get(q2);
+          const c =
+            typeof A === "number" && typeof B === "number" ? A - B : String(A ?? "").localeCompare(String(B ?? ""));
+          return sortAsc ? c : -c;
+        });
+        document.getElementById("snvcount").textContent = `${fmt(rows.length)} substitutions`;
+        const tb = document.querySelector("#snvtbl tbody");
+        tb.innerHTML = rows
+          .slice(0, 3000)
+          .map(
+            (a) =>
+              `<tr><td>${a.v.ntpos ?? "–"}</td><td><code>${esc(a.v.nt)}</code></td><td>${esc(a.v.pos_mut)}</td>` +
+              `<td><span class="sw" style="background:${clsColor(a.v.cls)}"></span> ${esc(clsLabel(a.v.cls))}</td>` +
+              `<td>${sci(a.f_raw)}</td><td>${sci(a.f_cor)}</td><td>${a.pct == null ? "–" : fmt(a.pct, 1) + " %"}</td></tr>`,
+          )
+          .join("");
+      }
+      document.querySelectorAll("#snvtbl thead th").forEach((th) =>
+        th.addEventListener("click", () => {
+          const k = th.dataset.k;
+          if (sortK === k) sortAsc = !sortAsc;
+          else {
+            sortK = k;
+            sortAsc = true;
+          }
+          drawTable();
+        }),
+      );
+      document.getElementById("snvq").addEventListener("input", drawTable);
+
+      function drawDetail() {
+        drawBias();
+        drawBee();
+        drawHist();
+        drawTable();
+      }
+
+      /* ---------- run-level tiles (over all files) ---------- */
+      function tiles() {
+        let rawTot = 0,
+          corTot = 0,
+          adjusted = 0,
+          nSnv = 0;
+        for (const v of V) {
+          let anyR = false,
+            anyAdj = false;
+          for (let i = 0; i < FILES.length; i++) {
+            const r = v.raw[i],
+              c = v.cor[i];
+            if (r != null) {
+              rawTot += r;
+              anyR = true;
+              corTot += c == null ? 0 : c;
+              if (c != null && c !== r) anyAdj = true;
+            }
+          }
+          if (anyAdj) adjusted++;
+          if (v.cls && anyR) nSnv++;
+        }
+        const pct = rawTot > 0 ? ((rawTot - corTot) / rawTot) * 100 : 0;
+        const t = [
+          ["Sequencing files", fmt(FILES.length)],
+          ["Single-nucleotide variants", fmt(nSnv)],
+          [
+            `Variants adjusted (≥1 file) <button class="ibtn" type="button" data-info="Counted if adjusted in ≥1 file (a union), so larger than any single file; the DETAIL section shows per-file counts.">i</button>`,
+            fmt(adjusted),
+          ],
+          ["Counts removed", (pct >= 0 ? "" : "+") + fmt(pct, 1) + " %"],
+        ];
+        document.getElementById("tiles").innerHTML = t
+          .map(([k, v]) => `<div class="tile"><div class="k">${k}</div><div class="v">${v}</div></div>`)
+          .join("");
+      }
+
+      /* ---------- info buttons ---------- */
+      document.addEventListener("click", (e) => {
+        const prev = document.querySelector(".ipop");
+        if (prev) prev.remove();
+        const b = e.target.closest(".ibtn");
+        if (!b) return;
+        e.preventDefault();
+        e.stopPropagation();
+        const pop = document.createElement("div");
+        pop.className = "ipop";
+        pop.textContent = b.dataset.info;
+        document.body.appendChild(pop);
+        const r = b.getBoundingClientRect();
+        pop.style.left = Math.max(8, Math.min(r.left, innerWidth - pop.offsetWidth - 8)) + "px";
+        pop.style.top =
+          (r.bottom + 6 + pop.offsetHeight > innerHeight ? r.top - pop.offsetHeight - 6 : r.bottom + 6) + "px";
+      });
+
+      /* ---------- go ---------- */
+      // The comparison + selection panels (tiles, correlation matrix, the two scatters) render
+      // synchronously so the page is usable at once. The single-file detail section (positional bias,
+      // beeswarm, magnitude, table) is the heaviest work, so it is deferred to the next task tick - the
+      // page paints first, then the detail fills in - which noticeably cuts the perceived load time.
+      tiles();
+      drawCorr();
+      buildGroups();
+      GROUPS.forEach((g) => drawScatter(g));
+      buildSeg();
+      drawLegend();
+      setTimeout(drawDetail, 0);
+      addEventListener("resize", () => {
+        drawCorr();
+        GROUPS.forEach((g) => drawScatter(g));
+        drawDetail();
+      });
+      // Close the enlarged scatter once the matrix it belongs to has scrolled out of sight.
+      const _corrHost = document.getElementById("c-corr");
+      if (_corrHost && "IntersectionObserver" in window) {
+        new IntersectionObserver(
+          (entries) => {
+            if (corrOpen && entries.some((e) => !e.isIntersecting)) closeScatter();
+          },
+          { threshold: 0 },
+        ).observe(_corrHost);
+      }
+    </script>
+  </body>
+</html>
diff --git a/assets/methods_description_template.yml b/assets/methods_description_template.yml
index e4e7ab3..c8e5159 100644
--- a/assets/methods_description_template.yml
+++ b/assets/methods_description_template.yml
@@ -1,29 +1,25 @@
-id: "nf-core-dmscore-methods-description"
+id: "nf-core-deepmutscan-methods-description"
 description: "Suggested text and references to use when describing pipeline usage within the methods section of a publication."
-section_name: "nf-core/dmscore Methods Description"
-section_href: "https://github.com/nf-core/dmscore"
+section_name: "nf-core/deepmutscan Methods Description"
+section_href: "https://github.com/nf-core/deepmutscan"
 plot_type: "html"
-## TODO nf-core: Update the HTML below to your preferred methods description, e.g. add publication citation for this pipeline
 ## You inject any metadata in the Nextflow '${workflow}' object
 data: |
   <h4>Methods</h4>
-  <p>Data was processed using nf-core/dmscore v${workflow.manifest.version} ${doi_text} of the nf-core collection of workflows (<a href="https://doi.org/10.1038/s41587-020-0439-x">Ewels <em>et al.</em>, 2020</a>), utilising reproducible software environments from the Bioconda (<a href="https://doi.org/10.1038/s41592-018-0046-7">Grüning <em>et al.</em>, 2018</a>) and Biocontainers (<a href="https://doi.org/10.1093/bioinformatics/btx192">da Veiga Leprevost <em>et al.</em>, 2017</a>) projects.</p>
-  <p>The pipeline was executed with Nextflow v${workflow.nextflow.version} (<a href="https://doi.org/10.1038/nbt.3820">Di Tommaso <em>et al.</em>, 2017</a>) with the following command:</p>
+  <p>Data was processed using nf-core/deepmutscan v${workflow.manifest.version} ${doi_text} (Wehnert <em>et al.</em>, in preparation), part of the nf-core collection of workflows (<a href="https://doi.org/10.1038/s41587-020-0439-x">Ewels <em>et al.</em>, 2020</a>), with Nextflow v${workflow.nextflow.version} and the following command:</p>
   <pre><code>${workflow.commandLine}</code></pre>
   <p>${tool_citations}</p>
   <h4>References</h4>
   <ul>
-    <li>Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: <a href="https://doi.org/10.1038/nbt.3820">10.1038/nbt.3820</a></li>
+    <li>Wehnert, B., <em>et al.</em> nf-core/deepmutscan. <em>bioRxiv</em> preprint (in preparation).</li>
     <li>Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: <a href="https://doi.org/10.1038/s41587-020-0439-x">10.1038/s41587-020-0439-x</a></li>
-    <li>Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: <a href="https://doi.org/10.1038/s41592-018-0046-7">10.1038/s41592-018-0046-7</a></li>
-    <li>da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: <a href="https://doi.org/10.1093/bioinformatics/btx192">10.1093/bioinformatics/btx192</a></li>
-    ${tool_bibliography}
+  ${tool_bibliography}
   </ul>
   <div class="alert alert-info">
     <h5>Notes:</h5>
     <ul>
       ${nodoi_text}
       <li>The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!</li>
-      <li>You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.</li>
+      <li>Software versions of all tools used in this run are listed in the "Software Versions" section of this report.</li>
     </ul>
   </div>
diff --git a/assets/multiqc_config.yml b/assets/multiqc_config.yml
index 2f6ceb9..bcfed63 100644
--- a/assets/multiqc_config.yml
+++ b/assets/multiqc_config.yml
@@ -1,13 +1,11 @@
 report_comment: >
-  This report has been generated by the <a href="https://github.com/nf-core/dmscore/tree/dev" target="_blank">nf-core/dmscore</a>
-  analysis pipeline. For information about how to interpret these results, please see the
-  <a href="https://nf-co.re/dmscore/dev/docs/output" target="_blank">documentation</a>.
+  This report has been generated by the <a href="https://github.com/nf-core/deepmutscan/releases/tag/1.0.0" target="_blank">nf-core/deepmutscan</a> analysis pipeline. For information about how to interpret these results, please see the <a href="https://nf-co.re/deepmutscan/1.0.0/docs/output" target="_blank">documentation</a>.
 report_section_order:
-  "nf-core-dmscore-methods-description":
+  "nf-core-deepmutscan-methods-description":
     order: -1000
   software_versions:
     order: -1001
-  "nf-core-dmscore-summary":
+  "nf-core-deepmutscan-summary":
     order: -1002
 
 export_plots: true
diff --git a/assets/nf-core-deepmutscan_logo_light.png b/assets/nf-core-deepmutscan_logo_light.png
new file mode 100644
index 0000000..d6c8e55
Binary files /dev/null and b/assets/nf-core-deepmutscan_logo_light.png differ
diff --git a/assets/nf-core-dmscore_logo_light.png b/assets/nf-core-dmscore_logo_light.png
deleted file mode 100644
index 3faa4ca..0000000
Binary files a/assets/nf-core-dmscore_logo_light.png and /dev/null differ
diff --git a/assets/samplesheet.csv b/assets/samplesheet.csv
index 5f653ab..5b5d503 100644
--- a/assets/samplesheet.csv
+++ b/assets/samplesheet.csv
@@ -1,3 +1,5 @@
-sample,fastq_1,fastq_2
-SAMPLE_PAIRED_END,/path/to/fastq/files/AEG588A1_S1_L002_R1_001.fastq.gz,/path/to/fastq/files/AEG588A1_S1_L002_R2_001.fastq.gz
-SAMPLE_SINGLE_END,/path/to/fastq/files/AEG588A4_S4_L003_R1_001.fastq.gz,
+sample,type,replicate,file1,file2
+ORF1,input,1,/reads/forward1.fastq.gz,/reads/reverse1.fastq.gz
+ORF1,input,2,/reads/forward2.fastq.gz,/reads/reverse2.fastq.gz
+ORF1,output,1,/reads/forward3.fastq.gz,/reads/reverse3.fastq.gz
+ORF1,output,2,/reads/forward4.fastq.gz,/reads/reverse4.fastq.gz
diff --git a/assets/schema_input.json b/assets/schema_input.json
index 2d9d8f6..09ae9af 100644
--- a/assets/schema_input.json
+++ b/assets/schema_input.json
@@ -1,7 +1,7 @@
 {
     "$schema": "https://json-schema.org/draft/2020-12/schema",
-    "$id": "https://raw.githubusercontent.com/nf-core/dmscore/master/assets/schema_input.json",
-    "title": "nf-core/dmscore pipeline - params.input schema",
+    "$id": "https://raw.githubusercontent.com/nf-core/deepmutscan/master/assets/schema_input.json",
+    "title": "nf-core/deepmutscan pipeline - params.input schema",
     "description": "Schema for the file provided with params.input",
     "type": "array",
     "items": {
@@ -9,25 +9,38 @@
         "properties": {
             "sample": {
                 "type": "string",
-                "pattern": "^\\S+$",
-                "errorMessage": "Sample name must be provided and cannot contain spaces",
-                "meta": ["id"]
+                "pattern": "^[^\\s/]+$",
+                "errorMessage": "Sample name must be provided, cannot contain spaces, and must not include special characters",
+                "meta": ["sample"]
             },
-            "fastq_1": {
+            "file1": {
                 "type": "string",
                 "format": "file-path",
                 "exists": true,
-                "pattern": "^\\S+\\.f(ast)?q\\.gz$",
-                "errorMessage": "FastQ file for reads 1 must be provided, cannot contain spaces and must have extension '.fq.gz' or '.fastq.gz'"
+                "pattern": "^\\S+\\.(fastq|fq)(\\.gz)?$",
+                "errorMessage": "FASTQ file for read 1 must be provided, cannot contain spaces and must have the extension '.fastq.gz', '.fq.gz', '.fastq' or '.fq'"
             },
-            "fastq_2": {
+            "file2": {
                 "type": "string",
                 "format": "file-path",
                 "exists": true,
-                "pattern": "^\\S+\\.f(ast)?q\\.gz$",
-                "errorMessage": "FastQ file for reads 2 cannot contain spaces and must have extension '.fq.gz' or '.fastq.gz'"
+                "pattern": "^\\S+\\.(fastq|fq)(\\.gz)?$",
+                "errorMessage": "FASTQ file for read 2 must be provided (paired-end reads are required), cannot contain spaces and must have the extension '.fastq.gz', '.fq.gz', '.fastq' or '.fq'"
+            },
+            "type": {
+                "type": "string",
+                "enum": ["input", "output", "wildtype"],
+                "errorMessage": "Type must be one of: input, output or wildtype",
+                "meta": ["type"]
+            },
+            "replicate": {
+                "type": "integer",
+                "minimum": 1,
+                "errorMessage": "Replicate must be a positive integer",
+                "meta": ["replicate"]
             }
         },
-        "required": ["sample", "fastq_1"]
-    }
+        "required": ["sample", "type", "replicate", "file1", "file2"]
+    },
+    "uniqueEntries": ["sample", "type", "replicate"]
 }
diff --git a/assets/sendmail_template.txt b/assets/sendmail_template.txt
index cdd2e19..4c84df3 100644
--- a/assets/sendmail_template.txt
+++ b/assets/sendmail_template.txt
@@ -9,12 +9,12 @@ Content-Type: text/html; charset=utf-8
 $email_html
 
 --nfcoremimeboundary
-Content-Type: image/png;name="nf-core-dmscore_logo.png"
+Content-Type: image/png;name="nf-core-deepmutscan_logo.png"
 Content-Transfer-Encoding: base64
 Content-ID: <nfcorepipelinelogo>
-Content-Disposition: inline; filename="nf-core-dmscore_logo_light.png"
+Content-Disposition: inline; filename="nf-core-deepmutscan_logo_light.png"
 
-<% out << new File("$projectDir/assets/nf-core-dmscore_logo_light.png").
+<% out << new File("$projectDir/assets/nf-core-deepmutscan_logo_light.png").
     bytes.
     encodeBase64().
     toString().
diff --git a/assets/slackreport.json b/assets/slackreport.json
deleted file mode 100644
index e5aa3f8..0000000
--- a/assets/slackreport.json
+++ /dev/null
@@ -1,34 +0,0 @@
-{
-    "attachments": [
-        {
-            "fallback": "Plain-text summary of the attachment.",
-            "color": "<% if (success) { %>good<% } else { %>danger<%} %>",
-            "author_name": "nf-core/dmscore ${version} - ${runName}",
-            "author_icon": "https://www.nextflow.io/docs/latest/_static/favicon.ico",
-            "text": "<% if (success) { %>Pipeline completed successfully!<% } else { %>Pipeline completed with errors<% } %>",
-            "fields": [
-                {
-                    "title": "Command used to launch the workflow",
-                    "value": "```${commandLine}```",
-                    "short": false
-                }
-                <%
-                    if (!success) { %>
-                    ,
-                    {
-                        "title": "Full error message",
-                        "value": "```${errorReport}```",
-                        "short": false
-                    },
-                    {
-                        "title": "Pipeline configuration",
-                        "value": "<% out << summary.collect{ k,v -> k == "hook_url" ? "_${k}_: (_hidden_)" : ( ( v.class.toString().contains('Path') || ( v.class.toString().contains('String') && v.contains('/') ) ) ? "_${k}_: `${v}`" : (v.class.toString().contains('DateTime') ? ("_${k}_: " + v.format(java.time.format.DateTimeFormatter.ofLocalizedDateTime(java.time.format.FormatStyle.MEDIUM))) : "_${k}_: ${v}") ) }.join(",\n") %>",
-                        "short": false
-                    }
-                    <% }
-                %>
-            ],
-            "footer": "Completed at <% out << dateComplete.format(java.time.format.DateTimeFormatter.ofLocalizedDateTime(java.time.format.FormatStyle.MEDIUM)) %> (duration: ${duration})"
-        }
-    ]
-}
diff --git a/assets/summary_report/summary_report_template.html b/assets/summary_report/summary_report_template.html
new file mode 100644
index 0000000..bc178a9
--- /dev/null
+++ b/assets/summary_report/summary_report_template.html
@@ -0,0 +1,1912 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <title>nf-core/deepmutscan · report · __SAMPLE__</title>
+    <style>
+      :root {
+        --bg: #ffffff;
+        --panel: #f7f8fa;
+        --ink: #1a1f2b;
+        --ink2: #5b6470;
+        --muted: #98a2b3;
+        --line: #e6e9ef;
+        --grid: #eef1f5;
+        --accent: #2f6bd8;
+        --accent-soft: #d7e2f7;
+        --na: #dddddd;
+        --ring: #ffffff;
+        --s1: #2a78d6;
+        --s2: #1baf7a;
+        --s3: #eda100;
+        --s4: #008300;
+        --s5: #4a3aa7;
+        --s6: #e34948;
+        --s7: #e87ba4;
+        --s8: #eb6834;
+      }
+      /* Light is the report's default: it is what gets printed, screenshotted and put in a paper, so
+         it must not depend on the reader's OS setting. Dark is opt-in through the header toggle only,
+         which is why there is deliberately no prefers-color-scheme rule here. */
+      :root[data-theme="dark"] {
+        --bg: #0f1219;
+        --panel: #161b24;
+        --ink: #e8ecf3;
+        --ink2: #a8b2c1;
+        --muted: #6b7688;
+        --line: #232a36;
+        --grid: #1c222d;
+        --accent: #5b90ee;
+        --accent-soft: #22304a;
+        --na: #2b323f;
+        --ring: #0f1219;
+        --s1: #3987e5;
+        --s2: #199e70;
+        --s3: #c98500;
+        --s4: #008300;
+        --s5: #9085e9;
+        --s6: #e66767;
+        --s7: #d55181;
+        --s8: #d95926;
+      }
+      :root[data-theme="light"] {
+        --bg: #ffffff;
+        --panel: #f7f8fa;
+        --ink: #1a1f2b;
+        --ink2: #5b6470;
+        --muted: #98a2b3;
+        --line: #e6e9ef;
+        --grid: #eef1f5;
+        --accent: #2f6bd8;
+        --accent-soft: #d7e2f7;
+        --na: #dddddd;
+        --ring: #ffffff;
+        --s1: #2a78d6;
+        --s2: #1baf7a;
+        --s3: #eda100;
+        --s4: #008300;
+        --s5: #4a3aa7;
+        --s6: #e34948;
+        --s7: #e87ba4;
+        --s8: #eb6834;
+      }
+      * {
+        box-sizing: border-box;
+      }
+      html,
+      body {
+        height: 100%;
+      }
+      body {
+        margin: 0;
+        background: var(--bg);
+        color: var(--ink);
+        font:
+          14px/1.5 -apple-system,
+          BlinkMacSystemFont,
+          "Segoe UI",
+          Roboto,
+          Helvetica,
+          Arial,
+          sans-serif;
+        -webkit-font-smoothing: antialiased;
+        display: flex;
+        flex-direction: column;
+      }
+      header {
+        display: flex;
+        align-items: center;
+        gap: 14px;
+        padding: 14px 22px;
+        border-bottom: 1px solid var(--line);
+        flex: none;
+      }
+      header .logo {
+        height: 50px;
+        width: auto;
+        display: block;
+      }
+      /* Two logo files ship with the pipeline: the wordmark is near-black in one and white in the
+         other. Swap them with the theme, or "deepmutscan" becomes unreadable on the dark background. */
+      header .logo-dark {
+        display: none;
+      }
+      :root[data-theme="dark"] header .logo-light {
+        display: none;
+      }
+      :root[data-theme="dark"] header .logo-dark {
+        display: block;
+      }
+      header .theme {
+        background: none;
+        border: 1px solid var(--line);
+        color: var(--ink2);
+        border-radius: 7px;
+        padding: 5px 9px;
+        cursor: pointer;
+        font: inherit;
+        font-size: 13px;
+        line-height: 1;
+      }
+      header .theme:hover {
+        border-color: var(--accent);
+        color: var(--accent);
+      }
+      header .title {
+        color: var(--muted);
+        font-size: 18px;
+        font-weight: 500;
+        border-left: 1px solid var(--line);
+        padding-left: 14px;
+      }
+      header .spacer {
+        flex: 1;
+      }
+      header .sub {
+        color: var(--ink2);
+        font-size: 13px;
+      }
+      .shell {
+        display: flex;
+        flex: 1;
+        min-height: 0;
+      }
+      nav {
+        width: 210px;
+        flex: none;
+        border-right: 1px solid var(--line);
+        padding: 14px 10px;
+        overflow-y: auto;
+      }
+      nav button {
+        display: block;
+        width: 100%;
+        text-align: left;
+        background: none;
+        border: 0;
+        border-radius: 8px;
+        padding: 8px 11px;
+        margin-bottom: 2px;
+        color: var(--ink2);
+        font-size: 13.5px;
+        cursor: pointer;
+        font-family: inherit;
+      }
+      nav button:hover {
+        background: var(--panel);
+        color: var(--ink);
+      }
+      nav button.on {
+        background: var(--accent-soft);
+        color: var(--accent);
+        font-weight: 600;
+      }
+      main {
+        flex: 1;
+        min-width: 0;
+        overflow-y: auto;
+        padding: 22px 26px 60px;
+      }
+      /* a page heading + selector that pins to the top while the panels below scroll (Library QC),
+         mirroring the error-correction report's per-file section */
+      .stick {
+        position: sticky;
+        top: -22px;
+        z-index: 6;
+        background: var(--bg);
+        padding: 22px 0 8px;
+        margin: -22px 0 10px;
+        box-shadow: 0 8px 10px -8px rgba(0, 0, 0, 0.3);
+      }
+      .stick h1 {
+        margin: 0 0 8px;
+      }
+      /* the Variant-effects structure spins on its own and is not meant to be grabbed - interaction
+         lives in the full inspection tool - so mouse events on its canvas are ignored */
+      #f-struct canvas {
+        pointer-events: none;
+      }
+      .sect {
+        display: none;
+        max-width: 1180px;
+      }
+      .sect.on {
+        display: block;
+      }
+      h1 {
+        font-size: 19px;
+        font-weight: 650;
+        margin: 0 0 4px;
+        letter-spacing: -0.01em;
+      }
+      .lead {
+        color: var(--ink2);
+        font-size: 13px;
+        margin: 0 0 18px;
+      }
+      .tiles {
+        display: grid;
+        grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
+        gap: 12px;
+        margin-bottom: 22px;
+      }
+      .tile {
+        background: var(--panel);
+        border: 1px solid var(--line);
+        border-radius: 12px;
+        padding: 14px 16px;
+      }
+      .tile .k {
+        color: var(--ink2);
+        font-size: 12px;
+      }
+      .tile .v {
+        font-size: 21px;
+        font-weight: 650;
+        letter-spacing: -0.02em;
+        margin-top: 3px;
+      }
+      .panel {
+        background: var(--panel);
+        border: 1px solid var(--line);
+        border-radius: 14px;
+        padding: 16px 18px;
+        margin-bottom: 18px;
+      }
+      .panel h2 {
+        font-size: 14px;
+        font-weight: 600;
+        margin: 0 0 2px;
+        display: flex;
+        align-items: center;
+        gap: 2px;
+      }
+      .panel .desc {
+        color: var(--ink2);
+        font-size: 12.5px;
+        margin: 0 0 12px;
+      }
+      .row {
+        display: flex;
+        align-items: center;
+        gap: 8px;
+        flex-wrap: wrap;
+        margin-bottom: 12px;
+      }
+      .chip {
+        display: inline-flex;
+        align-items: center;
+        gap: 6px;
+        padding: 3px 10px 3px 8px;
+        border: 1px solid var(--line);
+        border-radius: 999px;
+        background: var(--bg);
+        color: var(--ink2);
+        font-size: 12px;
+        cursor: pointer;
+        user-select: none;
+      }
+      .chip:hover {
+        color: var(--ink);
+      }
+      .chip .sw {
+        width: 10px;
+        height: 10px;
+        border-radius: 3px;
+        flex: none;
+      }
+      .chip.off {
+        opacity: 0.38;
+      }
+      .chip.off .sw {
+        background: var(--muted) !important;
+      }
+      .act {
+        display: inline-block;
+        background: var(--bg);
+        border: 1px solid var(--line);
+        border-radius: 8px;
+        padding: 5px 11px;
+        color: var(--ink2);
+        font-size: 12px;
+        cursor: pointer;
+        font-family: inherit;
+        text-decoration: none;
+        line-height: 1.4;
+      }
+      .act:hover {
+        color: var(--ink);
+        border-color: var(--muted);
+      }
+      .act::before {
+        content: "↓ ";
+        color: var(--muted);
+      }
+      button.act::before {
+        content: none;
+      }
+      svg {
+        display: block;
+        width: 100%;
+        height: auto;
+        overflow: visible;
+      }
+      .grid {
+        stroke: var(--grid);
+        stroke-width: 1;
+      }
+      .axis {
+        stroke: var(--line);
+        stroke-width: 1;
+      }
+      .tick {
+        fill: var(--muted);
+        font-size: 10px;
+      }
+      .albl {
+        fill: var(--ink2);
+        font-size: 11px;
+      }
+      .opts {
+        align-items: center;
+        gap: 10px;
+        margin-bottom: 8px;
+      }
+      .seg {
+        display: inline-flex;
+        border: 1px solid var(--line);
+        border-radius: 7px;
+        overflow: hidden;
+      }
+      .seg button {
+        background: none;
+        border: 0;
+        border-right: 1px solid var(--line);
+        color: var(--ink2);
+        font: inherit;
+        font-size: 12px;
+        padding: 5px 10px;
+        cursor: pointer;
+      }
+      .seg button:last-child {
+        border-right: 0;
+      }
+      .seg button.on {
+        background: var(--accent-soft);
+        color: var(--accent);
+        font-weight: 600;
+      }
+      .sw-lbl {
+        display: inline-flex;
+        align-items: center;
+        gap: 6px;
+        font-size: 12px;
+        color: var(--ink2);
+        cursor: pointer;
+      }
+      .cellbox {
+        border-top: 1px solid var(--line);
+        margin-top: 14px;
+        padding-top: 14px;
+      }
+      .cellbox h3 {
+        margin: 0 0 10px;
+        font-size: 16px;
+      }
+      .cellbox h4 {
+        margin: 16px 0 8px;
+        font-size: 13px;
+        color: var(--ink2);
+        font-weight: 600;
+      }
+      .cellbox code {
+        font-family: ui-monospace, SFMono-Regular, Menlo, monospace;
+        font-size: 12px;
+      }
+      .tiles.small .tile .v {
+        font-size: 16px;
+      }
+      .tile .v.small {
+        font-size: 13px !important;
+      }
+      .tag {
+        font-size: 11px;
+        font-weight: 500;
+        color: var(--ink2);
+        background: var(--panel);
+        border: 1px solid var(--line);
+        border-radius: 20px;
+        padding: 2px 8px;
+        vertical-align: middle;
+      }
+      .cites {
+        margin: 4px 0 14px;
+        padding-left: 22px;
+      }
+      .cites li {
+        margin-bottom: 10px;
+        line-height: 1.5;
+        color: var(--ink);
+      }
+      .tt {
+        position: fixed;
+        pointer-events: none;
+        background: var(--ink);
+        color: var(--bg);
+        font-size: 12px;
+        padding: 5px 8px;
+        border-radius: 7px;
+        opacity: 0;
+        transition: opacity 0.08s;
+        white-space: nowrap;
+        z-index: 30;
+        box-shadow: 0 4px 14px rgba(0, 0, 0, 0.18);
+      }
+      table {
+        width: 100%;
+        border-collapse: collapse;
+        font-variant-numeric: tabular-nums;
+      }
+      .tblwrap {
+        max-height: 380px;
+        overflow: auto;
+        border: 1px solid var(--line);
+        border-radius: 10px;
+      }
+      th,
+      td {
+        padding: 7px 12px;
+        text-align: right;
+        border-bottom: 1px solid var(--line);
+        white-space: nowrap;
+        font-size: 12.5px;
+      }
+      th:first-child,
+      td:first-child {
+        text-align: left;
+      }
+      thead th {
+        position: sticky;
+        top: 0;
+        background: var(--panel);
+        font-size: 12px;
+        color: var(--ink2);
+        font-weight: 600;
+        z-index: 1;
+      }
+      .ibtn {
+        display: inline-flex;
+        align-items: center;
+        justify-content: center;
+        width: 15px;
+        height: 15px;
+        border-radius: 50%;
+        border: 1px solid var(--line);
+        background: var(--bg);
+        color: var(--ink2);
+        font-size: 10px;
+        font-weight: 700;
+        line-height: 1;
+        cursor: pointer;
+        margin-left: 6px;
+        padding: 0;
+        font-family: inherit;
+      }
+      .ibtn:hover {
+        color: var(--ink);
+        border-color: var(--muted);
+      }
+      .ipop {
+        position: fixed;
+        z-index: 40;
+        max-width: 320px;
+        background: var(--panel);
+        color: var(--ink);
+        border: 1px solid var(--line);
+        border-radius: 9px;
+        padding: 9px 11px;
+        font-size: 12px;
+        line-height: 1.45;
+        box-shadow: 0 8px 26px rgba(0, 0, 0, 0.18);
+      }
+      .frame {
+        width: 100%;
+        height: 78vh;
+        border: 1px solid var(--line);
+        border-radius: 10px;
+        background: var(--bg);
+      }
+      .note {
+        border-left: 3px solid var(--accent);
+        background: var(--panel);
+        border-radius: 0 8px 8px 0;
+        padding: 10px 14px;
+        font-size: 12.5px;
+        color: var(--ink2);
+        margin-bottom: 16px;
+      }
+      .cite {
+        color: var(--muted);
+        font-size: 11.5px;
+        line-height: 1.6;
+      }
+      .cite a {
+        color: var(--ink2);
+      }
+      .empty {
+        color: var(--muted);
+        font-size: 12.5px;
+        padding: 10px 0;
+      }
+      .sm {
+        display: grid;
+        grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
+        gap: 14px;
+      }
+    </style>
+  </head>
+  <body>
+    <header>
+      <a
+        href="https://nf-co.re/deepmutscan"
+        target="_blank"
+        rel="noopener noreferrer"
+        aria-label="nf-core/deepmutscan"
+        style="display: inline-block; line-height: 0"
+        ><img
+          class="logo logo-light"
+          alt="nf-core/deepmutscan"
+          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"
+      /></a>
+      <span class="title">report</span>
+      <span class="spacer"></span>
+      <span class="sub" id="run-tag"></span>
+      <button class="theme" id="theme" type="button" title="Switch between light and dark"></button>
+    </header>
+
+    <div class="shell">
+      <nav id="nav"></nav>
+      <main id="main"></main>
+    </div>
+
+    <div class="tt" id="tt"></div>
+
+    <!-- 3Dmol.js, inlined by the build only when this run has a structure; empty otherwise -->
+    <script>
+      /*__THREEDMOL_JS__*/
+    </script>
+    <script id="report-data" type="application/json">
+      __REPORT_DATA__
+    </script>
+    <script>
+      const DATA = JSON.parse(document.getElementById("report-data").textContent);
+      const SVGNS = "http://www.w3.org/2000/svg";
+      const el = (n, a = {}) => {
+        const e = document.createElementNS(SVGNS, n);
+        for (const k in a) e.setAttribute(k, a[k]);
+        return e;
+      };
+      const tt = document.getElementById("tt");
+      const showTT = (e, html) => {
+        tt.innerHTML = html;
+        tt.style.opacity = 1;
+        tt.style.left = Math.min(e.clientX + 12, innerWidth - tt.offsetWidth - 8) + "px";
+        tt.style.top = e.clientY - tt.offsetHeight - 10 + "px";
+      };
+      const hideTT = () => (tt.style.opacity = 0);
+      const fmt = (x, d = 0) =>
+        x == null || isNaN(x) ? "–" : Number(x).toLocaleString(undefined, { maximumFractionDigits: d });
+      const esc = (s) => String(s).replace(/&/g, "&amp;").replace(/</g, "&lt;").replace(/"/g, "&quot;");
+
+      /* ---------- theme: light unless the reader opts into dark ---------- */
+      // localStorage throws outright when this file is opened from an opaque origin (a sandboxed or
+      // data: URI frame), so every access is guarded -- an unguarded read is exactly what breaks the
+      // embedded MultiQC report. Losing the preference is fine; throwing here would kill the script.
+      const store = {
+        get(k) {
+          try {
+            return localStorage.getItem(k);
+          } catch (e) {
+            return null;
+          }
+        },
+        set(k, v) {
+          try {
+            localStorage.setItem(k, v);
+          } catch (e) {}
+        },
+      };
+      const themeBtn = document.getElementById("theme");
+      let THEME = "light";
+      // Push the theme into every embedded report too. The frames share this document's origin
+      // (they are srcdoc, not data: URIs), so their theme controls are reachable directly: the EC
+      // report keys off data-theme like this page, MultiQC off Bootstrap's data-bs-theme. A frame
+      // mounted later is themed on mount via applyTheme(frame).
+      function applyTheme(root) {
+        (root ? [root] : document.querySelectorAll(".frame")).forEach((f) => {
+          const doc = f.contentDocument;
+          if (!doc || !doc.documentElement) return;
+          doc.documentElement.setAttribute("data-theme", THEME);
+          doc.documentElement.setAttribute("data-bs-theme", THEME);
+        });
+      }
+      function setTheme(t) {
+        THEME = t;
+        document.documentElement.setAttribute("data-theme", t);
+        themeBtn.textContent = t === "dark" ? "☀ Light" : "☾ Dark";
+        store.set("dms-theme", t);
+        applyTheme();
+      }
+      setTheme(store.get("dms-theme") === "dark" ? "dark" : "light");
+      themeBtn.addEventListener("click", () =>
+        setTheme(document.documentElement.getAttribute("data-theme") === "dark" ? "light" : "dark"),
+      );
+
+      const SAMPLES = DATA.samples || [];
+      const SIDS = SAMPLES.map((s) => s.id);
+      // Default matches the window the pipeline's R plots use, so the smoothed curves here and the
+      // published PDFs describe the same thing; the Library-QC control lets the reader change it live.
+      let WINDOW = Number((DATA.run || {}).sliding_window_size) || 10;
+      // Reads must span every varying base (plus --min_flank), so counts and counts-per-coverage are
+      // only comparable within a class; pooling them makes a curve that tracks class composition
+      // rather than the library. The R plots draw one line per class for the same reason.
+      const VB = [
+        ["1", "1 varying base"],
+        ["2", "2 varying bases"],
+        ["3", "3 varying bases"],
+      ];
+      // Colour groups the files by role: inputs share a blue family, outputs a red/orange one, so a plot
+      // reads "before vs after selection" at a glance. Replicates within a role are separated by
+      // lightness (kept in a band that stays legible on both the light and dark panel). Wildtype/quality
+      // helper samples get their own hues; anything with no parseable type falls back to the accent.
+      // A sample keeps its colour no matter what is filtered. Beyond 8 samples an overlay would have to
+      // crowd the lightness band, so the comparison falls back to small multiples instead.
+      const MAXHUE = 8;
+      const COLOR = {};
+      (function buildPalette() {
+        const FAM = { input: 212, output: 14, wildtype: 265, quality: 150 };
+        const groups = {};
+        SAMPLES.forEach((s) => (groups[s.type || "other"] = groups[s.type || "other"] || []).push(s));
+        for (const [type, arr] of Object.entries(groups)) {
+          arr.sort((a, b) => (a.replicate ?? 0) - (b.replicate ?? 0) || (a.id < b.id ? -1 : 1));
+          const hue = FAM[type];
+          arr.forEach((s, i) => {
+            if (hue == null) {
+              COLOR[s.id] = "var(--accent)";
+              return;
+            }
+            const n = arr.length;
+            const L = n <= 1 ? 55 : 66 - (20 * i) / (n - 1); // 66%..46%
+            COLOR[s.id] = `hsl(${hue} 68% ${L}%)`;
+          });
+        }
+      })();
+      const OVERLAY_OK = SIDS.length <= MAXHUE;
+      const sel = new Set(SIDS);
+
+      /* ---------- info buttons ---------- */
+      document.addEventListener("click", (e) => {
+        const prev = document.querySelector(".ipop");
+        if (prev) prev.remove();
+        const b = e.target.closest(".ibtn");
+        if (!b) return;
+        e.preventDefault();
+        e.stopPropagation();
+        const pop = document.createElement("div");
+        pop.className = "ipop";
+        pop.textContent = b.dataset.info;
+        document.body.appendChild(pop);
+        const r = b.getBoundingClientRect();
+        pop.style.left = Math.max(8, Math.min(r.left, innerWidth - pop.offsetWidth - 8)) + "px";
+        pop.style.top =
+          (r.bottom + 6 + pop.offsetHeight > innerHeight ? r.top - pop.offsetHeight - 6 : r.bottom + 6) + "px";
+      });
+      const info = (t) => `<button class="ibtn" type="button" data-info="${esc(t)}">i</button>`;
+
+      /* ---------- generic charts ---------- */
+      // Width a string actually occupies once rendered, so margins can be derived instead of guessed.
+      // The svg must already be in the document for this to return anything but 0.
+      function textW(svg, s, cls) {
+        const t = el("text", { class: cls, x: -9999, y: -9999 });
+        t.textContent = s;
+        svg.appendChild(t);
+        const w = t.getComputedTextLength();
+        t.remove();
+        return w;
+      }
+
+      const TICK_PX = 10, // must track .tick font-size
+        LBL_PX = 11; //     must track .albl font-size
+
+      // Rolling mean over ORF positions, matching rollapply(width=w, mean(na.rm=TRUE), fill="extend")
+      // in the pipeline's R plots: centre-aligned, gaps ignored rather than treated as zero, and the
+      // partial windows at each end filled by extending the first/last complete value.
+      function rollmean(vals, w) {
+        const L = vals.length;
+        if (!(w > 1) || L < w) return vals.slice();
+        const out = new Array(L).fill(null);
+        const lo = Math.ceil(w / 2) - 1,
+          hi = L - Math.floor(w / 2) - 1;
+        for (let i = lo; i <= hi; i++) {
+          let sum = 0,
+            n = 0;
+          for (let j = i - lo; j < i - lo + w; j++) {
+            if (vals[j] != null && !isNaN(vals[j])) {
+              sum += vals[j];
+              n++;
+            }
+          }
+          out[i] = n ? sum / n : null;
+        }
+        for (let i = 0; i < lo; i++) out[i] = out[lo];
+        for (let i = hi + 1; i < L; i++) out[i] = out[hi];
+        return out;
+      }
+
+      // Series for one sample: pull `key` off the chosen varying-bases class (or the position list for
+      // coverage, which is a property of the position and has no class), then optionally smooth.
+      function series(s, key, cls, smooth) {
+        const src = cls == null ? s.positions : (s.classes || {})[cls] || [];
+        if (!src.length) return [];
+        const pos = src.map((d) => d.pos);
+        let v = src.map((d) => (d[key] == null || isNaN(d[key]) ? null : d[key]));
+        if (smooth) v = rollmean(v, WINDOW);
+        return pos.map((p, i) => ({ pos: p, v: v[i] })).filter((d) => d.v != null);
+      }
+
+      // Class comparison is only legible in one plot, so all three varying-bases classes are always
+      // drawn together and selection controls opacity rather than presence: a class you deselect fades
+      // but stays for reference. Coverage has no class (classesSel = null) and is coloured by sample.
+      const CLASS_COLOR = { 1: "var(--s1)", 2: "var(--s2)", 3: "var(--s3)" };
+      const selSamples = () => SAMPLES.filter((s) => sel.has(s.id));
+
+      // one line per (selected sample x drawn class); x = ORF position
+      function lineChart(host, key, ylab, logY, classesSel, smooth, hline) {
+        host.innerHTML = "";
+        const chosen = selSamples();
+        if (!chosen.length) {
+          host.innerHTML = '<div class="empty">No files selected.</div>';
+          return;
+        }
+        // build the layers: coverage is one per sample; a split metric is sample x all three classes,
+        // faded where the class is deselected.
+        const layers = [];
+        chosen.forEach((s) => {
+          if (classesSel == null) {
+            const pts = series(s, key, null, smooth);
+            if (pts.length) layers.push({ pts, color: COLOR[s.id], opacity: 0.9, label: s.id });
+          } else {
+            [1, 2, 3].forEach((c) => {
+              const pts = series(s, key, String(c), smooth);
+              if (!pts.length) return;
+              const on = classesSel.has(String(c));
+              layers.push({
+                pts,
+                color: CLASS_COLOR[c],
+                opacity: on ? 0.95 : 0.16,
+                on,
+                label: (chosen.length > 1 ? s.id + " · " : "") + c + (c === 1 ? " varying base" : " varying bases"),
+              });
+            });
+          }
+        });
+        const live = layers.filter((l) => l.pts.length);
+        if (!live.length) {
+          host.innerHTML = '<div class="empty">No data for this metric in the selected files.</div>';
+          return;
+        }
+        const W = 1020,
+          H = 260,
+          m = { l: 0, r: 14, t: 10, b: 0 };
+        const svg = el("svg", { viewBox: `0 0 ${W} ${H}` });
+        host.appendChild(svg);
+
+        let maxP = 1,
+          lo = Infinity,
+          hi = -Infinity;
+        live.forEach((r) =>
+          r.pts.forEach((p) => {
+            if (p.pos > maxP) maxP = p.pos;
+            if (!logY || p.v > 0) {
+              if (p.v < lo) lo = p.v;
+              if (p.v > hi) hi = p.v;
+            }
+          }),
+        );
+        // The x extent must span the whole ORF even when only a subset of positions carry a class, or
+        // the axis silently rescales as classes are toggled. Pin it to the coverage series' length.
+        chosen.forEach((s) =>
+          s.positions.forEach((p) => {
+            if (p.pos > maxP) maxP = p.pos;
+          }),
+        );
+        if (hline != null && hline > 0 && (!logY || hline > 0)) {
+          if (hline > hi) hi = hline;
+          if (hline < lo) lo = hline;
+        }
+        if (!isFinite(lo)) {
+          host.innerHTML = '<div class="empty">No data for this metric.</div>';
+          return;
+        }
+        if (!logY) lo = 0;
+        const yv = (v) => (logY ? Math.log10(Math.max(v, lo)) : v);
+        const y0 = yv(lo),
+          y1 = yv(hi) || 1;
+        const label = (gv) => (logY ? gv.toExponential(1) : fmt(gv, gv < 10 ? 4 : 0));
+        const gvs = [];
+        for (let i = 0; i <= 4; i++) gvs.push(logY ? Math.pow(10, y0 + ((y1 - y0) * i) / 4) : lo + ((hi - lo) * i) / 4);
+
+        // Derive the margins from what the text really measures: the rotated y title needs its own
+        // band, then a gap, then the widest tick. Hardcoding these is what let the centred x title
+        // collide with the centre x tick.
+        // The rotated y title occupies a band one font-size wide about its baseline, so it needs
+        // LBL_PX plus padding on both sides before the widest tick may start. Measured, not guessed:
+        // with the previous constants the title cleared the ticks by 0.4px, which reads as touching.
+        const maxYW = Math.max(...gvs.map((g) => textW(svg, label(g), "tick")));
+        m.l = Math.ceil(LBL_PX + 18 + maxYW + 8);
+        m.b = TICK_PX + 8 + LBL_PX + 8;
+        const iw = W - m.l - m.r,
+          ih = H - m.t - m.b;
+        const x = (p) => m.l + ((p - 1) / Math.max(1, maxP - 1)) * iw;
+        const y = (v) => m.t + ih - ((yv(v) - y0) / (y1 - y0 || 1)) * ih;
+
+        gvs.forEach((gv) => {
+          const gy = y(gv);
+          svg.appendChild(el("line", { class: "grid", x1: m.l, x2: m.l + iw, y1: gy, y2: gy }));
+          const t = el("text", { class: "tick", x: m.l - 8, y: gy + 3, "text-anchor": "end" });
+          t.textContent = label(gv);
+          svg.appendChild(t);
+        });
+        const step = Math.max(1, Math.round(maxP / 10));
+        for (let p = 1; p <= maxP; p += step) {
+          const t = el("text", { class: "tick", x: x(p), y: m.t + ih + TICK_PX + 5, "text-anchor": "middle" });
+          t.textContent = p;
+          svg.appendChild(t);
+        }
+        svg.appendChild(el("line", { class: "axis", x1: m.l, x2: m.l + iw, y1: m.t + ih, y2: m.t + ih }));
+        const yl = el("text", {
+          class: "albl",
+          x: -(m.t + ih / 2),
+          y: LBL_PX,
+          transform: "rotate(-90)",
+          "text-anchor": "middle",
+        });
+        yl.textContent = ylab;
+        svg.appendChild(yl);
+        const xl = el("text", { class: "albl", x: m.l + iw / 2, y: H - 4, "text-anchor": "middle" });
+        xl.textContent = "ORF residue position";
+        svg.appendChild(xl);
+        if (hline != null && hline > 0) {
+          svg.appendChild(
+            el("line", {
+              x1: m.l,
+              x2: m.l + iw,
+              y1: y(hline),
+              y2: y(hline),
+              stroke: "var(--ink2)",
+              "stroke-width": 1,
+              "stroke-dasharray": "5 4",
+            }),
+          );
+          const t = el("text", { class: "tick", x: m.l + iw - 4, y: y(hline) - 4, "text-anchor": "end" });
+          t.textContent = "required coverage";
+          svg.appendChild(t);
+        }
+        // Draw faded layers first so the selected ones sit on top.
+        [...live]
+          .sort((a, b) => a.opacity - b.opacity)
+          .forEach((L) => {
+            const pathPts = L.pts.filter((p) => !logY || p.v > 0);
+            if (!pathPts.length) return;
+            let d = "";
+            pathPts.forEach((p, i) => (d += (i ? " L " : "M ") + x(p.pos) + " " + y(p.v)));
+            svg.appendChild(
+              el("path", {
+                d,
+                fill: "none",
+                stroke: L.color,
+                "stroke-width": 1.6,
+                "stroke-linejoin": "round",
+                "stroke-opacity": L.opacity,
+              }),
+            );
+          });
+        const ov = el("rect", { x: m.l, y: m.t, width: iw, height: ih, fill: "transparent" });
+        svg.appendChild(ov);
+        ov.addEventListener("mousemove", (e) => {
+          const r = svg.getBoundingClientRect(),
+            px = ((e.clientX - r.left) / r.width) * W;
+          const pp = Math.round(((px - m.l) / iw) * (maxP - 1)) + 1;
+          // Deselected (faded) layers stay out of the tooltip so it reads the current selection.
+          const parts = live
+            .filter((L) => L.opacity > 0.5)
+            .map((L) => {
+              const d = L.pts.find((z) => z.pos === pp);
+              return d
+                ? `<span style="color:${L.color}">&#9679;</span> ${esc(L.label)}: <b>${fmt(d.v, d.v < 10 ? 5 : 0)}</b>`
+                : null;
+            })
+            .filter(Boolean);
+          if (!parts.length) return hideTT();
+          showTT(
+            e,
+            `<b>position ${pp}</b>${smooth ? ` <span style="opacity:.7">(rolling mean, w=${WINDOW})</span>` : ""}<br>` +
+              parts.join("<br>"),
+          );
+        });
+        ov.addEventListener("mouseleave", hideTT);
+      }
+
+      // position x amino-acid grid for one sample
+      function heatChart(host, s) {
+        host.innerHTML = "";
+        if (!s || !s.heatmap || !s.heatmap.cells.length) {
+          host.innerHTML = '<div class="empty">No heatmap data for this sample.</div>';
+          return;
+        }
+        // Amino acids grouped by side-chain chemistry (hydrophobic, aromatic, basic, acidic,
+        // polar, proline, stop), matching the order used by the R heatmap figures.
+        const AAS = "GAVLMIFYWKRHDESTCNQP*".split("");
+        const cells = s.heatmap.cells;
+        const maxP = Math.max(...cells.map((c) => c.p));
+        const vals = cells.map((c) => c.v).filter((v) => v > 0);
+        const lo = Math.log10(Math.min(...vals)),
+          hi = Math.log10(Math.max(...vals));
+        const W = 1020,
+          m = { l: 26, r: 10, t: 8, b: 26 };
+        const cw = (W - m.l - m.r) / maxP,
+          ch = 11;
+        const H = m.t + m.b + ch * AAS.length;
+        const svg = el("svg", { viewBox: `0 0 ${W} ${H}` });
+        host.appendChild(svg);
+        const VIR = [0x440154, 0x414487, 0x2a788e, 0x22a884, 0x7ad151, 0xfde725];
+        const lerp = (a, b, t) => a + (b - a) * t;
+        const hex2 = (h) => [(h >> 16) & 255, (h >> 8) & 255, h & 255];
+        const ramp = (t) => {
+          t = Math.max(0, Math.min(1, t));
+          const n = VIR.length - 1,
+            xx = t * n,
+            i = Math.min(n - 1, Math.floor(xx)),
+            f = xx - i;
+          const a = hex2(VIR[i]),
+            b = hex2(VIR[i + 1]);
+          return `rgb(${Math.round(lerp(a[0], b[0], f))},${Math.round(lerp(a[1], b[1], f))},${Math.round(lerp(a[2], b[2], f))})`;
+        };
+        AAS.forEach((a, i) => {
+          const t = el("text", { class: "tick", x: m.l - 5, y: m.t + i * ch + 8, "text-anchor": "end" });
+          t.textContent = a;
+          svg.appendChild(t);
+        });
+        const step = Math.max(1, Math.round(maxP / 12));
+        for (let p = 1; p <= maxP; p += step) {
+          const t = el("text", { class: "tick", x: m.l + (p - 0.5) * cw, y: H - 8, "text-anchor": "middle" });
+          t.textContent = p;
+          svg.appendChild(t);
+        }
+        cells.forEach((c) => {
+          const ai = AAS.indexOf(c.a);
+          if (ai < 0) return;
+          const r = el("rect", {
+            x: m.l + (c.p - 1) * cw,
+            y: m.t + ai * ch,
+            width: Math.max(0.6, cw - 0.4),
+            height: ch - 0.6,
+            fill: c.v > 0 ? ramp((Math.log10(c.v) - lo) / (hi - lo || 1)) : "var(--na)",
+          });
+          r.addEventListener("mousemove", (e) =>
+            showTT(e, `<b>${(s.heatmap.wt[c.p] || "") + c.p + c.a}</b> · counts/cov ${c.v.toExponential(2)}`),
+          );
+          r.addEventListener("mouseleave", hideTT);
+          svg.appendChild(r);
+        });
+      }
+
+      /* ---------- sample chips ---------- */
+      function chips(host, onChange) {
+        function draw() {
+          host.innerHTML =
+            SIDS.map(
+              (id) =>
+                `<span class="chip${sel.has(id) ? "" : " off"}" data-s="${esc(id)}">` +
+                `<span class="sw" style="background:${COLOR[id]}"></span>${esc(id)}</span>`,
+            ).join("") + `<button class="act" data-all="1">All</button>`;
+        }
+        host.addEventListener("click", (e) => {
+          if (e.target.closest("[data-all]")) {
+            SIDS.forEach((s) => sel.add(s));
+          } else {
+            const c = e.target.closest(".chip");
+            if (!c) return;
+            const id = c.dataset.s;
+            // click isolates; clicking the isolated one brings everything back
+            if (sel.size === SIDS.length) {
+              sel.clear();
+              sel.add(id);
+            } else if (sel.has(id) && sel.size === 1) SIDS.forEach((s) => sel.add(s));
+            else if (sel.has(id)) sel.delete(id);
+            else sel.add(id);
+          }
+          draw();
+          onChange();
+        });
+        draw();
+      }
+
+      /* ---------- plot downloads ---------- */
+      // The report's own plots are what you download - PDF (vector, publication-quality) and PNG (a
+      // high-resolution raster for slides). Both are generated from the live chart, so the file is
+      // exactly what is on screen, including the current selection and smoothing.
+      function dlButtons(name) {
+        return `<button class="act" data-dl="png" data-name="${esc(name)}">PNG</button>`;
+      }
+      // Inline the computed colours: the chart paints from CSS variables that do not exist once the
+      // file is opened on its own, so a naive serialisation comes out black-on-black.
+      function inlineStyles(src) {
+        const svg = src.cloneNode(true);
+        const from = src.querySelectorAll("*"),
+          to = svg.querySelectorAll("*");
+        const PROPS = [
+          "fill",
+          "stroke",
+          "stroke-width",
+          "stroke-opacity",
+          "stroke-dasharray",
+          "font-size",
+          "font-family",
+          "text-anchor",
+          "opacity",
+        ];
+        for (let i = 0; i < from.length; i++) {
+          const cs = getComputedStyle(from[i]);
+          to[i].removeAttribute("class");
+          PROPS.forEach((p) => {
+            const v = cs.getPropertyValue(p);
+            if (v) to[i].setAttribute(p, v); // keep fill:none - dropping it would flood-fill the lines
+          });
+        }
+        svg.setAttribute("xmlns", SVGNS);
+        const vb = (svg.getAttribute("viewBox") || "0 0 1020 260").split(/\s+/).map(Number);
+        svg.setAttribute("width", vb[2]);
+        svg.setAttribute("height", vb[3]);
+        const bg = el("rect", {
+          x: 0,
+          y: 0,
+          width: vb[2],
+          height: vb[3],
+          fill: getComputedStyle(document.body).backgroundColor,
+        });
+        svg.insertBefore(bg, svg.firstChild);
+        return { svg, w: vb[2], h: vb[3] };
+      }
+      function saveBlob(blob, name) {
+        const u = URL.createObjectURL(blob);
+        const a = document.createElement("a");
+        a.href = u;
+        a.download = name;
+        a.click();
+        setTimeout(() => URL.revokeObjectURL(u), 4000);
+      }
+      // High-resolution PNG: 3x the on-screen size, which is enough for a full-width slide. The old
+      // export was 2x, which looked soft when scaled up.
+      const PNG_SCALE = 3;
+      function exportPNG(src, name) {
+        const { svg, w, h } = inlineStyles(src);
+        const txt = new XMLSerializer().serializeToString(svg);
+        const img = new Image();
+        img.onload = () => {
+          const c = document.createElement("canvas");
+          c.width = w * PNG_SCALE;
+          c.height = h * PNG_SCALE;
+          const ctx = c.getContext("2d");
+          ctx.scale(PNG_SCALE, PNG_SCALE);
+          ctx.drawImage(img, 0, 0);
+          c.toBlob((bl) => saveBlob(bl, name + ".png"), "image/png");
+        };
+        img.src = "data:image/svg+xml;base64," + btoa(unescape(encodeURIComponent(txt)));
+      }
+      // Render the chart to a high-resolution image once, for either target.
+      function rasterise(src, cb) {
+        const { svg, w, h } = inlineStyles(src);
+        const txt = new XMLSerializer().serializeToString(svg);
+        const img = new Image();
+        img.onload = () => {
+          const c = document.createElement("canvas");
+          c.width = w * PNG_SCALE;
+          c.height = h * PNG_SCALE;
+          const ctx = c.getContext("2d");
+          ctx.scale(PNG_SCALE, PNG_SCALE);
+          ctx.drawImage(img, 0, 0);
+          cb(c, w, h);
+        };
+        img.onerror = () => cb(null);
+        img.src = "data:image/svg+xml;base64," + btoa(unescape(encodeURIComponent(txt)));
+      }
+      // Download is PNG only (high-resolution raster of exactly what is on screen).
+      document.addEventListener("click", (e) => {
+        const b = e.target.closest("[data-dl]");
+        if (!b) return;
+        const src = b.closest(".panel").querySelector("svg");
+        if (!src) return;
+        const name = b.dataset.name;
+        rasterise(src, (c) => {
+          if (!c) return;
+          c.toBlob((bl) => saveBlob(bl, name + ".png"), "image/png");
+        });
+      });
+
+      /* ---------- lazy iframes: only mount the payload when the tab is first opened ---------- */
+      // Reports are held as strings and assigned to srcdoc, not src="data:...". A data: URI gives the
+      // frame an opaque origin, and MultiQC dies on the spot there (it reads localStorage unguarded
+      // while bootstrapping and never recovers). srcdoc inherits this document's origin instead.
+      // Keyed by group+name, not group alone: every per-file error-correction report shares the group
+      // "ec", so a group-keyed map silently collapses all six onto whichever was appended last.
+      const fkey = (r) => r.group + "" + r.name;
+      const FRAME_HTML = new Map((DATA.reports || []).map((r) => [fkey(r), r.html]));
+      function mountFrames(root) {
+        root.querySelectorAll("iframe[data-frame]").forEach((f) => {
+          const html = FRAME_HTML.get(f.dataset.frame);
+          if (html && !f.srcdoc) {
+            // srcdoc parses asynchronously, so the theme has to wait for load; do it once more on a
+            // short delay too, since MultiQC rewrites <html> during its own bootstrap.
+            f.addEventListener("load", () => {
+              applyTheme(f);
+              setTimeout(() => applyTheme(f), 400);
+            });
+            f.srcdoc = html;
+          }
+        });
+      }
+
+      /* ---------- sections ---------- */
+      // [dataKey, title, description, logY, classSplit]
+      // classSplit marks the metrics that are only meaningful within one varying-bases class. Coverage
+      // is a property of the position itself, so it has no class.
+      const QC_PLOTS = [
+        [
+          "cov",
+          "Sequencing coverage along the ORF",
+          "Reads covering each residue position, averaged over the substitutions measured there. Dips mark regions the library or the sequencing reached poorly. The dashed line is the coverage needed to reach the targeted counts per variant (--aimed_cov), assuming all 21 residue outcomes are equally represented.",
+          true,
+          false,
+        ],
+        [
+          "cpc",
+          "Counts per coverage along the ORF",
+          "Variant counts normalised by the coverage at that position, i.e. the frequency at which the library's variants appear. This is the coverage-independent view of library representation.",
+          true,
+          true,
+        ],
+      ];
+
+      // The fitness-coloured structure viewer is built once, up front, into an off-screen holder, so it
+      // is warmed up (WebGL context created, model parsed, already spinning) by the time the reader opens
+      // the Variant effects page. It is then moved into that page's panel on show, and moved back to the
+      // holder before any page switch — so main.innerHTML = "" never destroys its WebGL context, and the
+      // model never has to be re-parsed. `attach`/`detach` are no-ops when there is no structure.
+      const STRUCT = (function () {
+        const S = DATA.structure;
+        if (!S || !window.$3Dmol) return { attach() {}, detach() {} };
+        const CW = [0x3a4cc0, 0x6f8fe8, 0xc3cfe2, 0xf2f2f2, 0xf2c0a4, 0xe26a52, 0xb40426];
+        const a = S.fmax || 1;
+        const colFit = (f) => {
+          let t = 1 - (f + a) / (2 * a);
+          t = Math.max(0, Math.min(1, t));
+          const n = CW.length - 1,
+            x = t * n,
+            i = Math.min(n - 1, Math.floor(x)),
+            fr = x - i;
+          const p = CW[i],
+            q = CW[i + 1],
+            lp = (u, v) => Math.round(u + (v - u) * fr);
+          return (
+            (lp((p >> 16) & 255, (q >> 16) & 255) << 16) |
+            (lp((p >> 8) & 255, (q >> 8) & 255) << 8) |
+            lp(p & 255, q & 255)
+          );
+        };
+        const GREY = 0xcfd5df;
+        const resiColor = {};
+        for (const k in S.resi_fit) resiColor[k] = colFit(S.resi_fit[k]);
+        const OFF = "position:fixed;left:-99999px;top:0;width:980px;height:560px;";
+        const holder = document.createElement("div");
+        holder.style.cssText = OFF;
+        document.body.appendChild(holder);
+        let viewer = null;
+        try {
+          // Frame rate, not fidelity, is what matters for a small auto-spinning thumbnail, and 3Dmol's
+          // defaults are tuned the other way: `cartoonQuality: 10` tessellates the ribbon far more
+          // finely than this size can show, and `antialias: true` additionally *doubles* the internal
+          // devicePixelRatio (see Renderer.setSize), so the canvas is rendered at 4x the pixels it
+          // displays. Both are dropped here - on a laptop GPU that is the difference between a stuttery
+          // and a smooth spin, and at this scale the visual difference is not perceptible.
+          viewer = $3Dmol.createViewer(holder, { backgroundAlpha: 0, antialias: false, cartoonQuality: 4 });
+          viewer.addModel(S.pdb, "pdb");
+          viewer.setStyle(
+            {},
+            { cartoon: { colorfunc: (atom) => (atom.resi in resiColor ? resiColor[atom.resi] : GREY) } },
+          );
+          viewer.zoomTo();
+          // zoomTo() fits the whole model, which on a wide panel leaves the fold floating in dead space
+          // above and below; tighten it so the structure fills the frame. Kept modest so the silhouette
+          // still never clips as it turns.
+          viewer.zoom(1.35);
+          viewer.render();
+        } catch (e) {
+          viewer = null;
+        }
+        // 3Dmol's own viewer.spin() drives the rotation from setInterval(..., 25): a hard 40 fps
+        // ceiling that is also not aligned to the display refresh, so on a 60 Hz screen successive
+        // frames land one or two refreshes apart and the spin visibly judders no matter how fast the
+        // GPU is. It also keeps ticking while the panel is off-screen. Drive the rotation from
+        // requestAnimationFrame instead: vsync-locked (so it is smooth), time-based (so the speed is
+        // the same on a 60 or 120 Hz display), and paused whenever the structure is not on screen.
+        // viewer.rotate() re-renders internally, so no separate render call is needed per frame.
+        const DEG_PER_SEC = 16; // identical to the old 0.4 deg per 25 ms tick
+        let raf = 0,
+          last = 0;
+        function frame(t) {
+          // Clamp the step: browsers throttle rAF hard in a background tab, so without this the first
+          // frame after the reader comes back would carry the whole elapsed time and snap the model
+          // round by hundreds of degrees.
+          if (last) viewer.rotate((DEG_PER_SEC * Math.min(t - last, 100)) / 1000, "y");
+          last = t;
+          raf = requestAnimationFrame(frame);
+        }
+        function startSpin() {
+          if (viewer && !raf) {
+            last = 0;
+            raf = requestAnimationFrame(frame);
+          }
+        }
+        function stopSpin() {
+          if (raf) {
+            cancelAnimationFrame(raf);
+            raf = 0;
+          }
+        }
+        addEventListener("resize", () => {
+          if (viewer && holder.parentElement && holder.parentElement.id === "f-struct") viewer.resize();
+        });
+        return {
+          attach(target) {
+            if (!target) return;
+            if (!viewer) {
+              target.innerHTML = '<div class="empty">Structure could not be rendered in this browser.</div>';
+              return;
+            }
+            holder.style.cssText = "position:relative;width:100%;height:100%;";
+            target.appendChild(holder);
+            viewer.resize();
+            viewer.render();
+            startSpin();
+          },
+          detach() {
+            stopSpin(); // nothing is watching it off-screen - do not burn a frame budget on every page
+            if (viewer && holder.parentElement !== document.body) {
+              holder.style.cssText = OFF;
+              document.body.appendChild(holder);
+            }
+          },
+        };
+      })();
+
+      const SECT = [];
+
+      // --- overview ---
+      SECT.push({
+        id: "overview",
+        label: "Overview",
+        build(root) {
+          const r = DATA.run || {};
+          const nvar = SAMPLES.reduce((a, s) => a + (s.ec ? s.ec.n_variants : 0), 0);
+          const tiles = [
+            ["Sequencing files", fmt(SIDS.length)],
+            ["Protein / sample", r.sample || "–"],
+            ["Reading frame", r.reading_frame || "–"],
+            ["Mutagenesis", r.mutagenesis_type || "–"],
+            ["Error correction", r.error_correction || "none"],
+            ["Pipeline version", r.version || "–"],
+          ];
+          let h = `<h1>Run overview</h1><p class="lead">A self-contained tour of this deep mutational scanning run — sequencing quality, library representation, error correction, variant effects. Every figure is redrawn from the run's own tables and the detailed tool reports are embedded, so the file reads and travels on its own.</p>`;
+          h += `<div class="tiles">${tiles.map(([k, v]) => `<div class="tile"><div class="k">${esc(k)}</div><div class="v">${esc(v)}</div></div>`).join("")}</div>`;
+          h += `<div class="panel"><h2>Sequencing files ${info("Every fastq pair in the run, by selection side (input/output) and replicate.")}</h2>`;
+          h += `<div class="tblwrap"><table><thead><tr><th>File</th><th>Library variants</th><th>Total counts</th></tr></thead><tbody>${SAMPLES.map(
+            (s) =>
+              `<tr><td>${esc(s.id)}</td><td>${s.ec ? fmt(s.ec.n_variants) : "–"}</td><td>${s.ec ? fmt(s.ec.total_corrected) : "–"}</td></tr>`,
+          ).join("")}</tbody></table></div></div>`;
+          // Run statistics: Nextflow's own execution report and timeline, which sit next to this file in
+          // pipeline_info/. Linked rather than parsed, because the trace is still being written while this
+          // report is built. Relative links resolve when the report is opened from the results folder.
+          const sfx = (DATA.run || {}).trace_report_suffix;
+          if (sfx)
+            h += `<div class="panel"><h2>Run statistics</h2><p class="desc">Task runtimes, CPU and memory usage of this run are in Nextflow's own reports, next to this file: <a href="pipeline_info/execution_report_${esc(
+              sfx,
+            )}.html" target="_blank" rel="noopener" style="color:var(--accent);font-weight:600">execution report ↗</a> · <a href="pipeline_info/execution_timeline_${esc(
+              sfx,
+            )}.html" target="_blank" rel="noopener" style="color:var(--accent);font-weight:600">timeline ↗</a></p></div>`;
+          // README: what this file is and how it maps onto the results folder. The report is meant as
+          // the way in - a reader should be able to orient here and then find the underlying files.
+          const ec = r.error_correction && r.error_correction !== "none";
+          const struct = [
+            ["deepmutscan_report.html", "This report — the guided overview of the whole run."],
+            ["fastqc/", "Per-file FastQC reports on raw read quality."],
+            ["multiqc/", "The aggregated MultiQC report (also embedded under Sequencing QC)."],
+            [
+              "library_QC/&lt;file&gt;/",
+              "Per-file library-representation plots: coverage, counts and counts-per-coverage along the ORF, count heatmaps and log-difference plots.",
+            ],
+            ...(ec
+              ? [
+                  [
+                    "library_QC/error_correction_report.html",
+                    "The sequencing-error-correction report (also under Error correction).",
+                  ],
+                ]
+              : []),
+            [
+              "fitness/default_results/",
+              "Fitness estimates, heatmap and correlation plots from the built-in estimator.",
+            ],
+            ["fitness/DiMSum_results/", "DiMSum output, if DiMSum ran: fitness table, diagnostics and its own report."],
+            ["fitness/mutscan_results/", "mutscan output, if it ran: edgeR/limma fitness tables and volcano plots."],
+            ["variant_effect_inspection_tool/", "The interactive 3D structure viewer, if a --pdb was given."],
+            ["intermediate_files/", "Alignments and per-step variant-count tables — the inputs behind every plot."],
+            ["pipeline_info/", "Nextflow execution report, timeline, trace and software versions."],
+          ];
+          h += `<div class="panel"><h2>About this report ${info("Self-contained overview from sequencing QC to variant effects; detailed reports are embedded and underlying tables re-plotted client-side.")}</h2>
+            <p class="desc">Guided entry point: pages follow the analysis order — sequencing quality, library representation, then (where run) error correction and variant effects. Plots are regenerated from the run's tables and the original detailed reports embedded, so the file opens offline and sends as one attachment.</p>
+            <h3 style="margin:14px 0 6px;font-size:13px;color:var(--ink2)">Where everything lives in the results folder</h3>
+            <div class="tblwrap"><table><thead><tr><th>Path</th><th>Contents</th></tr></thead><tbody>${struct
+              .map(([p, d]) => `<tr><td><code>${p}</code></td><td>${d}</td></tr>`)
+              .join("")}</tbody></table></div></div>`;
+          root.innerHTML = h;
+        },
+      });
+
+      // --- sequencing QC (MultiQC) ---
+      const mqc = (DATA.reports || []).find((r) => r.group === "multiqc");
+      const sdSamples = SAMPLES.filter((s) => s.seqdepth && s.seqdepth.length);
+      if (mqc || sdSamples.length)
+        SECT.push({
+          id: "seqqc",
+          label: "Sequencing QC",
+          build(root) {
+            let h = `<h1>Sequencing QC</h1><p class="lead">Read quality per fastq, and sequencing saturation per library.</p>`;
+            if (sdSamples.length)
+              h += `<div class="panel"><h2>Sequencing-depth saturation ${info(
+                "Expected fraction of the designed library detected (variants ≥ count threshold) as depth is rarefied from 1.0 to 0, by closed-form hypergeometric. A plateau indicates saturation; a rising tail, undersequencing.",
+              )}</h2><p class="desc">Detected variants (% of the possible library) against the fraction of achieved sequencing depth.</p><div class="row" id="sd-legend"></div><div id="sd-plot"></div></div>`;
+            if (mqc)
+              h += `<div class="panel"><h2>Read quality ${info(
+                "Aggregated MultiQC over all fastqs in the run.",
+              )}</h2><iframe class="frame" data-frame="${esc(fkey(mqc))}" title="MultiQC report"></iframe></div>`;
+            root.innerHTML = h;
+            mountFrames(root);
+            if (sdSamples.length) drawSeqDepth(sdSamples);
+          },
+        });
+
+      // Sequencing-depth rarefaction: one line per file, depth fraction (x) vs % of the library
+      // detected (y). Overlaid so saturation can be compared across files at a glance. `sdSel` holds the
+      // files currently drawn; it persists across page visits and is seeded (to inputs) on first draw.
+      const sdSel = new Set();
+      function drawSeqDepth(sdSamples) {
+        const host = document.getElementById("sd-plot");
+        if (!host) return;
+        const W = 1000,
+          H = 340,
+          m = { l: 56, r: 14, t: 12, b: 42 };
+        const iw = W - m.l - m.r,
+          ih = H - m.t - m.b;
+        const svg = el("svg", { viewBox: `0 0 ${W} ${H}` });
+        host.innerHTML = "";
+        host.appendChild(svg);
+        const x = (v) => m.l + v * iw; // depth fraction 0..1
+        const y = (v) => m.t + ih - (v / 100) * ih; // percent 0..100
+        // grid + axes
+        for (let i = 0; i <= 10; i++) {
+          const gx = m.l + (iw * i) / 10;
+          svg.appendChild(el("line", { class: "grid", x1: gx, x2: gx, y1: m.t, y2: m.t + ih }));
+          const t = el("text", { class: "tick", x: gx, y: m.t + ih + 16, "text-anchor": "middle" });
+          t.textContent = (i / 10).toFixed(1);
+          svg.appendChild(t);
+        }
+        for (let i = 0; i <= 5; i++) {
+          const gy = m.t + (ih * i) / 5;
+          svg.appendChild(el("line", { class: "grid", x1: m.l, x2: m.l + iw, y1: gy, y2: gy }));
+          const t = el("text", { class: "tick", x: m.l - 8, y: gy + 3, "text-anchor": "end" });
+          t.textContent = 100 - i * 20 + "%";
+          svg.appendChild(t);
+        }
+        svg.appendChild(el("line", { class: "axis", x1: m.l, x2: m.l + iw, y1: m.t + ih, y2: m.t + ih }));
+        const xl = el("text", { class: "albl", x: m.l + iw / 2, y: H - 6, "text-anchor": "middle" });
+        xl.textContent = "Fraction of sequencing depth";
+        svg.appendChild(xl);
+        const yl = el("text", {
+          class: "albl",
+          x: -(m.t + ih / 2),
+          y: 14,
+          transform: "rotate(-90)",
+          "text-anchor": "middle",
+        });
+        yl.textContent = "Variants detected (% of library)";
+        svg.appendChild(yl);
+        // Default to the input libraries only - they are what "how deeply did we sequence the starting
+        // pool" is usually asked of; outputs (and helper samples) are one legend click away. Fall back to
+        // all files if the run has no input-typed file.
+        if (sdSel.size === 0) {
+          const inputs = sdSamples.filter((s) => s.type === "input");
+          (inputs.length ? inputs : sdSamples).forEach((s) => sdSel.add(s.id));
+        }
+        sdSamples
+          .filter((s) => sdSel.has(s.id))
+          .forEach((s) => {
+            let d = "";
+            s.seqdepth.forEach((p, i) => (d += (i ? " L " : "M ") + x(p.x) + " " + y(p.y)));
+            const path = el("path", {
+              d,
+              fill: "none",
+              stroke: COLOR[s.id],
+              "stroke-width": 1.6,
+              "stroke-opacity": 0.9,
+            });
+            path.addEventListener("mousemove", (e) => showTT(e, `<b>${esc(s.id)}</b>`));
+            path.addEventListener("mouseleave", hideTT);
+            svg.appendChild(path);
+          });
+        const legend = document.getElementById("sd-legend");
+        legend.innerHTML = sdSamples
+          .map(
+            (s) =>
+              `<span class="chip${sdSel.has(s.id) ? "" : " off"}" data-sd="${esc(s.id)}"><span class="sw" style="background:${COLOR[s.id]}"></span>${esc(s.id)}</span>`,
+          )
+          .join("");
+        legend.onclick = (e) => {
+          const c = e.target.closest("[data-sd]");
+          if (!c) return;
+          const id = c.dataset.sd;
+          if (sdSel.has(id)) {
+            if (sdSel.size > 1) sdSel.delete(id); // never leave the plot empty
+          } else sdSel.add(id);
+          drawSeqDepth(sdSamples);
+        };
+      }
+
+      // --- library QC ---
+      if (SAMPLES.some((s) => s.positions.length))
+        SECT.push({
+          id: "libqc",
+          label: "Library QC",
+          build(root) {
+            // Heading + sequencing-file selector pin to the top while the plots scroll, so it stays clear
+            // which files the curves are showing and the selection is always reachable.
+            let h = `<div class="stick"><h1>Library QC</h1><div class="row" id="lchips"></div><div class="row"><label class="sw-lbl">rolling-mean window&nbsp;<input type="number" id="rm-window" min="1" max="200" value="${WINDOW}" style="width:60px" /></label></div></div>`;
+            h += `<p class="lead">Library build and sequencing quality: uniformity of variant representation along the ORF, per-position coverage, and where representation drops off — the basis for trusting the downstream fitness estimates.</p>`;
+            // One selection of varying-bases classes, shared by the two split plots. Comparing 1 vs 2
+            // vs 3 only reads clearly in a single plot, so all three are always drawn; this set just
+            // controls which are opaque. Default: the single-nucleotide class.
+            const vbSel = new Set(["1"]);
+            QC_PLOTS.forEach(([dataKey, title, desc, logY, split]) => {
+              h += `<div class="panel"><h2>${esc(title)} ${info(desc)}</h2><p class="desc">${esc(desc)}</p>`;
+              h += `<div class="row opts">`;
+              if (split)
+                h += `<span class="seg" id="vb-${dataKey}">${VB.map(
+                  ([v, lbl]) =>
+                    `<button data-vb="${v}"${vbSel.has(v) ? ' class="on"' : ""}><span class="sw" style="background:${CLASS_COLOR[v]}"></span>${esc(lbl)}</button>`,
+                ).join("")}</span>`;
+              h += `<label class="sw-lbl"><input type="checkbox" id="rm-${dataKey}" checked /> rolling mean ${info(
+                "Rolling mean over the window set above (default --sliding_window_size), gaps skipped rather than zero-filled. Off shows the unsmoothed per-position values.",
+              )}</label>`;
+              h += `<span class="spacer"></span><span class="row" id="dl-${dataKey}"></span></div>`;
+              h += `<div id="c-${dataKey}"></div></div>`;
+            });
+            h += `<div class="panel"><h2>Counts per coverage — one file at a time ${info("Counts ÷ coverage per substitution (rows) × residue (columns), log colour; grey = unobserved. Single file only.")}</h2>
+                  <div class="row" id="hsel"></div><div id="c-heat"></div></div>`;
+            root.innerHTML = h;
+
+            const redraw = () => {
+              QC_PLOTS.forEach(([dataKey, t, d, logY, split]) => {
+                const host = document.getElementById("c-" + dataKey);
+                const smooth = document.getElementById("rm-" + dataKey).checked;
+                const hl = dataKey === "cov" ? (DATA.run || {}).required_cov : null;
+                lineChart(host, dataKey, t.split(" along")[0], logY, split ? vbSel : null, smooth, hl);
+                document.getElementById("dl-" + dataKey).innerHTML = dlButtons(`${dataKey}_along_ORF`);
+              });
+              const first = SIDS.find((id) => sel.has(id));
+              heatChart(
+                document.getElementById("c-heat"),
+                SAMPLES.find((s) => s.id === first),
+              );
+              document.getElementById("hsel").innerHTML = SIDS.map(
+                (id) =>
+                  `<span class="chip${id === first ? "" : " off"}" data-h="${esc(id)}"><span class="sw" style="background:${COLOR[id]}"></span>${esc(id)}</span>`,
+              ).join("");
+            };
+            chips(document.getElementById("lchips"), redraw);
+            document.getElementById("rm-window").addEventListener("input", (e) => {
+              const w = parseInt(e.target.value, 10);
+              if (w >= 1) {
+                WINDOW = w;
+                redraw();
+              }
+            });
+            QC_PLOTS.forEach(([dataKey, t, d, logY, split]) => {
+              document.getElementById("rm-" + dataKey).addEventListener("change", redraw);
+              const seg = document.getElementById("vb-" + dataKey);
+              if (seg)
+                seg.addEventListener("click", (e) => {
+                  const b = e.target.closest("[data-vb]");
+                  if (!b) return;
+                  const v = b.dataset.vb;
+                  // Toggle, but never let the last class off - an empty plot is not a useful state.
+                  if (vbSel.has(v) && vbSel.size > 1) vbSel.delete(v);
+                  else vbSel.add(v);
+                  document
+                    .querySelectorAll(".seg [data-vb]")
+                    .forEach((x) => x.classList.toggle("on", vbSel.has(x.dataset.vb)));
+                  redraw();
+                });
+            });
+            document.getElementById("hsel").addEventListener("click", (e) => {
+              const c = e.target.closest("[data-h]");
+              if (!c) return;
+              heatChart(
+                document.getElementById("c-heat"),
+                SAMPLES.find((s) => s.id === c.dataset.h),
+              );
+              document.querySelectorAll("#hsel .chip").forEach((x) => x.classList.toggle("off", x !== c));
+            });
+            redraw();
+            if (!OVERLAY_OK)
+              root.insertAdjacentHTML(
+                "afterbegin",
+                `<div class="note">This run has ${SIDS.length} sequencing files — more than the palette can keep distinguishable. Isolate files with the chips to compare them reliably.</div>`,
+              );
+          },
+        });
+
+      // --- error correction ---
+      const ecReports = (DATA.reports || []).filter((r) => r.group === "ec");
+      if (SAMPLES.some((s) => s.ec))
+        SECT.push({
+          id: "ec",
+          label: "Error correction",
+          build(root) {
+            let h = `<h1>Error correction</h1><p class="lead">What sequencing-error correction removed. The full nucleotide-level report is embedded below.</p>`;
+            h += `<div class="panel"><h2>Removed per file ${info("Counts removed by correction as a fraction of raw; 'variants adjusted' counts variants that changed at all.")}</h2><div class="tblwrap"><table><thead><tr><th>File</th><th>Library variants</th><th>Variants adjusted</th><th>Raw counts</th><th>Corrected</th><th>% removed</th></tr></thead><tbody>`;
+            h += SAMPLES.filter((s) => s.ec)
+              .map(
+                (s) =>
+                  `<tr><td>${esc(s.id)}</td><td>${fmt(s.ec.n_variants)}</td><td>${fmt(s.ec.n_corrected)}</td><td>${fmt(s.ec.total_raw)}</td><td>${fmt(s.ec.total_corrected)}</td><td>${fmt(s.ec.pct_removed, 2)} %</td></tr>`,
+              )
+              .join("");
+            h += `</tbody></table></div></div>`;
+            // One run-level report now, carrying every file and its own selector, so there is nothing
+            // left for this page to switch between.
+            if (ecReports.length) {
+              h += `<div class="panel"><h2>Full report ${info(
+                "The full error-correction report; its file selector drives subset or single-file views.",
+              )}</h2><iframe class="frame" style="height:84vh" data-frame="${esc(fkey(ecReports[0]))}" title="Error-correction report"></iframe></div>`;
+            }
+            root.innerHTML = h;
+            mountFrames(root);
+          },
+        });
+
+      // --- variant effects ---
+      // Which estimators ran is a per-run choice (--fitness / --dimsum / --mutscan), so nothing here
+      // may assume any particular one is present: every block is driven off what actually arrived.
+      const METHODS = [
+        [
+          "default",
+          "Default",
+          "fitness/default_results/",
+          "Replicate-wise log-ratio of output to input counts, rescaled so synonymous variants sit at 0 and stops at -1.",
+        ],
+        [
+          "dimsum",
+          "DiMSum",
+          "fitness/DiMSum_results/dimsum_results/",
+          "Fits an error model across replicates and reports fitness with per-variant error estimates.",
+        ],
+        [
+          "mutscan",
+          "mutscan",
+          "fitness/mutscan_results/",
+          "Differential abundance via edgeR/limma, reporting log fold-changes with significance.",
+        ],
+      ].filter(
+        ([g]) =>
+          (DATA.reports || []).some((r) => r.group === g) ||
+          (DATA.downloads || []).some((d) => d.group === g) ||
+          (g === "default" && DATA.fitness),
+      );
+
+      if (DATA.fitness || METHODS.length)
+        SECT.push({
+          id: "fitness",
+          label: "Variant effects",
+          build(root) {
+            const F = DATA.fitness;
+            let h = `<h1>Variant effects</h1><p class="lead">How each substitution changes fitness. ${
+              METHODS.length ? "Estimators in this run: " + METHODS.map(([, n]) => n).join(", ") + "." : ""
+            }</p>`;
+            const hasStruct = DATA.structure && window.$3Dmol;
+            // The blue-white-red fitness scale, faithful to colFit: −fmax red (deleterious), 0 white,
+            // +fmax blue (fitness gain) — the same ramp the landscape heatmap uses, so one legend serves
+            // both. Placed BETWEEN the landscape and the structure when the two share a block.
+            const legendHTML = (() => {
+              const fm = (DATA.structure && DATA.structure.fmax) || 1;
+              const grad = "linear-gradient(to right,#b40426,#e26a52,#f2c0a4,#f2f2f2,#c3cfe2,#6f8fe8,#3a4cc0)";
+              return `<div style="max-width:360px;margin:16px auto"><div style="height:12px;border-radius:6px;background:${grad}"></div><div style="display:flex;justify-content:space-between;font-size:11px;color:var(--muted);margin-top:3px"><span>−${fm.toFixed(2)} · less fit</span><span>0 · neutral</span><span>+${fm.toFixed(2)} · fitter</span></div></div>`;
+            })();
+            const landInfo = info(
+              "Mean fitness per substitution across replicates: blue fitter than wildtype, red less fit, white neutral. Symmetric about 0 (anchored on synonymous variants), stops at −1; grey = unmeasured. Click a cell for the contributing nucleotide changes.",
+            );
+            const structInfo = info(
+              "Wildtype fold coloured by mean missense fitness per residue, on the landscape's scale; grey = unmeasured. Auto-spins; use the inspection tool for interaction.",
+            );
+            const structDesc = `<p class="desc">Each residue is coloured by its mean fitness${
+              DATA.structure && DATA.structure.identity < 0.9
+                ? ` (structure↔sequence identity ${(DATA.structure.identity * 100).toFixed(0)}%)`
+                : ""
+            }.${
+              (DATA.reports || []).some((r) => r.group === "viewer")
+                ? ` <a href="#" id="open-viewer" style="color:var(--accent);font-weight:600">Open the full interactive inspection tool ↗</a>`
+                : ""
+            }</p>`;
+            const landBlock = `<h2>Fitness landscape ${landInfo}</h2><div id="f-heat"></div><div id="f-cell"></div>`;
+            const structBlock = `<h2 style="margin-top:4px">Fitness on the structure ${structInfo}</h2>${structDesc}<div id="f-struct" style="height:560px;position:relative"></div>`;
+            // One block with the shared legend between the two views when both are present; otherwise
+            // whichever one exists, with the legend kept alongside the structure.
+            if (F && hasStruct) h += `<div class="panel">${landBlock}${legendHTML}${structBlock}</div>`;
+            else if (F) h += `<div class="panel">${landBlock}</div>`;
+            else if (hasStruct) h += `<div class="panel">${structBlock}${legendHTML}</div>`;
+            // Point to the full per-method outputs on disk: the landscape here is a summary, and each
+            // estimator writes its complete tables, diagnostics and report to the results folder.
+            if (METHODS.length)
+              h +=
+                `<div class="panel"><h2>Complete fitness outputs</h2><p class="desc">The landscape above summarises the built-in estimator. Every estimator's full tables, diagnostic plots and reports are written to the results folder:</p>` +
+                `<div class="tblwrap"><table><thead><tr><th>Path</th><th>Estimator</th></tr></thead><tbody>` +
+                METHODS.map(
+                  ([, n, path, desc]) => `<tr><td><code>${esc(path)}</code></td><td>${esc(n)} — ${esc(desc)}</td></tr>`,
+                ).join("") +
+                `</tbody></table></div></div>`;
+            root.innerHTML = h;
+            mountFrames(root);
+
+            // Move the pre-built, already-spinning structure viewer into this page's panel.
+            if (DATA.structure && window.$3Dmol) {
+              STRUCT.attach(document.getElementById("f-struct"));
+              const ov = document.getElementById("open-viewer");
+              if (ov) {
+                // Open the full inspection tool in a new tab from the copy embedded in this file, so the
+                // link keeps working after the report is downloaded and sent on (no server, no sidecar).
+                const vrep = (DATA.reports || []).find((r) => r.group === "viewer");
+                ov.onclick = (e) => {
+                  e.preventDefault();
+                  if (!vrep) return;
+                  const url = URL.createObjectURL(new Blob([vrep.html], { type: "text/html" }));
+                  window.open(url, "_blank", "noopener");
+                };
+              }
+            }
+
+            // fitness heatmap (diverging, fixed symmetric range)
+            (function () {
+              const host = document.getElementById("f-heat");
+              // Amino acids grouped by side-chain chemistry (hydrophobic, aromatic, basic, acidic,
+              // polar, proline, stop), matching the order used by the R heatmap figures.
+              const AAS = "GAVLMIFYWKRHDESTCNQP*".split("");
+              const cells = F.cells;
+              if (!cells.length) return (host.innerHTML = '<div class="empty">No fitness data.</div>');
+              const maxP = Math.max(...cells.map((c) => c.p));
+              const a = Math.max(...cells.map((c) => Math.abs(c.f))) || 1;
+              const W = 1020,
+                m = { l: 26, r: 10, t: 8, b: 26 },
+                cw = (W - m.l - m.r) / maxP,
+                ch = 11;
+              const H = m.t + m.b + ch * AAS.length;
+              const svg = el("svg", { viewBox: `0 0 ${W} ${H}` });
+              host.innerHTML = "";
+              host.appendChild(svg);
+              const CW = [0x3a4cc0, 0x6f8fe8, 0xc3cfe2, 0xf2f2f2, 0xf2c0a4, 0xe26a52, 0xb40426];
+              const hex2 = (x) => [(x >> 16) & 255, (x >> 8) & 255, x & 255];
+              const lerp = (p, q, t) => p + (q - p) * t;
+              const ramp = (t) => {
+                t = Math.max(0, Math.min(1, t));
+                const n = CW.length - 1,
+                  xx = t * n,
+                  i = Math.min(n - 1, Math.floor(xx)),
+                  f = xx - i;
+                const p = hex2(CW[i]),
+                  q = hex2(CW[i + 1]);
+                return `rgb(${Math.round(lerp(p[0], q[0], f))},${Math.round(lerp(p[1], q[1], f))},${Math.round(lerp(p[2], q[2], f))})`;
+              };
+              AAS.forEach((aa, i) => {
+                const t = el("text", { class: "tick", x: m.l - 5, y: m.t + i * ch + 8, "text-anchor": "end" });
+                t.textContent = aa;
+                svg.appendChild(t);
+              });
+              const step = Math.max(1, Math.round(maxP / 12));
+              for (let p = 1; p <= maxP; p += step) {
+                const t = el("text", { class: "tick", x: m.l + (p - 0.5) * cw, y: H - 8, "text-anchor": "middle" });
+                t.textContent = p;
+                svg.appendChild(t);
+              }
+              cells.forEach((c) => {
+                const ai = AAS.indexOf(c.a);
+                if (ai < 0) return;
+                const r = el("rect", {
+                  x: m.l + (c.p - 1) * cw,
+                  y: m.t + ai * ch,
+                  width: Math.max(0.6, cw - 0.4),
+                  height: ch - 0.6,
+                  fill: ramp(1 - (c.f + a) / (2 * a)),
+                });
+                r.addEventListener("mousemove", (e) =>
+                  showTT(
+                    e,
+                    `<b>${c.wt + c.p + c.a}</b> · fitness ${c.f.toFixed(2)}${c.sd != null ? " ± " + c.sd.toFixed(2) : ""}${c.syn ? " (syn)" : c.stop ? " (stop)" : ""}<span style="opacity:.7"> · click for detail</span>`,
+                  ),
+                );
+                r.addEventListener("mouseleave", hideTT);
+                r.style.cursor = "pointer";
+                r.addEventListener("click", () => {
+                  svg.querySelectorAll("rect.selcell").forEach((x) => {
+                    x.classList.remove("selcell");
+                    x.removeAttribute("stroke");
+                  });
+                  r.classList.add("selcell");
+                  r.setAttribute("stroke", "var(--ink)");
+                  r.setAttribute("stroke-width", "1.4");
+                  showCell(c);
+                });
+                svg.appendChild(r);
+              });
+            })();
+
+            // Detail for a clicked cell. One amino-acid substitution can be reached by several
+            // different codons, and the pipeline reports them together on one row - so the honest
+            // answer to "what nucleotide change is this?" is usually a list, not a single value.
+            function showCell(c) {
+              const host = document.getElementById("f-cell");
+              const nt = c.nt || [];
+              let x = `<div class="cellbox"><h3>${esc(c.wt + c.p + c.a)}${
+                c.syn ? ' <span class="tag">synonymous</span>' : c.stop ? ' <span class="tag">stop</span>' : ""
+              }</h3>`;
+              x += `<div class="tiles small">`;
+              x += `<div class="tile"><div class="k">Mean fitness</div><div class="v">${fmt(c.f, 3)}</div></div>`;
+              x += `<div class="tile"><div class="k">SD across replicates</div><div class="v">${
+                c.sd == null ? "–" : fmt(c.sd, 3)
+              }</div></div>`;
+              if (c.reps && c.reps.length)
+                x += `<div class="tile"><div class="k">Per replicate</div><div class="v small">${c.reps
+                  .map((v) => (v == null ? "–" : fmt(v, 2)))
+                  .join(" · ")}</div></div>`;
+              if (c.n_in != null)
+                x += `<div class="tile"><div class="k">Input counts</div><div class="v">${fmt(c.n_in)}</div></div>`;
+              if (c.n_out != null)
+                x += `<div class="tile"><div class="k">Output counts</div><div class="v">${fmt(c.n_out)}</div></div>`;
+              x += `<div class="tile"><div class="k">Coding variants</div><div class="v">${nt.length || "–"}</div></div>`;
+              x += `</div>`;
+              if (nt.length) {
+                x += `<h4>Nucleotide changes reaching this substitution ${info(
+                  "Every codon in the data producing this amino-acid change, as nucleotide substitutions vs the wildtype ORF. Pooled into one fitness value, so disagreement between them is worth checking.",
+                )}</h4>`;
+                x += `<div class="tblwrap"><table><thead><tr><th>Codon</th><th>Nucleotide change(s)</th><th>ORF positions</th></tr></thead><tbody>`;
+                nt.forEach((v) => {
+                  x += `<tr><td><code>${esc(v.wt_codon)} → ${esc(v.mut_codon)}</code></td><td><code>${esc(
+                    v.changes.join(", "),
+                  )}</code></td><td>${esc(v.positions.join(", "))}</td></tr>`;
+                });
+                x += `</tbody></table></div>`;
+              } else {
+                x += `<p class="desc">No nucleotide-level detail available for this substitution.</p>`;
+              }
+              x += `</div>`;
+              host.innerHTML = x;
+              host.scrollIntoView({ behavior: "smooth", block: "nearest" });
+            }
+          },
+        });
+
+      // --- citation ---
+      const CITE = DATA.citations || { pipeline: [], tools: [] };
+      // Format one reference as a readable line; the same data also produces the biblatex block.
+      function citeLine(c) {
+        const bits = [`<b>${esc(c.authors)}</b>`, esc(c.title) + "."];
+        if (c.journal) {
+          let j = `<i>${esc(c.journal)}</i>`;
+          if (c.year) j += " " + esc(c.year);
+          if (c.volume) j += ";" + esc(c.volume) + (c.number ? "(" + esc(c.number) + ")" : "");
+          if (c.pages) j += ":" + esc(c.pages);
+          bits.push(j + ".");
+        } else if (c.year) bits.push(esc(c.year) + ".");
+        if (c.doi)
+          bits.push(`doi:<a href="https://doi.org/${esc(c.doi)}" target="_blank" rel="noopener">${esc(c.doi)}</a>`);
+        else if (c.eprint)
+          bits.push(
+            `arXiv:<a href="https://arxiv.org/abs/${esc(c.eprint)}" target="_blank" rel="noopener">${esc(c.eprint)}</a>`,
+          );
+        else if (c.url) bits.push(`<a href="${esc(c.url)}" target="_blank" rel="noopener">${esc(c.url)}</a>`);
+        return bits.join(" ") + (c.note ? ` <span style="opacity:.7">${esc(c.note)}</span>` : "");
+      }
+      // Open the biblatex source in a new tab as plain text - a view, not a download, as asked. A new
+      // window's document is same-origin and writable even from file://, unlike a blob: URL.
+      function openBib(title, src) {
+        const w = window.open("", "_blank");
+        if (!w) return;
+        w.document.write(
+          `<!doctype html><title>${esc(title)}</title>` +
+            `<body style="margin:0"><pre style="margin:0;padding:20px;font:13px/1.5 ui-monospace,Menlo,monospace;white-space:pre-wrap">${esc(src)}</pre></body>`,
+        );
+        w.document.close();
+      }
+      window.__bib = { pipeline: CITE.biblatex_pipeline || "", tools: CITE.biblatex_tools || "" };
+      if ((CITE.pipeline || []).length || (CITE.tools || []).length)
+        SECT.push({
+          id: "citation",
+          label: "Citation",
+          build(root) {
+            const sect = (heading, items, which, note) =>
+              `<div class="panel"><h2>${esc(heading)}</h2>${note ? `<p class="desc">${note}</p>` : ""}` +
+              `<ol class="cites">${items.map((c) => `<li>${citeLine(c)}</li>`).join("")}</ol>` +
+              `<button class="act" data-bib="${which}">Get biblatex</button></div>`;
+            let h = `<h1>Citation</h1><p class="lead">If this run supports your work, cite the pipeline and the tools below — exactly those it invoked.</p>`;
+            h += sect(
+              "Pipeline",
+              CITE.pipeline || [],
+              "pipeline",
+              "Cite nf-core/deepmutscan together with the nf-core framework and Nextflow.",
+            );
+            h += sect("Tools used in this run", CITE.tools || [], "tools", "");
+            root.innerHTML = h;
+            root.addEventListener("click", (e) => {
+              const b = e.target.closest("[data-bib]");
+              if (!b) return;
+              openBib("biblatex — " + b.dataset.bib, window.__bib[b.dataset.bib]);
+            });
+          },
+        });
+
+      /* ---------- nav ---------- */
+      const nav = document.getElementById("nav"),
+        main = document.getElementById("main");
+      SECT.forEach((s, i) => {
+        const b = document.createElement("button");
+        b.textContent = s.label;
+        b.className = "";
+        b.onclick = () => {
+          nav.querySelectorAll("button").forEach((x) => x.classList.remove("on"));
+          b.classList.add("on");
+          show(s);
+        };
+        nav.appendChild(b);
+      });
+      const built = {};
+      function show(s) {
+        STRUCT.detach(); // rescue the live WebGL viewer before the page is wiped
+        main.innerHTML = "";
+        const d = document.createElement("div");
+        d.className = "sect on";
+        main.appendChild(d);
+        s.build(d);
+        // `main` is the scroll container, so without this a page opens at whatever offset the previous
+        // one was scrolled to - landing the reader in the middle of a page they have not seen the top of.
+        main.scrollTop = 0;
+      }
+      document.getElementById("run-tag").innerHTML =
+        "sample <strong>" + esc((DATA.run || {}).sample || "run") + "</strong> · " + SIDS.length + " sequencing files";
+      // Open on the Overview page - the guided entry point to the run.
+      const startIdx = 0;
+      nav.children[startIdx].classList.add("on");
+      show(SECT[startIdx]);
+    </script>
+  </body>
+</html>
diff --git a/assets/variant_effect_inspection_tool/3Dmol-min.js b/assets/variant_effect_inspection_tool/3Dmol-min.js
new file mode 100644
index 0000000..c63bd14
--- /dev/null
+++ b/assets/variant_effect_inspection_tool/3Dmol-min.js
@@ -0,0 +1,2 @@
+/*! For license information please see 3Dmol-min.js.LICENSE.txt */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports["3Dmol"]=e():t["3Dmol"]=e()}(this,(()=>(()=>{var __webpack_modules__={620:function(){"use strict";!function(t){if(t.TextEncoder&&t.TextDecoder)return!1;function e(t="utf-8"){if("utf-8"!==t)throw new RangeError(`Failed to construct 'TextEncoder': The encoding label provided ('${t}') is invalid.`)}function i(t="utf-8",e={fatal:!1}){if("utf-8"!==t)throw new RangeError(`Failed to construct 'TextDecoder': The encoding label provided ('${t}') is invalid.`);if(e.fatal)throw new Error("Failed to construct 'TextDecoder': the 'fatal' option is unsupported.")}Object.defineProperty(e.prototype,"encoding",{value:"utf-8"}),e.prototype.encode=function(t,e={stream:!1}){if(e.stream)throw new Error("Failed to encode: the 'stream' option is unsupported.");let i=0;const r=t.length;let s=0,n=Math.max(32,r+(r>>1)+7),a=new 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f=e[0].replace(/^\s+/,"").replace(/\s+/g," ").split(" "),p=Math.abs(parseFloat(f[0])),g=Math.abs(parseFloat(f[1])),m=Math.abs(parseFloat(f[2])),v=this.origin=new s.Vector3(0,0,0);this.size={x:p,y:g,z:m},this.unit=new s.Vector3(l.x,h.y,c.z),l=l.multiplyScalar(1/(a*p)),h=h.multiplyScalar(1/(a*g)),c=c.multiplyScalar(1/(a*m)),0==l.y&&0==l.z&&0==h.x&&0==h.z&&0==c.x&&0==c.y||(this.matrix=new s.Matrix4(l.x,h.x,c.x,0,l.y,h.y,c.y,0,l.z,h.z,c.z,0,0,0,0,1),this.matrix=this.matrix.multiplyMatrices(this.matrix,(new s.Matrix4).makeTranslation(v.x,v.y,v.z)),this.origin=new s.Vector3(0,0,0),this.unit=new s.Vector3(1,1,1)),e.splice(0,1);var _=e.join(" "),y=(_=_.replace(/^\s+/,"")).split(/[\s\r]+/);y.splice(p*g*m+1);for(var b=Float32Array.from(y,parseFloat),x=0;x<b.length;x++)b[x]=b[x]*u*.036749309;this.data=b},this.dx=function(t){var e,i=t.split(/[\n\r]+/),r=/gridpositions\s+counts\s+(\d+)\s+(\d+)\s+(\d+)/,n=/^origin\s+(\S+)\s+(\S+)\s+(\S+)/,a=/^delta\s+(\S+)\s+(\S+)\s+(\S+)/,o=/data follows/,l=0;for(l=0;l<i.length;l++){var h=i[l];if(e=r.exec(h)){var c=parseInt(e[1]),d=parseInt(e[2]),u=parseInt(e[3]);this.size={x:c,y:d,z:u}}else if(e=a.exec(h)){var f=parseFloat(e[1]);if(0==parseFloat(e[2])&&0==parseFloat(e[3])||console.warn("Non-orthogonal delta matrix not currently supported in dx format"),h=i[l+=1],null==(e=a.exec(h)))return void console.error("Parse error in dx delta matrix");var p=parseFloat(e[2]);if(0==parseFloat(e[1])&&0==parseFloat(e[3])||console.warn("Non-orthogonal delta matrix not currently supported in dx format"),h=i[l+=1],null==(e=a.exec(h)))return void console.error("Parse error in dx delta matrix");var g=parseFloat(e[3]);0==parseFloat(e[1])&&0==parseFloat(e[2])||console.warn("Non-orthogonal delta matrix not currently supported in dx format"),this.unit=new s.Vector3(f,p,g)}else if(e=n.exec(h)){var m=parseFloat(e[1]),v=parseFloat(e[2]),_=parseFloat(e[3]);this.origin=new s.Vector3(m,v,_)}else if(e=o.exec(h))break}if(l+=1,this.size&&this.origin&&this.unit&&this.size){var y=i.splice(l).join(" ").split(/[\s\r]+/);this.data=Float32Array.from(y,parseFloat)}else console.error("Error parsing dx format")},e=e.toLowerCase(),/\.gz$/.test(e)){e=e.replace(/\.gz$/,"");try{this[e]&&this.isbinary.has(e)?("string"==typeof t&&(t=(0,r.base64ToArray)(t)),t=(0,o.inflate)(t)):t=new TextDecoder("utf-8").decode((0,o.inflate)(t))}catch(t){console.error(t)}}if(this[e]&&(this.isbinary.has(e)&&"string"==typeof t&&(t=(0,r.base64ToArray)(t)),this[e](t)),i){if(i.negate)for(let t=0,e=this.data.length;t<e;t++)this.data[t]=-this.data[t];if(i.normalize){var a=0;for(let t=0,e=this.data.length;t<e;t++)a+=this.data[t];var l=a/this.data.length;a=0;for(let t=0,e=this.data.length;t<e;t++){var h=this.data[t]-l;a+=h*h}var c=a/this.data.length;for(let t=0,e=this.data.length;t<e;t++)this.data[t]=(this.data[t]-l)/c}}}getIndex(t,e,i){if(this.matrix){null==this.inversematrix&&(this.inversematrix=(new s.Matrix4).getInverse(this.matrix));var r=new s.Vector3(t,e,i);t=(r=r.applyMatrix4(this.inversematrix)).x,e=r.y,i=r.z}else t-=this.origin.x,e-=this.origin.y,i-=this.origin.z,t/=this.unit.x,e/=this.unit.y,i/=this.unit.z;return t=Math.round(t),e=Math.round(e),i=Math.round(i),t<0||t>=this.size.x||e<0||e>=this.size.y||i<0||i>=this.size.z?-1:t*this.size.y*this.size.z+e*this.size.z+i}getVal(t,e,i){let r=this.getIndex(t,e,i);return r<0?0:this.data[r]}cube(t){var e=t.split(/\r?\n/);if(!(e.length<6)){var i=(0,a.CUBE)(t,{}).modelData[0].cryst,r=e[2].replace(/^\s+/,"").replace(/\s+/g," ").split(" "),s=parseFloat(r[0]),n=Math.abs(s);this.origin=i.origin,this.size=i.size,this.unit=i.unit,this.matrix=i.matrix4;var o=6;s<0&&o++;var l=e.splice(n+o).join(" "),h=(l=l.replace(/^\s+/,"")).split(/[\s\r]+/);this.data=Float32Array.from(h,parseFloat)}}ccp4(t){var e={};t=new Int8Array(t);var i=new Int32Array(t.buffer,0,56),r=new Float32Array(t.buffer,0,56),n=new DataView(t.buffer);if(e.MAP=String.fromCharCode(n.getUint8(208),n.getUint8(209),n.getUint8(210),n.getUint8(211)),e.MACHST=[n.getUint8(212),n.getUint8(213)],17===e.MACHST[0]&&17===e.MACHST[1])for(var a=t.byteLength,o=0;o<a;o+=4)n.setFloat32(o,n.getFloat32(o),!0);e.NX=i[0],e.NY=i[1],e.NZ=i[2],e.MODE=i[3],e.NXSTART=i[4],e.NYSTART=i[5],e.NZSTART=i[6],e.MX=i[7],e.MY=i[8],e.MZ=i[9],e.xlen=r[10],e.ylen=r[11],e.zlen=r[12],e.alpha=r[13],e.beta=r[14],e.gamma=r[15],e.MAPC=i[16],e.MAPR=i[17],e.MAPS=i[18],e.DMIN=r[19],e.DMAX=r[20],e.DMEAN=r[21],e.ISPG=i[22],e.NSYMBT=i[23],e.LSKFLG=i[24],e.originX=r[49],e.originY=r[50],e.originZ=r[51],e.ARMS=r[54];var l=e,h=[l.xlen,0,0],c=[l.ylen*Math.cos(Math.PI/180*l.gamma),l.ylen*Math.sin(Math.PI/180*l.gamma),0],d=[l.zlen*Math.cos(Math.PI/180*l.beta),l.zlen*(Math.cos(Math.PI/180*l.alpha)-Math.cos(Math.PI/180*l.gamma)*Math.cos(Math.PI/180*l.beta))/Math.sin(Math.PI/180*l.gamma),0];d[2]=Math.sqrt(l.zlen*l.zlen*Math.sin(Math.PI/180*l.beta)*Math.sin(Math.PI/180*l.beta)-d[1]*d[1]);var u=[0,h,c,d],f=[0,l.MX,l.MY,l.MZ],p=[0,l.MAPC,l.MAPR,l.MAPS];this.matrix=new s.Matrix4,this.matrix.set(u[p[1]][0]/f[p[1]],u[p[2]][0]/f[p[2]],u[p[3]][0]/f[p[3]],0,u[p[1]][1]/f[p[1]],u[p[2]][1]/f[p[2]],u[p[3]][1]/f[p[3]],0,u[p[1]][2]/f[p[1]],u[p[2]][2]/f[p[2]],u[p[3]][2]/f[p[3]],0,0,0,0,1),this.matrix=this.matrix.multiplyMatrices(this.matrix,(new s.Matrix4).makeTranslation(l.NXSTART+l.originX,l.NYSTART+l.originY,l.NZSTART+l.originZ)),this.origin=new s.Vector3(0,0,0),this.unit=new s.Vector3(1,1,1),this.size={x:e.NX,y:e.NY,z:e.NZ},this.dimensionorder=[e.MAPC,e.MAPR,e.MAPS];var g=new Float32Array(t.buffer,1024+e.NSYMBT),m=e.NX,v=e.NY,_=e.NZ;this.data=new Float32Array(m*v*_);for(let t=0;t<m;t++)for(let e=0;e<v;e++)for(let i=0;i<_;i++)this.data[(t*v+e)*_+i]=g[(i*v+e)*m+t]}}},638:(t,e,i)=>{"use strict";var r;i.r(e),i.d(e,{BackSide:()=>n,Camera:()=>Camera,ClampToEdgeWrapping:()=>c,Coloring:()=>r,Cylinder:()=>M.Cylinder,DoubleSide:()=>a,EventDispatcher:()=>EventDispatcher,FloatType:()=>g,Fog:()=>Fog,FrontSide:()=>s,Geometry:()=>Geometry,GeometryGroup:()=>GeometryGroup,GeometryIDCount:()=>A,ImposterMaterial:()=>ImposterMaterial,InstancedMaterial:()=>InstancedMaterial,Light:()=>Light,Line:()=>Line,LineBasicMaterial:()=>LineBasicMaterial,LineStyle:()=>N,LinearFilter:()=>d,LinearMipMapLinearFilter:()=>f,Material:()=>Material,MaterialIdCount:()=>x,Matrix3:()=>l.Matrix3,Matrix4:()=>l.Matrix4,Mesh:()=>Mesh,MeshDoubleLambertMaterial:()=>MeshDoubleLambertMaterial,MeshLambertMaterial:()=>MeshLambertMaterial,MeshOutlineMaterial:()=>MeshOutlineMaterial,NearestFilter:()=>u,Object3D:()=>Object3D,Object3DIDCount:()=>C,Projector:()=>Projector,Quaternion:()=>l.Quaternion,R32Format:()=>_,RFormat:()=>v,RGBAFormat:()=>m,Ray:()=>l.Ray,Raycaster:()=>Raycaster,Renderer:()=>Renderer,Scene:()=>Scene,ShaderLib:()=>ct,ShaderUtils:()=>ut,Shading:()=>o,Sphere:()=>M.Sphere,SphereImposterMaterial:()=>SphereImposterMaterial,SphereImposterOutlineMaterial:()=>SphereImposterOutlineMaterial,Sprite:()=>Sprite,SpriteAlignment:()=>h,SpriteMaterial:()=>SpriteMaterial,SpritePlugin:()=>SpritePlugin,StickImposterMaterial:()=>StickImposterMaterial,StickImposterOutlineMaterial:()=>StickImposterOutlineMaterial,Texture:()=>Texture,TextureIdCount:()=>B,TextureOperations:()=>y,Triangle:()=>M.Triangle,UVMapping:()=>UVMapping,UnsignedByteType:()=>p,Vector2:()=>l.Vector2,Vector3:()=>l.Vector3,VolumetricMaterial:()=>VolumetricMaterial,basic:()=>V,clamp:()=>l.clamp,clone:()=>dt,conversionMatrix3:()=>l.conversionMatrix3,degToRad:()=>l.degToRad,instanced:()=>H,intersectObject:()=>U,lambert:()=>q,lambertdouble:()=>Z,outline:()=>K,screen:()=>Q,screenaa:()=>$,sphereimposter:()=>tt,sphereimposteroutline:()=>it,sprite:()=>rt,stickimposter:()=>at,stickimposteroutline:()=>ot,volumetric:()=>ht}),function(t){t[t.NoColors=0]="NoColors",t[t.FaceColors=1]="FaceColors",t[t.VertexColors=2]="VertexColors"}(r||(r={}));const s=0,n=1,a=2;var o;!function(t){t[t.NoShading=0]="NoShading",t[t.FlatShading=1]="FlatShading",t[t.SmoothShading=2]="SmoothShading"}(o||(o={}));var l=i(529);const h={topLeft:new l.Vector2(1,-1),topCenter:new l.Vector2(0,-1),topRight:new l.Vector2(-1,-1),centerLeft:new l.Vector2(1,0),center:new l.Vector2(0,0),centerRight:new l.Vector2(-1,0),bottomLeft:new l.Vector2(1,1),bottomCenter:new l.Vector2(0,1),bottomRight:new l.Vector2(-1,1)},c=1001,d=1006,u=1007,f=1008,p=1009,g=1010,m=1021,v=1022,_=1023;var y;!function(t){t[t.MultiplyOperation=0]="MultiplyOperation",t[t.MixOperation=1]="MixOperation",t[t.AddOperation=2]="AddOperation"}(y||(y={}));class EventDispatcher{constructor(){this.listeners={}}dispatchEvent(t){var e=this.listeners[t.type];if(void 0!==e){t.target=this;for(var i=0,r=e.length;i<r;i++)e[i].call(this,t)}}removeEventListener(t,e){if(e){var i=this.listeners[t].indexOf(e);-1!==i&&this.listeners[t].splice(i,1)}else this.listeners[t]=[]}addEventListener(t,e){void 0===this.listeners[t]&&(this.listeners[t]=[]),-1===this.listeners[t].indexOf(e)&&this.listeners[t].push(e)}}var b=i(222);class Material extends EventDispatcher{constructor(){super(...arguments),this.id=x++,this.name="",this.side=s,this.opacity=1,this.transparent=!1,this.depthTest=!0,this.depthWrite=!0,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.alphaTest=0,this.visible=!0,this.needsUpdate=!0,this.outline=!1,this.wireframe=!1,this.shaded=!1}setValues(t={}){if(void 0!==t)for(var e in t){var i=t[e];if(void 0!==i){if(e in this){var r=this[e];r instanceof b.Color&&i instanceof b.Color?r.copy(i):r instanceof b.Color?r.set(i):r instanceof l.Vector3&&i instanceof l.Vector3?r.copy(i):this[e]=i}}else console.warn("$3Dmol.Material: '"+e+"' parameter is undefined.")}}clone(t=new Material){return t.name=this.name,t.side=this.side,t.opacity=this.opacity,t.transparent=this.transparent,t.depthTest=this.depthTest,t.depthWrite=this.depthWrite,t.polygonOffset=this.polygonOffset,t.polygonOffsetFactor=this.polygonOffsetFactor,t.polygonOffsetUnits=this.polygonOffsetUnits,t.alphaTest=this.alphaTest,t.overdraw=this.overdraw,t.visible=this.visible,t}makeShaded(t){this.shaded=!0,this.uniforms&&(this.uniforms.shading={type:"i",value:t},this.uniforms.vWidth={type:"f",value:1},this.uniforms.vHeight={type:"f",value:1})}dispose(){this.dispatchEvent({type:"dispose"})}}let x=0;class LineBasicMaterial extends Material{constructor(t){super(),this.color=new b.Color(16777215),this.linewidth=1,this.linecap="round",this.linejoin="round",this.vertexColors=!1,this.fog=!0,this.shaderID="basic",this.setValues(t)}clone(t=new LineBasicMaterial){return super.clone.call(this,t),t.color.copy(this.color),t}}const w=65535;class GeometryGroup{constructor(t=0){this.vertexArray=null,this.colorArray=null,this.normalArray=null,this.radiusArray=null,this.faceArray=null,this.lineArray=null,this.atomArray=Array(),this.vertices=0,this.faceidx=0,this.lineidx=0,this.__inittedArrays=!1,this.id=t}setColor(t){var e=this.vertexArray,i=this.colorArray;if(!e)throw new Error("vertex array not initialized");if(!i)throw new Error("color array not initialized");let r=b.CC.color(t);for(var s=0;s<e.length;s+=3)i[s]=r.r,i[s+1]=r.g,i[s+2]=r.b}setColors(t){var e=this.vertexArray,i=this.colorArray;if(!e)throw new Error("vertex array not initialized");if(!i)throw new Error("color array not initialized");if(e.length==i.length)for(var r=0;r<e.length;r+=3){var s=t(e[r],e[r+1],e[r+2]);s instanceof b.Color||(s=b.CC.color(s)),i[r]=s.r,i[r+1]=s.g,i[r+2]=s.b}else console.log("Cannot re-color geometry group due to mismatched lengths.")}getNumVertices(){return this.vertices}getVertices(){return this.vertexArray}getCentroid(){if(!this.vertexArray)throw new Error("vertex array not initialized");for(var t,e,i,r,s=new l.Vector3,n=0;n<this.vertices;++n)t=3*n,e=this.vertexArray[t],i=this.vertexArray[t+1],r=this.vertexArray[t+2],s.x+=e,s.y+=i,s.z+=r;return s.divideScalar(this.vertices),s}setNormals(){var t=this.faceArray,e=this.vertexArray,i=this.normalArray;if(this.vertices&&this.faceidx){if(!t)throw new Error("face array not initialized");if(!e)throw new Error("vertex array not initialized");if(!i)throw new Error("normal array not initialized");for(var r,s,n,a,o,h,c,d=0;d<t.length/3;++d)r=3*t[3*d],s=3*t[3*d+1],n=3*t[3*d+2],a=new l.Vector3(e[r],e[r+1],e[r+2]),o=new l.Vector3(e[s],e[s+1],e[s+2]),h=new l.Vector3(e[n],e[n+1],e[n+2]),a.subVectors(a,o),h.subVectors(h,o),h.cross(a),(c=h).normalize(),i[r]+=c.x,i[s]+=c.x,i[n]+=c.x,i[r+1]+=c.y,i[s+1]+=c.y,i[n+1]+=c.y,i[r+2]+=c.z,i[s+2]+=c.z,i[n+2]+=c.z}}setLineIndices(){if(this.faceidx&&(!this.lineArray||this.lineArray.length!=2*this.faceidx||this.lineidx!=2*this.faceidx)){var t=this.faceArray,e=this.lineArray=new Uint16Array(2*this.faceidx);if(this.lineidx=2*this.faceidx,!t)throw new Error("face array not initialized");for(var i=0;i<this.faceidx/3;++i){var r=3*i,s=2*r,n=t[r],a=t[r+1],o=t[r+2];e[s]=n,e[s+1]=a,e[s+2]=n,e[s+3]=o,e[s+4]=a,e[s+5]=o}}}vrml(t,e){var i,r,s,n,a,o,l,h,c,d,u,f,p,g,m,v,_,y,b="";if(b+=t+"Shape {\n"+t+" appearance Appearance {\n"+t+"  material Material {\n"+t+"   diffuseColor "+(null===(i=null==e?void 0:e.color)||void 0===i?void 0:i.r)+" "+(null===(r=null==e?void 0:e.color)||void 0===r?void 0:r.g)+" "+(null===(s=null==e?void 0:e.color)||void 0===s?void 0:s.b)+"\n",e.wireframe&&this.colorArray){let e=this.colorArray;b+=t+"    emissiveColor "+e[0]+" "+e[1]+" "+e[2]+"\n"}(null==e?void 0:e.transparent)&&(b+=t+"   transparency "+(1-e.opacity)+"\n"),b+=t+"  }\n",b+=t+" }\n";var x=t;if(t+=" ",e instanceof LineBasicMaterial||e.wireframe){let i,r,s;b+=t+"geometry IndexedLineSet {\n"+t+" colorPerVertex TRUE\n"+t+" coord Coordinate {\n"+t+"  point [\n";for(let e=0;e<this.vertices;++e){let l=3*e;i=null===(n=this.vertexArray)||void 0===n?void 0:n[l],r=null===(a=this.vertexArray)||void 0===a?void 0:a[l+1],s=null===(o=this.vertexArray)||void 0===o?void 0:o[l+2],b+=t+"   "+i+" "+r+" "+s+",\n"}if(b+=t+"  ]\n",b+=t+" }\n",this.colorArray&&!e.wireframe){b+=t+" color Color {\n"+t+"  color [\n";for(let e=0;e<this.vertices;++e){let n=3*e;i=this.colorArray[n],r=this.colorArray[n+1],s=this.colorArray[n+2],b+=t+"   "+i+" "+r+" "+s+",\n"}b+=t+"  ]\n",b+=t+" }\n"}if(b+=t+" coordIndex [\n",e.wireframe&&this.faceArray)for(let e=0;e<this.faceidx;e+=3)i=null===(l=this.faceArray)||void 0===l?void 0:l[e],r=null===(h=this.faceArray)||void 0===h?void 0:h[e+1],s=null===(c=this.faceArray)||void 0===c?void 0:c[e+2],b+=t+"  "+i+", "+r+", "+s+", -1,\n";else for(let e=0;e<this.vertices-1;e+=2)b+=t+"  "+e+", "+(e+1)+", -1,\n";b+=t+" ]\n",b+=t+"}\n"}else{let e,i,r;b+=t+"geometry IndexedFaceSet {\n"+t+" colorPerVertex TRUE\n"+t+" normalPerVertex TRUE\n"+t+" solid FALSE\n",b+=t+" coord Coordinate {\n"+t+"  point [\n";for(let s=0;s<this.vertices;++s){let n=3*s;e=null===(d=this.vertexArray)||void 0===d?void 0:d[n],i=null===(u=this.vertexArray)||void 0===u?void 0:u[n+1],r=null===(f=this.vertexArray)||void 0===f?void 0:f[n+2],b+=t+"   "+e+" "+i+" "+r+",\n"}b+=t+"  ]\n",b+=t+" }\n",b+=t+" normal Normal {\n"+t+"  vector [\n";for(let s=0;s<this.vertices;++s){let n=3*s;e=null===(p=this.normalArray)||void 0===p?void 0:p[n],i=null===(g=this.normalArray)||void 0===g?void 0:g[n+1],r=null===(m=this.normalArray)||void 0===m?void 0:m[n+2],b+=t+"   "+e+" "+i+" "+r+",\n"}if(b+=t+"  ]\n",b+=t+" }\n",this.colorArray){b+=t+" color Color {\n"+t+"  color [\n";for(let s=0;s<this.vertices;++s){let n=3*s;e=this.colorArray[n],i=this.colorArray[n+1],r=this.colorArray[n+2],b+=t+"   "+e+" "+i+" "+r+",\n"}b+=t+"  ]\n",b+=t+" }\n"}b+=t+" coordIndex [\n";for(let s=0;s<this.faceidx;s+=3)e=null===(v=this.faceArray)||void 0===v?void 0:v[s],i=null===(_=this.faceArray)||void 0===_?void 0:_[s+1],r=null===(y=this.faceArray)||void 0===y?void 0:y[s+2],b+=t+"  "+e+", "+i+", "+r+", -1,\n";b+=t+" ]\n",b+=t+"}\n"}return b+=x+"}"}truncateArrayBuffers(t=!0,e=!1){var i=this.vertexArray,r=this.colorArray,s=this.normalArray,n=this.faceArray,a=this.lineArray,o=this.radiusArray;this.vertexArray=(null==i?void 0:i.subarray(0,3*this.vertices))||null,this.colorArray=(null==r?void 0:r.subarray(0,3*this.vertices))||null,t?(this.normalArray=(null==s?void 0:s.subarray(0,3*this.vertices))||null,this.faceArray=(null==n?void 0:n.subarray(0,this.faceidx))||null,this.lineidx>0?this.lineArray=(null==a?void 0:a.subarray(0,this.lineidx))||null:this.lineArray=new Uint16Array(0)):(this.normalArray=new Float32Array(0),this.faceArray=new Uint16Array(0),this.lineArray=new Uint16Array(0)),o&&(this.radiusArray=o.subarray(0,this.vertices)),e&&(this.normalArray&&(this.normalArray=new Float32Array(this.normalArray)),this.faceArray&&(this.faceArray=new Uint16Array(this.faceArray)),this.lineArray&&(this.lineArray=new Uint16Array(this.lineArray)),this.vertexArray&&(this.vertexArray=new Float32Array(this.vertexArray)),this.colorArray&&(this.colorArray=new Float32Array(this.colorArray)),this.radiusArray&&(this.radiusArray=new Float32Array(this.radiusArray))),this.__inittedArrays=!0}}class Geometry extends EventDispatcher{constructor(t=!1,e=!1,i=!1){super(),this.name="",this.hasTangents=!1,this.dynamic=!0,this.verticesNeedUpdate=!1,this.elementsNeedUpdate=!1,this.normalsNeedUpdate=!1,this.colorsNeedUpdate=!1,this.buffersNeedUpdate=!1,this.imposter=!1,this.instanced=!1,this.geometryGroups=[],this.groups=0,this.id=A++,this.mesh=t,this.radii=e,this.offset=i}updateGeoGroup(t=0){var e,i=this.groups>0?this.geometryGroups[this.groups-1]:null;return(!i||i.vertices+t>((null===(e=null==i?void 0:i.vertexArray)||void 0===e?void 0:e.length)||0)/3)&&(i=this.addGeoGroup()),i}vrml(t,e){for(var i="",r=this.geometryGroups.length,s=0;s<r;s++){i+=this.geometryGroups[s].vrml(t,e)+",\n"}return i}addGeoGroup(){var t=new GeometryGroup(this.geometryGroups.length);return this.geometryGroups.push(t),this.groups=this.geometryGroups.length,t.vertexArray=new Float32Array(196605),t.colorArray=new Float32Array(196605),this.mesh&&(t.normalArray=new Float32Array(196605),t.faceArray=new Uint16Array(393210),t.lineArray=new Uint16Array(393210)),this.radii&&(t.radiusArray=new Float32Array(w)),t.useOffset=this.offset,t}setUpNormals(...t){for(var e=0;e<this.groups;e++){this.geometryGroups[e].setNormals(...t)}}setColors(...t){for(var e=this.geometryGroups.length,i=0;i<e;i++){this.geometryGroups[i].setColors(...t)}}setColor(...t){for(var e=this.geometryGroups.length,i=0;i<e;i++){this.geometryGroups[i].setColor(...t)}}setUpWireframe(...t){for(var e=0;e<this.groups;e++){this.geometryGroups[e].setLineIndices(...t)}}initTypedArrays(){for(var t=0;t<this.groups;t++){var e=this.geometryGroups[t];!0!==e.__inittedArrays&&e.truncateArrayBuffers(this.mesh,!1)}}dispose(){this.dispatchEvent({type:"dispose"})}get vertices(){for(var t=0,e=0;e<this.groups;e++)t+=this.geometryGroups[e].vertices;return t}}let A=0,C=0;class Object3D{constructor(){this.id=C++,this.name="",this.children=[],this.position=new l.Vector3,this.rotation=new l.Vector3,this.matrix=new l.Matrix4,this.matrixWorld=new l.Matrix4,this.quaternion=new l.Quaternion,this.eulerOrder="XYZ",this.up=new l.Vector3(0,1,0),this.scale=new l.Vector3(1,1,1),this.matrixAutoUpdate=!0,this.matrixWorldNeedsUpdate=!0,this.rotationAutoUpdate=!0,this.useQuaternion=!1,this.visible=!0}lookAt(t){this.matrix.lookAt(t,this.position,this.up),this.rotationAutoUpdate&&(!0===this.useQuaternion?console.error("Unimplemented math operation."):this.rotation instanceof l.Vector3&&this.rotation.setEulerFromRotationMatrix(this.matrix,this.eulerOrder))}add(t){if(t!==this){t.parent=this,this.children.push(t);for(var e=this;void 0!==e.parent;)e=e.parent;void 0!==e&&e instanceof Scene&&e.__addObject(t)}else console.error("Can't add $3Dmol.Object3D to itself")}remove(t){var e=this.children.indexOf(t);if(-1!==e){t.parent=void 0,this.children.splice(e,1);for(var i=this;void 0!==i.parent;)i=i.parent;void 0!==i&&i instanceof Scene&&i.__removeObject(t)}}vrml(t){t||(t=" ");var e=2*Math.atan2(this.quaternion.lengthxyz(),this.quaternion.w),i=0,r=0,s=0;if(0!=e){let t=Math.sin(e/2);i=this.quaternion.x/t,r=this.quaternion.y/t,s=this.quaternion.z/t}var n=t+"Transform {\n"+t+" center "+this.position.x+" "+this.position.y+" "+this.position.z+"\n"+t+" rotation "+i+" "+r+" "+s+" "+e+"\n"+t+" children [\n";this.geometry&&(n+=this.geometry.vrml(t,this.material));for(var a=0;a<this.children.length;a++)n+=this.children[a].vrml(t+" ")+",\n";return n+=" ]\n",n+="}"}updateMatrix(){this.matrix.setPosition(this.position),!1===this.useQuaternion&&this.rotation instanceof l.Vector3?this.matrix.setRotationFromEuler(this.rotation,this.eulerOrder):this.matrix.setRotationFromQuaternion(this.quaternion),1===this.scale.x&&1===this.scale.y&&1===this.scale.z||this.matrix.scale(this.scale),this.matrixWorldNeedsUpdate=!0}updateMatrixWorld(t){!0===this.matrixAutoUpdate&&this.updateMatrix(),!0!==this.matrixWorldNeedsUpdate&&!0!==t||(void 0===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix)),this.matrixWorldNeedsUpdate=!1;for(var e=0;e<this.children.length;e++)this.children[e].updateMatrixWorld(!0)}clone(t){void 0===t&&(t=new Object3D),t.name=this.name,t.up.copy(this.up),t.position.copy(this.position),t.rotation instanceof l.Vector3&&this.rotation instanceof l.Vector3?t.rotation.copy(this.rotation):t.rotation=this.rotation,t.eulerOrder=this.eulerOrder,t.scale.copy(this.scale),t.rotationAutoUpdate=this.rotationAutoUpdate,t.matrix.copy(this.matrix),t.matrixWorld.copy(this.matrixWorld),t.quaternion.copy(this.quaternion),t.matrixAutoUpdate=this.matrixAutoUpdate,t.matrixWorldNeedsUpdate=this.matrixWorldNeedsUpdate,t.useQuaternion=this.useQuaternion,t.visible=this.visible;for(var e=0;e<this.children.length;e++){var i=this.children[e];t.add(i.clone())}return t}setVisible(t){this.visible=t;for(var e=0;e<this.children.length;e++){this.children[e].setVisible(t)}}}class Scene extends Object3D{constructor(){super(...arguments),this.fog=null,this.overrideMaterial=null,this.matrixAutoUpdate=!1,this.__objects=[],this.__lights=[],this.__objectsAdded=[],this.__objectsRemoved=[]}__addObject(t){if(t instanceof Light)-1===this.__lights.indexOf(t)&&this.__lights.push(t),t.target&&void 0===t.target.parent&&this.add(t.target);else if(-1===this.__objects.indexOf(t)){this.__objects.push(t),this.__objectsAdded.push(t);var e=this.__objectsRemoved.indexOf(t);-1!==e&&this.__objectsRemoved.splice(e,1)}for(var i=0;i<t.children.length;i++)this.__addObject(t.children[i])}__removeObject(t){var e;t instanceof Light?-1!==(e=this.__lights.indexOf(t))&&this.__lights.splice(e,1):-1!==(e=this.__objects.indexOf(t))&&(this.__objects.splice(e,1),this.__objectsRemoved.push(t),-1!==this.__objectsAdded.indexOf(t)&&this.__objectsAdded.splice(e,1));for(var i=0;i<t.children.length;i++)this.__removeObject(t.children[i])}}class Light extends Object3D{constructor(t,e=1){super(),this.position=new l.Vector3(0,1,0),this.target=new Object3D,this.castShadow=!1,this.onlyShadow=!1,this.color=new b.Color(t),this.intensity=e}}const S=new l.Matrix4;class Projector{static unprojectVector(t,e){return e.projectionMatrixInverse.getInverse(e.projectionMatrix),S.multiplyMatrices(e.matrixWorld,e.projectionMatrixInverse),t.applyProjection(S)}static projectVector(t,e){return e.matrixWorldInverse.getInverse(e.matrixWorld),S.multiplyMatrices(e.projectionMatrix,e.matrixWorldInverse),t.applyProjection(S)}projectVector(t,e){return Projector.projectVector(t,e)}unprojectVector(t,e){return Projector.unprojectVector(t,e)}}var M=i(99);const z=(t,e)=>t.distance-e.distance,T=new l.Matrix4;class Raycaster{constructor(t,e,i,r){this.precision=1e-4,this.linePrecision=.2,this.ray=new l.Ray(t,e),this.ray.direction.lengthSq()>0&&this.ray.direction.normalize(),this.near=r||0,this.far=i||1/0}set(t,e){this.ray.set(t,e)}setFromCamera(t,e){e.ortho?(this.ray.origin.set(t.x,t.y,(e.near+e.far)/(e.near-e.far)).unproject(e),this.ray.direction.set(0,0,-1).transformDirection(e.matrixWorld)):(this.ray.origin.setFromMatrixPosition(e.matrixWorld),this.ray.direction.set(t.x,t.y,t.z),e.projectionMatrixInverse.getInverse(e.projectionMatrix),T.multiplyMatrices(e.matrixWorld,e.projectionMatrixInverse),this.ray.direction.applyProjection(T),this.ray.direction.sub(this.ray.origin).normalize())}intersectObjects(t,e){for(var i=[],r=0,s=e.length;r<s;r++)U(t,e[r],this,i);return i.sort(z),i}}const E=t=>Math.min(Math.max(t,-1),1);var L=new M.Sphere,F=new M.Cylinder,I=new M.Triangle,O=new l.Vector3,D=new l.Vector3,k=new l.Vector3,R=new l.Vector3,P=new l.Vector3;function U(t,e,i,r){if(P.getPositionFromMatrix(t.matrixWorld),void 0===e.intersectionShape)return r;var s,n,a,o,l,h,c,d,u,f,p,g,m,v,_=e.intersectionShape,y=i.linePrecision,b=(y*=t.matrixWorld.getMaxScaleOnAxis())*y;if(void 0!==e.boundingSphere&&e.boundingSphere instanceof M.Sphere&&(L.copy(e.boundingSphere),L.applyMatrix4(t.matrixWorld),!i.ray.isIntersectionSphere(L)))return r;for(s=0,n=_.triangle.length;s<n;s++)if(_.triangle[s]instanceof M.Triangle){if(I.copy(_.triangle[s]),I.applyMatrix4(t.matrixWorld),a=I.getNormal(),(o=i.ray.direction.dot(a))>=0)continue;if(O.subVectors(I.a,i.ray.origin),(c=a.dot(O)/o)<0)continue;D.copy(i.ray.direction).multiplyScalar(c).add(i.ray.origin),D.sub(I.a),k.copy(I.b).sub(I.a),R.copy(I.c).sub(I.a);var x=k.dot(R),w=k.lengthSq(),A=R.lengthSq();if((g=(w*D.dot(R)-x*D.dot(k))/(w*A-x*x))<0||g>1)continue;if((p=(D.dot(k)-g*x)/w)<0||p>1||p+g>1)continue;r.push({clickable:e,distance:c})}for(s=0,n=_.cylinder.length;s<n;s++)if(_.cylinder[s]instanceof M.Cylinder){if(F.copy(_.cylinder[s]),F.applyMatrix4(t.matrixWorld),O.subVectors(F.c1,i.ray.origin),l=O.dot(F.direction),h=O.dot(i.ray.direction),0===(u=1-(o=E(i.ray.direction.dot(F.direction)))*o))continue;m=(o*h-l)/u,v=(h-o*l)/u,D.copy(F.direction).multiplyScalar(m).add(F.c1),k.copy(i.ray.direction).multiplyScalar(v).add(i.ray.origin),d=R.subVectors(D,k).lengthSq();var C=F.radius*F.radius;if(d<=C){if((p=o*(g=c=(f=(o*l-h)*(o*l-h)-u*(O.lengthSq()-l*l-C))<=0?Math.sqrt(d):(h-o*l-Math.sqrt(f))/u)-l)<0||p*p>F.lengthSq()||g<0)continue;r.push({clickable:e,distance:c})}}for(s=0,n=_.line.length;s<n;s+=2){D.copy(_.line[s]),D.applyMatrix4(t.matrixWorld),k.copy(_.line[s+1]),k.applyMatrix4(t.matrixWorld),R.subVectors(k,D);var S=R.lengthSq();R.normalize(),O.subVectors(D,i.ray.origin);var z=O.dot(R);h=O.dot(i.ray.direction),0!==(u=1-(o=E(i.ray.direction.dot(R)))*o)&&(m=(o*h-z)/u,v=(h-o*z)/u,D.add(R.multiplyScalar(m)),k.copy(i.ray.direction).multiplyScalar(v).add(i.ray.origin),(d=R.subVectors(k,D).lengthSq())<b&&m*m<S&&r.push({clickable:e,distance:v}))}for(s=0,n=_.sphere.length;s<n;s++)if(_.sphere[s]instanceof M.Sphere&&(L.copy(_.sphere[s]),L.applyMatrix4(t.matrixWorld),i.ray.isIntersectionSphere(L))){D.subVectors(L.center,i.ray.origin);var T=D.dot(i.ray.direction);if(f=T*T-(D.lengthSq()-L.radius*L.radius),T<0)return r;c=f<=0?T:T-Math.sqrt(f),r.push({clickable:e,distance:c})}return r}class UVMapping{}class Texture extends EventDispatcher{constructor(t,e){super(),this.id=B++,this.name="",this.image=t,this.mapping=new UVMapping,this.wrapS=c,this.wrapT=c,this.anisotropy=1,e?(this.format=v,this.type=g,this.premultiplyAlpha=!1,this.flipY=!1,this.unpackAlignment=1,this.magFilter=u,this.minFilter=u):(this.format=m,this.type=p,this.offset=new l.Vector2(0,0),this.repeat=new l.Vector2(1,1),this.premultiplyAlpha=!1,this.flipY=!0,this.unpackAlignment=4,this.magFilter=d,this.minFilter=f),this.needsUpdate=!1,this.onUpdate=null}clone(t=new Texture){return t.image=this.image,t.mapping=this.mapping,t.wrapS=this.wrapS,t.wrapT=this.wrapT,t.magFilter=this.magFilter,t.minFilter=this.minFilter,t.anisotropy=this.anisotropy,t.format=this.format,t.type=this.type,t.offset.copy(this.offset),t.repeat.copy(this.repeat),t.premultiplyAlpha=this.premultiplyAlpha,t.flipY=this.flipY,t.unpackAlignment=this.unpackAlignment,t}dispose(){this.dispatchEvent({type:"dispose"})}}let B=0;class ImposterMaterial extends Material{constructor(t){super(),this.color=new b.Color(16777215),this.ambient=new b.Color(1048575),this.emissive=new b.Color(0),this.imposter=!0,this.wrapAround=!1,this.wrapRGB=new l.Vector3(1,1,1),this.map=void 0,this.lightMap=null,this.specularMap=null,this.envMap=null,this.reflectivity=1,this.refractionRatio=.98,this.fog=!0,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.shading=o.SmoothShading,this.shaderID=null,this.vertexColors=r.NoColors,this.skinning=!1,this.setValues(t)}clone(t=new ImposterMaterial){return super.clone.call(this,t),t.color.copy(this.color),t.ambient.copy(this.ambient),t.emissive.copy(this.emissive),t.wrapAround=this.wrapAround,t.wrapRGB.copy(this.wrapRGB),t.map=this.map,t.lightMap=this.lightMap,t.specularMap=this.specularMap,t.envMap=this.envMap,t.combine=this.combine,t.reflectivity=this.reflectivity,t.refractionRatio=this.refractionRatio,t.fog=this.fog,t.shading=this.shading,t.shaderID=this.shaderID,t.vertexColors=this.vertexColors,t.skinning=this.skinning,t.morphTargets=this.morphTargets,t.morphNormals=this.morphNormals,t}}class InstancedMaterial extends Material{constructor(t){super(),this.color=new b.Color(16777215),this.ambient=new b.Color(1048575),this.emissive=new b.Color(0),this.wrapAround=!1,this.wrapRGB=new l.Vector3(1,1,1),this.map=void 0,this.lightMap=null,this.specularMap=null,this.envMap=null,this.reflectivity=1,this.refractionRatio=.98,this.fog=!0,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.shading=o.SmoothShading,this.shaderID="instanced",this.vertexColors=r.NoColors,this.skinning=!1,this.sphere=null,this.setValues(t)}clone(t=new InstancedMaterial){return super.clone.call(this,t),t.color.copy(this.color),t.ambient.copy(this.ambient),t.emissive.copy(this.emissive),t.wrapAround=this.wrapAround,t.wrapRGB.copy(this.wrapRGB),t.map=this.map,t.lightMap=this.lightMap,t.specularMap=this.specularMap,t.envMap=this.envMap,t.combine=this.combine,t.reflectivity=this.reflectivity,t.refractionRatio=this.refractionRatio,t.fog=this.fog,t.shading=this.shading,t.shaderID=this.shaderID,t.vertexColors=this.vertexColors,t.skinning=this.skinning,t.morphTargets=this.morphTargets,t.morphNormals=this.morphNormals,t.sphere=this.sphere,t}}class MeshLambertMaterial extends Material{constructor(t){super(),this.color=new b.Color(16777215),this.ambient=new b.Color(1048575),this.emissive=new b.Color(0),this.wrapAround=!1,this.wrapRGB=new l.Vector3(1,1,1),this.map=void 0,this.lightMap=null,this.specularMap=null,this.envMap=null,this.reflectivity=1,this.refractionRatio=.98,this.fog=!0,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.shading=o.SmoothShading,this.shaderID="lambert",this.vertexColors=r.NoColors,this.skinning=!1,this.setValues(t)}clone(t=new MeshLambertMaterial){return super.clone.call(this,t),t.color.copy(this.color),t.ambient.copy(this.ambient),t.emissive.copy(this.emissive),t.wrapAround=this.wrapAround,t.wrapRGB.copy(this.wrapRGB),t.map=this.map,t.lightMap=this.lightMap,t.specularMap=this.specularMap,t.envMap=this.envMap,t.combine=this.combine,t.reflectivity=this.reflectivity,t.refractionRatio=this.refractionRatio,t.fog=this.fog,t.shading=this.shading,t.shaderID=this.shaderID,t.vertexColors=this.vertexColors,t.skinning=this.skinning,t.morphTargets=this.morphTargets,t.morphNormals=this.morphNormals,t}}class MeshDoubleLambertMaterial extends MeshLambertMaterial{constructor(t){super(t),this.shaderID="lambertdouble",this.side=a,this.outline=!1}clone(t=new MeshDoubleLambertMaterial){return super.clone.call(this,t),t}}class MeshOutlineMaterial extends Material{constructor(t){super(),t=t||{},this.fog=!0,this.shaderID="outline",this.wireframe=!1,this.outlineColor=b.CC.color(t.color||new b.Color(0,0,0)),this.outlineWidth=t.width||.1,this.outlinePushback=t.pushback||1,this.outlineMaxPixels=t.maxpixels||0}clone(t=new MeshOutlineMaterial){return super.clone.call(this,t),t.fog=this.fog,t.shaderID=this.shaderID,t.wireframe=this.wireframe,t.outlineColor=this.outlineColor,t.outlineWidth=this.outlineWidth,t.outlinePushback=this.outlinePushback,t.outlineMaxPixels=this.outlineMaxPixels,t}}class SphereImposterMaterial extends ImposterMaterial{constructor(t){super(t),this.shaderID="sphereimposter",this.setValues(t)}clone(t=new SphereImposterMaterial){return super.clone.call(this,t),t}}class SphereImposterOutlineMaterial extends ImposterMaterial{constructor(t){super(t),t=t||{},this.shaderID="sphereimposteroutline",this.outlineColor=b.CC.color(t.color||new b.Color(0,0,0)),this.outlineWidth=t.width||.1,this.outlinePushback=t.pushback||1,this.outlineMaxPixels=t.maxpixels||0,this.setValues(t)}clone(t=new SphereImposterOutlineMaterial){return super.clone.call(this,t),t.outlineColor=this.outlineColor,t.outlineWidth=this.outlineWidth,t.outlinePushback=this.outlinePushback,t.outlineMaxPixels=this.outlineMaxPixels,t}}class SpriteMaterial extends Material{constructor(t){super(),this.color=new b.Color(16777215),this.map=new Texture,this.useScreenCoordinates=!0,this.fog=!1,this.uvOffset=new l.Vector2(0,0),this.uvScale=new l.Vector2(1,1),this.depthTest=!this.useScreenCoordinates,this.sizeAttenuation=!this.useScreenCoordinates,this.screenOffset=this.screenOffset,this.scaleByViewPort=!this.sizeAttenuation,this.alignment=h.center.clone(),this.setValues(t),void 0===(t=t||{}).depthTest&&(this.depthTest=!this.useScreenCoordinates),void 0===t.sizeAttenuation&&(this.sizeAttenuation=!this.useScreenCoordinates),void 0===t.scaleByViewPort&&(this.scaleByViewPort=!this.sizeAttenuation)}clone(t=new SpriteMaterial){return super.clone.call(this,t),t.color.copy(this.color),t.map=this.map,t.useScreenCoordinates=this.useScreenCoordinates,t.screenOffset=this.screenOffset,t.sizeAttenuation=this.sizeAttenuation,t.scaleByViewport=this.scaleByViewPort,t.alignment.copy(this.alignment),t.uvOffset.copy(this.uvOffset),t}}class StickImposterMaterial extends ImposterMaterial{constructor(t){super(t),this.shaderID="stickimposter",this.setValues(t)}clone(t=new StickImposterMaterial){return super.clone.call(this,t),t}}class StickImposterOutlineMaterial extends ImposterMaterial{constructor(t={}){super(t),this.shaderID="stickimposteroutline",this.outlineColor=new b.Color(0,0,0),this.outlineWidth=.1,this.outlinePushback=1,this.outlineMaxPixels=0,t.color&&(this.outlineColor=b.CC.color(t.color)),t.width&&(this.outlineWidth=t.width),t.pushback&&(this.outlinePushback=t.pushback),t.maxpixels&&(this.outlineMaxPixels=t.maxpixels),this.setValues(t)}clone(t=new StickImposterOutlineMaterial){return super.clone.call(this,t),t.outlineColor=this.outlineColor,t.outlineWidth=this.outlineWidth,t.outlinePushback=this.outlinePushback,t.outlineMaxPixels=this.outlineMaxPixels,t}}class VolumetricMaterial extends Material{constructor(t){super(),this.transparent=!1,this.volumetric=!0,this.color=new b.Color(16777215),this.transferfn=null,this.map=void 0,this.extent=[],this.maxdepth=100,this.unit=0,this.texmatrix=null,this.transfermin=-1,this.transfermax=1,this.subsamples=5,this.shaderID="volumetric",this.side=s,this.setValues(t)}clone(t=new VolumetricMaterial){return super.clone.call(this,t),t.transparent=this.transparent,t.volumetric=this.volumetric,t.color=this.color,t.transferfn=this.transferfn,t.map=this.map,t.extent=this.extent,t.maxdepth=this.maxdepth,t.unit=this.unit,t.texmatrix=this.texmatrix,t.transfermin=this.transfermin,t.transfermax=this.transfermax,t.subsamples=this.subsamples,t.shaderID=this.shaderID,t.side=this.side,t}}var N;!function(t){t[t.LineStrip=0]="LineStrip",t[t.LinePieces=1]="LinePieces"}(N||(N={}));class Line extends Object3D{constructor(t,e=new LineBasicMaterial({color:16777215*Math.random()}),i=N.LineStrip){super(),this.geometry=t,this.material=e,this.type=i}clone(t=new Line(this.geometry,this.material,this.type)){return super.clone.call(this,t),t}}class Mesh extends Object3D{constructor(t,e){super(),this.geometry=t,this.material=e}clone(t){return void 0===t&&(t=new Mesh(this.geometry,this.material)),super.clone.call(this,t),t}}class Sprite extends Object3D{constructor(t=new SpriteMaterial){super(),this.material=t,this.rotation3d=this.rotation,this.rotation=0}updateMatrix(){this.matrix.setPosition(this.position),this.rotation3d.set(0,0,this.rotation),this.matrix.setRotationFromEuler(this.rotation3d),1===this.scale.x&&1===this.scale.y||this.matrix.scale(this.scale),this.matrixWorldNeedsUpdate=!0}clone(t=new Sprite(this.material)){return Object3D.prototype.clone.call(this,t),t}}const G={opacity:{type:"f",value:1},fogColor:{type:"c",value:new b.Color(1,1,1)},fogNear:{type:"f",value:1},fogFar:{type:"f",value:2e3}},V={vertexShader:"uniform mat4 modelViewMatrix;\nuniform mat4 projectionMatrix;\nuniform mat4 viewMatrix;\nuniform mat3 normalMatrix;\n\nattribute vec3 position;\nattribute vec3 color;\n\nvarying vec3 vColor;\n\nvoid main() {\n\n    vColor = color;\n    vec4 mvPosition = modelViewMatrix * vec4( position, 1.0 );\n    gl_Position = projectionMatrix * mvPosition;\n\n}".replace("#define GLSLIFY 1",""),fragmentShader:"uniform mat4 viewMatrix;\nuniform float opacity;\nuniform vec3 fogColor;\nuniform float fogNear;\nuniform float fogFar;\nvarying vec3 vColor;\n//DEFINEFRAGCOLOR\nvoid main() {\n    gl_FragColor = vec4( vColor, opacity );\n    float depth = gl_FragCoord.z / gl_FragCoord.w;\n    float fogFactor = smoothstep( fogNear, fogFar, depth );\n    gl_FragColor = mix( gl_FragColor, vec4( fogColor, gl_FragColor.w ), fogFactor );\n}".replace("#define GLSLIFY 1",""),uniforms:G},j={opacity:{type:"f",value:1},fogColor:{type:"c",value:new b.Color(1,1,1)},fogNear:{type:"f",value:1},fogFar:{type:"f",value:2e3},directionalLightColor:{type:"fv",value:[]},directionalLightDirection:{type:"fv",value:[]}},H={fragmentShader:"uniform mat4 viewMatrix;\nuniform float opacity;\n\nuniform vec3 fogColor;\nuniform float fogNear;\nuniform float fogFar;\n#ifdef SHADED\nuniform highp sampler2D shading;\n#endif\nvarying vec3 vLightFront;\nvarying vec3 vColor;\n//DEFINEFRAGCOLOR\n\nvoid main() {\n\n    gl_FragColor = vec4( vec3 ( 1.0 ), opacity );\n\n    #ifndef WIREFRAME\n    gl_FragColor.xyz *= vLightFront;\n    #endif\n\n#ifdef SHADED\n    ivec2 dim = textureSize(shading,0);\n    float shadowFactor = texture2D(shading,vec2(gl_FragCoord.x/float(dim.x),gl_FragCoord.y/float(dim.y))).r;\n    vColor *= shadowFactor;\n#endif\n    gl_FragColor = gl_FragColor * vec4( vColor, opacity );\n    float depth = gl_FragCoord.z / gl_FragCoord.w;\n\n    float fogFactor = smoothstep( fogNear, fogFar, depth );\n\n    gl_FragColor = mix( gl_FragColor, vec4( fogColor, gl_FragColor.w ), fogFactor );\n\n}\n\n\n".replace("#define GLSLIFY 1",""),vertexShader:"\n\nuniform mat4 modelViewMatrix;\nuniform mat4 projectionMatrix;\nuniform mat4 viewMatrix;\nuniform mat3 normalMatrix;\nuniform vec3 directionalLightColor[ 1 ];\nuniform vec3 directionalLightDirection[ 1 ];\n\nattribute vec3 offset;\nattribute vec3 position;\nattribute vec3 normal;\nattribute vec3 color;\nattribute float radius;\n\nvarying vec3 vColor;\nvarying vec3 vLightFront;\n\nvoid main() {\n\n    vColor = color;\n\n    vec3 objectNormal = normal;\n    vec3 transformedNormal = normalMatrix * objectNormal;\n    vec4 mvPosition = modelViewMatrix * vec4( position * radius + offset, 1.0 );\n\n    vLightFront = vec3( 0.0 );\n\n    transformedNormal = normalize( transformedNormal );\n\n    vec4 lDirection = viewMatrix * vec4( directionalLightDirection[ 0 ], 0.0 );\n    vec3 dirVector = normalize( lDirection.xyz );\n    float dotProduct = dot( transformedNormal, dirVector );\n    vec3 directionalLightWeighting = vec3( max( dotProduct, 0.0 ) );\n\n    vLightFront += directionalLightColor[ 0 ] * directionalLightWeighting;\n\n    gl_Position = projectionMatrix * mvPosition;\n}\n\n".replace("#define GLSLIFY 1",""),uniforms:j},W={opacity:{type:"f",value:1},fogColor:{type:"c",value:new b.Color(1,1,1)},fogNear:{type:"f",value:1},fogFar:{type:"f",value:2e3},directionalLightColor:{type:"fv",value:[]},directionalLightDirection:{type:"fv",value:[]}},q={fragmentShader:"uniform mat4 viewMatrix;\nuniform float opacity;\n\nuniform vec3 fogColor;\nuniform float fogNear;\nuniform float fogFar;\n#ifdef SHADED\nuniform highp sampler2D shading;\n#endif\nvarying vec3 vLightFront;\nvarying vec3 vColor;\n//DEFINEFRAGCOLOR\n\nvoid main() {\n\n    gl_FragColor = vec4( vec3 ( 1.0 ), opacity );\n\n    #ifndef WIREFRAME\n    gl_FragColor.xyz *= vLightFront;\n    #endif\n#ifdef SHADED\n    ivec2 dim = textureSize(shading,0);\n    float shadowFactor = texture2D(shading,vec2(gl_FragCoord.x/float(dim.x),gl_FragCoord.y/float(dim.y))).r;\n    vColor *= shadowFactor;\n#endif\n    gl_FragColor = gl_FragColor * vec4( vColor, opacity );\n    float depth = gl_FragCoord.z / gl_FragCoord.w;\n\n    float fogFactor = smoothstep( fogNear, fogFar, depth );\n\n    gl_FragColor = mix( gl_FragColor, vec4( fogColor, gl_FragColor.w ), fogFactor );\n\n}".replace("#define GLSLIFY 1",""),vertexShader:"\nuniform mat4 modelViewMatrix;\nuniform mat4 projectionMatrix;\nuniform mat4 viewMatrix;\nuniform mat3 normalMatrix;\nuniform vec3 directionalLightColor[ 1 ];\nuniform vec3 directionalLightDirection[ 1 ];\n\nattribute vec3 position;\nattribute vec3 normal;\nattribute vec3 color;\n\nvarying vec3 vColor;\nvarying vec3 vLightFront;\n\nvoid main() {\n\n    vColor = color;\n\n    vec3 objectNormal = normal;\n    vec3 transformedNormal = normalMatrix * objectNormal;\n    vec4 mvPosition = modelViewMatrix * vec4( position, 1.0 );\n\n    vLightFront = vec3( 0.0 );\n\n    transformedNormal = normalize( transformedNormal );\n\n    vec4 lDirection = viewMatrix * vec4( directionalLightDirection[ 0 ], 0.0 );\n    vec3 dirVector = normalize( lDirection.xyz );\n    float dotProduct = dot( transformedNormal, dirVector );\n    vec3 directionalLightWeighting = vec3( max( dotProduct, 0.0 ) );\n\n    vLightFront += directionalLightColor[ 0 ] * directionalLightWeighting;\n\n    gl_Position = projectionMatrix * mvPosition;\n}".replace("#define GLSLIFY 1",""),uniforms:W},Y={opacity:{type:"f",value:1},fogColor:{type:"c",value:new b.Color(1,1,1)},fogNear:{type:"f",value:1},fogFar:{type:"f",value:2e3},directionalLightColor:{type:"fv",value:[]},directionalLightDirection:{type:"fv",value:[]}},Z={fragmentShader:"\n#ifdef SHADED\nuniform highp sampler2D shading;\n#endif\n\nuniform mat4 viewMatrix;\nuniform float opacity;\n\nuniform vec3 fogColor;\nuniform float fogNear;\nuniform float fogFar;\n\nvarying vec3 vLightFront;\nvarying vec3 vLightBack;\n\nvarying vec3 vColor;\n\n\n//DEFINEFRAGCOLOR\n\nvoid main() {\n\n    gl_FragColor = vec4( vec3 ( 1.0 ), opacity );\n\n    #ifndef WIREFRAME\n    if ( gl_FrontFacing )\n       gl_FragColor.xyz *= vLightFront;\n    else\n       gl_FragColor.xyz *= vLightBack;\n    #endif\n\n    vec3 color = vColor;\n#ifdef SHADED\n    ivec2 dim = textureSize(shading,0);\n    float shadowFactor = texture2D(shading,vec2(gl_FragCoord.x/float(dim.x),gl_FragCoord.y/float(dim.y))).r;\n    color *= shadowFactor;\n#endif\n    gl_FragColor = gl_FragColor * vec4( color, opacity );\n    float depth = gl_FragCoord.z / gl_FragCoord.w;\n\n    float fogFactor = smoothstep( fogNear, fogFar, depth );\n\n    gl_FragColor = mix( gl_FragColor, vec4( fogColor, gl_FragColor.w ), fogFactor );\n\n}\n\n\n".replace("#define GLSLIFY 1",""),vertexShader:"\n\nuniform mat4 modelViewMatrix;\nuniform mat4 projectionMatrix;\nuniform mat4 viewMatrix;\nuniform mat3 normalMatrix;\nuniform vec3 directionalLightColor[ 1 ];\nuniform vec3 directionalLightDirection[ 1 ];\n\nattribute vec3 position;\nattribute vec3 normal;\nattribute vec3 color;\n\nvarying vec3 vColor;\nvarying vec3 vLightFront;\nvarying vec3 vLightBack;\n\nvoid main() {\n\n    vColor = color;\n\n    vec3 objectNormal = normal;\n    vec3 transformedNormal = normalMatrix * objectNormal;\n    vec4 mvPosition = modelViewMatrix * vec4( position, 1.0 );\n\n    vLightFront = vec3( 0.0 );\n    vLightBack = vec3( 0.0 );\n\n    transformedNormal = normalize( transformedNormal );\n\n    vec4 lDirection = viewMatrix * vec4( directionalLightDirection[ 0 ], 0.0 );\n    vec3 dirVector = normalize( lDirection.xyz );\n    float dotProduct = dot( transformedNormal, dirVector );\n    vec3 directionalLightWeighting = vec3( max( dotProduct, 0.0 ) );\n    vec3 directionalLightWeightingBack = vec3( max( -dotProduct, 0.0 ) );\n\n    vLightFront += directionalLightColor[ 0 ] * directionalLightWeighting;\n    vLightBack += directionalLightColor[ 0 ] * directionalLightWeightingBack;\n\n    gl_Position = projectionMatrix * mvPosition;\n}\n\n".replace("#define GLSLIFY 1",""),uniforms:Y},X={opacity:{type:"f",value:1},outlineColor:{type:"c",value:new b.Color(0,0,0)},fogColor:{type:"c",value:new b.Color(1,1,1)},fogNear:{type:"f",value:1},fogFar:{type:"f",value:2e3},outlineWidth:{type:"f",value:.1},outlinePushback:{type:"f",value:1},outlineMaxPixels:{type:"f",value:0}},K={fragmentShader:"\n\nuniform float opacity;\nuniform vec3 outlineColor;\nuniform vec3 fogColor;\nuniform float fogNear;\nuniform float fogFar;\n//DEFINEFRAGCOLOR\n\nvoid main() {\n    gl_FragColor = vec4( outlineColor, 1 );\n\n    float depth = gl_FragCoord.z / gl_FragCoord.w;\n    float fogFactor = smoothstep( fogNear, fogFar, depth );\n    gl_FragColor = mix( gl_FragColor, vec4( fogColor, gl_FragColor.w ), fogFactor );    \n}\n\n\n".replace("#define GLSLIFY 1",""),vertexShader:"\n\nuniform mat4 modelViewMatrix;\nuniform mat4 projectionMatrix;\nuniform float outlineWidth;\nuniform float outlinePushback;\nuniform float vWidth;\nuniform float vHeight;\nuniform float outlineMaxPixels;\n\nattribute vec3 position;\nattribute vec3 normal;\nattribute vec3 color;\n\nvoid main() {\n\n    vec4 norm = modelViewMatrix*vec4(normalize(normal),0.0);\n    vec4 mvPosition = modelViewMatrix * vec4( position, 1.0 );\n    mvPosition.xy += norm.xy*outlineWidth;\n    vec4 outpos = projectionMatrix * mvPosition;\n\n    if(outlineMaxPixels > 0.0) {\n        vec4 unadjusted = projectionMatrix*modelViewMatrix * vec4( position, 1.0 );\n        float w = outpos.w;\n        //normalize homogeneous coords\n        unadjusted /= unadjusted.w;\n        outpos /= outpos.w;\n        vec2 diff = outpos.xy-unadjusted.xy;\n        //put into pixels\n        diff.x *= vWidth;\n        diff.y *= vHeight;\n        if ( length(diff) > outlineMaxPixels) {\n            vec2 ndiff = normalize(diff)*outlineMaxPixels;\n            ndiff.x /= vWidth;\n            ndiff.y /= vHeight;\n            outpos.xy = unadjusted.xy;\n            outpos.xy += ndiff;\n        }\n        outpos *= w; //if I don't do this things blow up\n    }\n    gl_Position = outpos;\n    mvPosition.z -= outlinePushback; //go backwards in model space\n    vec4 pushpos = projectionMatrix*mvPosition; //project to get z in projection space, I'm probably missing some simple math to do the same thing..\n    gl_Position.z = gl_Position.w*pushpos.z/pushpos.w;\n}\n\n".replace("#define GLSLIFY 1",""),uniforms:X},Q={fragmentShader:"uniform sampler2D colormap;\nvarying highp vec2 vTexCoords;\nuniform vec2 dimensions;\n//DEFINEFRAGCOLOR\nvoid main (void) {\n   gl_FragColor = texture2D(colormap, vTexCoords);\n\n   //gl_FragColor.g = gl_FragColor.b =  gl_FragColor.r; //debug shading \n}\n        ".replace("#define GLSLIFY 1",""),vertexShader:"attribute vec2 vertexPosition;\nvarying highp vec2 vTexCoords;\nconst vec2 scale = vec2(0.5, 0.5);\n\nvoid main() {\n   vTexCoords  = vertexPosition * scale + scale; // scale vertex attribute to [0,1] range\n   gl_Position = vec4(vertexPosition, 0.0, 1.0);\n}\n        ".replace("#define GLSLIFY 1",""),uniforms:{}},$={fragmentShader:"uniform highp sampler2D colormap;\nvarying highp vec2 vTexCoords;\n\n\n// Basic FXAA implementation based on the code on geeks3d.com \n#define FXAA_REDUCE_MIN (1.0/ 128.0)\n#define FXAA_REDUCE_MUL (1.0 / 8.0)\n#define FXAA_SPAN_MAX 8.0\n\n\nvec4 applyFXAA(vec2 fragCoord, highp sampler2D tex)\n{\n    vec4 color;\n    ivec2 dim = textureSize(tex,0);\n    vec2 dimensions = vec2(float(dim.x),float(dim.y));\n    vec2 inverseVP = vec2(1.0 / dimensions.x, 1.0 / dimensions.y);\n    vec4 rgbNW = texture2D(tex, fragCoord + vec2(-1.0, -1.0) * inverseVP);\n    vec4 rgbNE = texture2D(tex, fragCoord + vec2(1.0, -1.0) * inverseVP);\n    vec4 rgbSW = texture2D(tex, fragCoord + vec2(-1.0, 1.0) * inverseVP);\n    vec4 rgbSE = texture2D(tex, fragCoord + vec2(1.0, 1.0) * inverseVP);\n    vec4 rgbM  = texture2D(tex, fragCoord );\n    vec3 luma = vec3(0.299, 0.587, 0.114);\n    float lumaNW = dot(rgbNW.xyz, luma);\n    float lumaNE = dot(rgbNE.xyz, luma);\n    float lumaSW = dot(rgbSW.xyz, luma);\n    float lumaSE = dot(rgbSE.xyz, luma);\n    float lumaM  = dot(rgbM.xyz,  luma);\n    float lumaMin = min(lumaM, min(min(lumaNW, lumaNE), min(lumaSW, lumaSE)));\n    float lumaMax = max(lumaM, max(max(lumaNW, lumaNE), max(lumaSW, lumaSE)));\n\n    vec2 dir;\n    dir.x = -((lumaNW + lumaNE) - (lumaSW + lumaSE));\n    dir.y =  ((lumaNW + lumaSW) - (lumaNE + lumaSE));\n\n    float dirReduce = max((lumaNW + lumaNE + lumaSW + lumaSE) *\n                        (0.25 * FXAA_REDUCE_MUL), FXAA_REDUCE_MIN);\n\n    float rcpDirMin = 1.0 / (min(abs(dir.x), abs(dir.y)) + dirReduce);\n    dir = min(vec2(FXAA_SPAN_MAX, FXAA_SPAN_MAX),\n            max(vec2(-FXAA_SPAN_MAX, -FXAA_SPAN_MAX),\n            dir * rcpDirMin)) * inverseVP;\n\n    vec4 rgbA = 0.5 * (\n        texture2D(tex, fragCoord + dir * (1.0 / 3.0 - 0.5)) +\n        texture2D(tex, fragCoord  + dir * (2.0 / 3.0 - 0.5)));\n    vec4 rgbB = rgbA * 0.5 + 0.25 * (\n        texture2D(tex, fragCoord  + dir * -0.5) +\n        texture2D(tex, fragCoord  + dir * 0.5));\n\n    float lumaB = dot(rgbB.xyz, luma);\n    if ((lumaB < lumaMin) || (lumaB > lumaMax))\n        color = rgbA;\n    else\n        color = rgbB;\n\n    return color;\n}\n\n\n//DEFINEFRAGCOLOR\nvoid main (void) {\n    ivec2 dim = textureSize(colormap,0);\n\n  gl_FragColor = applyFXAA(vTexCoords, colormap);\n}\n        ".replace("#define GLSLIFY 1",""),vertexShader:"attribute vec2 vertexPosition;\nvarying highp vec2 vTexCoords;\nconst vec2 scale = vec2(0.5, 0.5);\n\nvoid main() {\n   vTexCoords  = vertexPosition * scale + scale; // scale vertex attribute to [0,1] range\n   gl_Position = vec4(vertexPosition, 0.0, 1.0);\n}\n        ".replace("#define GLSLIFY 1",""),uniforms:{}},J={opacity:{type:"f",value:1},fogColor:{type:"c",value:new b.Color(1,1,1)},fogNear:{type:"f",value:1},fogFar:{type:"f",value:2e3},directionalLightColor:{type:"fv",value:[]},directionalLightDirection:{type:"fv",value:[]}},tt={vertexShader:"uniform mat4 modelViewMatrix;\nuniform mat4 projectionMatrix;\nuniform mat4 viewMatrix;\nuniform mat3 normalMatrix;\nuniform vec3 directionalLightColor[ 1 ];\nuniform vec3 directionalLightDirection[ 1 ];\n\nattribute vec3 position;\nattribute vec3 normal;\nattribute vec3 color;\n\nvarying vec2 mapping;\nvarying vec3 vColor;\nvarying float rval;\nvarying vec3 vLight;\nvarying vec3 center;\n\nvoid main() {\n\n    vColor = color;\n    vec4 mvPosition = modelViewMatrix * vec4( position, 1.0 );\n    center = mvPosition.xyz;\n    vec4 projPosition = projectionMatrix * mvPosition;\n    vec4 adjust = projectionMatrix* vec4(normal,0.0); adjust.z = 0.0; adjust.w = 0.0;\n    vec4 lDirection = viewMatrix * vec4( directionalLightDirection[ 0 ], 0.0 );\n    vLight = normalize( lDirection.xyz );\n    mapping = normal.xy;\n    rval = abs(normal.x);\n    gl_Position = projPosition+adjust;\n\n}\n".replace("#define GLSLIFY 1",""),fragmentShader:"\nuniform mat4 viewMatrix;\nuniform float opacity;\nuniform mat4 projectionMatrix;\n\nuniform vec3 fogColor;\nuniform float fogNear;\nuniform float fogFar;\nuniform float uDepth;\nuniform vec3 directionalLightColor[ 1 ];\n\nvarying vec3 vColor;\nvarying vec2 mapping;\nvarying float rval;\nvarying vec3 vLight;\nvarying vec3 center;\n\n#ifdef SHADED\nuniform highp sampler2D shading;\n#endif\n\n//DEFINEFRAGCOLOR\n\nvoid main() {\n    float lensqr = dot(mapping,mapping);\n    float rsqr = rval*rval;\n    if(lensqr > rsqr)\n       discard;\n    float z = sqrt(rsqr-lensqr);\n    vec3 cameraPos = center+ vec3(mapping.x,mapping.y,z);\n    vec4 clipPos = projectionMatrix * vec4(cameraPos, 1.0);\n    float ndcDepth = clipPos.z / clipPos.w;\n    gl_FragDepthEXT = ((gl_DepthRange.diff * ndcDepth) + gl_DepthRange.near + gl_DepthRange.far) / 2.0;\n    vec3 norm = normalize(vec3(mapping.x,mapping.y,z));\n    float dotProduct = dot( norm, vLight );\n    vec3 directionalLightWeighting = vec3( max( dotProduct, 0.0 ) );\n    vec3 vLight = directionalLightColor[ 0 ] * directionalLightWeighting;\n    vec3 color = vLight*vColor;\n#ifdef SHADED\n    ivec2 dim = textureSize(shading,0);\n    float shadowFactor = texture2D(shading,vec2(gl_FragCoord.x/float(dim.x),gl_FragCoord.y/float(dim.y))).r;\n    color *= shadowFactor;\n#endif    \n    gl_FragColor = vec4(color, opacity*opacity );\n    float fogFactor = smoothstep( fogNear, fogFar, gl_FragDepthEXT/gl_FragCoord.w );\n    gl_FragColor = mix( gl_FragColor, vec4( fogColor, gl_FragColor.w ), fogFactor );\n}\n\n".replace("#define GLSLIFY 1",""),uniforms:J},et={opacity:{type:"f",value:1},outlineColor:{type:"c",value:new b.Color(0,0,0)},fogColor:{type:"c",value:new b.Color(1,1,1)},fogNear:{type:"f",value:1},fogFar:{type:"f",value:2e3},outlineWidth:{type:"f",value:.1},outlinePushback:{type:"f",value:1},outlineMaxPixels:{type:"f",value:0}},it={fragmentShader:"\n\nuniform float opacity;\nuniform vec3 outlineColor;\nuniform vec3 fogColor;\nuniform float fogNear;\nuniform float fogFar;\nuniform mat4 projectionMatrix;\nvarying vec2 mapping;\nvarying float rval;\nvarying vec3 center;\n\nuniform float outlinePushback;\n\n//DEFINEFRAGCOLOR\n\nvoid main() {\n    float lensqr = dot(mapping,mapping);\n    float rsqr = rval*rval;\n    if(lensqr > rsqr)\n       discard;\n    float z = sqrt(rsqr-lensqr);\n    vec3 cameraPos = center+ vec3(mapping.x,mapping.y,z-outlinePushback);\n    vec4 clipPos = projectionMatrix * vec4(cameraPos, 1.0);\n    float ndcDepth = clipPos.z / clipPos.w;\n    gl_FragDepthEXT = ((gl_DepthRange.diff * ndcDepth) + gl_DepthRange.near + gl_DepthRange.far) / 2.0;\n    gl_FragColor = vec4(outlineColor, 1 );\n}\n\n\n".replace("#define GLSLIFY 1",""),vertexShader:"\n\nuniform mat4 modelViewMatrix;\nuniform mat4 projectionMatrix;\nuniform float outlineWidth;\nuniform float outlinePushback;\nuniform float outlineMaxPixels;\nuniform float vWidth;\nuniform float vHeight;\n\nattribute vec3 position;\nattribute vec3 normal;\nattribute vec3 color;\n\nvarying vec2 mapping;\nvarying float rval;\nvarying vec3 center;\n\nvoid main() {\n\n    vec4 mvPosition = modelViewMatrix * vec4( position, 1.0 );\n    center = mvPosition.xyz;\n    vec4 projPosition = projectionMatrix * mvPosition;\n    vec2 norm = normal.xy + vec2(sign(normal.x)*outlineWidth,sign(normal.y)*outlineWidth);\n\n    vec4 adjust = projectionMatrix* vec4(norm,normal.z,1.0); \n    mapping = norm.xy;\n    rval = abs(norm.x);\n    gl_Position = projPosition+vec4(adjust.xy,0.0,0.0);\n\n    if(outlineMaxPixels > 0.0) {\n        vec4 unadjusted = projectionMatrix*vec4(center.x+normal.x, center.y,center.z,1.0); \n        vec4 ccoord = projectionMatrix*vec4(center.xyz,1.0);\n        adjust = projectionMatrix* vec4(center.x+norm.x,center.y,center.z,1.0); \n        //subtract center \n        unadjusted.xyz -= ccoord.xyz;\n        adjust.xyz -= ccoord.xyz;\n        unadjusted /= unadjusted.w;\n        adjust /= adjust.w;\n        float diff = abs(adjust.x-unadjusted.x);\n        diff *= vWidth;\n        if(diff > outlineMaxPixels) {\n            \n            float fixlen = abs(unadjusted.x) + outlineMaxPixels/vWidth;\n            //adjsut reval by ratio of lengths\n            rval *= fixlen/abs(adjust.x);\n        }\n\n    }\n}\n\n".replace("#define GLSLIFY 1",""),uniforms:et},rt={fragmentShader:"\n\nuniform vec3 color;\nuniform sampler2D map;\nuniform float opacity;\n\nuniform int fogType;\nuniform vec3 fogColor;\nuniform float fogDensity;\nuniform float fogNear;\nuniform float fogFar;\nuniform float alphaTest;\n\nvarying vec2 vUV;\n//DEFINEFRAGCOLOR\n\nvoid main() {\n\n    vec4 texture = texture2D(map, vUV);\n\n    if (texture.a <= alphaTest) discard;\n\n    gl_FragColor = vec4(color * texture.xyz, texture.a * opacity);\n\n    if (fogType > 0) {\n\n        float depth = gl_FragCoord.z / gl_FragCoord.w;\n        float fogFactor = 0.0;\n\n        if (fogType == 1) {\n            fogFactor = smoothstep(fogNear, fogFar, depth);\n        }\n\n        else {\n            const float LOG2 = 1.442695;\n            float fogFactor = exp2(- fogDensity * fogDensity * depth * depth * LOG2);\n            fogFactor = 1.0 - clamp(fogFactor, 0.0, 1.0);\n        }\n\n        gl_FragColor = mix(gl_FragColor, vec4(fogColor, gl_FragColor.w), fogFactor);\n\n    }\n}\n\n".replace("#define GLSLIFY 1",""),vertexShader:"\n\nuniform int useScreenCoordinates;\nuniform vec3 screenPosition;\nuniform mat4 modelViewMatrix;\nuniform mat4 projectionMatrix;\nuniform float rotation;\nuniform vec2 scale;\nuniform vec2 alignment;\nuniform vec2 uvOffset;\nuniform vec2 uvScale;\n\nattribute vec2 position;\nattribute vec2 uv;\n\nvarying vec2 vUV;\n\nvoid main() {\n\n    vUV = uvOffset + uv * uvScale;\n\n    vec2 alignedPosition = position + alignment;\n\n    vec2 rotatedPosition;\n    rotatedPosition.x = ( cos(rotation) * alignedPosition.x - sin(rotation) * alignedPosition.y ) * scale.x;\n    rotatedPosition.y = ( sin(rotation) * alignedPosition.x + cos(rotation) * alignedPosition.y ) * scale.y;\n\n    vec4 finalPosition;\n\n    if(useScreenCoordinates != 0) {\n        finalPosition = vec4(screenPosition.xy + rotatedPosition, screenPosition.z, 1.0);\n    }\n\n    else {\n        finalPosition = projectionMatrix * modelViewMatrix * vec4(0.0, 0.0, 0.0, 1.0); finalPosition /= finalPosition.w;\n        finalPosition.xy += rotatedPosition; \n    }\n\n    gl_Position = finalPosition;\n\n}\n\n".replace("#define GLSLIFY 1",""),uniforms:{}},st={opacity:{type:"f",value:1},fogColor:{type:"c",value:new b.Color(1,1,1)},fogNear:{type:"f",value:1},fogFar:{type:"f",value:2e3},directionalLightColor:{type:"fv",value:[]},directionalLightDirection:{type:"fv",value:[]}},nt="uniform float opacity;\nuniform mat4 projectionMatrix;\n\nuniform vec3 fogColor;\nuniform float fogNear;\nuniform float fogFar;\n\nvarying vec3 vLight;\nvarying vec3 vColor;\nvarying vec3 cposition;\nvarying vec3 p1;\nvarying vec3 p2;\nvarying float r;\n\n#ifdef SHADED\nuniform highp sampler2D shading;\n#endif\n\n//DEFINEFRAGCOLOR\n\n//cylinder-ray intersection testing taken from http://mrl.nyu.edu/~dzorin/cg05/lecture12.pdf\n//also useful: http://stackoverflow.com/questions/9595300/cylinder-impostor-in-glsl\n//with a bit more care (caps) this could be a general cylinder imposter (see also outline)\nvoid main() {\n    vec3 color = abs(vColor);\n    vec3 pos = cposition;\n    vec3 p = pos; //ray point\n    vec3 v = vec3(0.0,0.0,-1.0); //ray normal - orthographic\n    if(projectionMatrix[3][3] == 0.0) v = normalize(pos); //ray normal - perspective\n    vec3 pa = p1; //cyl start\n    vec3 va = normalize(p2-p1); //cyl norm\n    vec3 tmp1 = v-(dot(v,va)*va);\n    vec3 deltap = p-pa;\n    float A = dot(tmp1,tmp1);\n    if(A == 0.0) discard;\n    vec3 tmp2 = deltap-(dot(deltap,va)*va);\n    float B = 2.0*dot(tmp1, tmp2);\n    float C = dot(tmp2,tmp2)-r*r;\n//quadratic equation!\n    float det = (B*B) - (4.0*A*C);\n    if(det < 0.0) discard;\n    float sqrtDet = sqrt(det);\n    float posT = (-B+sqrtDet)/(2.0*A);\n    float negT = (-B-sqrtDet)/(2.0*A);\n    float intersectionT = min(posT,negT);\n    vec3 qi = p+v*intersectionT;\n    float dotp1 = dot(va,qi-p1);\n    float dotp2 = dot(va,qi-p2);\n    vec3 norm;\n    if( dotp1 < 0.0 || dotp2 > 0.0) { //(p-c)^2 + 2(p-c)vt +v^2+t^2 - r^2 = 0\n       vec3 cp;\n       if( dotp1 < 0.0) {  \n//        if(vColor.x < 0.0 ) discard; //color sign bit indicates if we should cap or not\n        cp = p1;\n       } else {\n//          if(vColor.y < 0.0 ) discard;\n          cp = p2;\n       }\n       vec3 diff = p-cp;\n       A = dot(v,v);\n       B = dot(diff,v)*2.0;\n       C = dot(diff,diff)-r*r;\n       det = (B*B) - (4.0*C);\n       if(det < 0.0) discard;\n       sqrtDet = sqrt(det);\n       posT = (-B+sqrtDet)/(2.0);\n       negT = (-B-sqrtDet)/(2.0);\n       float t = min(posT,negT);\n       qi = p+v*t; \n       norm = normalize(qi-cp); \n    } else {\n       norm = normalize(qi-(dotp1*va + p1));\n    }\n    vec4 clipPos = projectionMatrix * vec4(qi, 1.0);\n    float ndcDepth = clipPos.z / clipPos.w;\n    float depth = ((gl_DepthRange.diff * ndcDepth) + gl_DepthRange.near + gl_DepthRange.far) / 2.0;\n    gl_FragDepthEXT = depth;",at={fragmentShader:[nt,"    float dotProduct = dot( norm, vLight );\n    vec3 light = vec3( max( dotProduct, 0.0 ) );\n    color *= light;\n#ifdef SHADED\n    ivec2 dim = textureSize(shading,0);\n    float shadowFactor = texture2D(shading,vec2(gl_FragCoord.x/float(dim.x),gl_FragCoord.y/float(dim.y))).r;\n    color *= shadowFactor;\n#endif    \n    gl_FragColor = vec4(color, opacity*opacity );\n    float fogFactor = smoothstep( fogNear, fogFar, depth );\n    gl_FragColor = mix( gl_FragColor, vec4( fogColor, gl_FragColor.w ), fogFactor );\n}"].join("\n"),vertexShader:"\n\nuniform mat4 modelViewMatrix;\nuniform mat4 projectionMatrix;\nuniform mat4 viewMatrix;\nuniform mat3 normalMatrix;\nuniform vec3 directionalLightColor[ 1 ];\nuniform vec3 directionalLightDirection[ 1 ];\n\nattribute vec3 position;\nattribute vec3 normal;\nattribute vec3 color;\nattribute float radius;\n\nvarying vec3 vColor;\nvarying vec3 vLight;\nvarying vec3 cposition;\nvarying vec3 p1;\nvarying vec3 p2;\nvarying float r;\n\nvoid main() {\n\n    vColor = color; vColor.z = abs(vColor.z); //z indicates which vertex and so would vary\n    r = abs(radius);\n    vec4 to = modelViewMatrix*vec4(normal, 1.0); //normal is other point of cylinder\n    vec4 pt = modelViewMatrix*vec4(position, 1.0);\n    vec4 mvPosition = pt;\n    p1 = pt.xyz; p2 = to.xyz;\n    vec3 norm = to.xyz-pt.xyz;\n    float mult = 1.1; //slop to account for perspective of sphere\n    if(length(p1) > length(p2)) { //billboard at level of closest point\n       mvPosition = to;\n    }\n    vec3 n = normalize(mvPosition.xyz);\n    bool isperspective = (projectionMatrix[3][3] == 0.0);\n//intersect with the plane defined by the camera looking at the billboard point\n    if(color.z >= 0.0) { //p1\n       if(isperspective) { //perspective\n         vec3 pnorm = normalize(p1);\n         float t = dot(mvPosition.xyz-p1,n)/dot(pnorm,n);\n         mvPosition.xyz = p1+t*pnorm; \n       } else { //orthographic\n         mvPosition.xyz = p1;\n       }\n    } else {\n      if(isperspective) { //perspective\n         vec3 pnorm = normalize(p2);\n         float t = dot(mvPosition.xyz-p2,n)/dot(pnorm,n);\n         mvPosition.xyz = p2+t*pnorm;\n       } else { //orthographic\n         mvPosition.xyz = p2;\n       } \n       mult *= -1.0;\n    }\n\n    if(isperspective) { //perspective\n      vec3 cr = normalize(cross(mvPosition.xyz,norm))*radius;\n      vec3 doublecr = normalize(cross(mvPosition.xyz,cr))*radius;\n      mvPosition.xyz +=  mult*(cr + doublecr).xyz;\n    } else {\n      vec3 cr = normalize(cross(vec3(0.0,0.0,-1.0),norm))*radius;\n      vec3 doublecr = normalize(cross(vec3(0.0,0.0,-1.0),cr))*radius;\n      mvPosition.xyz +=  mult*(cr + doublecr).xyz;     \n    }\n\n    cposition = mvPosition.xyz;\n    gl_Position = projectionMatrix * mvPosition;\n    vec4 lDirection = viewMatrix * vec4( directionalLightDirection[ 0 ], 0.0 );\n    vLight = normalize( lDirection.xyz )*directionalLightColor[0]; //not really sure this is right, but color is always white so..\n}\n\n",uniforms:st},ot={fragmentShader:nt+"gl_FragColor = vec4(color,1.0);}",vertexShader:"\n\nuniform mat4 modelViewMatrix;\nuniform mat4 projectionMatrix;\nuniform mat4 viewMatrix;\nuniform mat3 normalMatrix;\nuniform vec3 directionalLightColor[ 1 ];\nuniform vec3 directionalLightDirection[ 1 ];\nuniform vec3 outlineColor;\nuniform float outlineWidth;\nuniform float outlinePushback;\nuniform float outlineMaxPixels;\nuniform float vWidth;\nuniform mat4 projinv;\n\n\nattribute vec3 position;\nattribute vec3 normal;\nattribute vec3 color;\nattribute float radius;\n\nvarying vec3 vColor;\nvarying vec3 vLight;\nvarying vec3 cposition;\nvarying vec3 p1;\nvarying vec3 p2;\nvarying float r;\n\nvoid main() {\n\n    vColor = outlineColor;\n    float rad = radius+sign(radius)*outlineWidth;\n    r = abs(rad);\n\n    vec4 to = modelViewMatrix*vec4(normal, 1.0); //normal is other point of cylinder\n    vec4 pt = modelViewMatrix*vec4(position, 1.0);\n//pushback\n    float scale = 1.0;\n    if(projectionMatrix[3][3] != 0.0) { //orthographic\n        to.z -= outlinePushback;\n        pt.z -= outlinePushback;\n    } else { //perspective\n        vec4 midbefore = pt;\n        if(length(to.xyz) < length(pt)) {\n            midbefore = to;\n        }\n        vec4 midafter = midbefore;\n        midafter.xyz += normalize(midbefore.xyz)*outlinePushback;\n\n        to.xyz += normalize(to.xyz)*outlinePushback;\n        pt.xyz += normalize(pt.xyz)*outlinePushback;\n\n        //figure out a scaling factor for radius to account for perspective setback\n        vec4 midbeforer = vec4(midbefore.x+rad,midbefore.y, midbefore.z, midbefore.w);\n        vec4 midafterr = vec4(midafter.x+rad,midafter.y, midafter.z, midafter.w);\n\n        vec4 mb = projectionMatrix*midbefore;\n        vec4 mbr = projectionMatrix*midbeforer;\n        vec4 ma = projectionMatrix*midafter;\n        vec4 mar = projectionMatrix*midafterr;\n        mb /= mb.w;\n        mbr /= mbr.w;\n        ma /= ma.w;\n        mar /= mar.w;\n        scale = abs((mbr.x-mb.x)/(mar.x-ma.x));\n        rad *= scale;\n        r = abs(rad);\n    }\n    vec4 mvPosition = pt;\n    p1 = pt.xyz; p2 = to.xyz;\n    vec3 norm = to.xyz-pt.xyz;\n    float mult = 1.1; //slop to account for perspective of sphere\n    if(length(p1) > length(p2)) { //billboard at level of closest point\n       mvPosition = to;\n    }\n\n    vec3 n = normalize(mvPosition.xyz);\n//intersect with the plane defined by the camera looking at the billboard point\n    if(color.z >= 0.0) { //p1\n       vec3 pnorm = normalize(p1);\n       float t = dot(mvPosition.xyz-p1,n)/dot(pnorm,n);\n       mvPosition.xyz = p1+t*pnorm;\n    } else {\n       vec3 pnorm = normalize(p2);\n       float t = dot(mvPosition.xyz-p2,n)/dot(pnorm,n);\n       mvPosition.xyz = p2+t*pnorm;\n       mult *= -1.0;\n    }\n\n    if(outlineMaxPixels > 0.0) {\n        vec4 cpos = mvPosition;\n        vec4 unadjusted = projectionMatrix*vec4(cpos.x+abs(scale*radius), cpos.y,cpos.z,cpos.w); \n        vec4 ccoord = projectionMatrix*cpos;\n        vec4 adjust = projectionMatrix*vec4(cpos.x+r,cpos.y,cpos.z,cpos.w); \n        unadjusted /= unadjusted.w;\n        adjust /= adjust.w;\n        unadjusted.xyz -= ccoord.xyz/ccoord.w;\n        adjust.xyz -= ccoord.xyz/ccoord.w;\n        float diff = abs(adjust.x-unadjusted.x);\n        diff *= vWidth; //this should now be in pixels\n        if(diff > outlineMaxPixels) {\n            float fixlen = abs(unadjusted.x) + outlineMaxPixels/vWidth; \n            vec4 pcoord = ccoord;\n            pcoord.x += fixlen*pcoord.w;\n            vec4 altc = projinv*pcoord;\n            r= abs(altc.x-cpos.x);\n        }\n    }\n\n    vec3 cr = normalize(cross(mvPosition.xyz,norm))*rad;\n    vec3 doublecr = normalize(cross(mvPosition.xyz,cr))*rad;\n    mvPosition.xy +=  mult*(cr + doublecr).xy;\n    cposition = mvPosition.xyz;\n    gl_Position = projectionMatrix * mvPosition;\n    vLight = vec3(1.0,1.0,1.0);\n}\n\n",uniforms:{opacity:{type:"f",value:1},fogColor:{type:"c",value:new b.Color(1,1,1)},fogNear:{type:"f",value:1},fogFar:{type:"f",value:2e3},outlineColor:{type:"c",value:new b.Color(0,0,0)},outlineWidth:{type:"f",value:.1},outlinePushback:{type:"f",value:1},outlineMaxPixels:{type:"f",value:0},projinv:{type:"mat4",value:[]}}},lt={opacity:{type:"f",value:1},fogColor:{type:"c",value:new b.Color(1,1,1)},fogNear:{type:"f",value:1},fogFar:{type:"f",value:2e3},data:{type:"i",value:3},colormap:{type:"i",value:4},depthmap:{type:"i",value:5},step:{type:"f",value:1},maxdepth:{type:"f",value:100},subsamples:{type:"f",value:5},textmat:{type:"mat4",value:[]},projinv:{type:"mat4",value:[]},transfermin:{type:"f",value:-.2},transfermax:{type:"f",value:.2}},ht={fragmentShader:"\nuniform highp sampler3D data;\nuniform highp sampler2D colormap;\nuniform highp sampler2D depthmap;\n\n\nuniform mat4 textmat;\nuniform mat4 projinv;\nuniform mat4 projectionMatrix;\n\nuniform float step;\nuniform float subsamples;\nuniform float maxdepth;\nuniform float transfermin;\nuniform float transfermax;\nin  vec4 mvPosition;\nout vec4 color;\nvoid main(void) {\n\n   vec4 pos = mvPosition;\n   bool seengood = false;\n   float i = 0.0;\n   color = vec4(1,1,1,0);\n   float increment = 1.0/subsamples;\n   float maxsteps = (maxdepth*subsamples/step);\n//there's probably a better way to do this..\n//calculate farthest possible point in model coordinates\n   vec4 maxpos = vec4(pos.x,pos.y,pos.z-maxdepth,1.0);\n// convert to projection\n   maxpos = projectionMatrix*maxpos;\n   vec4 startp = projectionMatrix*pos;\n// homogonize\n   maxpos /= maxpos.w;\n   startp /= startp.w;\n//take x,y from start and z from max\n   maxpos = vec4(startp.x,startp.y,maxpos.z,1.0);\n//convert back to model space\n   maxpos = projinv*maxpos;\n   maxpos /= maxpos.w;\n   float incr = step/subsamples;\n//get depth from depthmap\n//startp is apparently [-1,1]\n   vec2 tpos = startp.xy/2.0+0.5;\n   float depth = texture(depthmap, tpos).r;\n//compute vector between start and end\n   vec4 direction = maxpos-pos;\n   for( i = 0.0; i <= maxsteps; i++) {\n      vec4 pt = (pos+(i/maxsteps)*direction);\n      vec4 ppt = projectionMatrix*pt;\n      float ptdepth = ppt.z/ppt.w;\n      ptdepth = ((gl_DepthRange.diff * ptdepth) + gl_DepthRange.near + gl_DepthRange.far) / 2.0;\n      if(ptdepth > depth) break;\n      pt = textmat*pt;\n//       pt /= pt.w;\n      if(pt.x >= -0.01 && pt.y >= -0.01 && pt.z >= -0.01 && pt.x <= 1.01 && pt.y <= 1.01 && pt.z <= 1.01) {\n         seengood = true;\n      } else if(seengood) {\n         break;\n      }\n      if( pt.x < -0.01 || pt.x > 1.01 || pt.y < -0.01 || pt.y > 1.01 || pt.z < -0.01 || pt.z > 1.01  ){\n          color.a = 0.0;\n          continue;\n      }\n      else {\n         float val = texture(data, pt.zyx).r;\n         if(isinf(val)) continue; //masked out\n         float cval = (val-transfermin)/(transfermax-transfermin); //scale to texture 0-1 range\n         vec4 val_color = texture(colormap, vec2(cval,0.5));\n         color.rgb = color.rgb*color.a + (1.0-color.a)*val_color.a*val_color.rgb;\n         color.a += (1.0 - color.a) * val_color.a; \n         if(color.a > 0.0) color.rgb /= color.a;\n//          color = vec4(pt.x, pt.y, pt.z, 1.0);\n      }\n//       color = vec4(pt.x, pt.y, pt.z, 0.0)\n    }\n}\n\n        ".replace("#define GLSLIFY 1",""),vertexShader:"uniform mat4 modelViewMatrix;\nuniform mat4 projectionMatrix;\nuniform mat4 viewMatrix;\n\nin vec3 position;\nout vec4 mvPosition;\nvoid main() {\n\n    mvPosition = modelViewMatrix * vec4( position, 1.0 );\n    gl_Position = projectionMatrix*mvPosition;\n}\n".replace("#define GLSLIFY 1",""),uniforms:lt},ct={basic:V,blur:{fragmentShader:"const float INV_TOTAL_SAMPLES_FACTOR = 1.0 / 9.0;\nuniform highp sampler2D inTex;\nvarying highp vec2 vTexCoords;\n    \nvoid main() {\n \n vec2 texelSize = 1.0 / vec2(textureSize(inTex,0));\n float blurred_visibility_factor = 0.0f;\n for (int t = -1; t <= 1; ++t) {\n  for (int s = -1; s <= 1; ++s) {\n   vec2 offset = vec2(float(s), float(t)) * texelSize;\n   blurred_visibility_factor += texture2D(inTex, vTexCoords + offset).r;\n  }\n }\n    \n gl_FragDepthEXT = blurred_visibility_factor * INV_TOTAL_SAMPLES_FACTOR;\n \n}",vertexShader:"attribute vec2 vertexPosition;\nvarying highp vec2 vTexCoords;\nconst vec2 scale = vec2(0.5, 0.5);\n\nvoid main() {\n   vTexCoords  = vertexPosition * scale + scale; // scale vertex attribute to [0,1] range\n   gl_Position = vec4(vertexPosition, 0.0, 1.0);\n}\n        ",uniforms:{}},instanced:H,lambert:q,lambertdouble:Z,outline:K,screen:Q,screenaa:$,ssao:{fragmentShader:"uniform sampler2D depthmap;\nvarying highp vec2 vTexCoords;\nuniform float vWidth;\nuniform float vHeight;\nuniform float total_strength;\nuniform float radius;\nuniform mat4 projinv;\nuniform mat4 projectionMatrix;\n\n//code for pseudorandom vector from chatgpt\nfloat hash(vec3 p) {\n    p = fract(p * vec3(0.1031, 0.1030, 0.0973));\n    p += dot(p, p.yzx + 33.33);\n    return fract((p.x + p.y) * p.z);\n}\n\n// Generate a pseudorandom vec3 from a seed vec3\nvec3 pseudorandomVec3(vec3 seed) {\n    vec3 randomVec;\n    randomVec.x = hash(seed);\n    randomVec.y = hash(seed + vec3(1.0, 0.0, 17.1));\n    randomVec.z = hash(seed + vec3(0.0, 13.23, 0.0));\n    return randomVec;\n}\n\nvoid main(void)\n{   \n   const float base = 0.2;\n   const float area = 0.75;\n   const int cycles = 1;\n\n   const int samples = 64;\n   vec3 sample_sphere[64] = vec3[](\n      vec3(0.091258,-0.510164,0.000000),\n      vec3(-0.204347,-0.872967,0.187199),\n      vec3(0.009690,-0.263696,-0.110414),\n      vec3(0.175208,-0.563987,0.228527),\n      vec3(-0.001824,-0.003113,-0.000323),\n      vec3(0.411134,-0.719869,-0.261530),\n      vec3(-0.074272,-0.377368,0.276290),\n      vec3(-0.147773,-0.381587,-0.284529),\n      vec3(0.173317,-0.199635,0.063295),\n      vec3(-0.186452,-0.199460,0.076965),\n      vec3(0.143985,-0.308160,-0.307687),\n      vec3(0.053194,-0.148286,0.169589),\n      vec3(-0.547656,-0.486476,-0.317378),\n      vec3(0.020804,-0.015092,-0.004574),\n      vec3(-0.038006,-0.043165,0.054059),\n      vec3(-0.094795,-0.443908,-0.731525),\n      vec3(0.547552,-0.396466,0.461477),\n      vec3(-0.176886,-0.089989,0.007315),\n      vec3(0.074401,-0.048840,-0.074039),\n      vec3(-0.008240,-0.075697,0.178197),\n      vec3(-0.307880,-0.185053,-0.368943),\n      vec3(0.309520,-0.108483,0.041646),\n      vec3(-0.773478,-0.292946,0.538166),\n      vec3(0.184487,-0.231594,-0.820065),\n      vec3(0.207318,-0.100531,0.361797),\n      vec3(-0.173306,-0.037737,-0.055289),\n      vec3(0.548102,-0.105342,-0.253237),\n      vec3(-0.119342,-0.043907,0.285162),\n      vec3(-0.270247,-0.087861,-0.751357),\n      vec3(0.449312,-0.039777,0.236146),\n      vec3(-0.743773,-0.036095,0.196056),\n      vec3(0.148819,-0.004300,-0.231448),\n      vec3(0.008773,0.000809,0.051047),\n      vec3(-0.461467,0.027390,-0.357386),\n      vec3(0.169626,0.013338,-0.014053),\n      vec3(-0.043786,0.007095,0.047331),\n      vec3(0.004821,0.140371,-0.988260),\n      vec3(0.092402,0.023994,0.101860),\n      vec3(-0.295335,0.061530,-0.027372),\n      vec3(0.024903,0.007537,-0.018901),\n      vec3(-0.081463,0.125402,0.447794),\n      vec3(-0.397119,0.231805,-0.631062),\n      vec3(0.163853,0.059014,0.044905),\n      vec3(-0.495220,0.214357,0.254139),\n      vec3(0.306123,0.373687,-0.825715),\n      vec3(0.021665,0.026737,0.053220),\n      vec3(-0.208231,0.117129,-0.098685),\n      vec3(0.139749,0.080968,-0.043086),\n      vec3(-0.153599,0.182814,0.262090),\n      vec3(-0.159673,0.496777,-0.743568),\n      vec3(0.134797,0.117152,0.095753),\n      vec3(-0.155626,0.120533,0.019395),\n      vec3(0.042311,0.054462,-0.049709),\n      vec3(0.001257,0.031288,0.034468),\n      vec3(-0.002271,0.003199,-0.002304),\n      vec3(0.662104,0.717307,0.033854),\n      vec3(-0.373100,0.576021,0.308274),\n      vec3(0.024233,0.231316,-0.173688),\n      vec3(0.161311,0.420217,0.234273),\n      vec3(-0.045248,0.078031,-0.010411),\n      vec3(0.167453,0.376942,-0.094872),\n      vec3(-0.056194,0.433247,0.173218),\n      vec3(-0.016224,0.123149,-0.035569),\n      vec3(0.067127,0.407641,0.028479)\n   );\n\n   float depth = texture2D(depthmap, vTexCoords).r;\n   if(depth == 1.0) {\n      discard;\n   }\n\n   vec4 screen_position = vec4(vTexCoords, depth,1.0);\n   vec4 pos = projinv*screen_position;\n   pos /= pos.w;\n   vec3 position = pos.xyz;\n\n   //approximate the normal from the depth map; I find this simpler\n   //than trying to recompute the exact normal within every possible object shader\n\n   //pixel offset positions in screen space\n   ivec2 dim = textureSize(depthmap,0);\n   vec2 offset1 = vec2(0.0,1.0/float(dim.y));\n   vec2 offset2 = vec2(1.0/float(dim.x),0.0);\n   float depth1 = texture2D(depthmap, vTexCoords + offset1).r;\n   float depth2 = texture2D(depthmap, vTexCoords + offset2).r;\n   \n   vec3 p1 = vec3(screen_position.xy+offset1, depth1 - depth);\n   vec3 p2 = vec3(screen_position.xy+offset2, depth2 - depth);\n\n   //convert to model space\n   vec4 pos1 = projinv*vec4(p1,1);\n   pos1 /= pos1.w;\n   vec4 pos2 = projinv*vec4(p2,1);\n   pos2 /= pos2.w;\n\n   vec3 normal = normalize(cross(pos1.xyz-position, pos2.xyz-position)); //model normal, important we normalize in model space\n\n   //pseudo random number\n\n   float occlusion = 0.0;\n   for(int c = 0; c < cycles; c++) {\n   vec3 random = normalize(pseudorandomVec3(position+float(c)));\n   for(int i=0; i < samples; i++) {\n\n      vec3 ray = radius * reflect(sample_sphere[i],random);\n      vec3 hemi_ray = position + sign(dot(ray,normal)) * ray; //model space\n      vec4 hemi_screen = projectionMatrix*vec4(hemi_ray,1.0);\n      hemi_screen /= hemi_screen.w;\n      \n      float occ_depth = texture2D(depthmap, clamp(hemi_screen.xy,0.0,1.0)).r;\n      float difference = hemi_screen.z - occ_depth;\n      \n      occlusion += step(0.0, difference) * (1.0-smoothstep(0.0, area, difference));\n   }\n   }\n   float ao = 1.0 - total_strength * occlusion * (1.0 / float(cycles*samples));\n   gl_FragDepthEXT = clamp(ao+base,0.0,1.0);\n\n}",vertexShader:"attribute vec2 vertexPosition;\nvarying highp vec2 vTexCoords;\nconst vec2 scale = vec2(0.5, 0.5);\n\nvoid main() {\n   vTexCoords  = vertexPosition * scale + scale; // scale vertex attribute to [0,1] range\n   gl_Position = vec4(vertexPosition, 0.0, 1.0);\n}\n        ",uniforms:{total_strength:{type:"f",value:1},radius:{type:"f",value:5},projinv:{type:"mat4",value:[]}}},sphereimposter:tt,sphereimposteroutline:it,sprite:rt,stickimposter:at,stickimposteroutline:ot,volumetric:ht};function dt(t){let e={};for(const r in t){e[r]={},e[r].type=t[r].type;var i=t[r].value;i instanceof b.Color?e[r].value=i.clone():"number"==typeof i?e[r].value=i:i instanceof Array?e[r].value=[]:console.error("Error copying shader uniforms from ShaderLib: unknown type for uniform")}return e}const ut={clone:dt};class Camera extends Object3D{constructor(t=50,e=1,i=.1,r=2e3,s=!1){super(),this.projectionMatrix=new l.Matrix4,this.projectionMatrixInverse=new l.Matrix4,this.matrixWorldInverse=new l.Matrix4,this.fov=t,this.aspect=e,this.near=i,this.far=r;var n=this.position.z;this.right=n*Math.tan(Math.PI/180*t),this.left=-this.right,this.top=this.right/this.aspect,this.bottom=-this.top,this.ortho=!!s,this.updateProjectionMatrix()}lookAt(t){this.matrix.lookAt(this.position,t,this.up),this.rotationAutoUpdate&&(!1===this.useQuaternion&&this.rotation instanceof l.Vector3?this.rotation.setEulerFromRotationMatrix(this.matrix,this.eulerOrder):console.error("Unimplemented math operation."))}updateProjectionMatrix(){this.ortho?this.projectionMatrix.makeOrthographic(this.left,this.right,this.top,this.bottom,this.near,this.far):this.projectionMatrix.makePerspective(this.fov,this.aspect,this.near,this.far),this.projectionMatrixInverse.getInverse(this.projectionMatrix)}}class Fog{constructor(t,e=1,i=1e3){this.name="",this.color=new b.Color(t),this.near=e,this.far=i}clone(){return new Fog(this.color.getHex(),this.near,this.far)}}class SpritePlugin{constructor(){this.sprite={vertices:null,faces:null,vertexBuffer:null,elementBuffer:null,program:null,attributes:{},uniforms:null}}init(t){this.gl=t.context,this.renderer=t,this.precision=t.getPrecision(),this.sprite.vertices=new Float32Array(16),this.sprite.faces=new Uint16Array(6);var e=0;this.sprite.vertices[e++]=-1,this.sprite.vertices[e++]=-1,this.sprite.vertices[e++]=0,this.sprite.vertices[e++]=0,this.sprite.vertices[e++]=1,this.sprite.vertices[e++]=-1,this.sprite.vertices[e++]=1,this.sprite.vertices[e++]=0,this.sprite.vertices[e++]=1,this.sprite.vertices[e++]=1,this.sprite.vertices[e++]=1,this.sprite.vertices[e++]=1,this.sprite.vertices[e++]=-1,this.sprite.vertices[e++]=1,this.sprite.vertices[e++]=0,this.sprite.vertices[e++]=1,e=0,this.sprite.faces[e++]=0,this.sprite.faces[e++]=1,this.sprite.faces[e++]=2,this.sprite.faces[e++]=0,this.sprite.faces[e++]=2,this.sprite.faces[e++]=3,this.sprite.vertexBuffer=this.gl.createBuffer(),this.sprite.elementBuffer=this.gl.createBuffer(),this.gl.bindBuffer(this.gl.ARRAY_BUFFER,this.sprite.vertexBuffer),this.gl.bufferData(this.gl.ARRAY_BUFFER,this.sprite.vertices,this.gl.STATIC_DRAW),this.gl.bindBuffer(this.gl.ELEMENT_ARRAY_BUFFER,this.sprite.elementBuffer),this.gl.bufferData(this.gl.ELEMENT_ARRAY_BUFFER,this.sprite.faces,this.gl.STATIC_DRAW),this.sprite.program=this.createProgram(ct.sprite,this.precision||1),this.sprite.attributes={};const i={};this.sprite.attributes.position=this.gl.getAttribLocation(this.sprite.program,"position"),this.sprite.attributes.uv=this.gl.getAttribLocation(this.sprite.program,"uv"),i.uvOffset=this.gl.getUniformLocation(this.sprite.program,"uvOffset"),i.uvScale=this.gl.getUniformLocation(this.sprite.program,"uvScale"),i.rotation=this.gl.getUniformLocation(this.sprite.program,"rotation"),i.scale=this.gl.getUniformLocation(this.sprite.program,"scale"),i.alignment=this.gl.getUniformLocation(this.sprite.program,"alignment"),i.color=this.gl.getUniformLocation(this.sprite.program,"color"),i.map=this.gl.getUniformLocation(this.sprite.program,"map"),i.opacity=this.gl.getUniformLocation(this.sprite.program,"opacity"),i.useScreenCoordinates=this.gl.getUniformLocation(this.sprite.program,"useScreenCoordinates"),i.screenPosition=this.gl.getUniformLocation(this.sprite.program,"screenPosition"),i.modelViewMatrix=this.gl.getUniformLocation(this.sprite.program,"modelViewMatrix"),i.projectionMatrix=this.gl.getUniformLocation(this.sprite.program,"projectionMatrix"),i.fogType=this.gl.getUniformLocation(this.sprite.program,"fogType"),i.fogDensity=this.gl.getUniformLocation(this.sprite.program,"fogDensity"),i.fogNear=this.gl.getUniformLocation(this.sprite.program,"fogNear"),i.fogFar=this.gl.getUniformLocation(this.sprite.program,"fogFar"),i.fogColor=this.gl.getUniformLocation(this.sprite.program,"fogColor"),i.alphaTest=this.gl.getUniformLocation(this.sprite.program,"alphaTest"),this.sprite.uniforms=i}render(t,e,i,r,s){var n,a,o,l,h,c,d,u,f,p;if(!this.gl)throw new Error("WebGLRenderer not initialized");let g=[];null===(n=null==t?void 0:t.__webglSprites)||void 0===n||n.forEach((t=>{(s&&0==t.material.depthTest||!s&&t.material.depthTest)&&g.push(t)}));let m=g.length;if(!m)return;const v=this.sprite.attributes,_=this.sprite.uniforms;if(!_)throw new Error("Uniforms not defined");var y=.5*i,b=.5*r;this.gl.useProgram(this.sprite.program),this.gl.enableVertexAttribArray(v.position),this.gl.enableVertexAttribArray(v.uv),this.gl.disable(this.gl.CULL_FACE),this.gl.enable(this.gl.BLEND),this.gl.bindBuffer(this.gl.ARRAY_BUFFER,this.sprite.vertexBuffer),this.gl.vertexAttribPointer(v.position,2,this.gl.FLOAT,!1,16,0),this.gl.vertexAttribPointer(v.uv,2,this.gl.FLOAT,!1,16,8),this.gl.bindBuffer(this.gl.ELEMENT_ARRAY_BUFFER,this.sprite.elementBuffer),this.gl.uniformMatrix4fv(_.projectionMatrix,!1,e.projectionMatrix.elements),this.gl.activeTexture(this.gl.TEXTURE0),this.gl.uniform1i(_.map,0);var x,w=0,A=0,C=t.fog;let S,M,z,T;C?(this.gl.uniform3f(_.fogColor,C.color.r,C.color.g,C.color.b),this.gl.uniform1f(_.fogNear,C.near),this.gl.uniform1f(_.fogFar,C.far),this.gl.uniform1i(_.fogType,1),w=1,A=1):(this.gl.uniform1i(_.fogType,0),w=0,A=0);let E=[];for(x=0;x<m;x++)S=g[x],M=S.material,M&&(0!=M.depthTest||s)&&S.visible&&0!==M.opacity&&(M.useScreenCoordinates?S.z=-S.position.z:(S._modelViewMatrix.multiplyMatrices(e.matrixWorldInverse,S.matrixWorld),S.z=-S._modelViewMatrix.elements[14]));for(g.sort(ft),x=0;x<m;x++)if(S=g[x],M=S.material,M&&S.visible&&0!==M.opacity&&M.map&&M.map.image&&M.map.image.width){this.gl.uniform1f((null==_?void 0:_.alphaTest)||null,M.alphaTest);var L=M.map.image.width,F=M.map.image.height;E[0]=L*this.renderer.devicePixelRatio/i,E[1]=F*this.renderer.devicePixelRatio/r,!0===M.useScreenCoordinates?(this.gl.uniform1i(_.useScreenCoordinates,1),this.gl.uniform3f(_.screenPosition,(S.position.x*this.renderer.devicePixelRatio-y)/y,(b-S.position.y*this.renderer.devicePixelRatio)/b,Math.max(0,Math.min(1,S.position.z)))):(this.gl.uniform1i(_.useScreenCoordinates,0),this.gl.uniformMatrix4fv(_.modelViewMatrix,!1,S._modelViewMatrix.elements)),T=t.fog&&M.fog?A:0,w!==T&&(this.gl.uniform1i(_.fogType,T),w=T),z=1/(M.scaleByViewport?r:1),E[0]*=z*S.scale.x,E[1]*=z*S.scale.y;let e=null===(a=null==M?void 0:M.alignment)||void 0===a?void 0:a.x,s=null===(o=null==M?void 0:M.alignment)||void 0===o?void 0:o.y;M.screenOffset&&(e=(e||0)+2*M.screenOffset.x/L,s=(s||0)+2*M.screenOffset.y/F),this.gl.uniform2f(_.uvScale,(null===(l=null==M?void 0:M.uvScale)||void 0===l?void 0:l.x)||1,(null===(h=null==M?void 0:M.uvScale)||void 0===h?void 0:h.y)||1),this.gl.uniform2f(_.uvOffset,(null===(c=null==M?void 0:M.uvOffset)||void 0===c?void 0:c.x)||0,(null===(d=null==M?void 0:M.uvOffset)||void 0===d?void 0:d.y)||0),this.gl.uniform2f(_.alignment,e||0,s||0),this.gl.uniform1f(_.opacity,M.opacity),this.gl.uniform3f(_.color,(null===(u=null==M?void 0:M.color)||void 0===u?void 0:u.r)||0,(null===(f=null==M?void 0:M.color)||void 0===f?void 0:f.g)||0,(null===(p=null==M?void 0:M.color)||void 0===p?void 0:p.b)||0),this.gl.uniform1f(_.rotation,S.rotation),this.gl.uniform2fv(_.scale,E),this.renderer.setDepthTest(M.depthTest),this.renderer.setDepthWrite(M.depthWrite),this.renderer.setTexture(M.map,0),this.gl.drawElements(this.gl.TRIANGLES,6,this.gl.UNSIGNED_SHORT,0)}this.gl.enable(this.gl.CULL_FACE)}createProgram(t,e){if(!this.gl)throw new Error("WebGL Rendering context not found");var i=this.gl.createProgram();if(!i)throw new Error("Error creating webgl program");var r=this.gl.createShader(this.gl.FRAGMENT_SHADER),s=this.gl.createShader(this.gl.VERTEX_SHADER);if(!r)throw new Error("Unable to create fragment shader SpritePlugin.createProgram");if(!s)throw new Error("Unable to create vertex shader SpritePlugin.createProgram");var n="precision "+e+" float;\n";if(this.gl.shaderSource(r,n+t.fragmentShader),this.gl.shaderSource(s,n+t.vertexShader),this.gl.compileShader(r),this.gl.compileShader(s),!this.gl.getShaderParameter(r,this.gl.COMPILE_STATUS)||!this.gl.getShaderParameter(s,this.gl.COMPILE_STATUS))throw new Error(`Error compiling shader: \n      ${this.gl.getShaderInfoLog(r)} \n      ${this.gl.getShaderInfoLog(s)}`);return this.gl.attachShader(i,r),this.gl.attachShader(i,s),this.gl.linkProgram(i),this.gl.getProgramParameter(i,this.gl.LINK_STATUS)||console.error("Could not initialize shader"),i}}function ft(t,e){return t.z!==e.z?e.z-t.z:e.id-t.id}var pt=null,gt=null;class Renderer{constructor(t){this.context=null,this.devicePixelRatio=1,this.sortObjects=!0,this.autoUpdateObjects=!0,this.autoUpdateScene=!0,this.info={memory:{programs:0,geometries:0,textures:0},render:{calls:0,vertices:0,faces:0,points:0}},this._offscreen=null,this._bitmap=null,this._programs=[],this._programs_counter=0,this._webglversion=1,this._currentProgram=null,this._currentMaterialId=-1,this._currentGeometryGroupHash=null,this._currentCamera=null,this._geometryGroupCounter=0,this._oldDoubleSided=-1,this._oldFlipSided=-1,this._oldDepthTest=-1,this._oldDepthWrite=-1,this._oldPolygonOffset=null,this._oldLineWidth=null,this._viewportWidth=0,this._viewportHeight=0,this._currentWidth=0,this._currentHeight=0,this._enabledAttributes={},this._vector3=new l.Vector3,this._worldInverse=new l.Matrix4,this._projInverse=new l.Matrix4,this._textureMatrix=new l.Matrix4,this._fullProjModelMatrix=new l.Matrix4,this._fullProjModelMatrixInv=new l.Matrix4,this._direction=new l.Vector3,this._lightsNeedUpdate=!0,this._lights={ambient:[0,0,0],directional:{length:0,colors:[],positions:[]},point:{length:0,colors:[],positions:[],distances:[]},spot:{length:0,colors:[],positions:[],distances:[],directions:[],anglesCos:[],exponents:[]},hemi:{length:0,skyColors:[],groundColors:[],positions:[]}},this.sprites=new SpritePlugin,this._screenshader=null,this._AOshader=null,this._blurshader=null,this._vertexattribpos=null,this._aovertexattribpos=null,this._blurvertexattribpos=null,this._screenQuadVBO=null,this._fb=null,this._targetTexture=null,this._depthTexture=null,this._shadingTexture=null,this._scratchTexture=null,this._AOstrength=1,this._AOradius=5,this.SHADE_TEXTURE=3,t=t||{},this.row=t.row,this.col=t.col,this.rows=t.rows,this.cols=t.cols,this._canvas=void 0!==t.canvas?t.canvas:document.createElement("canvas"),this._precision=void 0!==t.precision?t.precision:"highp",this._alpha=void 0===t.alpha||t.alpha,this._premultipliedAlpha=void 0===t.premultipliedAlpha||t.premultipliedAlpha,this._antialias=void 0!==t.antialias&&t.antialias,this._preserveDrawingBuffer=void 0!==t.preserveDrawingBuffer&&t.preserveDrawingBuffer,this._clearColor=void 0!==t.clearColor?new b.Color(t.clearColor):new b.Color(0),this._clearAlpha=void 0!==t.clearAlpha?t.clearAlpha:0,this._outlineMaterial=new MeshOutlineMaterial(t.outline),this._outlineSphereImposterMaterial=new SphereImposterOutlineMaterial(t.outline),this._outlineStickImposterMaterial=new StickImposterOutlineMaterial(t.outline),this._outlineEnabled=!!t.outline,this._AOEnabled=!!t.ambientOcclusion,t.ambientOcclusion&&void 0!==t.ambientOcclusion.strength&&(this._AOstrength=parseFloat(t.ambientOcclusion.strength)),0==this._AOstrength&&(this._AOEnabled=!1),t.ambientOcclusion&&void 0!==t.ambientOcclusion.radius&&(this._AOradius=parseFloat(t.ambientOcclusion.radius)),this.domElement=this._canvas,this._canvas.id=t.id,0!=t.containerWidth&&0!=t.containerHeight&&(this.initGL(),this.setDefaultGLState(),this.context=this._gl,this.isWebGL1()?this._extInstanced=this._gl.getExtension("ANGLE_instanced_arrays"):this._extInstanced={vertexAttribDivisorANGLE:this._gl.vertexAttribDivisor.bind(this._gl),drawElementsInstancedANGLE:this._gl.drawElementsInstanced.bind(this._gl)},this._extFragDepth=this._gl.getExtension("EXT_frag_depth"),this._extFloatLinear=this._gl.getExtension("OES_texture_float_linear"),this._extColorBufferFloat=this._gl.getExtension("EXT_color_buffer_float"),this.sprites.init(this))}supportedExtensions(){return{supportsAIA:Boolean(this._extInstanced),supportsImposters:Boolean(this._extFragDepth)||!this.isWebGL1(),regen:!1}}getContext(){return this._gl}getCanvas(){return this._canvas}isLost(){return null==this._gl||this._gl.isContextLost()}getPrecision(){return this._precision}setClearColorHex(t,e){this._clearColor.setHex(t),this._clearAlpha=e,this.isLost()||this._gl.clearColor(this._clearColor.r,this._clearColor.g,this._clearColor.b,this._clearAlpha)}enableOutline(t){this._outlineMaterial=new MeshOutlineMaterial(t),this._outlineSphereImposterMaterial=new SphereImposterOutlineMaterial(t),this._outlineStickImposterMaterial=new StickImposterOutlineMaterial(t),this._outlineEnabled=!0}disableOutline(){this._outlineEnabled=!1}enableAmbientOcclusion(t){t&&(t.strength&&(this._AOstrength=t.strength),t.scale&&(this._AOradius=t.scale)),this._AOEnabled=this._AOstrength>0}disableAmbientOcclusion(){this._AOEnabled=!1}setViewport(){if(this._offscreen&&(this._offscreen.width=this._canvas.width,this._offscreen.height=this._canvas.height),null!=this.rows&&null!=this.cols&&null!=this.row&&null!=this.col){var t=this._canvas.width/this.cols,e=this._canvas.height/this.rows;this._viewportWidth=t,this._viewportHeight=e,this.isLost()||(this._gl.enable(this._gl.SCISSOR_TEST),this._gl.scissor(t*this.col,e*this.row,t,e),this._gl.viewport(t*this.col,e*this.row,t,e))}}setSize(t,e){if(this.devicePixelRatio=void 0!==window.devicePixelRatio?window.devicePixelRatio:1,this._antialias&&this.devicePixelRatio<2&&(this.devicePixelRatio*=2),null!=this.rows&&null!=this.cols&&null!=this.row&&null!=this.col){var i=t/this.cols,r=e/this.rows;this._canvas.width=t*this.devicePixelRatio,this._canvas.height=e*this.devicePixelRatio,this._viewportWidth=i*this.devicePixelRatio,this._viewportHeight=r*this.devicePixelRatio,this._canvas.style.width=t+"px",this._canvas.style.height=e+"px",this.setViewport()}else this._viewportWidth=this._canvas.width=t*this.devicePixelRatio,this._viewportHeight=this._canvas.height=e*this.devicePixelRatio,this._canvas.style.width=t+"px",this._canvas.style.height=e+"px",this.isLost()||this._gl.viewport(0,0,this._gl.drawingBufferWidth,this._gl.drawingBufferHeight);this.initFrameBuffer()}clear(t,e,i){var r=0;(void 0===t||t)&&(r|=this._gl.COLOR_BUFFER_BIT),(void 0===e||e)&&(r|=this._gl.DEPTH_BUFFER_BIT),(void 0===i||i)&&(r|=this._gl.STENCIL_BUFFER_BIT),this._gl.clear(r)}setMaterialFaces(t,e){var i=t.side===a,r=t.side===n;t.imposter||(r=e?!r:r),this._oldDoubleSided!==i&&(i?this._gl.disable(this._gl.CULL_FACE):this._gl.enable(this._gl.CULL_FACE),this._oldDoubleSided=i),this._oldFlipSided!==r&&(r?this._gl.frontFace(this._gl.CW):this._gl.frontFace(this._gl.CCW),this._oldFlipSided=r),this._gl.cullFace(this._gl.BACK)}setDepthTest(t){this._oldDepthTest!==t&&(t?this._gl.enable(this._gl.DEPTH_TEST):this._gl.disable(this._gl.DEPTH_TEST),this._oldDepthTest=t)}setDepthWrite(t){this._oldDepthWrite!==t&&(this._gl.depthMask(t),this._oldDepthWrite=t)}setBlending(t){t?(this._gl.enable(this._gl.BLEND),this._gl.blendEquationSeparate(this._gl.FUNC_ADD,this._gl.FUNC_ADD),this._gl.blendFuncSeparate(this._gl.SRC_ALPHA,this._gl.ONE_MINUS_SRC_ALPHA,this._gl.ONE,this._gl.ONE_MINUS_SRC_ALPHA)):this._gl.disable(this._gl.BLEND)}initMaterial(t,e,i,r){var s,n;if(t.addEventListener("dispose",this.onMaterialDispose.bind(this)),n=t.shaderID){var a=ct[n];t.vertexShader=a.vertexShader,t.fragmentShader=a.fragmentShader,t.uniforms=ut.clone(a.uniforms),t.shaded&&t.makeShaded(this.SHADE_TEXTURE)}s={wireframe:t.wireframe,fragdepth:t.imposter,volumetric:t.volumetric,shaded:t.shaded},t.program=this.buildProgram(t.fragmentShader,t.vertexShader,t.uniforms,s)}renderBuffer(t,e,i,r,s,n){var a,o;if(r.visible&&(a=this.setProgram(t,e,i,r,n,this))){o=a.attributes;var l,h,c=!1,d=r.wireframe?1:0,u=16777215*s.id+2*a.id+d;if(u!==this._currentGeometryGroupHash&&(this._currentGeometryGroupHash=u,c=!0),c&&(this.disableAttributes(),o.position>=0&&(this._gl.bindBuffer(this._gl.ARRAY_BUFFER,s.__webglVertexBuffer),this.enableAttribute(o.position),this._gl.vertexAttribPointer(o.position,3,this._gl.FLOAT,!1,0,0)),o.color>=0&&(this._gl.bindBuffer(this._gl.ARRAY_BUFFER,s.__webglColorBuffer),this.enableAttribute(o.color),this._gl.vertexAttribPointer(o.color,3,this._gl.FLOAT,!1,0,0)),o.normal>=0&&(this._gl.bindBuffer(this._gl.ARRAY_BUFFER,s.__webglNormalBuffer),this.enableAttribute(o.normal),this._gl.vertexAttribPointer(o.normal,3,this._gl.FLOAT,!1,0,0)),o.offset>=0&&(this._gl.bindBuffer(this._gl.ARRAY_BUFFER,s.__webglOffsetBuffer),this.enableAttribute(o.offset),this._gl.vertexAttribPointer(o.offset,3,this._gl.FLOAT,!1,0,0)),o.radius>=0&&(this._gl.bindBuffer(this._gl.ARRAY_BUFFER,s.__webglRadiusBuffer),this.enableAttribute(o.radius),this._gl.vertexAttribPointer(o.radius,1,this._gl.FLOAT,!1,0,0))),n instanceof Mesh){if("instanced"===r.shaderID){var f=r.sphere.geometryGroups[0];c&&(this._gl.bindBuffer(this._gl.ARRAY_BUFFER,s.__webglVertexBuffer),this._gl.bufferData(this._gl.ARRAY_BUFFER,f.vertexArray,this._gl.STATIC_DRAW),this._gl.bindBuffer(this._gl.ARRAY_BUFFER,s.__webglNormalBuffer),this._gl.bufferData(this._gl.ARRAY_BUFFER,f.normalArray,this._gl.STATIC_DRAW),this._gl.bindBuffer(this._gl.ELEMENT_ARRAY_BUFFER,s.__webglFaceBuffer),this._gl.bufferData(this._gl.ELEMENT_ARRAY_BUFFER,f.faceArray,this._gl.STATIC_DRAW)),l=f.faceidx,this._extInstanced.vertexAttribDivisorANGLE(o.offset,1),this._extInstanced.vertexAttribDivisorANGLE(o.radius,1),this._extInstanced.vertexAttribDivisorANGLE(o.color,1),this._extInstanced.drawElementsInstancedANGLE(this._gl.TRIANGLES,l,this._gl.UNSIGNED_SHORT,0,s.radiusArray.length),this._extInstanced.vertexAttribDivisorANGLE(o.offset,0),this._extInstanced.vertexAttribDivisorANGLE(o.radius,0),this._extInstanced.vertexAttribDivisorANGLE(o.color,0)}else r.wireframe?(h=s.lineidx,this.setLineWidth(r.wireframeLinewidth),c&&this._gl.bindBuffer(this._gl.ELEMENT_ARRAY_BUFFER,s.__webglLineBuffer),this._gl.drawElements(this._gl.LINES,h,this._gl.UNSIGNED_SHORT,0)):(l=s.faceidx,c&&this._gl.bindBuffer(this._gl.ELEMENT_ARRAY_BUFFER,s.__webglFaceBuffer),this._gl.drawElements(this._gl.TRIANGLES,l,this._gl.UNSIGNED_SHORT,0));this.info.render.calls++,this.info.render.vertices+=l,this.info.render.faces+=l/3}else n instanceof Line&&(h=s.vertices,this.setLineWidth(r.linewidth),this._gl.drawArrays(this._gl.LINES,0,h),this.info.render.calls++)}}clearShading(){this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.DEPTH_ATTACHMENT,this._gl.TEXTURE_2D,this._shadingTexture,0),this.clear(!1,!0,!1),this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.DEPTH_ATTACHMENT,this._gl.TEXTURE_2D,this._depthTexture,0)}setShading(t,e,i){let r=t.__lights,s=t.fog,n=[];for(let e=0,r=t.__webglObjects.length;e<r;e++){let r=t.__webglObjects[e];r.render&&r[i]&&n.push(r)}if(0==n.length)return;this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.DEPTH_ATTACHMENT,this._gl.TEXTURE_2D,this._shadingTexture,0),this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.COLOR_ATTACHMENT0,this._gl.TEXTURE_2D,null,0),this.renderObjects(t.__webglObjects,!0,i+"Depth",e,r,s,!1),this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.DEPTH_ATTACHMENT,this._gl.TEXTURE_2D,this._scratchTexture,0),this.clear(!1,!0,!1),this._gl.useProgram(this._AOshader),this._currentProgram=this._AOshader,this.setDepthTest(-1),this.setDepthWrite(-1);let a=this._AOshader.uniforms;this._gl.uniform1f(a.total_strength,this._AOstrength),this._gl.uniform1f(a.radius,this._AOradius),this._fullProjModelMatrix=new l.Matrix4,this._fullProjModelMatrixInv=new l.Matrix4;let o=n[0].object;this._fullProjModelMatrix.multiplyMatrices(e.projectionMatrix,o._modelViewMatrix),this._fullProjModelMatrixInv.getInverse(this._fullProjModelMatrix),this._gl.uniformMatrix4fv(a.projectionMatrix,!1,this._fullProjModelMatrix.elements),this._gl.uniformMatrix4fv(a.projinv,!1,this._fullProjModelMatrixInv.elements),this._gl.bindBuffer(this._gl.ARRAY_BUFFER,this._screenQuadVBO),this._gl.enableVertexAttribArray(this._aovertexattribpos),this._gl.vertexAttribPointer(this._aovertexattribpos,2,this._gl.FLOAT,!1,0,0),this._gl.activeTexture(this._gl.TEXTURE0),this._gl.bindTexture(this._gl.TEXTURE_2D,this._shadingTexture),this._gl.drawArrays(this._gl.TRIANGLES,0,6),this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.DEPTH_ATTACHMENT,this._gl.TEXTURE_2D,this._shadingTexture,0),this.clear(!1,!0,!1),this._gl.useProgram(this._blurshader),this._currentProgram=this._blurshader,this.setDepthTest(-1),this.setDepthWrite(-1),this._gl.bindBuffer(this._gl.ARRAY_BUFFER,this._screenQuadVBO),this._gl.enableVertexAttribArray(this._blurvertexattribpos),this._gl.vertexAttribPointer(this._blurvertexattribpos,2,this._gl.FLOAT,!1,0,0),this._gl.activeTexture(this._gl.TEXTURE0),this._gl.bindTexture(this._gl.TEXTURE_2D,this._scratchTexture),this._gl.drawArrays(this._gl.TRIANGLES,0,6),this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.COLOR_ATTACHMENT0,this._gl.TEXTURE_2D,this._targetTexture,0),this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.DEPTH_ATTACHMENT,this._gl.TEXTURE_2D,this._depthTexture,0)}render(t,e){if(e instanceof Camera==!1)return void console.error("Renderer.render: camera is not an instance of Camera.");var i,r,s,n,a,o=t.__lights,l=t.fog;if(this._currentMaterialId=-1,this._lightsNeedUpdate=!0,this.autoUpdateScene&&t.updateMatrixWorld(),void 0===e.parent&&e.updateMatrixWorld(),e.matrixWorldInverse.getInverse(e.matrixWorld),this.isLost())return;this.autoUpdateObjects&&this.initWebGLObjects(t),this.info.render.calls=0,this.info.render.vertices=0,this.info.render.faces=0,this.info.render.points=0,this._currentWidth=this._viewportWidth,this._currentHeight=this._viewportHeight,this.setViewport(),this.setFrameBuffer(),this._gl.clearColor(this._clearColor.r,this._clearColor.g,this._clearColor.b,this._clearAlpha),this.clear(!0,!0,!0),a=t.__webglObjects;let h=!1,c=this._AOEnabled;for(i=0,r=a.length;i<r;i++)n=(s=a[i]).object,s.render=!1,n.visible&&(this.setupMatrices(n,e),this.unrollBufferMaterial(s),s.render=!0,s.volumetric&&(h=!0),s.hasAO&&(c=!0));if(this.setBlending(!1),c&&this.setShading(t,e,"opaque"),this.renderObjects(t.__webglObjects,!0,"opaque",e,o,l,!1),c&&this.clearShading(),this.renderSprites(t,e,!1),this.renderObjects(t.__webglObjects,!0,"transparentDepth",e,o,l,!0),this.renderObjects(t.__webglObjects,!1,"transparent",e,o,l,!0),h&&this._fb&&(this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.DEPTH_ATTACHMENT,this._gl.TEXTURE_2D,null,0),this.renderObjects(t.__webglObjects,!1,"volumetric",e,o,l,!0)),this.renderFrameBuffertoScreen(),this.setDepthTest(!0),this.setDepthWrite(!0),this.renderSprites(t,e,!0),this._bitmap){const t=this._offscreen.transferToImageBitmap();this._bitmap.transferFromImageBitmap(t),t.close()}}setFrameBuffer(){if(this.isWebGL1()||!this._fb)return;let t=this._viewportWidth,e=this._viewportHeight;this._gl.enable(this._gl.SCISSOR_TEST),this._gl.scissor(0,0,t,e),this._gl.viewport(0,0,t,e),this._gl.bindTexture(this._gl.TEXTURE_2D,this._targetTexture),this._gl.texImage2D(this._gl.TEXTURE_2D,0,this._gl.RGBA,t,e,0,this._gl.RGBA,this._gl.UNSIGNED_BYTE,null),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_MIN_FILTER,this._gl.LINEAR),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_MAG_FILTER,this._gl.LINEAR),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_WRAP_S,this._gl.CLAMP_TO_EDGE),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_WRAP_T,this._gl.CLAMP_TO_EDGE),this._gl.bindTexture(this._gl.TEXTURE_2D,this._depthTexture),this._gl.texImage2D(this._gl.TEXTURE_2D,0,this._gl.DEPTH_COMPONENT32F,t,e,0,this._gl.DEPTH_COMPONENT,this._gl.FLOAT,null),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_MIN_FILTER,this._gl.NEAREST),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_MAG_FILTER,this._gl.NEAREST),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_WRAP_S,this._gl.CLAMP_TO_EDGE),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_WRAP_T,this._gl.CLAMP_TO_EDGE),this._shadingTexture&&(this._gl.deleteTexture(this._shadingTexture),this._shadingTexture=this._gl.createTexture(),this._gl.bindTexture(this._gl.TEXTURE_2D,this._shadingTexture),this._gl.texImage2D(this._gl.TEXTURE_2D,0,this._gl.DEPTH_COMPONENT32F,t,e,0,this._gl.DEPTH_COMPONENT,this._gl.FLOAT,null),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_MIN_FILTER,this._gl.NEAREST),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_MAG_FILTER,this._gl.NEAREST),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_WRAP_S,this._gl.CLAMP_TO_EDGE),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_WRAP_T,this._gl.CLAMP_TO_EDGE),this._gl.deleteTexture(this._scratchTexture),this._scratchTexture=this._gl.createTexture(),this._gl.bindTexture(this._gl.TEXTURE_2D,this._scratchTexture),this._gl.texImage2D(this._gl.TEXTURE_2D,0,this._gl.DEPTH_COMPONENT32F,t,e,0,this._gl.DEPTH_COMPONENT,this._gl.FLOAT,null),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_MIN_FILTER,this._gl.NEAREST),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_MAG_FILTER,this._gl.NEAREST),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_WRAP_S,this._gl.CLAMP_TO_EDGE),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_WRAP_T,this._gl.CLAMP_TO_EDGE)),this._gl.bindFramebuffer(this._gl.FRAMEBUFFER,this._fb),this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.COLOR_ATTACHMENT0,this._gl.TEXTURE_2D,this._targetTexture,0),this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.DEPTH_ATTACHMENT,this._gl.TEXTURE_2D,this._shadingTexture,0),this._gl.clearDepth(1),this._gl.clear(this._gl.DEPTH_BUFFER_BIT),this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.DEPTH_ATTACHMENT,this._gl.TEXTURE_2D,this._depthTexture,0)}initFrameBuffer(){if(this.isWebGL1())return;let t=this._viewportWidth,e=this._viewportHeight;this._gl.enable(this._gl.SCISSOR_TEST),this._gl.scissor(0,0,t,e),this._gl.viewport(0,0,t,e),this._targetTexture=this._gl.createTexture(),this._depthTexture=this._gl.createTexture(),this._shadingTexture=this._gl.createTexture(),this._scratchTexture=this._gl.createTexture(),this._fb=this._gl.createFramebuffer();var i=this._antialias?ct.screenaa:ct.screen;this._screenshader=this.buildProgram(i.fragmentShader,i.vertexShader,i.uniforms,{}),this._vertexattribpos=this._gl.getAttribLocation(this._screenshader,"vertexPosition");this._screenQuadVBO=this._gl.createBuffer(),this._gl.bindBuffer(this._gl.ARRAY_BUFFER,this._screenQuadVBO),this._gl.bufferData(this._gl.ARRAY_BUFFER,new Float32Array([1,1,-1,1,-1,-1,-1,-1,1,-1,1,1]),this._gl.STATIC_DRAW);let r=ct.ssao;this._AOshader=this.buildProgram(r.fragmentShader,r.vertexShader,r.uniforms,{}),this._aovertexattribpos=this._gl.getAttribLocation(this._AOshader,"vertexPosition");let s=ct.blur;this._blurshader=this.buildProgram(s.fragmentShader,s.vertexShader,s.uniforms,{}),this._blurvertexattribpos=this._gl.getAttribLocation(this._blurshader,"vertexPosition")}renderFrameBuffertoScreen(){this.isWebGL1()||null===this._fb||(this.setViewport(),this._gl.bindFramebuffer(this._gl.FRAMEBUFFER,null),this._gl.clear(this._gl.COLOR_BUFFER_BIT|this._gl.DEPTH_BUFFER_BIT),this._gl.frontFace(this._gl.CCW),this._gl.cullFace(this._gl.BACK),this._gl.useProgram(this._screenshader),this._currentProgram=this._screenshader,this.setDepthTest(-1),this.setDepthWrite(-1),this._gl.bindBuffer(this._gl.ARRAY_BUFFER,this._screenQuadVBO),this._gl.enableVertexAttribArray(this._vertexattribpos),this._gl.vertexAttribPointer(this._vertexattribpos,2,this._gl.FLOAT,!1,0,0),this._gl.activeTexture(this._gl.TEXTURE0),this._gl.bindTexture(this._gl.TEXTURE_2D,this._targetTexture),this._gl.drawArrays(this._gl.TRIANGLES,0,6))}initWebGLObjects(t){if(t.__webglObjects||(t.__webglObjects=[],t.__webglObjectsImmediate=[],t.__webglSprites=[],t.__webglFlares=[]),t.__objectsAdded.length){for(;t.__objectsAdded.length;)this.addObject(t.__objectsAdded[0],t),t.__objectsAdded.splice(0,1);this._currentGeometryGroupHash=-1}for(;t.__objectsRemoved.length;)this.removeObject(t.__objectsRemoved[0],t),t.__objectsRemoved.splice(0,1);for(var e=0,i=t.__webglObjects.length;e<i;e++)this.updateObject(t.__webglObjects[e].object)}getYRatio(){return void 0!==this.rows&&void 0!==this.row?this.rows:1}getXRatio(){return void 0!==this.cols&&void 0!==this.col?this.cols:1}getAspect(t,e){null!=t&&null!=e||(t=this._canvas.width,e=this._canvas.height);var i=t/e;null!=this.rows&&null!=this.cols&&null!=this.row&&null!=this.col&&(i=t/this.cols/(e/this.rows));return i}setTexture(t,e,i){if(t.needsUpdate){t.__webglInit||(t.__webglInit=!0,t.addEventListener("dispose",this.onTextureDispose.bind(this)),t.__webglTexture=this._gl.createTexture(),this.info.memory.textures++),this._gl.activeTexture(this._gl.TEXTURE0+e);var r=i?this._gl.TEXTURE_3D:this._gl.TEXTURE_2D;this._gl.bindTexture(r,t.__webglTexture),this._gl.pixelStorei(this._gl.UNPACK_FLIP_Y_WEBGL,t.flipY),this._gl.pixelStorei(this._gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL,t.premultiplyAlpha),this._gl.pixelStorei(this._gl.UNPACK_ALIGNMENT,t.unpackAlignment),this._gl.pixelStorei(this._gl.PACK_ALIGNMENT,t.unpackAlignment);var s=this.paramToGL(t.format),n=this.paramToGL(t.type);if(i)this.setTextureParameters(this._gl.TEXTURE_3D,t),this._gl.texImage3D(this._gl.TEXTURE_3D,0,this._gl.R32F,t.image.size.z,t.image.size.y,t.image.size.x,0,this._gl.RED,this._gl.FLOAT,t.image.data);else{var a=t.image,o=a.width,l=a.height;void 0===o&&(o=a.length,s==this._gl.RGBA&&(o/=4),l=1),this.setTextureParameters(this._gl.TEXTURE_2D,t),this.isWebGL1()?this._gl.texImage2D(this._gl.TEXTURE_2D,0,s,s,n,t.image):this._gl.texImage2D(this._gl.TEXTURE_2D,0,s,o,l,0,s,n,t.image)}t.needsUpdate=!1,t.onUpdate&&t.onUpdate()}else this._gl.activeTexture(this._gl.TEXTURE0+e),i?this._gl.bindTexture(this._gl.TEXTURE_3D,t.__webglTexture):this._gl.bindTexture(this._gl.TEXTURE_2D,t.__webglTexture)}supportsVolumetric(){return!this.isWebGL1()}enableAttribute(t){this._enabledAttributes[t]||(this._gl.enableVertexAttribArray(t),this._enabledAttributes[t]=!0)}disableAttributes(){for(let t in this._enabledAttributes)this._enabledAttributes[t]&&(this._gl.disableVertexAttribArray(t),this._enabledAttributes[t]=!1)}setPolygonOffset(t,e,i){this._oldPolygonOffset!==t&&(t?this._gl.enable(this._gl.POLYGON_OFFSET_FILL):this._gl.disable(this._gl.POLYGON_OFFSET_FILL))}setLineWidth(t){t!==this._oldLineWidth&&(this._gl.lineWidth(t),this._oldLineWidth=t)}deallocateGeometry(t){if(t.__webglInit=void 0,void 0!==t.__webglVertexBuffer&&this._gl.deleteBuffer(t.__webglVertexBuffer),void 0!==t.__webglColorBuffer&&this._gl.deleteBuffer(t.__webglColorBuffer),void 0!==t.geometryGroups)for(var e=0,i=t.groups;e<i;e++){var r=t.geometryGroups[e];void 0!==r.__webglVertexBuffer&&this._gl.deleteBuffer(r.__webglVertexBuffer),void 0!==r.__webglColorBuffer&&this._gl.deleteBuffer(r.__webglColorBuffer),void 0!==r.__webglNormalBuffer&&this._gl.deleteBuffer(r.__webglNormalBuffer),void 0!==r.__webglFaceBuffer&&this._gl.deleteBuffer(r.__webglFaceBuffer),void 0!==r.__webglLineBuffer&&this._gl.deleteBuffer(r.__webglLineBuffer)}}deallocateMaterial(t){var e=t.program;if(void 0!==e){var i,r,s;t.program=void 0;var n=!1;for(i=0,r=this._programs.length;i<r;i++)if((s=this._programs[i]).program===e){s.usedTimes--,0===s.usedTimes&&(n=!0);break}if(!0===n){var a=[];for(i=0,r=this._programs.length;i<r;i++)(s=this._programs[i]).program!==e&&a.push(s);this._programs=a,this._gl.deleteProgram(e),this.info.memory.programs--}}}deallocateTexture(t){if(t.image&&t.image.__webglTextureCube)this._gl.deleteTexture(t.image.__webglTextureCube);else{if(!t.__webglInit)return;t.__webglInit=!1,this._gl.deleteTexture(t.__webglTexture)}}onGeometryDispose(t){var e=t.target;e.removeEventListener("dispose",this.onGeometryDispose),this.deallocateGeometry(e),this.info.memory.geometries--}onTextureDispose(t){var e=t.target;e.removeEventListener("dispose",this.onTextureDispose),this.deallocateTexture(e),this.info.memory.textures--}onMaterialDispose(t){var e=t.target;e.removeEventListener("dispose",this.onMaterialDispose),this.deallocateMaterial(e)}getShader(t,e){var i;return this.isWebGL1()||e.startsWith("#version")||(e=e.replace(/gl_FragDepthEXT/g,"gl_FragDepth"),e="#version 300 es\n"+(e=(e=(e=(e=(e="fragment"==t?e.replace(/varying/g,"in"):e.replace(/varying/g,"out")).replace(/attribute/g,"in")).replace(/texture2D/g,"texture")).replace(/\/\/DEFINEFRAGCOLOR/g,"out vec4 glFragColor;")).replace(/gl_FragColor/g,"glFragColor"))),"fragment"===t?i=this._gl.createShader(this._gl.FRAGMENT_SHADER):"vertex"===t&&(i=this._gl.createShader(this._gl.VERTEX_SHADER)),null==i?null:(this._gl.shaderSource(i,e),this._gl.compileShader(i),this._gl.getShaderParameter(i,this._gl.COMPILE_STATUS)?i:(console.error(this._gl.getShaderInfoLog(i)),console.error("could not initialize shader"),null))}buildProgram(t,e,i,r){var s,n,a,o,l=[];for(s in l.push(t),l.push(e),r)l.push(s),l.push(r[s]);for(o=l.join(),s=0,n=this._programs.length;s<n;s++){var h=this._programs[s];if(h.code===o)return h.usedTimes++,h.program}if(this.isWebGL1()&&r.volumetric)throw new Error("Volumetric rendering requires webgl2 which is not supported by your hardware.");if(null==(a=this._gl.createProgram()))return null;var c,d,u,f="precision "+this._precision+" float;",p=[r.volumetric?"#version 300 es":"",f].join("\n"),g=[r.volumetric?"#version 300 es":"",r.fragdepth&&this.isWebGL1()?"#extension GL_EXT_frag_depth: enable":"",r.shaded?"#define SHADED 1":"",r.wireframe?"#define WIREFRAME 1":"",f].join("\n"),m=this.getShader("fragment",g+t),v=this.getShader("vertex",p+e);for(d in null!=v&&this._gl.attachShader(a,v),null!=m&&this._gl.attachShader(a,m),this._gl.linkProgram(a),this._gl.getProgramParameter(a,this._gl.LINK_STATUS)||console.error("Could not initialize shader"),a.uniforms={},a.attributes={},c=["viewMatrix","modelViewMatrix","projectionMatrix","normalMatrix","vWidth","vHeight"],i)c.push(d);for(u=0;u<c.length;u++){var _=c[u];a.uniforms[_]=this._gl.getUniformLocation(a,_)}for(c=["position","normal","color","lineDistance","offset","radius"],u=0;u<c.length;u++){var y=c[u];a.attributes[y]=this._gl.getAttribLocation(a,y)}return a.id=this._programs_counter++,this._programs.push({program:a,code:o,usedTimes:1}),this.info.memory.programs=this._programs.length,a}setProgram(t,e,i,r,s,n){if(r.needsUpdate&&(r.program&&this.deallocateMaterial(r),this.initMaterial(r,e,i,s),r.needsUpdate=!1),null==r.program)return null;var a=!1,o=r.program,l=o.uniforms,h=r.uniforms;if(o!=this._currentProgram&&(this._gl.useProgram(o),this._currentProgram=o,a=!0),r.id!=this._currentMaterialId&&(this._currentMaterialId=r.id,a=!0),t!=this._currentCamera&&(this._currentCamera=t,a=!0),l.projectionMatrix&&this._gl.uniformMatrix4fv(l.projectionMatrix,!1,t.projectionMatrix.elements),l.modelViewMatrix&&this._gl.uniformMatrix4fv(l.modelViewMatrix,!1,s._modelViewMatrix.elements),l.normalMatrix&&this._gl.uniformMatrix3fv(l.normalMatrix,!1,s._normalMatrix.elements),l.projinv&&(this._projInverse.getInverse(t.projectionMatrix),this._gl.uniformMatrix4fv(l.projinv,!1,this._projInverse.elements)),l.viewMatrix&&this._gl.uniformMatrix4fv(l.viewMatrix,!1,t.matrixWorldInverse.elements),l.vWidth&&this._gl.uniform1f(l.vWidth,this._viewportWidth),l.vHeight&&this._gl.uniform1f(l.vHeight,this._viewportHeight),a){if(h.fogColor.value=i.color,h.fogNear.value=i.near,h.fogFar.value=i.far,r.shaderID.startsWith("lambert")||"instanced"===r.shaderID||r.shaderID.endsWith("imposter"))this._lightsNeedUpdate&&(this.setupLights(o,e),this._lightsNeedUpdate=!1),h.directionalLightColor.value=this._lights.directional.colors,h.directionalLightDirection.value=this._lights.directional.positions;else if(r.shaderID.endsWith("outline"))h.outlineColor.value=r.outlineColor,h.outlineWidth.value=r.outlineWidth,h.outlinePushback.value=r.outlinePushback,h.outlineMaxPixels.value=r.outlineMaxPixels*this.devicePixelRatio;else if("volumetric"===r.shaderID){s._modelViewMatrix.getScale(this._direction),this._worldInverse.getInverse(s._modelViewMatrix),this._projInverse.getInverse(t.projectionMatrix),this._textureMatrix.multiplyMatrices(s.material.texmatrix,this._worldInverse),this._gl.uniformMatrix4fv(l.textmat,!1,this._textureMatrix.elements),this._gl.uniformMatrix4fv(l.projinv,!1,this._projInverse.elements);let e=Math.min(Math.min(this._direction.x,this._direction.y),this._direction.z);h.step.value=s.material.unit*e,h.maxdepth.value=s.material.maxdepth*e,h.transfermax.value=s.material.transfermax,h.transfermin.value=s.material.transfermin,h.subsamples.value=s.material.subsamples,n.setTexture(s.material.transferfn,4,!1),n.setTexture(s.material.map,3,!0),this._gl.activeTexture(this._gl.TEXTURE5),this._gl.bindTexture(this._gl.TEXTURE_2D,this._depthTexture)}h.opacity.value=r.opacity,this.loadMaterialUniforms(l,h)}return h.shading&&(3==h.shading.value?(this._gl.activeTexture(this._gl.TEXTURE0+this.SHADE_TEXTURE),this._gl.bindTexture(this._gl.TEXTURE_2D,this._shadingTexture)):console.error("Invalid shading textures.")),o}loadMaterialUniforms(t,e){var i,r,s,n;for(i in e)t[i]&&(r=e[i].type,s=e[i].value,n=t[i],"f"===r?this._gl.uniform1f(n,s):"i"===r?this._gl.uniform1i(n,s):"fv"===r?this._gl.uniform3fv(n,s):"c"===r?this._gl.uniform3f(n,s.r,s.g,s.b):"f4"===r&&this._gl.uniform4f(n,s[0],s[1],s[2],s[3]))}addObject(t,e){var i,r,s,n;if(!t.__webglInit&&(t.__webglInit=!0,t._modelViewMatrix=new l.Matrix4,t._normalMatrix=new l.Matrix3,void 0!==t.geometry&&void 0===t.geometry.__webglInit&&(t.geometry.__webglInit=!0,t.geometry.addEventListener("dispose",this.onGeometryDispose.bind(this))),t instanceof Mesh||t instanceof Line))for(i=0,r=(s=t.geometry).geometryGroups.length;i<r;i++)(n=s.geometryGroups[i]).id=this._geometryGroupCounter++,n.__webglVertexBuffer||(t instanceof Mesh?(this.createMeshBuffers(n),s.elementsNeedUpdate=!0,s.normalsNeedUpdate=!0):t instanceof Line&&this.createLineBuffers(n),s.verticesNeedUpdate=!0,s.colorsNeedUpdate=!0);if(!t.__webglActive){if(t instanceof Mesh||t instanceof Line)for(i=0,r=(s=t.geometry).geometryGroups.length;i<r;i++)n=s.geometryGroups[i],this.addBuffer(e.__webglObjects,n,t);else t instanceof Sprite&&e.__webglSprites.push(t);t.__webglActive=!0}}updateObject(t){var e,i=t.geometry;if(t instanceof Mesh||t instanceof Line){for(var r=0,s=i.geometryGroups.length;r<s;r++)e=i.geometryGroups[r],(i.verticesNeedUpdate||i.elementsNeedUpdate||i.colorsNeedUpdate||i.normalsNeedUpdate)&&this.setBuffers(e,this._gl.STATIC_DRAW);i.verticesNeedUpdate=!1,i.elementsNeedUpdate=!1,i.normalsNeedUpdate=!1,i.colorsNeedUpdate=!1,i.buffersNeedUpdate=!1}}removeObject(t,e){t instanceof Mesh||t instanceof Line?this.removeInstances(e.__webglObjects,t):t instanceof Sprite&&this.removeInstancesDirect(e.__webglSprites,t),t.__webglActive=!1}removeInstances(t,e){for(var i=t.length-1;i>=0;--i)t[i].object===e&&t.splice(i,1)}removeInstancesDirect(t,e){for(var i=t.length-1;i>=0;--i)t[i]===e&&t.splice(i,1)}unrollBufferMaterial(t){var e=t.object.material;if(e.volumetric)t.opaque=null,t.transparent=null,t.volumetric=e;else if(e.transparent){if(t.opaque=null,t.volumetric=null,t.transparent=e,!e.wireframe){let i=e.clone();i.opacity=0,t.transparentDepth=i}}else{if(t.opaque=e,t.transparent=null,t.volumetric=null,!e.wireframe){let i=e.clone();i.opacity=0,t.opaqueDepth=i}e.hasAO&&(t.hasAO=!0),(this._AOEnabled||t.hasAO)&&(t.opaqueShaded=e.clone(),t.opaqueShaded.shaded=!0)}}setBuffers(t,e){var i=t.vertexArray,r=t.colorArray;if(void 0!==t.__webglOffsetBuffer?(this._gl.bindBuffer(this._gl.ARRAY_BUFFER,t.__webglOffsetBuffer),this._gl.bufferData(this._gl.ARRAY_BUFFER,i,e)):(this._gl.bindBuffer(this._gl.ARRAY_BUFFER,t.__webglVertexBuffer),this._gl.bufferData(this._gl.ARRAY_BUFFER,i,e)),this._gl.bindBuffer(this._gl.ARRAY_BUFFER,t.__webglColorBuffer),this._gl.bufferData(this._gl.ARRAY_BUFFER,r,e),t.normalArray&&void 0!==t.__webglNormalBuffer){var s=t.normalArray;this._gl.bindBuffer(this._gl.ARRAY_BUFFER,t.__webglNormalBuffer),this._gl.bufferData(this._gl.ARRAY_BUFFER,s,e)}if(t.radiusArray&&void 0!==t.__webglRadiusBuffer&&(this._gl.bindBuffer(this._gl.ARRAY_BUFFER,t.__webglRadiusBuffer),this._gl.bufferData(this._gl.ARRAY_BUFFER,t.radiusArray,e)),t.faceArray&&void 0!==t.__webglFaceBuffer){var n=t.faceArray;this._gl.bindBuffer(this._gl.ELEMENT_ARRAY_BUFFER,t.__webglFaceBuffer),this._gl.bufferData(this._gl.ELEMENT_ARRAY_BUFFER,n,e)}if(t.lineArray&&void 0!==t.__webglLineBuffer){var a=t.lineArray;this._gl.bindBuffer(this._gl.ELEMENT_ARRAY_BUFFER,t.__webglLineBuffer),this._gl.bufferData(this._gl.ELEMENT_ARRAY_BUFFER,a,e)}}createMeshBuffers(t){t.radiusArray&&(t.__webglRadiusBuffer=this._gl.createBuffer()),t.useOffset&&(t.__webglOffsetBuffer=this._gl.createBuffer()),t.__webglVertexBuffer=this._gl.createBuffer(),t.__webglNormalBuffer=this._gl.createBuffer(),t.__webglColorBuffer=this._gl.createBuffer(),t.__webglFaceBuffer=this._gl.createBuffer(),t.__webglLineBuffer=this._gl.createBuffer(),this.info.memory.geometries++}createLineBuffers(t){t.__webglVertexBuffer=this._gl.createBuffer(),t.__webglColorBuffer=this._gl.createBuffer(),this.info.memory.geometries++}addBuffer(t,e,i){t.push({buffer:e,object:i,opaque:null,transparent:null})}setupMatrices(t,e){t._modelViewMatrix.multiplyMatrices(e.matrixWorldInverse,t.matrixWorld),t._normalMatrix.getInverse(t._modelViewMatrix),t._normalMatrix.transpose()}filterFallback(t){return this._gl.LINEAR}setTextureParameters(t,e){t==this._gl.TEXTURE_2D?(this._gl.texParameteri(t,this._gl.TEXTURE_WRAP_S,this._gl.CLAMP_TO_EDGE),this._gl.texParameteri(t,this._gl.TEXTURE_WRAP_T,this._gl.CLAMP_TO_EDGE),this._gl.texParameteri(t,this._gl.TEXTURE_MAG_FILTER,this.filterFallback(e.magFilter)),this._gl.texParameteri(t,this._gl.TEXTURE_MIN_FILTER,this.filterFallback(e.minFilter))):(this._gl.texParameteri(t,this._gl.TEXTURE_WRAP_S,this._gl.CLAMP_TO_EDGE),this._gl.texParameteri(t,this._gl.TEXTURE_WRAP_T,this._gl.CLAMP_TO_EDGE),this._gl.texParameteri(t,this._gl.TEXTURE_WRAP_R,this._gl.CLAMP_TO_EDGE),this._extColorBufferFloat&&this._extFloatLinear?(this._gl.texParameteri(t,this._gl.TEXTURE_MAG_FILTER,this._gl.LINEAR),this._gl.texParameteri(t,this._gl.TEXTURE_MIN_FILTER,this._gl.LINEAR)):(this._gl.texParameteri(t,this._gl.TEXTURE_MAG_FILTER,this._gl.NEAREST),this._gl.texParameteri(t,this._gl.TEXTURE_MIN_FILTER,this._gl.NEAREST)))}paramToGL(t){return t===p?this._gl.UNSIGNED_BYTE:t===m?this._gl.RGBA:t===u?this._gl.NEAREST:0}setupLights(t,e){var i,r,s,n,a,o=this._lights,l=o.directional.colors,h=o.directional.positions,c=0,d=0;for(i=0,r=e.length;i<r;i++)if(n=(s=e[i]).color,a=s.intensity,s instanceof Light){if(this._direction.getPositionFromMatrix(s.matrixWorld),this._vector3.getPositionFromMatrix(s.target.matrixWorld),this._direction.sub(this._vector3),this._direction.normalize(),0===this._direction.x&&0===this._direction.y&&0===this._direction.z)continue;h[d]=this._direction.x,h[d+1]=this._direction.y,h[d+2]=this._direction.z,l[d]=n.r*a,l[d+1]=n.g*a,l[d+2]=n.b*a,d+=3,c++}o.ambient[0]=0,o.ambient[1]=0,o.ambient[2]=0,o.directional.length=c}initGL(){try{if(!OffscreenCanvas||null!=this.rows&&null!=this.cols&&null!=this.row&&null!=this.col){if(!(this._gl=this._canvas.getContext("webgl2",{alpha:this._alpha,premultipliedAlpha:this._premultipliedAlpha,antialias:this._antialias,preserveDrawingBuffer:this._preserveDrawingBuffer}))&&!(this._gl=this._canvas.getContext("experimental-webgl",{alpha:this._alpha,premultipliedAlpha:this._premultipliedAlpha,antialias:this._antialias,preserveDrawingBuffer:this._preserveDrawingBuffer}))&&!(this._gl=this._canvas.getContext("webgl",{alpha:this._alpha,premultipliedAlpha:this._premultipliedAlpha,antialias:this._antialias,preserveDrawingBuffer:this._preserveDrawingBuffer})))throw"Error creating WebGL context."}else(null==gt||gt.isContextLost())&&(pt=new OffscreenCanvas(this._canvas.width,this._canvas.height),gt=pt.getContext("webgl2",{alpha:!0,premultipliedAlpha:this._premultipliedAlpha,antialias:this._antialias,preserveDrawingBuffer:this._preserveDrawingBuffer})),this._offscreen=pt,this._gl=gt,this._bitmap=this._canvas.getContext("bitmaprenderer",{alpha:!0});var t=this._gl.getParameter(this._gl.VERSION);this._webglversion=parseInt(t[6])}catch(t){console.error(t)}}isWebGL1(){return 1==this._webglversion}setDefaultGLState(){this._gl.clearDepth(1),this._gl.clearStencil(0),this._gl.enable(this._gl.DEPTH_TEST),this._gl.depthFunc(this._gl.LEQUAL),this._gl.frontFace(this._gl.CCW),this._gl.cullFace(this._gl.BACK),this._gl.enable(this._gl.CULL_FACE),this._gl.enable(this._gl.BLEND),this._gl.blendEquation(this._gl.FUNC_ADD),this._gl.blendFunc(this._gl.SRC_ALPHA,this._gl.ONE_MINUS_SRC_ALPHA),this._gl.clearColor(this._clearColor.r,this._clearColor.g,this._clearColor.b,this._clearAlpha)}renderObjects(t,e,i,r,s,n,a){var o,l,h,c,d,u,f;e?(d=t.length-1,u=-1,f=-1):(d=0,u=t.length,f=1);for(var p=d;p!==u;p+=f)if((o=t[p]).render){if(l=o.object,h=o.buffer,c=o[i],(o.hasAO||this._AOEnabled)&&o[i+"Shaded"]&&(c=o[i+"Shaded"]),!c)continue;this.setBlending(a),this.setDepthTest(c.depthTest),this.setDepthWrite(c.depthWrite),this.setPolygonOffset(c.polygonOffset,c.polygonOffsetFactor,c.polygonOffsetUnits);var g=l._modelViewMatrix.isReflected();if(this.setMaterialFaces(c,g),this.renderBuffer(r,s,n,c,h,l),(this._outlineEnabled||c.outline)&&!c.wireframe&&"basic"!==c.shaderID&&0!==c.opacity){let t=this._outlineMaterial;"sphereimposter"==c.shaderID?t=this._outlineSphereImposterMaterial:"stickimposter"==c.shaderID&&(t=this._outlineStickImposterMaterial),this.renderBuffer(r,s,n,t,h,l)}}}renderSprites(t,e,i){this._currentGeometryGroupHash=-1,this._currentProgram=null,this._currentCamera=null,this._oldDepthWrite=-1,this._oldDepthTest=-1,this._oldDoubleSided=-1,this._currentMaterialId=-1,this._oldFlipSided=-1,this._lightsNeedUpdate=!0,this.sprites.render(t,e,this._currentWidth,this._currentHeight,i),this._currentGeometryGroupHash=-1,this._currentProgram=null,this._currentCamera=null,this._oldDepthWrite=-1,this._oldDepthTest=-1,this._oldDoubleSided=-1,this._currentMaterialId=-1,this._oldFlipSided=-1}}},529:(t,e,i)=>{"use strict";i.r(e),i.d(e,{Matrix3:()=>Matrix3,Matrix4:()=>Matrix4,Quaternion:()=>Quaternion,Ray:()=>Ray,Vector2:()=>Vector2,Vector3:()=>Vector3,clamp:()=>r,conversionMatrix3:()=>d,degToRad:()=>n});class Quaternion{constructor(t,e,i,r){this.x=t||0,this.y=e||0,this.z=i||0,this.w=void 0!==r?r:1}set(t,e,i,r){return this.x=t,this.y=e,this.z=i,this.w=r,this}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this.w=t.w,this}conjugate(){return this.x*=-1,this.y*=-1,this.z*=-1,this}inverse(){return this.conjugate().normalize()}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w)}lengthxyz(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)}normalize(){let t=this.length();return 0===t?(this.x=0,this.y=0,this.z=0,this.w=1):(t=1/t,this.x*=t,this.y*=t,this.z*=t,this.w*=t),this}multiply(t){return this.multiplyQuaternions(this,t)}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this.w*=t,this}multiplyQuaternions(t,e){const i=t.x,r=t.y,s=t.z,n=t.w,a=e.x,o=e.y,l=e.z,h=e.w;return this.x=i*h+n*a+r*l-s*o,this.y=r*h+n*o+s*a-i*l,this.z=s*h+n*l+i*o-r*a,this.w=n*h-i*a-r*o-s*l,this}sub(t){return this.x-=t.x,this.y-=t.y,this.z-=t.z,this.w-=t.w,this}clone(){return new Quaternion(this.x,this.y,this.z,this.w)}setFromEuler(t){const e=Math.cos(t.x/2),i=Math.cos(t.y/2),r=Math.cos(t.z/2),s=Math.sin(t.x/2),n=Math.sin(t.y/2),a=Math.sin(t.z/2);return this.x=s*i*r+e*n*a,this.y=e*n*r-s*i*a,this.z=e*i*a+s*n*r,this.w=e*i*r-s*n*a,this}}class Vector2{constructor(t,e){this.x=t||0,this.y=e||0}set(t,e){return this.x=t,this.y=e,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this}copy(t){return this.x=t.x,this.y=t.y,this}clone(){return new Vector2(this.x,this.y)}}function r(t,e,i){return Math.min(Math.max(t,e),i)}const s=Math.PI/180;function n(t){return t*s}var a,o,l,h,c;class Matrix4{constructor(t=1,e=0,i=0,r=0,s=0,n=1,a=0,o=0,l=0,h=0,c=1,d=0,u=0,f=0,p=0,g=1){void 0!==t&&"number"!=typeof t?this.elements=new Float32Array(t):(this.elements=new Float32Array(16),this.elements[0]=t,this.elements[4]=e,this.elements[8]=i,this.elements[12]=r,this.elements[1]=s,this.elements[5]=n,this.elements[9]=a,this.elements[13]=o,this.elements[2]=l,this.elements[6]=h,this.elements[10]=c,this.elements[14]=d,this.elements[3]=u,this.elements[7]=f,this.elements[11]=p,this.elements[15]=g)}makeScale(t,e,i){throw new Error("Method not implemented.")}set(t,e,i,r,s,n,a,o,l,h,c,d,u,f,p,g){const m=this.elements;return m[0]=t,m[4]=e,m[8]=i,m[12]=r,m[1]=s,m[5]=n,m[9]=a,m[13]=o,m[2]=l,m[6]=h,m[10]=c,m[14]=d,m[3]=u,m[7]=f,m[11]=p,m[15]=g,this}identity(){return this.set(1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1),this}copy(t){const e=t.elements;return this.set(e[0],e[4],e[8],e[12],e[1],e[5],e[9],e[13],e[2],e[6],e[10],e[14],e[3],e[7],e[11],e[15]),this}matrix3FromTopLeft(){const t=this.elements;return new Matrix3(t[0],t[4],t[8],t[1],t[5],t[9],t[2],t[6],t[10])}setRotationFromEuler(t,e){const i=this.elements,{x:r,y:s,z:n}=t,a=Math.cos(r),o=Math.sin(r),l=Math.cos(s),h=Math.sin(s),c=Math.cos(n),d=Math.sin(n);if(void 0===e||"XYZ"===e){const t=a*c,e=a*d,r=o*c,s=o*d;i[0]=l*c,i[4]=-l*d,i[8]=h,i[1]=e+r*h,i[5]=t-s*h,i[9]=-o*l,i[2]=s-t*h,i[6]=r+e*h,i[10]=a*l}else console.error(`Error with matrix4 setRotationFromEuler. Order: ${e}`);return this}setRotationFromQuaternion(t){const e=this.elements,{x:i,y:r,z:s,w:n}=t,a=i+i,o=r+r,l=s+s,h=i*a,c=i*o,d=i*l,u=r*o,f=r*l,p=s*l,g=n*a,m=n*o,v=n*l;return e[0]=1-(u+p),e[4]=c-v,e[8]=d+m,e[1]=c+v,e[5]=1-(h+p),e[9]=f-g,e[2]=d-m,e[6]=f+g,e[10]=1-(h+u),this}multiplyMatrices(t,e){const i=t.elements,r=e.elements,s=this.elements,n=i[0],a=i[4],o=i[8],l=i[12],h=i[1],c=i[5],d=i[9],u=i[13],f=i[2],p=i[6],g=i[10],m=i[14],v=i[3],_=i[7],y=i[11],b=i[15],x=r[0],w=r[4],A=r[8],C=r[12],S=r[1],M=r[5],z=r[9],T=r[13],E=r[2],L=r[6],F=r[10],I=r[14],O=r[3],D=r[7],k=r[11],R=r[15];return s[0]=n*x+a*S+o*E+l*O,s[4]=n*w+a*M+o*L+l*D,s[8]=n*A+a*z+o*F+l*k,s[12]=n*C+a*T+o*I+l*R,s[1]=h*x+c*S+d*E+u*O,s[5]=h*w+c*M+d*L+u*D,s[9]=h*A+c*z+d*F+u*k,s[13]=h*C+c*T+d*I+u*R,s[2]=f*x+p*S+g*E+m*O,s[6]=f*w+p*M+g*L+m*D,s[10]=f*A+p*z+g*F+m*k,s[14]=f*C+p*T+g*I+m*R,s[3]=v*x+_*S+y*E+b*O,s[7]=v*w+_*M+y*L+b*D,s[11]=v*A+_*z+y*F+b*k,s[15]=v*C+_*T+y*I+b*R,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}makeTranslation(t,e,i){return this.set(1,0,0,t,0,1,0,e,0,0,1,i,0,0,0,1),this}snap(t){t||(t=4);const e=Math.pow(10,4),i=this.elements;for(let t=0;t<16;t++){const r=Math.round(i[t]);r===Math.round(i[t]*e)/e&&(i[t]=r)}return this}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t){const e=this.elements;return e[12]=t.x,e[13]=t.y,e[14]=t.z,this}translate(t){const e=this.elements;return e[12]+=t.x,e[13]+=t.y,e[14]+=t.z,this}getInverse(t,e){const i=this.elements,r=t.elements,s=r[0],n=r[4],a=r[8],o=r[12],l=r[1],h=r[5],c=r[9],d=r[13],u=r[2],f=r[6],p=r[10],g=r[14],m=r[3],v=r[7],_=r[11],y=r[15];i[0]=c*g*v-d*p*v+d*f*_-h*g*_-c*f*y+h*p*y,i[4]=o*p*v-a*g*v-o*f*_+n*g*_+a*f*y-n*p*y,i[8]=a*d*v-o*c*v+o*h*_-n*d*_-a*h*y+n*c*y,i[12]=o*c*f-a*d*f-o*h*p+n*d*p+a*h*g-n*c*g,i[1]=d*p*m-c*g*m-d*u*_+l*g*_+c*u*y-l*p*y,i[5]=a*g*m-o*p*m+o*u*_-s*g*_-a*u*y+s*p*y,i[9]=o*c*m-a*d*m-o*l*_+s*d*_+a*l*y-s*c*y,i[13]=a*d*u-o*c*u+o*l*p-s*d*p-a*l*g+s*c*g,i[2]=h*g*m-d*f*m+d*u*v-l*g*v-h*u*y+l*f*y,i[6]=o*f*m-n*g*m-o*u*v+s*g*v+n*u*y-s*f*y,i[10]=n*d*m-o*h*m+o*l*v-s*d*v-n*l*y+s*h*y,i[14]=o*h*u-n*d*u-o*l*f+s*d*f+n*l*g-s*h*g,i[3]=c*f*m-h*p*m-c*u*v+l*p*v+h*u*_-l*f*_,i[7]=n*p*m-a*f*m+a*u*v-s*p*v-n*u*_+s*f*_,i[11]=a*h*m-n*c*m-a*l*v+s*c*v+n*l*_-s*h*_,i[15]=n*c*u-a*h*u+a*l*f-s*c*f-n*l*p+s*h*p;const b=s*i[0]+l*i[4]+u*i[8]+m*i[12];if(0===b){const t="Matrix4.getInverse(): can't invert matrix, determinant is 0";if(e)throw new Error(t);return console.warn(t),this.identity(),this}return this.multiplyScalar(1/b),this}isReflected(){const t=this.elements,e=t[0],i=t[4],r=t[8],s=t[1],n=t[5],a=t[9],o=t[2],l=t[6],h=t[10];return e*n*h+s*l*r+o*i*a-o*n*r-s*i*h-e*l*a<0}scale(t){const e=this.elements,{x:i}=t,{y:r}=t,{z:s}=t;return e[0]*=i,e[4]*=r,e[8]*=s,e[1]*=i,e[5]*=r,e[9]*=s,e[2]*=i,e[6]*=r,e[10]*=s,e[3]*=i,e[7]*=r,e[11]*=s,this}getMaxScaleOnAxis(){const t=this.elements,e=t[0]*t[0]+t[1]*t[1]+t[2]*t[2],i=t[4]*t[4]+t[5]*t[5]+t[6]*t[6],r=t[8]*t[8]+t[9]*t[9]+t[10]*t[10];return Math.sqrt(Math.max(e,Math.max(i,r)))}makeFrustum(t,e,i,r,s,n){const a=this.elements,o=2*s/(e-t),l=2*s/(r-i),h=(e+t)/(e-t),c=(r+i)/(r-i),d=-(n+s)/(n-s),u=-2*n*s/(n-s);return a[0]=o,a[4]=0,a[8]=h,a[12]=0,a[1]=0,a[5]=l,a[9]=c,a[13]=0,a[2]=0,a[6]=0,a[10]=d,a[14]=u,a[3]=0,a[7]=0,a[11]=-1,a[15]=0,this}makePerspective(t,e,i,r){const s=i*Math.tan(n(.5*t)),a=-s,o=a*e,l=s*e;return this.makeFrustum(o,l,a,s,i,r)}makeOrthographic(t,e,i,r,s,n){const a=this.elements,o=1/(e-t),l=1/(i-r),h=1/(n-s),c=(e+t)*o,d=(i+r)*l,u=(n+s)*h;return a[0]=2*o,a[4]=0,a[8]=0,a[12]=-c,a[1]=0,a[5]=2*l,a[9]=0,a[13]=-d,a[2]=0,a[6]=0,a[10]=-2*h,a[14]=-u,a[3]=0,a[7]=0,a[11]=0,a[15]=1,this}isEqual(t){const e=t.elements,i=this.elements;return i[0]===e[0]&&i[4]===e[4]&&i[8]===e[8]&&i[12]===e[12]&&i[1]===e[1]&&i[5]===e[5]&&i[9]===e[9]&&i[13]===e[13]&&i[2]===e[2]&&i[6]===e[6]&&i[10]===e[10]&&i[14]===e[14]&&i[3]===e[3]&&i[7]===e[7]&&i[11]===e[11]&&i[15]===e[15]}clone(){const t=this.elements;return new Matrix4(t[0],t[4],t[8],t[12],t[1],t[5],t[9],t[13],t[2],t[6],t[10],t[14],t[3],t[7],t[11],t[15])}isIdentity(){const t=this.elements;return 1===t[0]&&0===t[4]&&0===t[8]&&0===t[12]&&0===t[1]&&1===t[5]&&0===t[9]&&0===t[13]&&0===t[2]&&0===t[6]&&1===t[10]&&0===t[14]&&0===t[3]&&0===t[7]&&0===t[11]&&1===t[15]}isNearlyIdentity(t){return this.clone().snap(t).isIdentity()}getScale(t){const e=this.elements;return t=t||new Vector3,l.set(e[0],e[1],e[2]),h.set(e[4],e[5],e[6]),c.set(e[8],e[9],e[10]),t.x=l.length(),t.y=h.length(),t.z=c.length(),t}lookAt(t,e,i){const r=this.elements;return c.subVectors(t,e).normalize(),0===c.length()&&(c.z=1),l.crossVectors(i,c).normalize(),0===l.length()&&(c.x+=1e-4,l.crossVectors(i,c).normalize()),h.crossVectors(c,l),r[0]=l.x,r[4]=h.x,r[8]=c.x,r[1]=l.y,r[5]=h.y,r[9]=c.y,r[2]=l.z,r[6]=h.z,r[10]=c.z,this}compose(t,e,i){const r=this.elements;return a.identity(),a.setRotationFromQuaternion(e),o.makeScale(i.x,i.y,i.z),this.multiplyMatrices(a,o),r[12]=t.x,r[13]=t.y,r[14]=t.z,this}}a=new Matrix4,o=new Matrix4;class Vector3{constructor(t,e,i){this.x=t||0,this.y=e||0,this.z=i||0,this.atomid=void 0}set(t,e,i){return this.x=t,this.y=e,this.z=i,this}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this}add(t){return this.x+=t.x,this.y+=t.y,this.z+=t.z,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this}multiplyVectors(t,e){return this.x=t.x*e.x,this.y=t.y*e.y,this.z=t.z*e.z,this}sub(t){return this.x-=t.x,this.y-=t.y,this.z-=t.z,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this}divideScalar(t){return 0!==t?(this.x/=t,this.y/=t,this.z/=t):(this.x=0,this.y=0,this.z=0),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,i=this.y-t.y,r=this.z-t.z;return e*e+i*i+r*r}applyMatrix3(t){const{x:e}=this,{y:i}=this,{z:r}=this,s=t.elements;return this.x=s[0]*e+s[3]*i+s[6]*r,this.y=s[1]*e+s[4]*i+s[7]*r,this.z=s[2]*e+s[5]*i+s[8]*r,this}applyMatrix4(t){const{x:e}=this,{y:i}=this,{z:r}=this,s=t.elements;return this.x=s[0]*e+s[4]*i+s[8]*r+s[12],this.y=s[1]*e+s[5]*i+s[9]*r+s[13],this.z=s[2]*e+s[6]*i+s[10]*r+s[14],this}applyProjection(t){const{x:e}=this,{y:i}=this,{z:r}=this,s=t.elements,n=s[3]*e+s[7]*i+s[11]*r+s[15];return this.x=(s[0]*e+s[4]*i+s[8]*r+s[12])/n,this.y=(s[1]*e+s[5]*i+s[9]*r+s[13])/n,this.z=(s[2]*e+s[6]*i+s[10]*r+s[14])/n,this}applyQuaternion(t){const{x:e}=this,{y:i}=this,{z:r}=this,s=t.x,n=t.y,a=t.z,o=t.w,l={};l.x=2*(i*a-r*n),l.y=2*(r*s-e*a),l.z=2*(e*n-i*s);const h={};return h.x=l.y*a-l.z*n,h.y=l.z*s-l.x*a,h.z=l.x*n-l.y*s,this.x=e+o*l.x+h.x,this.y=i+o*l.y+h.y,this.z=r+o*l.z+h.z,this}negate(){return this.multiplyScalar(-1)}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z}normalize(){return this.divideScalar(this.length())}cross(t){const{x:e}=this,{y:i}=this,{z:r}=this;return this.x=i*t.z-r*t.y,this.y=r*t.x-e*t.z,this.z=e*t.y-i*t.x,this}crossVectors(t,e){return this.x=t.y*e.z-t.z*e.y,this.y=t.z*e.x-t.x*e.z,this.z=t.x*e.y-t.y*e.x,this}equals(t){return this.x==t.x&&this.y==t.y&&this.z==t.z}getPositionFromMatrix(t){return this.x=t.elements[12],this.y=t.elements[13],this.z=t.elements[14],this}setEulerFromRotationMatrix(t,e){const i=t.elements,s=i[0],n=i[4],a=i[8],o=i[5],l=i[9],h=i[6],c=i[10];return void 0===e||"XYZ"===e?(this.y=Math.asin(r(a,-1,1)),Math.abs(a)<.99999?(this.x=Math.atan2(-l,c),this.z=Math.atan2(-n,s)):(this.x=Math.atan2(h,o),this.z=0)):console.error(`Error with vector's setEulerFromRotationMatrix: Unknown order: ${e}`),this}rotateAboutVector(t,e){t.normalize();const i=Math.cos(e),r=Math.sin(e),s=this.clone().multiplyScalar(i),n=t.clone().cross(this).multiplyScalar(r),a=t.clone().multiplyScalar(t.clone().dot(this)).multiplyScalar(1-i),o=s.add(n).add(a);return this.x=o.x,this.y=o.y,this.z=o.z,this}setFromMatrixPosition(t){const e=t.elements;return this.x=e[12],this.y=e[13],this.z=e[14],this}transformDirection(t){const{x:e}=this,{y:i}=this,{z:r}=this,s=t.elements;return this.x=s[0]*e+s[4]*i+s[8]*r,this.y=s[1]*e+s[5]*i+s[9]*r,this.z=s[2]*e+s[6]*i+s[10]*r,this.normalize()}clone(){return new Vector3(this.x,this.y,this.z)}unproject(t){const e=a;return e.multiplyMatrices(t.matrixWorld,e.getInverse(t.projectionMatrix)),this.applyMatrix4(e)}}l=new Vector3,h=new Vector3,c=new Vector3;class Matrix3{constructor(t=1,e=0,i=0,r=0,s=1,n=0,a=0,o=0,l=1){this.elements=new Float32Array(9),this.set(t,e,i,r,s,n,a,o,l)}set(t,e,i,r,s,n,a,o,l){const h=this.elements;return h[0]=t,h[3]=e,h[6]=i,h[1]=r,h[4]=s,h[7]=n,h[2]=a,h[5]=o,h[8]=l,this}identity(){return this.set(1,0,0,0,1,0,0,0,1),this}copy(t){const e=t.elements;this.set(e[0],e[3],e[6],e[1],e[4],e[7],e[2],e[5],e[8])}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[3]*=t,e[6]*=t,e[1]*=t,e[4]*=t,e[7]*=t,e[2]*=t,e[5]*=t,e[8]*=t,this}getInverse3(t){const e=t.elements,i=this.elements;i[0]=e[4]*e[8]-e[5]*e[7],i[3]=e[6]*e[5]-e[3]*e[8],i[6]=e[3]*e[7]-e[6]*e[4],i[1]=e[7]*e[2]-e[1]*e[8],i[4]=e[0]*e[8]-e[6]*e[2],i[7]=e[1]*e[6]-e[0]*e[7],i[2]=e[1]*e[5]-e[2]*e[4],i[5]=e[2]*e[3]-e[0]*e[5],i[8]=e[0]*e[4]-e[1]*e[3];const r=e[0]*i[0]+e[3]*i[1]+e[6]*i[2];return this.multiplyScalar(1/r),this}getInverse(t,e){const i=t.elements,r=this.elements;r[0]=i[10]*i[5]-i[6]*i[9],r[1]=-i[10]*i[1]+i[2]*i[9],r[2]=i[6]*i[1]-i[2]*i[5],r[3]=-i[10]*i[4]+i[6]*i[8],r[4]=i[10]*i[0]-i[2]*i[8],r[5]=-i[6]*i[0]+i[2]*i[4],r[6]=i[9]*i[4]-i[5]*i[8],r[7]=-i[9]*i[0]+i[1]*i[8],r[8]=i[5]*i[0]-i[1]*i[4];const s=i[0]*r[0]+i[1]*r[3]+i[2]*r[6];if(0===s){const t="Matrix3.getInverse(): can't invert matrix, determinant is 0";if(e)throw new Error(t);return console.warn(t),this.identity(),this}return this.multiplyScalar(1/s),this}getDeterminant(){const t=this.elements;return t[0]*t[4]*t[8]+t[1]*t[5]*t[6]+t[2]*t[3]*t[7]-t[2]*t[4]*t[6]-t[1]*t[3]*t[8]-t[0]*t[5]*t[7]}transpose(){let t;const e=this.elements;return t=e[1],e[1]=e[3],e[3]=t,t=e[2],e[2]=e[6],e[6]=t,t=e[5],e[5]=e[7],e[7]=t,this}clone(){const t=this.elements;return new Matrix3(t[0],t[3],t[6],t[1],t[4],t[7],t[2],t[5],t[8])}getMatrix4(){const t=this.elements;return new Matrix4(t[0],t[3],t[6],0,t[1],t[4],t[7],0,t[2],t[5],t[8],0)}}class Ray{constructor(t,e){this.origin=void 0!==t?t:new Vector3,this.direction=void 0!==e?e:new Vector3}set(t,e){return this.origin.copy(t),this.direction.copy(e),this}copy(t){return this.origin.copy(t.origin),this.direction.copy(t.direction),this}at(t,e){return(e||new Vector3).copy(this.direction).multiplyScalar(t).add(this.origin)}recast(t){const e=l;return this.origin.copy(this.at(t,e)),this}closestPointToPoint(t,e){const i=e||new Vector3;i.subVectors(t,this.origin);const r=i.dot(this.direction);return i.copy(this.direction).multiplyScalar(r).add(this.origin)}distanceToPoint(t){const e=l,i=e.subVectors(t,this.origin).dot(this.direction);return e.copy(this.direction).multiplyScalar(i).add(this.origin),e.distanceTo(t)}isIntersectionCylinder(){}isIntersectionSphere(t){return this.distanceToPoint(t.center)<=t.radius}isIntersectionPlane(t){return 0!==t.normal.dot(this.direction)||0===t.distanceToPoint(this.origin)}distanceToPlane(t){const e=t.normal.dot(this.direction);if(0===e)return 0===t.distanceToPoint(this.origin)?0:void 0;return-(this.origin.dot(t.normal)+t.constant)/e}intersectPlane(t,e){const i=this.distanceToPlane(t);if(void 0!==i)return this.at(i,e)}applyMatrix4(t){return this.direction.add(this.origin).applyMatrix4(t),this.origin.applyMatrix4(t),this.direction.sub(this.origin),this}clone(){return(new Ray).copy(this)}}function d(t,e,i,r,s,n){r=r*Math.PI/180,s=s*Math.PI/180,n=n*Math.PI/180;const a=t=>t*t,o=Math.cos(r),l=Math.cos(s),h=Math.cos(n),c=Math.sin(n);return new Matrix3(t,e*h,i*l,0,e*c,i*(o-l*h)/c,0,0,i*Math.sqrt(1-a(o)-a(l)-a(h)+2*o*l*h)/c)}},99:(t,e,i)=>{"use 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0!==t._symmetry_equiv_pos_as_xyz&&!r)for(let e=0;e<t._symmetry_equiv_pos_as_xyz.length;e++){const i=t._symmetry_equiv_pos_as_xyz[e].replace(/["' ]/g,"").split(",").map((function(t){return t.replace(/-/g,"+-")}));let r=new n.Matrix4(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1);for(let t=0;t<3;t++){const e=i[t].split("+");for(let i=0;i<e.length;i++){const s=e[i];if(""===s)continue;const n=l(s);s.match("x")?r.elements[t+0]=n:s.match("y")?r.elements[t+4]=n:s.match("z")?r.elements[t+8]=n:r.elements[t+12]=n}}const o=a.getMatrix4(),h=(new n.Matrix4).getInverse(o,!0);r=(new n.Matrix4).multiplyMatrices(r,h),r=(new n.Matrix4).multiplyMatrices(o,r),s[s.length-1].symmetries.push(r)}}var f,p,g,_;for(let t=0;t<i.length;t++)!a||e.duplicateAssemblyAtoms&&!e.dontConnectDuplicatedAtoms||b(i[t],e),m(i[t],e.hbondCutoff),v(s[t].symmetries,i[t],e,s[t].cryst),e.duplicateAssemblyAtoms&&!e.dontConnectDuplicatedAtoms&&a&&b(i[t],e);return i}let 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i=[],r={};i.modelData=[];for(var s=t.split(/\r?\n|\r/);s.length>0;){var n=z(s,e,r),a=n[0],o=n[1];if(s=n[2],0!=a.length){if(e.multimodel&&e.onemol&&i.length>0)for(var l=i[0].length,h=0;h<a.length;h++){var c=a[h];c.index=h;for(var d=0;d<c.bonds.length;d++)c.bonds[d]+=l;i[0].push(c)}else i.modelData.push(o),i.push(a);if(!e.multimodel)break}}return i}function E(t,e){var i=[[]],r=!e.noSecondaryStructure;i.modelData=[{symmetries:[]}];var s,n=[],a=t.split(/\r?\n|\r/);for(let t=0;t<a.length;t++){var o=(s=a[t].replace(/^\s*/,"")).substring(0,6);if(0==o.indexOf("END")){if(e.multimodel){e.onemol||i.push([]);continue}break}if("ATOM  "==o||"HETATM"==o){var l;let t=parseInt(s.substring(6,11)),e=s.substring(12,16).replace(/ /g,""),r=s.substring(17,20).trim(),a=s.substring(21,22),o=parseInt(s.substring(22,26));var 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e=c?""==c[t]?1:parseInt(c[t]):1;this.C.push([[i[t],r[t],s[t],n[t]],[a[t],o[t],l[t],h[t]],e])}}}}class Residues{constructor(){this.R={}}add(t){null==this.R[t.lchain]&&(this.R[t.lchain]={}),null==this.R[t.lchain][t.lresi]&&(this.R[t.lchain][t.lresi]={}),null==this.R[t.lchain][t.lresi][t.lresn]&&(this.R[t.lchain][t.lresi][t.lresn]=[]),this.R[t.lchain][t.lresi][t.lresn].push(t),this.R[t.lchain][t.lresi][t.lresn][t.atom]=t}geta([t,e,i,r]){if(void 0!==this.R[t]&&void 0!==this.R[t][e]&&void 0!==this.R[t][e][i])return this.R[t][e][i][r]}setBonds(t,e){for(let e in this.R)for(let i in this.R[e])for(let r in this.R[e][i]){let s=this.R[e][i][r];for(let e=0;e<s.length;e++)for(let i=e+1;i<s.length;i++){let n=s[e],a=s[i],o=t.order(r,n.atom,a.atom);n.altLoc!=a.altLoc&&""!=n.altLoc&&""!=a.altLoc&&(o=0),o>0&&(n.bonds.push(a.index),a.bonds.push(n.index),n.bondOrder.push(o),a.bondOrder.push(o))}}for(let t of e.C){let e=t[0],i=t[1],r=t[2],s=this.geta(e),n=this.geta(i);null!=s&&null!=n&&(s.bonds.push(n.index),n.bonds.push(s.index),s.bondOrder.push(r),n.bondOrder.push(r))}}}function X(t,e){var i=!e.keepH,r=e.altLoc?e.altLoc:"A",s=!e.noComputeSecondaryStructure;const a=!e.doAssembly,o=void 0===e.assignBonds||e.assignBonds;if("string"==typeof t)try{t=(0,L.base64ToArray)(t)}catch(e){t=(new TextEncoder).encode(t)}else t=new Uint8Array(t);var l=Z.decodeMsgpack(t);31==l&&(t=(0,L.inflateString)(t,!1),l=Z.decodeMsgpack(t));var h=[],c=h.modelData=[],d=l.dataBlocks.length;if(0==d)return h;e.multimodel||(d=1);for(let t=0;t<d;t++){let s=h.length;const o=[];c.push({symmetries:[]}),h.push([]);const u=l.dataBlocks[t],f=Object.create(null);for(const t of u.categories)f[t.name.substr(1)]=Y(t);let p={},g=f.struct_conf;if(g){let t=g.getField("conf_type_id"),e=g.getField("beg_label_asym_id"),i=g.getField("beg_label_seq_id"),r=g.getField("end_label_seq_id");for(let s=0;s<t.length;s++)if(t[s].startsWith("H")){let t=e[s],n=i[s],a=r[s];t in p||(p[t]={}),p[t][n]="h1";for(let e=n+1;e<a;e++)p[t][e]="h";p[t][a]="h2"}}let m=f.struct_sheet_range;if(m){let t=m.getField("id"),e=m.getField("beg_label_asym_id"),i=m.getField("beg_label_seq_id"),r=m.getField("end_label_seq_id");for(let s=0;s<t.length;s++){let t=e[s],n=i[s],a=r[s];t in p||(p[t]={}),p[t][n]="s1";for(let e=n+1;e<a;e++)p[t][e]="s";p[t][a]="s2"}}let v=f.pdbx_struct_oper_list,_=v.getField("id");if(_&&!a){let t=v.getField("matrix[1][1]"),e=v.getField("matrix[1][2]"),i=v.getField("matrix[1][3]"),r=v.getField("matrix[2][1]"),s=v.getField("matrix[2][2]"),a=v.getField("matrix[2][3]"),o=v.getField("matrix[3][1]"),l=v.getField("matrix[3][2]"),h=v.getField("matrix[3][3]"),d=v.getField("vector[1]"),u=v.getField("vector[2]"),f=v.getField("vector[3]");for(let p=0;p<_.length;p++){const g=new n.Matrix4(t[p],e[p],i[p],d[p],r[p],s[p],a[p],u[p],o[p],l[p],h[p],f[p]);c[c.length-1].symmetries.push(g)}}let y=new Connectivity(f.chem_comp_bond),b=new Residues,x=new StructConn(f.struct_conn),w=f.atom_site,A=w.rowCount,C=w.getField("group_PDB"),S=w.getField("Cartn_x"),z=w.getField("Cartn_y"),T=w.getField("Cartn_z"),E=w.getField("auth_asym_id"),L=w.getField("label_asym_id"),F=w.getField("auth_seq_id"),I=w.getField("label_seq_id"),O=w.getField("auth_comp_id"),D=w.getField("label_comp_id"),k=w.getField("auth_atom_id"),R=w.getField("label_atom_id"),P=w.getField("type_symbol"),U=w.getField("B_iso_or_equiv"),B=w.getField("id"),N=w.getField("label_alt_id"),G=w.getField("pdbx_PDB_model_num"),V=G?G[0]:0;for(let t=0;t<A;t++){if(void 0!==C&&"TER"===C[t])continue;if(G&&G[t]!=V){if(V=G[t],!e.multimodel)break;e.onemol||(h.push([]),c.push(c[c.length-1]),V=G[t],b.setBonds(y,x),b=new Residues)}const s={};s.x=S[t],s.y=z[t],s.z=T[t],s.chain=E?E[t]:L?L[t]:void 0,s.lchain=L?L[t]:void 0,s.resi=F?F[t]:I?I[t]:void 0,s.lresi=I?I[t]:void 0,s.resn=O?O[t].trim():D?D[t].trim():void 0,s.lresn=D?D[t].trim():void 0,s.atom=k?k[t].replace(/"/gm,""):R?R[t].replace(/"/gm,""):void 0,s.icode=N?N[t]:void 0,s.altLoc=s.icode,s.hetflag=!C||"HETA"===C[t]||"HETATM"===C[t];let n="X";P&&(n=P[t].replace(/\(?\+?\d+.*/,"")),s.elem=n[0].toUpperCase()+n.substring(1,2).toLowerCase(),U&&(s.b=U[t]),i&&"H"==s.elem||(""!=s.altLoc&&s.altLoc!=r&&"*"!=r||(s.bonds=[],s.ss="c",s.serial=B[t],s.model=V,s.bondOrder=[],s.properties={},s.index=h[h.length-1].length,o[s.serial]=[h.length,s.index],h[h.length-1].push(s),b.add(s)))}if(b.setBonds(y,x),!M(p))for(let t=s;t<h.length;t++){let e=h[t];for(let t=0;t<e.length;t++){const i=e[t];if(void 0!==i&&(i.lchain in p&&i.lresi in p[i.lchain])){const t=p[i.lchain][i.lresi];i.ss=t[0],t.length>1&&("1"==t[1]?i.ssbegin=!0:"2"==t[1]&&(i.ssend=!0))}}}e.multimodel&&t<d-1&&(e.onemol||(h.push([]),c.push({symmetries:[]})))}for(let t=0;t<h.length;t++)!o||e.duplicateAssemblyAtoms&&!e.dontConnectDuplicatedAtoms||b(h[t],e),s&&m(h[t],e.hbondCutoff),v(c[t].symmetries,h[t],e,c[t].cryst),e.duplicateAssemblyAtoms&&!e.dontConnectDuplicatedAtoms&&o&&b(h[t],e);return h}const K={vasp:c.VASP,VASP:c.VASP,cube:d.CUBE,CUBE:d.CUBE,xyz:f,XYZ:f,sdf:p,SDF:p,json:g,cdjson:g,CDJSON:g,mcif:x,cif:x,CIF:x,mol2:A,MOL2:A,pdb:T,PDB:T,pdbqt:T,PDBQT:T,pqr:E,PQR:E,mmtf:k,MMTF:k,prmtop:R,PRMTOP:R,gro:P,GRO:P,lammpstrj:B,LAMMPSTRJ:B,bcif:X,BCIF:X};var Q,$=i(529),J=i(99);!function(t){t[t.VDW=1]="VDW",t[t.MS=2]="MS",t[t.SAS=3]="SAS",t[t.SES=4]="SES"}(Q||(Q={}));var tt=!1;function et(t){tt=t}(window.navigator.userAgent.indexOf("MSIE ")>=0||window.navigator.userAgent.indexOf("Trident/")>=0)&&(tt=!0);class MarchingCubeInitializer{constructor(){this.ISDONE=2,this.edgeTable=new Uint32Array([0,0,0,0,0,0,0,2816,0,0,0,1792,0,3328,3584,3840,0,0,0,138,0,21,0,134,0,0,0,652,0,2067,3865,3600,0,0,0,42,0,0,0,294,0,0,21,28,0,3875,1049,3360,0,168,162,170,0,645,2475,2210,0,687,293,172,4010,3747,3497,3232,0,0,0,0,0,69,0,900,0,0,0,1792,138,131,1608,1920,0,81,0,2074,84,85,84,86,0,81,0,3676,330,1105,1881,1616,0,0,0,42,0,69,0,502,0,0,21,3580,138,2035,1273,1520,2816,104,2337,106,840,581,367,102,2816,3695,3429,3180,1898,1635,1385,1120,0,0,0,0,0,0,0,3910,0,0,69,588,42,2083,41,2880,0,0,0,1722,0,2293,4095,3830,0,255,757,764,2538,2291,3065,2800,0,0,81,338,0,3925,1119,3414,84,855,85,340,2130,2899,89,2384,1792,712,194,1162,4036,3781,3535,3270,708,719,197,204,3018,2755,2505,2240,0,0,0,0,168,420,168,1958,162,162,676,2988,170,163,680,928,3328,3096,3328,3642,52,53,1855,1590,2340,2111,2869,2620,298,51,825,560,3584,3584,3090,3482,1668,1941,1183,1430,146,2975,2069,2460,154,915,153,400,3840,3592,3329,3082,1796,1541,1295,1030,2818,2575,2309,2060,778,515,265,0]),this.triTable=[[],[],[],[],[],[],[],[11,9,8],[],[],[],[8,10,9],[],[10,8,11],[9,11,10],[8,10,9,8,11,10],[],[],[],[1,7,3],[],[4,2,0],[],[2,1,7],[],[],[],[2,7,3,2,9,7],[],[1,4,11,1,0,4],[3,8,0,11,9,4,11,10,9],[4,11,9,11,10,9],[],[],[],[5,3,1],[],[],[],[2,5,8,2,1,5],[],[],[2,4,0],[3,2,4],[],[0,9,1,8,10,5,8,11,10],[3,4,0,3,10,4],[5,8,10,8,11,10],[],[3,5,7],[7,1,5],[1,7,3,1,5,7],[],[9,2,0,9,7,2],[0,3,8,1,7,11,1,5,7],[11,1,7,1,5,7],[],[9,1,0,5,3,2,5,7,3],[8,2,5,8,0,2],[2,5,3,5,7,3],[3,9,1,3,8,9,7,11,10,7,10,5],[9,1,0,10,7,11,10,5,7],[3,8,0,7,10,5,7,11,10],[11,5,7,11,10,5],[],[],[],[],[],[0,6,2],[],[7,2,9,7,9,8],[],[],[],[8,10,9],[7,1,3],[7,1,0],[6,9,3,6,10,9],[7,10,8,10,9,8],[],[6,0,4],[],[11,1,4,11,3,1],[2,4,6],[2,0,4,2,4,6],[2,4,6],[1,4,2,4,6,2],[],[6,0,4],[],[2,11,3,6,9,4,6,10,9],[8,6,1,8,1,3],[10,0,6,0,4,6],[8,0,3,9,6,10,9,4,6],[10,4,6,10,9,4],[],[],[],[5,3,1],[],[0,6,2],[],[7,4,8,5,2,1,5,6,2],[],[],[2,4,0],[7,4,8,2,11,3,10,5,6],[7,1,3],[5,6,10,0,9,1,8,7,4],[5,6,10,7,0,3,7,4,0],[10,5,6,4,8,7],[9,11,8],[3,5,6],[0,5,11,0,11,8],[6,3,5,3,1,5],[3,9,6,3,8,9],[9,6,0,6,2,0],[0,3,8,2,5,6,2,1,5],[1,6,2,1,5,6],[9,11,8],[1,0,9,6,10,5,11,3,2],[6,10,5,2,8,0,2,11,8],[3,2,11,10,5,6],[10,5,6,9,3,8,9,1,3],[0,9,1,5,6,10],[8,0,3,10,5,6],[10,5,6],[],[],[],[],[],[],[],[1,10,2,9,11,6,9,8,11],[],[],[6,0,2],[3,6,9,3,2,6],[3,5,1],[0,5,1,0,11,5],[0,3,5],[6,9,11,9,8,11],[],[],[],[4,5,9,7,1,10,7,3,1],[],[11,6,7,2,4,5,2,0,4],[11,6,7,8,0,3,1,10,2,9,4,5],[6,7,11,1,10,2,9,4,5],[],[4,1,0,4,5,1,6,7,3,6,3,2],[9,4,5,0,6,7,0,2,6],[4,5,9,6,3,2,6,7,3],[6,7,11,5,3,8,5,1,3],[6,7,11,4,1,0,4,5,1],[4,5,9,3,8,0,11,6,7],[9,4,5,7,11,6],[],[],[0,6,4],[8,6,4,8,1,6],[],[0,10,2,0,9,10,4,8,11,4,11,6],[10,2,1,6,0,3,6,4,0],[10,2,1,11,4,8,11,6,4],[4,2,6],[1,0,9,2,4,8,2,6,4],[2,4,0,2,6,4],[8,2,4,2,6,4],[11,4,1,11,6,4],[0,9,1,4,11,6,4,8,11],[3,6,0,6,4,0],[8,6,4,8,11,6],[10,8,9],[6,3,9,6,7,3],[6,7,1],[10,7,1,7,3,1],[7,11,6,8,10,2,8,9,10],[11,6,7,10,0,9,10,2,0],[2,1,10,7,11,6,8,0,3],[1,10,2,6,7,11],[7,2,6,7,9,2],[1,0,9,3,6,7,3,2,6],[7,0,6,0,2,6],[2,7,3,2,6,7],[7,11,6,3,9,1,3,8,9],[9,1,0,11,6,7],[0,3,8,11,6,7],[11,6,7],[],[],[],[],[5,3,7],[8,5,2,8,7,5],[5,3,7],[1,10,2,5,8,7,5,9,8],[1,7,5],[1,7,5],[9,2,7,9,7,5],[11,3,2,8,5,9,8,7,5],[1,3,7,1,7,5],[0,7,1,7,5,1],[9,3,5,3,7,5],[9,7,5,9,8,7],[8,10,11],[3,4,10,3,10,11],[8,10,11],[5,9,4,1,11,3,1,10,11],[2,4,5],[5,2,4,2,0,4],[0,3,8,5,9,4,10,2,1],[2,1,10,9,4,5],[2,8,5,2,11,8],[3,2,11,1,4,5,1,0,4],[9,4,5,8,2,11,8,0,2],[11,3,2,9,4,5],[8,5,3,5,1,3],[5,0,4,5,1,0],[3,8,0,4,5,9],[9,4,5],[11,9,10],[11,9,10],[1,11,4,1,10,11],[8,7,4,11,1,10,11,3,1],[2,7,9,2,9,10],[4,8,7,0,10,2,0,9,10],[2,1,10,0,7,4,0,3,7],[10,2,1,8,7,4],[1,7,4],[3,2,11,4,8,7,9,1,0],[11,4,2,4,0,2],[2,11,3,7,4,8],[4,1,7,1,3,7],[1,0,9,8,7,4],[3,4,0,3,7,4],[8,7,4],[8,9,10,8,10,11],[3,9,11,9,10,11],[0,10,8,10,11,8],[10,3,1,10,11,3],[2,8,10,8,9,10],[9,2,0,9,10,2],[8,0,3,1,10,2],[10,2,1],[1,11,9,11,8,9],[11,3,2,0,9,1],[11,0,2,11,8,0],[11,3,2],[8,1,3,8,9,1],[9,1,0],[8,0,3],[]],this.edgeTable2=[0,265,515,778,2060,2309,2575,2822,1030,1295,1541,1804,3082,3331,3593,3840,400,153,915,666,2460,2197,2975,2710,1430,1183,1941,1692,3482,3219,3993,3728,560,825,51,314,2620,2869,2111,2358,1590,1855,1077,1340,3642,3891,3129,3376,928,681,419,170,2988,2725,2479,2214,1958,1711,1445,1196,4010,3747,3497,3232,2240,2505,2755,3018,204,453,719,966,3270,3535,3781,4044,1226,1475,1737,1984,2384,2137,2899,2650,348,85,863,598,3414,3167,3925,3676,1370,1107,1881,1616,2800,3065,2291,2554,764,1013,255,502,3830,4095,3317,3580,1786,2035,1273,1520,2912,2665,2403,2154,876,613,367,102,3942,3695,3429,3180,1898,1635,1385,1120,1120,1385,1635,1898,3180,3429,3695,3942,102,367,613,876,2154,2403,2665,2912,1520,1273,2035,1786,3580,3317,4095,3830,502,255,1013,764,2554,2291,3065,2800,1616,1881,1107,1370,3676,3925,3167,3414,598,863,85,348,2650,2899,2137,2384,1984,1737,1475,1226,4044,3781,3535,3270,966,719,453,204,3018,2755,2505,2240,3232,3497,3747,4010,1196,1445,1711,1958,2214,2479,2725,2988,170,419,681,928,3376,3129,3891,3642,1340,1077,1855,1590,2358,2111,2869,2620,314,51,825,560,3728,3993,3219,3482,1692,1941,1183,1430,2710,2975,2197,2460,666,915,153,400,3840,3593,3331,3082,1804,1541,1295,1030,2822,2575,2309,2060,778,515,265,0],this.triTable2=[[],[8,3,0],[9,0,1],[8,3,1,8,1,9],[11,2,3],[11,2,0,11,0,8],[11,2,3,0,1,9],[2,1,11,1,9,11,11,9,8],[10,1,2],[8,3,0,1,2,10],[9,0,2,9,2,10],[3,2,8,2,10,8,8,10,9],[10,1,3,10,3,11],[1,0,10,0,8,10,10,8,11],[0,3,9,3,11,9,9,11,10],[8,10,9,8,11,10],[8,4,7],[3,0,4,3,4,7],[1,9,0,8,4,7],[9,4,1,4,7,1,1,7,3],[2,3,11,7,8,4],[7,11,4,11,2,4,4,2,0],[3,11,2,4,7,8,9,0,1],[2,7,11,2,1,7,1,4,7,1,9,4],[10,1,2,8,4,7],[2,10,1,0,4,7,0,7,3],[4,7,8,0,2,10,0,10,9],[2,7,3,2,9,7,7,9,4,2,10,9],[8,4,7,11,10,1,11,1,3],[11,4,7,1,4,11,1,11,10,1,0,4],[3,8,0,7,11,4,11,9,4,11,10,9],[7,11,4,4,11,9,11,10,9],[9,5,4],[3,0,8,4,9,5],[5,4,0,5,0,1],[4,8,5,8,3,5,5,3,1],[11,2,3,9,5,4],[9,5,4,8,11,2,8,2,0],[3,11,2,1,5,4,1,4,0],[8,5,4,2,5,8,2,8,11,2,1,5],[2,10,1,9,5,4],[0,8,3,5,4,9,10,1,2],[10,5,2,5,4,2,2,4,0],[3,4,8,3,2,4,2,5,4,2,10,5],[5,4,9,1,3,11,1,11,10],[0,9,1,4,8,5,8,10,5,8,11,10],[3,4,0,3,10,4,4,10,5,3,11,10],[4,8,5,5,8,10,8,11,10],[9,5,7,9,7,8],[0,9,3,9,5,3,3,5,7],[8,0,7,0,1,7,7,1,5],[1,7,3,1,5,7],[11,2,3,8,9,5,8,5,7],[9,2,0,9,7,2,2,7,11,9,5,7],[0,3,8,2,1,11,1,7,11,1,5,7],[2,1,11,11,1,7,1,5,7],[1,2,10,5,7,8,5,8,9],[9,1,0,10,5,2,5,3,2,5,7,3],[5,2,10,8,2,5,8,5,7,8,0,2],[10,5,2,2,5,3,5,7,3],[3,9,1,3,8,9,7,11,10,7,10,5],[9,1,0,10,7,11,10,5,7],[3,8,0,7,10,5,7,11,10],[11,5,7,11,10,5],[11,7,6],[0,8,3,11,7,6],[9,0,1,11,7,6],[7,6,11,3,1,9,3,9,8],[2,3,7,2,7,6],[8,7,0,7,6,0,0,6,2],[1,9,0,3,7,6,3,6,2],[7,6,2,7,2,9,2,1,9,7,9,8],[1,2,10,6,11,7],[2,10,1,7,6,11,8,3,0],[11,7,6,10,9,0,10,0,2],[7,6,11,3,2,8,8,2,10,8,10,9],[6,10,7,10,1,7,7,1,3],[6,10,1,6,1,7,7,1,0,7,0,8],[9,0,3,6,9,3,6,10,9,6,3,7],[6,10,7,7,10,8,10,9,8],[8,4,6,8,6,11],[11,3,6,3,0,6,6,0,4],[0,1,9,4,6,11,4,11,8],[1,9,4,11,1,4,11,3,1,11,4,6],[3,8,2,8,4,2,2,4,6],[2,0,4,2,4,6],[1,9,0,3,8,2,2,8,4,2,4,6],[9,4,1,1,4,2,4,6,2],[10,1,2,11,8,4,11,4,6],[10,1,2,11,3,6,6,3,0,6,0,4],[0,2,10,0,10,9,4,11,8,4,6,11],[2,11,3,6,9,4,6,10,9],[8,4,6,8,6,1,6,10,1,8,1,3],[1,0,10,10,0,6,0,4,6],[8,0,3,9,6,10,9,4,6],[10,4,6,10,9,4],[9,5,4,7,6,11],[4,9,5,3,0,8,11,7,6],[6,11,7,4,0,1,4,1,5],[6,11,7,4,8,5,5,8,3,5,3,1],[4,9,5,6,2,3,6,3,7],[9,5,4,8,7,0,0,7,6,0,6,2],[4,0,1,4,1,5,6,3,7,6,2,3],[7,4,8,5,2,1,5,6,2],[6,11,7,1,2,10,9,5,4],[11,7,6,8,3,0,1,2,10,9,5,4],[11,7,6,10,5,2,2,5,4,2,4,0],[7,4,8,2,11,3,10,5,6],[4,9,5,6,10,7,7,10,1,7,1,3],[5,6,10,0,9,1,8,7,4],[5,6,10,7,0,3,7,4,0],[10,5,6,4,8,7],[5,6,9,6,11,9,9,11,8],[0,9,5,0,5,3,3,5,6,3,6,11],[0,1,5,0,5,11,5,6,11,0,11,8],[11,3,6,6,3,5,3,1,5],[9,5,6,3,9,6,3,8,9,3,6,2],[5,6,9,9,6,0,6,2,0],[0,3,8,2,5,6,2,1,5],[1,6,2,1,5,6],[1,2,10,5,6,9,9,6,11,9,11,8],[1,0,9,6,10,5,11,3,2],[6,10,5,2,8,0,2,11,8],[3,2,11,10,5,6],[10,5,6,9,3,8,9,1,3],[0,9,1,5,6,10],[8,0,3,10,5,6],[10,5,6],[10,6,5],[8,3,0,10,6,5],[0,1,9,5,10,6],[10,6,5,9,8,3,9,3,1],[3,11,2,10,6,5],[6,5,10,2,0,8,2,8,11],[1,9,0,6,5,10,11,2,3],[1,10,2,5,9,6,9,11,6,9,8,11],[1,2,6,1,6,5],[0,8,3,2,6,5,2,5,1],[5,9,6,9,0,6,6,0,2],[9,6,5,3,6,9,3,9,8,3,2,6],[11,6,3,6,5,3,3,5,1],[0,5,1,0,11,5,5,11,6,0,8,11],[0,5,9,0,3,5,3,6,5,3,11,6],[5,9,6,6,9,11,9,8,11],[10,6,5,4,7,8],[5,10,6,7,3,0,7,0,4],[5,10,6,0,1,9,8,4,7],[4,5,9,6,7,10,7,1,10,7,3,1],[7,8,4,2,3,11,10,6,5],[11,6,7,10,2,5,2,4,5,2,0,4],[11,6,7,8,0,3,1,10,2,9,4,5],[6,7,11,1,10,2,9,4,5],[7,8,4,5,1,2,5,2,6],[4,1,0,4,5,1,6,7,3,6,3,2],[9,4,5,8,0,7,0,6,7,0,2,6],[4,5,9,6,3,2,6,7,3],[6,7,11,4,5,8,5,3,8,5,1,3],[6,7,11,4,1,0,4,5,1],[4,5,9,3,8,0,11,6,7],[9,4,5,7,11,6],[10,6,4,10,4,9],[8,3,0,9,10,6,9,6,4],[1,10,0,10,6,0,0,6,4],[8,6,4,8,1,6,6,1,10,8,3,1],[2,3,11,6,4,9,6,9,10],[0,10,2,0,9,10,4,8,11,4,11,6],[10,2,1,11,6,3,6,0,3,6,4,0],[10,2,1,11,4,8,11,6,4],[9,1,4,1,2,4,4,2,6],[1,0,9,3,2,8,2,4,8,2,6,4],[2,4,0,2,6,4],[3,2,8,8,2,4,2,6,4],[1,4,9,11,4,1,11,1,3,11,6,4],[0,9,1,4,11,6,4,8,11],[11,6,3,3,6,0,6,4,0],[8,6,4,8,11,6],[6,7,10,7,8,10,10,8,9],[9,3,0,6,3,9,6,9,10,6,7,3],[6,1,10,6,7,1,7,0,1,7,8,0],[6,7,10,10,7,1,7,3,1],[7,11,6,3,8,2,8,10,2,8,9,10],[11,6,7,10,0,9,10,2,0],[2,1,10,7,11,6,8,0,3],[1,10,2,6,7,11],[7,2,6,7,9,2,2,9,1,7,8,9],[1,0,9,3,6,7,3,2,6],[8,0,7,7,0,6,0,2,6],[2,7,3,2,6,7],[7,11,6,3,9,1,3,8,9],[9,1,0,11,6,7],[0,3,8,11,6,7],[11,6,7],[11,7,5,11,5,10],[3,0,8,7,5,10,7,10,11],[9,0,1,10,11,7,10,7,5],[3,1,9,3,9,8,7,10,11,7,5,10],[10,2,5,2,3,5,5,3,7],[5,10,2,8,5,2,8,7,5,8,2,0],[9,0,1,10,2,5,5,2,3,5,3,7],[1,10,2,5,8,7,5,9,8],[2,11,1,11,7,1,1,7,5],[0,8,3,2,11,1,1,11,7,1,7,5],[9,0,2,9,2,7,2,11,7,9,7,5],[11,3,2,8,5,9,8,7,5],[1,3,7,1,7,5],[8,7,0,0,7,1,7,5,1],[0,3,9,9,3,5,3,7,5],[9,7,5,9,8,7],[4,5,8,5,10,8,8,10,11],[3,0,4,3,4,10,4,5,10,3,10,11],[0,1,9,4,5,8,8,5,10,8,10,11],[5,9,4,1,11,3,1,10,11],[3,8,4,3,4,2,2,4,5,2,5,10],[10,2,5,5,2,4,2,0,4],[0,3,8,5,9,4,10,2,1],[2,1,10,9,4,5],[8,4,5,2,8,5,2,11,8,2,5,1],[3,2,11,1,4,5,1,0,4],[9,4,5,8,2,11,8,0,2],[11,3,2,9,4,5],[4,5,8,8,5,3,5,1,3],[5,0,4,5,1,0],[3,8,0,4,5,9],[9,4,5],[7,4,11,4,9,11,11,9,10],[3,0,8,7,4,11,11,4,9,11,9,10],[11,7,4,1,11,4,1,10,11,1,4,0],[8,7,4,11,1,10,11,3,1],[2,3,7,2,7,9,7,4,9,2,9,10],[4,8,7,0,10,2,0,9,10],[2,1,10,0,7,4,0,3,7],[10,2,1,8,7,4],[2,11,7,2,7,1,1,7,4,1,4,9],[3,2,11,4,8,7,9,1,0],[7,4,11,11,4,2,4,0,2],[2,11,3,7,4,8],[9,1,4,4,1,7,1,3,7],[1,0,9,8,7,4],[3,4,0,3,7,4],[8,7,4],[8,9,10,8,10,11],[0,9,3,3,9,11,9,10,11],[1,10,0,0,10,8,10,11,8],[10,3,1,10,11,3],[3,8,2,2,8,10,8,9,10],[9,2,0,9,10,2],[8,0,3,1,10,2],[10,2,1],[2,11,1,1,11,9,11,8,9],[11,3,2,0,9,1],[11,0,2,11,8,0],[11,3,2],[8,1,3,8,9,1],[9,1,0],[8,0,3],[]]}march(t,e,i,r){let s=!!r.fulltable,n=r.hasOwnProperty("origin")&&r.origin.hasOwnProperty("x")?r.origin:{x:0,y:0,z:0},a=!!r.voxel,o=r.matrix,l=r.nX||0,h=r.nY||0,c=r.nZ||0,d=r.scale||1,u=null;u=r.unitCube?r.unitCube:{x:d,y:d,z:d};let f,p,g=new Int32Array(l*h*c);for(f=0,p=g.length;f<p;++f)g[f]=-1;let m=function(t,i,r,s,l,d){let f={x:0,y:0,z:0},p=l;if(!!!(s&1<<l)&&!!(s&1<<d)&&(p=d),1&p&&r++,2&p&&i++,4&p&&t++,o){let e=new $.Vector3(t,i,r);e=e.applyMatrix4(o),f={x:e.x,y:e.y,z:e.z}}else f.x=n.x+u.x*t,f.y=n.y+u.y*i,f.z=n.z+u.z*r;let m=(h*t+i)*c+r;return a?(e.push(f),e.length-1):(g[m]<0&&(g[m]=e.length,e.push(f)),g[m])},v=new Int32Array(12),_=s?this.edgeTable2:this.edgeTable,y=s?this.triTable2:this.triTable;for(f=0;f<l-1;++f)for(let r=0;r<h-1;++r)for(let s=0;s<c-1;++s){let n=0;for(let e=0;e<8;++e){n|=!!(t[(h*(f+((4&e)>>2))+r+((2&e)>>1))*c+s+(1&e)]&this.ISDONE)<<e}if(0===n||255===n)continue;let o=_[n];if(0===o)continue;let l=y[n];1&o&&(v[0]=m(f,r,s,n,0,1)),2&o&&(v[1]=m(f,r,s,n,1,3)),4&o&&(v[2]=m(f,r,s,n,3,2)),8&o&&(v[3]=m(f,r,s,n,2,0)),16&o&&(v[4]=m(f,r,s,n,4,5)),32&o&&(v[5]=m(f,r,s,n,5,7)),64&o&&(v[6]=m(f,r,s,n,7,6)),128&o&&(v[7]=m(f,r,s,n,6,4)),256&o&&(v[8]=m(f,r,s,n,0,4)),512&o&&(v[9]=m(f,r,s,n,1,5)),1024&o&&(v[10]=m(f,r,s,n,3,7)),2048&o&&(v[11]=m(f,r,s,n,2,6));for(let t=0;t<l.length;t+=3){let r=v[l[t]],s=v[l[t+1]],n=v[l[t+2]];a&&t>=3&&(e.push(e[r]),r=e.length-1,e.push(e[s]),s=e.length-1,e.push(e[n]),n=e.length-1),i.push(r),i.push(s),i.push(n)}}}laplacianSmooth(t,e,i){let r,s,n,a,o,l=new Array(e.length);for(r=0,s=e.length;r<s;r++)l[r]={x:0,y:0,z:0};let h,c=new Array(20);for(r=0;r<20;r++)c[r]=new Array(e.length);for(r=0,s=e.length;r<s;r++)c[0][r]=0;for(r=0,s=i.length/3;r<s;r++){let t=3*r,e=3*r+1,s=3*r+2;for(h=!0,n=0,a=c[0][i[t]];n<a;n++)if(i[e]==c[n+1][i[t]]){h=!1;break}for(h&&(c[0][i[t]]++,c[c[0][i[t]]][i[t]]=i[e]),h=!0,n=0,a=c[0][i[t]];n<a;n++)if(i[s]==c[n+1][i[t]]){h=!1;break}for(h&&(c[0][i[t]]++,c[c[0][i[t]]][i[t]]=i[s]),h=!0,n=0,a=c[0][i[e]];n<a;n++)if(i[t]==c[n+1][i[e]]){h=!1;break}for(h&&(c[0][i[e]]++,c[c[0][i[e]]][i[e]]=i[t]),h=!0,n=0,a=c[0][i[e]];n<a;n++)if(i[s]==c[n+1][i[e]]){h=!1;break}for(h&&(c[0][i[e]]++,c[c[0][i[e]]][i[e]]=i[s]),h=!0,n=0;n<c[0][i[s]];n++)if(i[t]==c[n+1][i[s]]){h=!1;break}for(h&&(c[0][i[s]]++,c[c[0][i[s]]][i[s]]=i[t]),h=!0,n=0,a=c[0][i[s]];n<a;n++)if(i[e]==c[n+1][i[s]]){h=!1;break}h&&(c[0][i[s]]++,c[c[0][i[s]]][i[s]]=i[e])}let d=.5;for(o=0;o<t;o++){for(r=0,s=e.length;r<s;r++)if(c[0][r]<3)l[r].x=e[r].x,l[r].y=e[r].y,l[r].z=e[r].z;else if(3==c[0][r]||4==c[0][r]){for(l[r].x=0,l[r].y=0,l[r].z=0,n=0,a=c[0][r];n<a;n++)l[r].x+=e[c[n+1][r]].x,l[r].y+=e[c[n+1][r]].y,l[r].z+=e[c[n+1][r]].z;l[r].x+=d*e[r].x,l[r].y+=d*e[r].y,l[r].z+=d*e[r].z,l[r].x/=d+c[0][r],l[r].y/=d+c[0][r],l[r].z/=d+c[0][r]}else{for(l[r].x=0,l[r].y=0,l[r].z=0,n=0,a=c[0][r];n<a;n++)l[r].x+=e[c[n+1][r]].x,l[r].y+=e[c[n+1][r]].y,l[r].z+=e[c[n+1][r]].z;l[r].x+=1*e[r].x,l[r].y+=1*e[r].y,l[r].z+=1*e[r].z,l[r].x/=1+c[0][r],l[r].y/=1+c[0][r],l[r].z/=1+c[0][r]}for(r=0,s=e.length;r<s;r++)e[r].x=l[r].x,e[r].y=l[r].y,e[r].z=l[r].z}}}let MarchingCube=new MarchingCubeInitializer;class PointGrid{constructor(t,e,i){this.data=new Int32Array(t*e*i*3),this.width=e,this.height=i}set(t,e,i,r){let s=3*((t*this.width+e)*this.height+i);this.data[s]=r.ix,this.data[s+1]=r.iy,this.data[s+2]=r.iz}get(t,e,i){let r=3*((t*this.width+e)*this.height+i);return{ix:this.data[r],iy:this.data[r+1],iz:this.data[r+2]}}}class ProteinSurface{constructor(){this.INOUT=1,this.ISDONE=2,this.ISBOUND=4,this.ptranx=0,this.ptrany=0,this.ptranz=0,this.probeRadius=1.4,this.defaultScaleFactor=2,this.scaleFactor=this.defaultScaleFactor,this.pHeight=0,this.pWidth=0,this.pLength=0,this.cutRadius=0,this.vpBits=null,this.vpDistance=null,this.vpAtomID=null,this.pminx=0,this.pminy=0,this.pminz=0,this.pmaxx=0,this.pmaxy=0,this.pmaxz=0,this.depty={},this.widxz={},this.faces=[],this.verts=[],this.vdwRadii={H:1.2,Li:1.82,Na:2.27,K:2.75,C:1.7,N:1.55,O:1.52,F:1.47,P:1.8,S:1.8,CL:1.75,BR:1.85,SE:1.9,ZN:1.39,CU:1.4,NI:1.63,X:2},this.nb=[new Int32Array([1,0,0]),new Int32Array([-1,0,0]),new Int32Array([0,1,0]),new Int32Array([0,-1,0]),new Int32Array([0,0,1]),new Int32Array([0,0,-1]),new Int32Array([1,1,0]),new Int32Array([1,-1,0]),new Int32Array([-1,1,0]),new Int32Array([-1,-1,0]),new Int32Array([1,0,1]),new Int32Array([1,0,-1]),new Int32Array([-1,0,1]),new Int32Array([-1,0,-1]),new Int32Array([0,1,1]),new Int32Array([0,1,-1]),new Int32Array([0,-1,1]),new Int32Array([0,-1,-1]),new Int32Array([1,1,1]),new Int32Array([1,1,-1]),new Int32Array([1,-1,1]),new Int32Array([-1,1,1]),new Int32Array([1,-1,-1]),new Int32Array([-1,-1,1]),new Int32Array([-1,1,-1]),new Int32Array([-1,-1,-1])],ProteinSurface.MarchingCube||(ProteinSurface.MarchingCube=new MarchingCubeInitializer)}getVDWIndex(t){return t.elem&&void 0!==this.vdwRadii[t.elem]?t.elem:"X"}getFacesAndVertices(t){let e={};for(let i=0,r=t.length;i<r;i++)e[t[i]]=!0;let i=this.verts;for(let t=0,e=i.length;t<e;t++)i[t].x=i[t].x/this.scaleFactor-this.ptranx,i[t].y=i[t].y/this.scaleFactor-this.ptrany,i[t].z=i[t].z/this.scaleFactor-this.ptranz;let r=[];for(let t=0,s=this.faces.length;t<s;t+=3){let s=this.faces[t],n=this.faces[t+1],a=this.faces[t+2],o=i[s].atomid,l=i[n].atomid,h=i[a].atomid,c=o;l<c&&(c=l),h<c&&(c=h),e[c]&&(s!==n&&n!==a&&s!==a&&(r.push(s),r.push(n),r.push(a)))}return this.vpBits=null,this.vpDistance=null,this.vpAtomID=null,{vertices:i,faces:r}}initparm(t,e,i){i>1e6&&(this.scaleFactor=this.defaultScaleFactor/2);let r=1/this.scaleFactor*5.5;this.pminx=t[0][0],this.pmaxx=t[1][0],this.pminy=t[0][1],this.pmaxy=t[1][1],this.pminz=t[0][2],this.pmaxz=t[1][2],e?(this.pminx-=this.probeRadius+r,this.pminy-=this.probeRadius+r,this.pminz-=this.probeRadius+r,this.pmaxx+=this.probeRadius+r,this.pmaxy+=this.probeRadius+r,this.pmaxz+=this.probeRadius+r):(this.pminx-=r,this.pminy-=r,this.pminz-=r,this.pmaxx+=r,this.pmaxy+=r,this.pmaxz+=r),this.pminx=Math.floor(this.pminx*this.scaleFactor)/this.scaleFactor,this.pminy=Math.floor(this.pminy*this.scaleFactor)/this.scaleFactor,this.pminz=Math.floor(this.pminz*this.scaleFactor)/this.scaleFactor,this.pmaxx=Math.ceil(this.pmaxx*this.scaleFactor)/this.scaleFactor,this.pmaxy=Math.ceil(this.pmaxy*this.scaleFactor)/this.scaleFactor,this.pmaxz=Math.ceil(this.pmaxz*this.scaleFactor)/this.scaleFactor,this.ptranx=-this.pminx,this.ptrany=-this.pminy,this.ptranz=-this.pminz,this.pLength=Math.ceil(this.scaleFactor*(this.pmaxx-this.pminx))+1,this.pWidth=Math.ceil(this.scaleFactor*(this.pmaxy-this.pminy))+1,this.pHeight=Math.ceil(this.scaleFactor*(this.pmaxz-this.pminz))+1,this.boundingatom(e),this.cutRadius=this.probeRadius*this.scaleFactor,this.vpBits=new Uint8Array(this.pLength*this.pWidth*this.pHeight),this.vpDistance=new Float64Array(this.pLength*this.pWidth*this.pHeight),this.vpAtomID=new Int32Array(this.pLength*this.pWidth*this.pHeight)}boundingatom(t){let e={};for(const i in this.vdwRadii){let r=this.vdwRadii[i];e[i]=t?(r+this.probeRadius)*this.scaleFactor+.5:r*this.scaleFactor+.5;let s=e[i]*e[i];this.widxz[i]=Math.floor(e[i])+1,this.depty[i]=new Int32Array(this.widxz[i]*this.widxz[i]);let n=0;for(let t=0;t<this.widxz[i];t++)for(let e=0;e<this.widxz[i];e++){let r=t*t+e*e;if(r>s)this.depty[i][n]=-1;else{let t=Math.sqrt(s-r);this.depty[i][n]=Math.floor(t)}n++}}}fillvoxels(t,e){for(let t=0,e=this.vpBits.length;t<e;t++)this.vpBits[t]=0,this.vpDistance[t]=-1,this.vpAtomID[t]=-1;for(let i in e){let r=t[e[i]];void 0!==r&&this.fillAtom(r,t)}for(let t=0,e=this.vpBits.length;t<e;t++)this.vpBits[t]&this.INOUT&&(this.vpBits[t]|=this.ISDONE)}fillAtom(t,e){let i=Math.floor(.5+this.scaleFactor*(t.x+this.ptranx)),r=Math.floor(.5+this.scaleFactor*(t.y+this.ptrany)),s=Math.floor(.5+this.scaleFactor*(t.z+this.ptranz)),n=this.getVDWIndex(t),a=0,o=this.pWidth*this.pHeight;for(let l=0,h=this.widxz[n];l<h;l++)for(let c=0;c<h;c++){if(-1!=this.depty[n][a])for(let h=-1;h<2;h++)for(let d=-1;d<2;d++)for(let u=-1;u<2;u++)if(0!==h&&0!==d&&0!==u){let f=h*l,p=u*c;for(let l=0;l<=this.depty[n][a];l++){let n=l*d,a=i+f,h=r+n,c=s+p;if(a<0||h<0||c<0||a>=this.pLength||h>=this.pWidth||c>=this.pHeight)continue;let u=a*o+h*this.pHeight+c;if(this.vpBits[u]&this.INOUT){let a=e[this.vpAtomID[u]];if(a.serial!=t.serial){let e=i+f-Math.floor(.5+this.scaleFactor*(a.x+this.ptranx)),o=r+n-Math.floor(.5+this.scaleFactor*(a.y+this.ptrany)),l=s+p-Math.floor(.5+this.scaleFactor*(a.z+this.ptranz));f*f+n*n+p*p<e*e+o*o+l*l&&(this.vpAtomID[u]=t.serial)}}else this.vpBits[u]|=this.INOUT,this.vpAtomID[u]=t.serial}}a++}}fillvoxelswaals(t,e){for(let t=0,e=this.vpBits.length;t<e;t++)this.vpBits[t]&=~this.ISDONE;for(let i in e){let r=t[e[i]];void 0!==r&&this.fillAtomWaals(r,t)}}fillAtomWaals(t,e){let i=0,r=Math.floor(.5+this.scaleFactor*(t.x+this.ptranx)),s=Math.floor(.5+this.scaleFactor*(t.y+this.ptrany)),n=Math.floor(.5+this.scaleFactor*(t.z+this.ptranz)),a=this.getVDWIndex(t),o=this.pWidth*this.pHeight;for(let l=0,h=this.widxz[a];l<h;l++)for(let c=0;c<h;c++){if(-1!=this.depty[a][i])for(let h=-1;h<2;h++)for(let d=-1;d<2;d++)for(let u=-1;u<2;u++)if(0!==h&&0!==d&&0!==u){let f=h*l,p=u*c;for(let l=0;l<=this.depty[a][i];l++){let i=l*d,a=r+f,h=s+i,c=n+p;if(a<0||h<0||c<0||a>=this.pLength||h>=this.pWidth||c>=this.pHeight)continue;let u=a*o+h*this.pHeight+c;if(this.vpBits[u]&this.ISDONE){let a=e[this.vpAtomID[u]];if(a.serial!=t.serial){let e=r+f-Math.floor(.5+this.scaleFactor*(a.x+this.ptranx)),o=s+i-Math.floor(.5+this.scaleFactor*(a.y+this.ptrany)),l=n+p-Math.floor(.5+this.scaleFactor*(a.z+this.ptranz));f*f+i*i+p*p<e*e+o*o+l*l&&(this.vpAtomID[u]=t.serial)}}else this.vpBits[u]|=this.ISDONE,this.vpAtomID[u]=t.serial}}i++}}buildboundary(){let t=this.pWidth*this.pHeight;for(let e=0;e<this.pLength;e++)for(let i=0;i<this.pHeight;i++)for(let r=0;r<this.pWidth;r++){let s=e*t+r*this.pHeight+i;if(this.vpBits[s]&this.INOUT){let n=0;for(;n<26;){let a=e+this.nb[n][0],o=i+this.nb[n][2],l=r+this.nb[n][1];if(a>-1&&a<this.pLength&&l>-1&&l<this.pWidth&&o>-1&&o<this.pHeight&&!(this.vpBits[a*t+l*this.pHeight+o]&this.INOUT)){this.vpBits[s]|=this.ISBOUND;break}n++}}}}fastdistancemap(){let t,e=new PointGrid(this.pLength,this.pWidth,this.pHeight),i=this.pWidth*this.pHeight,r=this.cutRadius*this.cutRadius,s=[],n=[];for(let r=0;r<this.pLength;r++)for(let n=0;n<this.pWidth;n++)for(let a=0;a<this.pHeight;a++)if(t=r*i+n*this.pHeight+a,this.vpBits[t]&=~this.ISDONE,this.vpBits[t]&this.INOUT&&this.vpBits[t]&this.ISBOUND){let i={ix:r,iy:n,iz:a};e.set(r,n,a,i),s.push(i),this.vpDistance[t]=0,this.vpBits[t]|=this.ISDONE,this.vpBits[t]&=~this.ISBOUND}do{n=this.fastoneshell(s,e),s=[];for(let e=0,a=n.length;e<a;e++)t=i*n[e].ix+this.pHeight*n[e].iy+n[e].iz,this.vpBits[t]&=~this.ISBOUND,this.vpDistance[t]<=1.0404*r&&s.push({ix:n[e].ix,iy:n[e].iy,iz:n[e].iz})}while(0!==s.length);s=[],n=[],e=null;let a=this.scaleFactor-.5;a<0&&(a=0);let o=r-.5/(.1+a);for(let e=0;e<this.pLength;e++)for(let r=0;r<this.pWidth;r++)for(let s=0;s<this.pHeight;s++)t=e*i+r*this.pHeight+s,this.vpBits[t]&=~this.ISBOUND,this.vpBits[t]&this.INOUT&&(this.vpBits[t]&this.ISDONE&&!(this.vpBits[t]&this.ISDONE&&this.vpDistance[t]>=o)||(this.vpBits[t]|=this.ISBOUND))}fastoneshell(t,e){let i,r,s,n,a,o,l,h,c,d=[];if(0===t.length)return d;let u={ix:-1,iy:-1,iz:-1},f=this.pWidth*this.pHeight;for(let p=0,g=t.length;p<g;p++){i=t[p].ix,r=t[p].iy,s=t[p].iz,h=e.get(i,r,s);for(let t=0;t<6;t++)u.ix=i+this.nb[t][0],u.iy=r+this.nb[t][1],u.iz=s+this.nb[t][2],u.ix<this.pLength&&u.ix>-1&&u.iy<this.pWidth&&u.iy>-1&&u.iz<this.pHeight&&u.iz>-1&&(c=u.ix*f+this.pHeight*u.iy+u.iz,this.vpBits[c]&this.INOUT&&!(this.vpBits[c]&this.ISDONE)?(e.set(u.ix,u.iy,s+this.nb[t][2],h),n=u.ix-h.ix,a=u.iy-h.iy,o=u.iz-h.iz,l=n*n+a*a+o*o,this.vpDistance[c]=l,this.vpBits[c]|=this.ISDONE,this.vpBits[c]|=this.ISBOUND,d.push({ix:u.ix,iy:u.iy,iz:u.iz})):this.vpBits[c]&this.INOUT&&this.vpBits[c]&this.ISDONE&&(n=u.ix-h.ix,a=u.iy-h.iy,o=u.iz-h.iz,l=n*n+a*a+o*o,l<this.vpDistance[c]&&(e.set(u.ix,u.iy,u.iz,h),this.vpDistance[c]=l,this.vpBits[c]&this.ISBOUND||(this.vpBits[c]|=this.ISBOUND,d.push({ix:u.ix,iy:u.iy,iz:u.iz})))))}for(let p=0,g=t.length;p<g;p++){i=t[p].ix,r=t[p].iy,s=t[p].iz,h=e.get(i,r,s);for(let t=6;t<18;t++)u.ix=i+this.nb[t][0],u.iy=r+this.nb[t][1],u.iz=s+this.nb[t][2],u.ix<this.pLength&&u.ix>-1&&u.iy<this.pWidth&&u.iy>-1&&u.iz<this.pHeight&&u.iz>-1&&(c=u.ix*f+this.pHeight*u.iy+u.iz,this.vpBits[c]&this.INOUT&&!(this.vpBits[c]&this.ISDONE)?(e.set(u.ix,u.iy,s+this.nb[t][2],h),n=u.ix-h.ix,a=u.iy-h.iy,o=u.iz-h.iz,l=n*n+a*a+o*o,this.vpDistance[c]=l,this.vpBits[c]|=this.ISDONE,this.vpBits[c]|=this.ISBOUND,d.push({ix:u.ix,iy:u.iy,iz:u.iz})):this.vpBits[c]&this.INOUT&&this.vpBits[c]&this.ISDONE&&(n=u.ix-h.ix,a=u.iy-h.iy,o=u.iz-h.iz,l=n*n+a*a+o*o,l<this.vpDistance[c]&&(e.set(u.ix,u.iy,u.iz,h),this.vpDistance[c]=l,this.vpBits[c]&this.ISBOUND||(this.vpBits[c]|=this.ISBOUND,d.push({ix:u.ix,iy:u.iy,iz:u.iz})))))}for(let p=0,g=t.length;p<g;p++){i=t[p].ix,r=t[p].iy,s=t[p].iz,h=e.get(i,r,s);for(let t=18;t<26;t++)u.ix=i+this.nb[t][0],u.iy=r+this.nb[t][1],u.iz=s+this.nb[t][2],u.ix<this.pLength&&u.ix>-1&&u.iy<this.pWidth&&u.iy>-1&&u.iz<this.pHeight&&u.iz>-1&&(c=u.ix*f+this.pHeight*u.iy+u.iz,this.vpBits[c]&this.INOUT&&!(this.vpBits[c]&this.ISDONE)?(e.set(u.ix,u.iy,s+this.nb[t][2],h),n=u.ix-h.ix,a=u.iy-h.iy,o=u.iz-h.iz,l=n*n+a*a+o*o,this.vpDistance[c]=l,this.vpBits[c]|=this.ISDONE,this.vpBits[c]|=this.ISBOUND,d.push({ix:u.ix,iy:u.iy,iz:u.iz})):this.vpBits[c]&this.INOUT&&this.vpBits[c]&this.ISDONE&&(n=u.ix-h.ix,a=u.iy-h.iy,o=u.iz-h.iz,l=n*n+a*a+o*o,l<this.vpDistance[c]&&(e.set(u.ix,u.iy,u.iz,h),this.vpDistance[c]=l,this.vpBits[c]&this.ISBOUND||(this.vpBits[c]|=this.ISBOUND,d.push({ix:u.ix,iy:u.iy,iz:u.iz})))))}return d}marchingcubeinit(t){for(let e=0,i=this.vpBits.length;e<i;e++)1==t?this.vpBits[e]&=~this.ISBOUND:4==t?(this.vpBits[e]&=~this.ISDONE,this.vpBits[e]&this.ISBOUND&&(this.vpBits[e]|=this.ISDONE),this.vpBits[e]&=~this.ISBOUND):2==t?this.vpBits[e]&this.ISBOUND&&this.vpBits[e]&this.ISDONE?this.vpBits[e]&=~this.ISBOUND:this.vpBits[e]&this.ISBOUND&&!(this.vpBits[e]&this.ISDONE)&&(this.vpBits[e]|=this.ISDONE):3==t&&(this.vpBits[e]&=~this.ISBOUND)}marchingcube(t){this.marchingcubeinit(t),this.verts=[],this.faces=[],ProteinSurface.MarchingCube.march(this.vpBits,this.verts,this.faces,{smooth:1,nX:this.pLength,nY:this.pWidth,nZ:this.pHeight});let e=this.pWidth*this.pHeight;for(let t=0,i=this.verts.length;t<i;t++)this.verts[t].atomid=this.vpAtomID[this.verts[t].x*e+this.pHeight*this.verts[t].y+this.verts[t].z];ProteinSurface.MarchingCube.laplacianSmooth(1,this.verts,this.faces)}}ProteinSurface.MarchingCube=new MarchingCubeInitializer;var it,rt,st=i(848);function nt(t,e){var i,r,s,n,a,o,l,h,c,d=[],u=t;for((u=[]).push(t[0]),i=1,r=t.length-1;i<r;i++)if(a=t[i],o=t[i+1],a.smoothen){var f=new $.Vector3((a.x+o.x)/2,(a.y+o.y)/2,(a.z+o.z)/2);f.atom=a.atom,u.push(f)}else u.push(a);for(u.push(t[t.length-1]),i=-1,s=u.length;i<=s-3;i++)if(n=u[-1===i?0:i],a=u[i+1],o=u[i+2],l=u[i===s-3?s-1:i+3],h=(new $.Vector3).subVectors(o,n).multiplyScalar(.5),c=(new $.Vector3).subVectors(l,a).multiplyScalar(.5),!o.skip)for(var p=0;p<e;p++){var g=1/e*p,m=a.x+g*h.x+g*g*(-3*a.x+3*o.x-2*h.x-c.x)+g*g*g*(2*a.x-2*o.x+h.x+c.x),v=a.y+g*h.y+g*g*(-3*a.y+3*o.y-2*h.y-c.y)+g*g*g*(2*a.y-2*o.y+h.y+c.y),_=a.z+g*h.z+g*g*(-3*a.z+3*o.z-2*h.z-c.z)+g*g*g*(2*a.z-2*o.z+h.z+c.z),y=new $.Vector3(m,v,_);y.atom=p<e/2?a.atom:o.atom,d.push(y)}return d.push(u[u.length-1]),d}!function(t){t[t.NONE=0]="NONE",t[t.FLAT=1]="FLAT",t[t.ROUND=2]="ROUND"}(it||(it={})),function(t){function e(t,e,i){var r,s,n,a,o,l=Math.hypot(t,e);l<1e-4?(s=0,n=1):(s=-t/l,n=e/l),e=-s*t+n*e,(r=Math.hypot(e,i))<1e-4?(a=0,o=1):(a=i/r,o=e/r);var h=new Float32Array(9);return h[0]=n,h[1]=s,h[2]=0,h[3]=-s*o,h[4]=n*o,h[5]=a,h[6]=s*a,h[7]=-n*a,h[8]=o,h}var i=new class CylVertexCache{constructor(){this.cache={};let t,e=[],i=Math.pow(2,4),r=2,s=Math.pow(2,r),n=i/s;for(e[0]=new $.Vector3(-1,0,0),e[n]=new $.Vector3(0,0,1),e[2*n]=new $.Vector3(1,0,0),e[3*n]=new $.Vector3(0,0,-1),r=3;r<=4;r++){for(s=Math.pow(2,r-1),n=i/s,t=0;t<s-1;t++)e[n/2+t*n]=e[t*n].clone().add(e[(t+1)*n]).normalize();t=s-1,e[n/2+t*n]=e[t*n].clone().add(e[0]).normalize()}this.basisVectors=e}getVerticesForRadius(t,e,i){if(void 0!==this.cache&&void 0!==this.cache[t]&&void 0!==this.cache[t][e+i])return this.cache[t][e+i];for(var r,s=this.basisVectors.length,n=[],a=[],o=0;o<s;o++)n.push(this.basisVectors[o].clone().multiplyScalar(t)),n.push(this.basisVectors[o].clone().multiplyScalar(t)),r=this.basisVectors[o].clone().normalize(),a.push(r),a.push(r);var l=[],h=10,c=s;var d,u,f=2*Math.PI,p=Math.PI,g=!1,m=!1;for(u=0;u<=h;u++){g=0===u||u===h,m=5===u;var v=[],_=[];for(d=0;d<=c;d++)if(m){var y=d<c?2*d:0;_.push(y+1),v.push(y)}else{var b=d/c,x=u/h;if(g&&0!==d)g&&v.push(n.length-1);else if(d<c){var w=new $.Vector3;w.x=-t*Math.cos(0+b*f)*Math.sin(0+x*p),w.y=1==e?0:t*Math.cos(0+x*p),w.z=t*Math.sin(0+b*f)*Math.sin(0+x*p),Math.abs(w.x)<1e-5&&(w.x=0),Math.abs(w.y)<1e-5&&(w.y=0),Math.abs(w.z)<1e-5&&(w.z=0),e==it.FLAT?(r=new $.Vector3(0,Math.cos(0+x*p),0)).normalize():(r=new $.Vector3(w.x,w.y,w.z)).normalize(),n.push(w),a.push(r),v.push(n.length-1)}else v.push(n.length-c)}m&&l.push(_),l.push(v)}var A={vertices:n,normals:a,verticesRows:l,w:c,h};return t in this.cache||(this.cache[t]={}),this.cache[t][e+i]=A,A}};t.drawCylinder=function(t,r,s,n,a,o=0,l=0){if(!r||!s)return;let h=function(t){if("string"==typeof t){let e=t;return"flat"==e.toLowerCase()?it.FLAT:"round"==e.toLowerCase()?it.ROUND:it.NONE}return t};o=h(o);var c=(l=h(l))||o;a=a||{r:0,g:0,b:0};var d,u,f,p,g,m,v=e(s.x-r.x,s.y-r.y,s.z-r.z),_=i.getVerticesForRadius(n,l,"to"),y=_.w,b=_.h,x=c?b*y+2:2*y,w=t.updateGeoGroup(x),A=_.vertices,C=_.normals,S=_.verticesRows,M=S[b/2],z=S[b/2+1],T=w.vertices,E=w.vertexArray,L=w.normalArray,F=w.colorArray,I=w.faceArray;for(f=0;f<y;++f){var O=2*f;p=v[0]*A[O].x+v[3]*A[O].y+v[6]*A[O].z,g=v[1]*A[O].x+v[4]*A[O].y+v[7]*A[O].z,m=v[5]*A[O].y+v[8]*A[O].z,d=3*(T+O),u=w.faceidx,E[d]=p+r.x,E[d+1]=g+r.y,E[d+2]=m+r.z,E[d+3]=p+s.x,E[d+4]=g+s.y,E[d+5]=m+s.z,L[d]=p,L[d+3]=p,L[d+1]=g,L[d+4]=g,L[d+2]=m,L[d+5]=m,F[d]=a.r,F[d+3]=a.r,F[d+1]=a.g,F[d+4]=a.g,F[d+2]=a.b,F[d+5]=a.b,I[u]=z[f]+T,I[u+1]=z[f+1]+T,I[u+2]=M[f]+T,I[u+3]=M[f]+T,I[u+4]=z[f+1]+T,I[u+5]=M[f+1]+T,w.faceidx+=6}if(c){var D,k,R,P,U,B,N,G,V,j,H,W,q,Y,Z,X,K,Q,$,J,tt,et,rt,st,nt,at,ot,lt,ht,ct,dt,ut,ft=o?b+1:b/2+1;for(g=l?0:b/2;g<ft;g++)if(g!==b/2){var pt=g<=b/2?s:r,gt=i.getVerticesForRadius(n,l,"to"),mt=i.getVerticesForRadius(n,o,"from");for(pt===s?(A=gt.vertices,C=gt.normals,S=gt.verticesRows):pt==r&&(A=mt.vertices,C=mt.normals,S=mt.verticesRows),p=0;p<y;p++)u=w.faceidx,ht=3*((D=S[g][p+1])+T),ct=3*((k=S[g][p])+T),dt=3*((R=S[g+1][p])+T),ut=3*((P=S[g+1][p+1])+T),U=v[0]*A[D].x+v[3]*A[D].y+v[6]*A[D].z,B=v[0]*A[k].x+v[3]*A[k].y+v[6]*A[k].z,N=v[0]*A[R].x+v[3]*A[R].y+v[6]*A[R].z,G=v[0]*A[P].x+v[3]*A[P].y+v[6]*A[P].z,V=v[1]*A[D].x+v[4]*A[D].y+v[7]*A[D].z,j=v[1]*A[k].x+v[4]*A[k].y+v[7]*A[k].z,H=v[1]*A[R].x+v[4]*A[R].y+v[7]*A[R].z,W=v[1]*A[P].x+v[4]*A[P].y+v[7]*A[P].z,q=v[5]*A[D].y+v[8]*A[D].z,Y=v[5]*A[k].y+v[8]*A[k].z,Z=v[5]*A[R].y+v[8]*A[R].z,X=v[5]*A[P].y+v[8]*A[P].z,E[ht]=U+pt.x,E[ct]=B+pt.x,E[dt]=N+pt.x,E[ut]=G+pt.x,E[ht+1]=V+pt.y,E[ct+1]=j+pt.y,E[dt+1]=H+pt.y,E[ut+1]=W+pt.y,E[ht+2]=q+pt.z,E[ct+2]=Y+pt.z,E[dt+2]=Z+pt.z,E[ut+2]=X+pt.z,F[ht]=a.r,F[ct]=a.r,F[dt]=a.r,F[ut]=a.r,F[ht+1]=a.g,F[ct+1]=a.g,F[dt+1]=a.g,F[ut+1]=a.g,F[ht+2]=a.b,F[ct+2]=a.b,F[dt+2]=a.b,F[ut+2]=a.b,K=v[0]*C[D].x+v[3]*C[D].y+v[6]*C[D].z,Q=v[0]*C[k].x+v[3]*C[k].y+v[6]*C[k].z,$=v[0]*C[R].x+v[3]*C[R].y+v[6]*C[R].z,J=v[0]*C[P].x+v[3]*C[P].y+v[6]*C[P].z,tt=v[1]*C[D].x+v[4]*C[D].y+v[7]*C[D].z,et=v[1]*C[k].x+v[4]*C[k].y+v[7]*C[k].z,rt=v[1]*C[R].x+v[4]*C[R].y+v[7]*C[R].z,st=v[1]*C[P].x+v[4]*C[P].y+v[7]*C[P].z,nt=v[5]*C[D].y+v[8]*C[D].z,at=v[5]*C[k].y+v[8]*C[k].z,ot=v[5]*C[R].y+v[8]*C[R].z,lt=v[5]*C[P].y+v[8]*C[P].z,0===g?(L[ht]=K,L[dt]=$,L[ut]=J,L[ht+1]=tt,L[dt+1]=rt,L[ut+1]=st,L[ht+2]=nt,L[dt+2]=ot,L[ut+2]=lt,I[u]=D+T,I[u+1]=R+T,I[u+2]=P+T,w.faceidx+=3):g===ft-1?(L[ht]=K,L[ct]=Q,L[dt]=$,L[ht+1]=tt,L[ct+1]=et,L[dt+1]=rt,L[ht+2]=nt,L[ct+2]=at,L[dt+2]=ot,I[u]=D+T,I[u+1]=k+T,I[u+2]=R+T,w.faceidx+=3):(L[ht]=K,L[ct]=Q,L[ut]=J,L[ht+1]=tt,L[ct+1]=et,L[ut+1]=st,L[ht+2]=nt,L[ct+2]=at,L[ut+2]=lt,L[ct]=Q,L[dt]=$,L[ut]=J,L[ct+1]=et,L[dt+1]=rt,L[ut+1]=st,L[ct+2]=at,L[dt+2]=ot,L[ut+2]=lt,I[u]=D+T,I[u+1]=k+T,I[u+2]=P+T,I[u+3]=k+T,I[u+4]=R+T,I[u+5]=P+T,w.faceidx+=6)}}w.vertices+=x},t.drawCone=function(t,r,s,n,a){if(!r||!s)return;a=a||{r:0,g:0,b:0};let o=new $.Vector3(s.x-r.x,s.y-r.y,s.z-r.z);var l=e(o.x,o.y,o.z);o=o.normalize();var h,c,d,u,f,p,g=i.basisVectors.length,m=i.basisVectors,v=g+2,_=t.updateGeoGroup(v),y=_.vertices,b=_.vertexArray,x=_.normalArray,w=_.colorArray,A=_.faceArray;for(b[h=3*y]=r.x,b[h+1]=r.y,b[h+2]=r.z,x[h]=-o.x,x[h+1]=-o.y,x[h+2]=-o.z,w[h]=a.r,w[h+1]=a.g,w[h+2]=a.b,b[h+3]=s.x,b[h+4]=s.y,b[h+5]=s.z,x[h+3]=o.x,x[h+4]=o.y,x[h+5]=o.z,w[h+3]=a.r,w[h+4]=a.g,w[h+5]=a.b,h+=6,d=0;d<g;++d){var C=m[d].clone();C.multiplyScalar(n),u=l[0]*C.x+l[3]*C.y+l[6]*C.z,f=l[1]*C.x+l[4]*C.y+l[7]*C.z,p=l[5]*C.y+l[8]*C.z,b[h]=u+r.x,b[h+1]=f+r.y,b[h+2]=p+r.z,x[h]=u,x[h+1]=f,x[h+2]=p,w[h]=a.r,w[h+1]=a.g,w[h+2]=a.b,h+=3}for(_.vertices+=g+2,c=_.faceidx,d=0;d<g;d++){var S=y+2+d,M=y+2+(d+1)%g;A[c]=S,A[c+1]=M,A[c+2]=y,A[c+=3]=S,A[c+1]=M,A[c+2]=y+1,c+=3}_.faceidx+=6*g};var r=new class SphereVertexCache{constructor(){this.cache=new Map}getVerticesForRadius(t,e){e=e||2,this.cache.has(e)||this.cache.set(e,new Map);let i=this.cache.get(e);if(i.has(t))return i.get(t);var r={vertices:[],verticesRows:[],normals:[]},s=16*e,n=10*e;t<1&&(s=10*e,n=8*e);var a,o,l=2*Math.PI,h=Math.PI;for(o=0;o<=n;o++){let e=[];for(a=0;a<=s;a++){let i=a/s,d=o/n,u=-t*Math.cos(0+i*l)*Math.sin(0+d*h),f=t*Math.cos(0+d*h),p=t*Math.sin(0+i*l)*Math.sin(0+d*h);var c=new $.Vector3(u,f,p);c.normalize(),r.vertices.push({x:u,y:f,z:p}),r.normals.push(c),e.push(r.vertices.length-1)}r.verticesRows.push(e)}return i.set(t,r),r}};t.drawSphere=function(t,e,i,s,n){var a=r.getVerticesForRadius(i,n),o=a.vertices,l=a.normals,h=t.updateGeoGroup(o.length),c=h.vertices,d=h.vertexArray,u=h.colorArray,f=h.faceArray,p=h.lineArray,g=h.normalArray;for(let t=0,i=o.length;t<i;++t){let i=3*(c+t),r=o[t];d[i]=r.x+e.x,d[i+1]=r.y+e.y,d[i+2]=r.z+e.z,u[i]=s.r,u[i+1]=s.g,u[i+2]=s.b}h.vertices+=o.length;let m=a.verticesRows,v=m.length-1;for(let t=0;t<v;t++){let e=m[t].length-1;for(let r=0;r<e;r++){let e=h.faceidx,s=h.lineidx,n=m[t][r+1]+c,a=3*n,d=m[t][r]+c,u=3*d,v=m[t+1][r]+c,_=3*v,y=m[t+1][r+1]+c,b=3*y,x=l[n-c],w=l[d-c],A=l[v-c],C=l[y-c];Math.abs(o[n-c].y)===i?(g[a]=x.x,g[_]=A.x,g[b]=C.x,g[a+1]=x.y,g[_+1]=A.y,g[b+1]=C.y,g[a+2]=x.z,g[_+2]=A.z,g[b+2]=C.z,f[e]=n,f[e+1]=v,f[e+2]=y,p[s]=n,p[s+1]=v,p[s+2]=n,p[s+3]=y,p[s+4]=v,p[s+5]=y,h.faceidx+=3,h.lineidx+=6):Math.abs(o[v-c].y)===i?(g[a]=x.x,g[u]=w.x,g[_]=A.x,g[a+1]=x.y,g[u+1]=w.y,g[_+1]=A.y,g[a+2]=x.z,g[u+2]=w.z,g[_+2]=A.z,f[e]=n,f[e+1]=d,f[e+2]=v,p[s]=n,p[s+1]=d,p[s+2]=n,p[s+3]=v,p[s+4]=d,p[s+5]=v,h.faceidx+=3,h.lineidx+=6):(g[a]=x.x,g[u]=w.x,g[b]=C.x,g[a+1]=x.y,g[u+1]=w.y,g[b+1]=C.y,g[a+2]=x.z,g[u+2]=w.z,g[b+2]=C.z,g[u]=w.x,g[_]=A.x,g[b]=C.x,g[u+1]=w.y,g[_+1]=A.y,g[b+1]=C.y,g[u+2]=w.z,g[_+2]=A.z,g[b+2]=C.z,f[e]=n,f[e+1]=d,f[e+2]=y,f[e+3]=d,f[e+4]=v,f[e+5]=y,p[s]=n,p[s+1]=d,p[s+2]=n,p[s+3]=y,p[s+4]=d,p[s+5]=v,p[s+6]=v,p[s+7]=y,h.faceidx+=6,h.lineidx+=8)}}}}(rt||(rt={}));const at=.5,ot=1.3,lt=.8,ht=.4,ct=.4;function dt(t,e,i,r){for(var n,a,o,l,h=0,c=e.length;h<c;h++){l=Math.round(h*(r.length-1)/c),o=s.CC.color(r[l]);var d=t.updateGeoGroup(2),u=d.vertexArray,f=d.colorArray,p=d.faceArray;u[a=3*(n=d.vertices)]=e[h].x,u[a+1]=e[h].y,u[a+2]=e[h].z,u[a+3]=i[h].x,u[a+4]=i[h].y,u[a+5]=i[h].z;for(var g=0;g<6;++g)f[a+3*g]=o.r,f[a+1+3*g]=o.g,f[a+2+3*g]=o.b;if(h>0){var m=[n,n+1,n-1,n-2],v=d.faceidx;p[v]=m[0],p[v+1]=m[1],p[v+2]=m[3],p[v+3]=m[1],p[v+4]=m[2],p[v+5]=m[3],d.faceidx+=6}d.vertices+=2}}function ut(t,e,i,r,n,a,o){o&&"default"!==o||(o="rectangle"),"edged"===o?function(t,e,i,r,n){if(!(e.length<2)){var a,o;if(a=e[0],o=e[e.length-1],a=nt(a,r),o=nt(o,r),!n)return dt(t,a,o,i);var l,h,c,d,u,f,p,g,m,v,_,y,b,x,w,A,C,S,M,z,T,E,L=[],F=[[0,2,-6,-8],[-4,-2,6,4],[7,-1,-5,3],[-3,5,1,-7]];for(b=0,x=a.length;b<x;b++){if(v=Math.round(b*(i.length-1)/x),m=s.CC.color(i[v]),L.push(h=a[b]),L.push(h),L.push(c=o[b]),L.push(c),b<x-1){var I=a[b+1].clone().sub(a[b]);l=o[b].clone().sub(a[b]).cross(I).normalize().multiplyScalar(n)}for(L.push(d=a[b].clone().add(l)),L.push(d),L.push(u=o[b].clone().add(l)),L.push(u),void 0!==h.atom&&(_=h.atom),z=(M=t.updateGeoGroup(8)).vertexArray,T=M.colorArray,E=M.faceArray,z[p=3*(f=M.vertices)]=h.x,z[p+1]=h.y,z[p+2]=h.z,z[p+3]=h.x,z[p+4]=h.y,z[p+5]=h.z,z[p+6]=c.x,z[p+7]=c.y,z[p+8]=c.z,z[p+9]=c.x,z[p+10]=c.y,z[p+11]=c.z,z[p+12]=d.x,z[p+13]=d.y,z[p+14]=d.z,z[p+15]=d.x,z[p+16]=d.y,z[p+17]=d.z,z[p+18]=u.x,z[p+19]=u.y,z[p+20]=u.z,z[p+21]=u.x,z[p+22]=u.y,z[p+23]=u.z,w=0;w<8;++w)T[p+3*w]=m.r,T[p+1+3*w]=m.g,T[p+2+3*w]=m.b;if(b>0){var O=void 0!==y&&void 0!==_&&y.serial!==_.serial;for(w=0;w<4;w++){var D=[f+F[w][0],f+F[w][1],f+F[w][2],f+F[w][3]];if(E[g=M.faceidx]=D[0],E[g+1]=D[1],E[g+2]=D[3],E[g+3]=D[1],E[g+4]=D[2],E[g+5]=D[3],M.faceidx+=6,_.clickable||y.clickable||_.hoverable||y.hoverable){var k=L[D[3]].clone(),R=L[D[0]].clone(),P=L[D[2]].clone(),U=L[D[1]].clone();if(k.atom=L[D[3]].atom||null,P.atom=L[D[2]].atom||null,R.atom=L[D[0]].atom||null,U.atom=L[D[1]].atom||null,O){var B=k.clone().add(R).multiplyScalar(.5),N=P.clone().add(U).multiplyScalar(.5),G=k.clone().add(U).multiplyScalar(.5);w%2==0?((y.clickable||y.hoverable)&&(A=new J.Triangle(B,G,k),C=new J.Triangle(N,P,G),S=new J.Triangle(G,P,k),y.intersectionShape.triangle.push(A),y.intersectionShape.triangle.push(C),y.intersectionShape.triangle.push(S)),(_.clickable||_.hoverable)&&(A=new J.Triangle(R,U,G),C=new J.Triangle(U,N,G),S=new J.Triangle(R,G,B),_.intersectionShape.triangle.push(A),_.intersectionShape.triangle.push(C),_.intersectionShape.triangle.push(S))):((_.clickable||_.hoverable)&&(A=new J.Triangle(B,G,k),C=new J.Triangle(N,P,G),S=new J.Triangle(G,P,k),_.intersectionShape.triangle.push(A),_.intersectionShape.triangle.push(C),_.intersectionShape.triangle.push(S)),(y.clickable||y.hoverable)&&(A=new J.Triangle(R,U,G),C=new J.Triangle(U,N,G),S=new J.Triangle(R,G,B),y.intersectionShape.triangle.push(A),y.intersectionShape.triangle.push(C),y.intersectionShape.triangle.push(S)))}else(_.clickable||_.hoverable)&&(A=new J.Triangle(R,U,k),C=new J.Triangle(U,P,k),_.intersectionShape.triangle.push(A),_.intersectionShape.triangle.push(C))}}}M.vertices+=8,y=_}var V=L.length-8;for(z=(M=t.updateGeoGroup(8)).vertexArray,T=M.colorArray,E=M.faceArray,p=3*(f=M.vertices),g=M.faceidx,b=0;b<4;b++){L.push(L[2*b]),L.push(L[V+2*b]);var j=L[2*b],H=L[V+2*b];z[p+6*b]=j.x,z[p+1+6*b]=j.y,z[p+2+6*b]=j.z,z[p+3+6*b]=H.x,z[p+4+6*b]=H.y,z[p+5+6*b]=H.z,T[p+6*b]=m.r,T[p+1+6*b]=m.g,T[p+2+6*b]=m.b,T[p+3+6*b]=m.r,T[p+4+6*b]=m.g,T[p+5+6*b]=m.b}V+=8,A=[f,f+2,f+6,f+4],C=[f+1,f+5,f+7,f+3],E[g]=A[0],E[g+1]=A[1],E[g+2]=A[3],E[g+3]=A[1],E[g+4]=A[2],E[g+5]=A[3],E[g+6]=C[0],E[g+7]=C[1],E[g+8]=C[3],E[g+9]=C[1],E[g+10]=C[2],E[g+11]=C[3],M.faceidx+=12,M.vertices+=8}}(t,e,i,r,n):"rectangle"!==o&&"oval"!==o&&"parabola"!==o||function(t,e,i,r,n,a,o){var l,h,c,d,u,f,p,g,m,v;if((c=e.length)<2||e[0].length<2)return;for(l=0;l<c;l++)e[l]=nt(e[l],r);if(d=e[0].length,!n)return dt(t,e[0],e[c-1],i);var _=[],y=[],b=[];for(h=0;h<c;h++)_.push(.25+1.5*Math.sqrt((c-1)*h-Math.pow(h,2))/(c-1)),y.push(.5),b.push(2*(Math.pow(h/c,2)-h/c)+.6);var x,w,A,C,S,M,z,T,E,L,F=[];for(h=0;h<2*c-1;h++)F[h]=[h,h+1,h+1-2*c,h-2*c];F[2*c-1]=[h,h+1-2*c,h+1-4*c,h-2*c];let I=t.updateGeoGroup();for(l=0;l<d;l++){let r=t.groups,a=!1;var O,D;for(I=t.updateGeoGroup(2*c),r!=t.groups&&l>0&&(l-=1,a=!0),M=Math.round(l*(i.length-1)/d),S=s.CC.color(i[M]),m=p,v=g,p=[],g=[],u=[],void 0!==e[0][l].atom&&(C=e[0][l].atom,"oval"===o?f=_:"rectangle"===o?f=y:"parabola"===o&&(f=b)),f||(f=y),h=0;h<c;h++)O=l<d-1?e[h][l+1].clone().sub(e[h][l]):e[h][l-1].clone().sub(e[h][l]).negate(),D=h<c-1?e[h+1][l].clone().sub(e[h][l]):e[h-1][l].clone().sub(e[h][l]).negate(),u[h]=D.cross(O).normalize().multiplyScalar(n*f[h]);for(h=0;h<c;h++)p[h]=e[h][l].clone().add(u[h].clone().negate());for(h=0;h<c;h++)g[h]=e[h][l].clone().add(u[h]);for(z=I.vertexArray,T=I.colorArray,E=I.faceArray,w=3*(x=I.vertices),h=0;h<c;h++)z[w+3*h+0]=p[h].x,z[w+3*h+1]=p[h].y,z[w+3*h+2]=p[h].z;for(h=0;h<c;h++)z[w+3*h+0+3*c]=g[c-1-h].x,z[w+3*h+1+3*c]=g[c-1-h].y,z[w+3*h+2+3*c]=g[c-1-h].z;for(h=0;h<2*c;++h)T[w+3*h+0]=S.r,T[w+3*h+1]=S.g,T[w+3*h+2]=S.b;if(l>0&&!a){for(h=0;h<2*c;h++)L=[x+F[h][0],x+F[h][1],x+F[h][2],x+F[h][3]],E[A=I.faceidx]=L[0],E[A+1]=L[1],E[A+2]=L[3],E[A+3]=L[1],E[A+4]=L[2],E[A+5]=L[3],I.faceidx+=6;if(C.clickable||C.hoverable){var k=[];for(h in k.push(new J.Triangle(m[0],p[0],p[c-1])),k.push(new J.Triangle(m[0],p[c-1],m[c-1])),k.push(new J.Triangle(m[c-1],p[c-1],g[c-1])),k.push(new J.Triangle(m[c-1],g[c-1],v[c-1])),k.push(new J.Triangle(g[0],v[0],v[c-1])),k.push(new J.Triangle(g[c-1],g[0],v[c-1])),k.push(new J.Triangle(p[0],m[0],v[0])),k.push(new J.Triangle(g[0],p[0],v[0])),k)C.intersectionShape.triangle.push(k[h])}}I.vertices+=2*c}for(z=I.vertexArray,T=I.colorArray,E=I.faceArray,w=3*(x=I.vertices),A=I.faceidx,l=0;l<c-1;l++)L=[l,l+1,2*c-2-l,2*c-1-l],E[A=I.faceidx]=L[0],E[A+1]=L[1],E[A+2]=L[3],E[A+3]=L[1],E[A+4]=L[2],E[A+5]=L[3],I.faceidx+=6;for(l=0;l<c-1;l++)L=[x-1-l,x-2-l,x-2*c+l+1,x-2*c+l],E[A=I.faceidx]=L[0],E[A+1]=L[1],E[A+2]=L[3],E[A+3]=L[1],E[A+4]=L[2],E[A+5]=L[3],I.faceidx+=6}(t,e,i,r,n,0,o)}function ft(t){return t&&"C"===t.elem&&"CA"===t.atom}function pt(t,e){if(t&&e&&t.chain===e.chain){if(!(t.hetflag||e.hetflag||t.reschain!==e.reschain||t.resi!==e.resi&&t.resi!==e.resi-1))return!0;if(t.resi<e.resi){var i=t.x-e.x,r=t.y-e.y,s=t.z-e.z,n=i*i+r*r+s*s;if("CA"==t.atom&&"CA"==e.atom&&n<16)return!0;if(("P"==t.atom||"P"==e.atom)&&n<64)return!0}}return!1}function gt(t,e,i,r,s){if(null!=e&&0!=e.vertices){s&&(e.initTypedArrays(),e.setUpNormals());var a=new n.MeshDoubleLambertMaterial;a.vertexColors=n.Coloring.FaceColors,"number"==typeof i&&i>=0&&i<1&&(a.transparent=!0,a.opacity=i),a.outline=r;var o=new n.Mesh(e,a);t.add(o)}}function mt(t,e,i,r,s,n,a,o,l){var h,c,d,u,f,p;if(r&&s&&a){var g=s.sub(r);g.normalize();var m=o[l];for(c=l+1;c<o.length&&(m=o[c]).atom!=a.atom;c++);if((m=m?new $.Vector3(m.x,m.y,m.z):new $.Vector3(0,0,0)).sub(r),"arrow start"===a.ss){var v=m.clone().multiplyScalar(.3).cross(s);r.add(v);var _=m.clone().cross(g).normalize();g.rotateAboutVector(_,.43)}for(a.style.cartoon.ribbon?h=a.style.cartoon.thickness||ht:a.style.cartoon.width?h=a.style.cartoon.width:"c"===a.ss?h="P"===a.atom?lt:at:"arrow start"===a.ss?(h=ot,f=!0):h="arrow end"===a.ss||"h"===a.ss&&a.style.cartoon.tubes||"tube start"===a.ss?at:ot,null!=n&&g.dot(n)<0&&g.negate(),g.multiplyScalar(h),c=0;c<e;c++)d=2*c/(e-1)-1,(u=new $.Vector3(r.x+d*g.x,r.y+d*g.y,r.z+d*g.z)).atom=a,i&&"s"===a.ss&&(u.smoothen=!0),t[c].push(u);if(f)for(g.multiplyScalar(2),c=0;c<e;c++)d=2*c/(e-1)-1,(u=new $.Vector3(r.x+d*g.x,r.y+d*g.y,r.z+d*g.z)).atom=a,u.smoothen=!1,u.skip=!0,t[c].push(u);return p=a.style.cartoon.style||"default",t.style?t.style!=p&&(console.log("Warning: a cartoon chain's strand-style is ambiguous"),t.style="default"):t.style=p,"arrow start"!==a.ss&&"arrow end"!==a.ss||(a.ss="s"),f}}const vt={C:!0,CA:!0,O:!0,P:!0,OP2:!0,O2P:!0,"O5'":!0,"O3'":!0,"C5'":!0,"C2'":!0,"O5*":!0,"O3*":!0,"C5*":!0,"C2*":!0,N1:!0,N3:!0},_t={DA:!0,DG:!0,A:!0,G:!0},yt={DT:!0,DC:!0,U:!0,C:!0,T:!0},bt={DA:!0,DG:!0,A:!0,G:!0,DT:!0,DC:!0,U:!0,C:!0,T:!0};function xt(t,e,i,a=10){let o=a,l=a;var h,c,d,u,f,p,g,m,v,_,y,b,x,w,A,C,S,M,z=new n.Geometry(!0),T=new n.Geometry(!0),E=[],F=[],I=1,O=!1,D={};for(var k in r.Gradient.builtinGradients)r.Gradient.builtinGradients.hasOwnProperty(k)&&(D[k]=new r.Gradient.builtinGradients[k](i[1],i[0]));var R=function(t,e){return i&&"spectrum"===e.color?e.colorscheme in D?D[e.colorscheme].valueToHex(t.resi):D.sinebow.valueToHex(t.resi):(0,L.getColorFromStyle)(t,e).getHex()};for(m=0;m<o;m++)F[m]=[];var P=!1,U=!1,B=[];for(m in e){if("C"===(u=e[m]).elem&&"CA"===u.atom){var N=pt(d,u);N&&"s"===u.ss?P=!0:P&&(d&&c&&d.style.cartoon.arrows&&c.style.cartoon.arrows&&(d.ss="arrow end",c.ss="arrow start"),P=!1),N&&("h"===d.ss||"tube start"==d.ss)&&d.style.cartoon.tubes?!U&&"tube start"!=d.ss&&u.style.cartoon.tubes&&(u.ss="tube start",U=!0):U&&("tube start"===d.ss?d.ss="tube end":c&&c.style.cartoon.tubes&&(c.ss="tube end"),U=!1),c=d,d=u}u&&u.atom in vt&&B.push(u)}U&&d.style.cartoon.tubes&&(d.ss="tube end",U=!1);var G=function(e){F[0].length>0&&ut(T,F,E,l,g,0,F.style);var i=[],r=null;if(e){for(m=0;m<o;m++)i[m]=F[m][F[m].length-1];r=E[E.length-1]}for(F=[],m=0;m<o;m++)F[m]=[];if(E=[],e){for(m=0;m<o;m++)F[m].push(i[m]);E.push(r)}gt(t,T,I,O,!0),gt(t,z,I,O,!1),T=new n.Geometry(!0),z=new n.Geometry(!0)};d=void 0;for(var V=0;V<B.length;V++){var j=(u=B[V]).resn.trim(),H=j in bt;if(I=1,h=u.style.cartoon,d&&d.style.cartoon&&(I=d.style.cartoon.opacity),d&&d.style.cartoon&&d.style.cartoon.outline&&(O=d.style.cartoon.outline),!d||!d.style.cartoon||u.style.cartoon&&d.style.cartoon.opacity==u.style.cartoon.opacity||G(d.chain==u.chain),"trace"===h.style){if(u.hetflag);else if("C"===u.elem&&"CA"===u.atom||H&&"P"===u.atom||"BB"===u.atom){if(p=R(u,h),g=(0,L.isNumeric)(h.thickness)?h.thickness:ht,pt(d,u))if(p==f){var W=s.CC.color(p);rt.drawCylinder(z,d,u,g,W,2,2)}else{var q=(new $.Vector3).addVectors(d,u).multiplyScalar(.5),Y=s.CC.color(f),Z=s.CC.color(p);rt.drawCylinder(z,d,q,g,Y,2,0),rt.drawCylinder(z,q,u,g,Z,0,2)}if((!0===u.clickable||u.hoverable)&&void 0!==u.intersectionShape){var X=new $.Vector3(u.x,u.y,u.z);u.intersectionShape.sphere.push(new J.Sphere(X,g))}d=u,f=p}}else{if(ft(u)||H&&("P"===u.atom||0==u.atom.indexOf("O5"))){if(M)if("tube end"===u.ss)M=!1,S=new $.Vector3(u.x,u.y,u.z),rt.drawCylinder(z,C,S,2,s.CC.color(f),1,1),u.ss="h";else{if(d.chain==u.chain&&"tube end"!==d.ss)continue;M=!1,d.ss="h",S=new $.Vector3(d.x,d.y,d.z),rt.drawCylinder(z,C,S,2,s.CC.color(f),1,1)}if(d&&(!pt(d,u)||"tube start"===d.ss)){for("tube start"===d.ss&&(M=!0,C=new $.Vector3(d.x,d.y,d.z),d.ss="h"),A&&(w=b?(new $.Vector3).addVectors(d,b).multiplyScalar(.5):new $.Vector3(d.x,d.y,d.z),rt.drawCylinder(z,w,A,ct,s.CC.color(A.color),0,2),mt(F,o,!0,b,x,y,d,B,V),E.push(p),w=null,A=null),F[0].length>0&&ut(T,F,E,l,g,0,F.style),F=[],m=0;m<o;m++)F[m]=[];E=[]}if(void 0===d||d.rescode!=u.rescode||d.resi!=u.resi){if(A&&null!=d){var K=(w=(new $.Vector3).addVectors(d,u).multiplyScalar(.5)).clone().sub(A).multiplyScalar(.02);w.add(K),rt.drawCylinder(z,w,A,ct,s.CC.color(A.color),0,2),w=null,A=null}p=R(u,h),E.push(p),g=(0,L.isNumeric)(h.thickness)?h.thickness:ht,d=u,(v=new $.Vector3(d.x,d.y,d.z)).resi=d.resi,f=p}!0!==u.clickable&&!0!==u.hoverable||void 0!==u.intersectionShape&&void 0!==u.intersectionShape.triangle||(u.intersectionShape={sphere:null,cylinder:[],line:[],triangle:[]})}else null!=d&&(ft(d)&&"O"===u.atom||H&&"P"===d.atom&&("OP2"===u.atom||"O2P"===u.atom)||H&&0==d.atom.indexOf("O5")&&0==u.atom.indexOf("C5"))?((_=new $.Vector3(u.x,u.y,u.z)).resi=u.resi,"OP2"!==u.atom&&"O2P"!==u.atom||(x=new $.Vector3(u.x,u.y,u.z))):H&&0==u.atom.indexOf("O3")?b=new $.Vector3(u.x,u.y,u.z):("N1"===u.atom&&j in _t||"N3"===u.atom&&j in yt)&&((A=new $.Vector3(u.x,u.y,u.z)).color=(0,L.getColorFromStyle)(u,h).getHex());_&&v&&_.resi===v.resi&&(mt(F,o,!0,v,_,y,d,B,V),y=_,v=null,_=null,E.push(p))}}A&&(w=b?(new $.Vector3).addVectors(d,b).multiplyScalar(.5):new $.Vector3(d.x,d.y,d.z),rt.drawCylinder(z,w,A,ct,s.CC.color(A.color),0,2),mt(F,o,!0,b,x,y,d,B,V),E.push(p)),G(!1)}class GLShape{static finalizeGeo(t){var e=t.updateGeoGroup(0);e.vertices>0&&e.truncateArrayBuffers(!0,!0)}static updateColor(t,e){var i,r,n;e=e||s.CC.color(e),t.colorsNeedUpdate=!0,e.constructor!==Array&&(i=e.r,r=e.g,n=e.b);for(let s in t.geometryGroups){let a=t.geometryGroups[s],o=a.colorArray;for(let t=0,s=a.vertices;t<s;++t){if(e.constructor===Array){let s=e[t];i=s.r,r=s.g,n=s.b}o[3*t]=i,o[3*t+1]=r,o[3*t+2]=n}}}static drawArrow(t,e,i){var r=i.start,s=i.end,n=i.radius,a=i.radiusRatio,o=i.mid,l=i.midpos;if(!r||!s)return;var h=e.updateGeoGroup(51),c=new $.Vector3(s.x,s.y,s.z).sub(r);if(l){let t=c.length();o=l>0?l/t:(t+l)/t}c.multiplyScalar(o);var d=new $.Vector3(r.x,r.y,r.z).add(c),u=c.clone().negate();let f=new $.Vector3(r.x,r.y,r.z);t.intersectionShape.cylinder.push(new J.Cylinder(f,d.clone(),n)),t.intersectionShape.sphere.push(new J.Sphere(f,n));var p=[];p[0]=c.clone(),Math.abs(p[0].x)>1e-4?p[0].y+=1:p[0].x+=1,p[0].cross(c),p[0].normalize(),p[4]=p[0].clone(),p[4].crossVectors(p[0],c),p[4].normalize(),p[8]=p[0].clone().negate(),p[12]=p[4].clone().negate(),p[2]=p[0].clone().add(p[4]).normalize(),p[6]=p[4].clone().add(p[8]).normalize(),p[10]=p[8].clone().add(p[12]).normalize(),p[14]=p[12].clone().add(p[0]).normalize(),p[1]=p[0].clone().add(p[2]).normalize(),p[3]=p[2].clone().add(p[4]).normalize(),p[5]=p[4].clone().add(p[6]).normalize(),p[7]=p[6].clone().add(p[8]).normalize(),p[9]=p[8].clone().add(p[10]).normalize(),p[11]=p[10].clone().add(p[12]).normalize(),p[13]=p[12].clone().add(p[14]).normalize(),p[15]=p[14].clone().add(p[0]).normalize();var g,m,v,_,y,b,x,w,A,C,S,M,z,T,E,L,F,I,O,D,k,R,P=h.vertices,U=h.vertexArray,B=h.faceArray,N=h.normalArray,G=h.lineArray;for(m=0,v=p.length;m<v;++m){g=3*(P+3*m);var V=p[m].clone().multiplyScalar(n).add(r),j=p[m].clone().multiplyScalar(n).add(d),H=p[m].clone().multiplyScalar(n*a).add(d);if(U[g]=V.x,U[g+1]=V.y,U[g+2]=V.z,U[g+3]=j.x,U[g+4]=j.y,U[g+5]=j.z,U[g+6]=H.x,U[g+7]=H.y,U[g+8]=H.z,m>0){var W=U[g-3],q=U[g-2],Y=U[g-1],Z=new $.Vector3(W,q,Y),X=new $.Vector3(s.x,s.y,s.z),K=d.clone(),Q=new $.Vector3(H.x,H.y,H.z);t.intersectionShape.triangle.push(new J.Triangle(Q,X,Z)),t.intersectionShape.triangle.push(new J.Triangle(Z.clone(),K,Q.clone()))}}h.vertices+=48,U[g=3*h.vertices]=r.x,U[g+1]=r.y,U[g+2]=r.z,U[g+3]=d.x,U[g+4]=d.y,U[g+5]=d.z,U[g+6]=s.x,U[g+7]=s.y,U[g+8]=s.z,h.vertices+=3;var tt=h.vertices-3,et=h.vertices-2,it=h.vertices-1,rt=3*tt,st=3*et,nt=3*it;for(m=0,v=p.length-1;m<v;++m){var at=P+3*m;g=3*at,y=h.faceidx,b=h.lineidx,z=3*(x=at),T=3*(w=at+1),E=3*(A=at+2),L=3*(C=at+4),F=3*(S=at+5),I=3*(M=at+3),O=D=p[m],k=R=p[m+1],N[z]=O.x,N[T]=D.x,N[I]=R.x,N[z+1]=O.y,N[T+1]=D.y,N[I+1]=R.y,N[z+2]=O.z,N[T+2]=D.z,N[I+2]=R.z,N[T]=D.x,N[L]=k.x,N[I]=R.x,N[T+1]=D.y,N[L+1]=k.y,N[I+1]=R.y,N[T+2]=D.z,N[L+2]=k.z,N[I+2]=R.z,N[E]=D.x,N[F]=k.x,N[E+1]=D.y,N[F+1]=k.y,N[E+2]=D.z,N[F+2]=k.z,B[y]=x,B[y+1]=w,B[y+2]=M,B[y+3]=w,B[y+4]=C,B[y+5]=M,B[y+6]=x,B[y+7]=M,B[y+8]=tt,B[y+9]=A,B[y+10]=et,B[y+11]=S,B[y+12]=A,B[y+13]=it,B[y+14]=S,G[b]=x,G[b+1]=w,G[b+2]=x,G[b+3]=M,G[b+4]=C,G[b+5]=M,G[b+6]=x,G[b+7]=M,G[b+8]=A,G[b+9]=w,G[b+10]=A,G[b+11]=S,G[b+12]=C,G[b+13]=S,G[b+14]=A,G[b+15]=it,G[b+16]=A,G[b+17]=S,G[b+18]=it,G[b+19]=S,h.faceidx+=15,h.lineidx+=20}_=[P+45,P+46,P+1,P,P+47,P+2],y=h.faceidx,b=h.lineidx,z=3*(x=_[0]),T=3*(w=_[1]),E=3*(A=_[4]),L=3*(C=_[2]),F=3*(S=_[5]),I=3*(M=_[3]),O=D=p[15],k=R=p[0],N[z]=O.x,N[T]=D.x,N[I]=R.x,N[z+1]=O.y,N[T+1]=D.y,N[I+1]=R.y,N[z+2]=O.z,N[T+2]=D.z,N[I+2]=R.z,N[T]=D.x,N[L]=k.x,N[I]=R.x,N[T+1]=D.y,N[L+1]=k.y,N[I+1]=R.y,N[T+2]=D.z,N[L+2]=k.z,N[I+2]=R.z,N[E]=D.x,N[F]=k.x,N[E+1]=D.y,N[F+1]=k.y,N[E+2]=D.z,N[F+2]=k.z,c.normalize(),u.normalize(),N[rt]=u.x,N[st]=N[nt]=c.x,N[rt+1]=u.y,N[st+1]=N[nt+1]=c.y,N[rt+2]=u.z,N[st+2]=N[nt+2]=c.z,B[y]=x,B[y+1]=w,B[y+2]=M,B[y+3]=w,B[y+4]=C,B[y+5]=M,B[y+6]=x,B[y+7]=M,B[y+8]=tt,B[y+9]=A,B[y+10]=et,B[y+11]=S,B[y+12]=A,B[y+13]=it,B[y+14]=S,G[b]=x,G[b+1]=w,G[b+2]=x,G[b+3]=M,G[b+4]=C,G[b+5]=M,G[b+6]=x,G[b+7]=M,G[b+8]=A,G[b+9]=w,G[b+10]=A,G[b+11]=S,G[b+12]=C,G[b+13]=S,G[b+14]=A,G[b+15]=it,G[b+16]=A,G[b+17]=S,G[b+18]=it,G[b+19]=S,h.faceidx+=15,h.lineidx+=20}static updateBoundingFromPoints(t,e,i,r){t.center.set(0,0,0);let s=1/0,n=1/0,a=1/0,o=-1/0,l=-1/0,h=-1/0;t.box&&(s=t.box.min.x,o=t.box.max.x,n=t.box.min.y,l=t.box.max.y,a=t.box.min.z,h=t.box.max.z);for(let t=0,e=r;t<e;t++){var c=i[3*t],d=i[3*t+1],u=i[3*t+2];c<s&&(s=c),d<n&&(n=d),u<a&&(a=u),c>o&&(o=c),d>l&&(l=d),u>h&&(h=u)}t.center.set((o+s)/2,(l+n)/2,(h+a)/2),t.radius=t.center.distanceTo({x:o,y:l,z:h}),t.box={min:{x:s,y:n,z:a},max:{x:o,y:l,z:h}}}static addCustomGeo(t,e,i,r,s){var n,a,o,l,h,c,d,u,f,p=e.addGeoGroup(),g=i.vertexArr,m=i.normalArr,v=i.faceArr;p.vertices=g.length,p.faceidx=v.length;var _=p.vertexArray,y=p.colorArray;for(r.constructor!==Array&&(u=r.r,f=r.g,l=r.b),c=0,d=p.vertices;c<d;++c)n=3*c,a=g[c],_[n]=a.x,_[n+1]=a.y,_[n+2]=a.z,r.constructor===Array&&(u=(h=r[c]).r,f=h.g,l=h.b),y[n]=u,y[n+1]=f,y[n+2]=l;if(s)for(c=0,d=p.faceidx/3;c<d;++c){o=v[n=3*c],l=v[n+1],h=v[n+2];var b=new $.Vector3,x=new $.Vector3,w=new $.Vector3;t.intersectionShape.triangle.push(new J.Triangle(b.copy(g[o]),x.copy(g[l]),w.copy(g[h])))}if(s){var A=new $.Vector3(0,0,0),C=0;for(let t=0;t<e.geometryGroups.length;t++)A.add(e.geometryGroups[t].getCentroid()),C++;A.divideScalar(C),GLShape.updateBoundingFromPoints(t.boundingSphere,{centroid:A},_,p.vertices)}if(p.faceArray=new Uint16Array(v),p.truncateArrayBuffers(!0,!0),m.length<p.vertices)p.setNormals();else{var S,M=p.normalArray=new Float32Array(3*p.vertices);for(c=0,d=p.vertices;c<d;++c)n=3*c,S=m[c],M[n]=S.x,M[n+1]=S.y,M[n+2]=S.z}p.setLineIndices(),p.lineidx=p.lineArray.length}static updateFromStyle(t,e){void 0!==e.color?(t.color=e.color||new s.Color,e.color instanceof s.Color||(t.color=s.CC.color(e.color))):t.color=s.CC.color(0),t.wireframe=!!e.wireframe,t.opacity=e.alpha?(0,$.clamp)(e.alpha,0,1):1,void 0!==e.opacity&&(t.opacity=(0,$.clamp)(e.opacity,0,1)),t.side=void 0!==e.side?e.side:n.DoubleSide,t.linewidth=void 0===e.linewidth?1:e.linewidth,t.clickable=!!e.clickable,t.callback=(0,L.makeFunction)(e.callback),t.hoverable=!!e.hoverable,t.hover_callback=(0,L.makeFunction)(e.hover_callback),t.unhover_callback=(0,L.makeFunction)(e.unhover_callback),t.contextMenuEnabled=!!e.contextMenuEnabled,t.hidden=e.hidden,t.frame=e.frame}constructor(t){this.color=16777215,this.hidden=!1,this.wireframe=!1,this.opacity=1,this.linewidth=1,this.clickable=!1,this.hoverable=!1,this.contextMenuEnabled=!1,this.side=n.DoubleSide,this.stylespec=t||{},this.boundingSphere=new J.Sphere,this.intersectionShape={sphere:[],cylinder:[],line:[],triangle:[]},GLShape.updateFromStyle(this,this.stylespec),this.components=[],this.shapeObj=null,this.renderedShapeObj=null,this.geo=new n.Geometry(!0),this.linegeo=new n.Geometry(!0)}updateStyle(t){for(var e in t)this.stylespec[e]=t[e];if(GLShape.updateFromStyle(this,this.stylespec),t.voldata&&t.volscheme){(0,L.adjustVolumeStyle)(t);const e=t.volscheme,i=t.voldata,r=s.CC,n=e.range()||[-1,1];this.geo.setColors((function(t,s,a){let o=i.getVal(t,s,a);return r.color(e.valueToHex(o,n))})),delete this.color}}addCustom(t){t.vertexArr=t.vertexArr||[],t.faceArr=t.faceArr||[],t.normalArr=t.normalArr||[],GLShape.drawCustom(this,this.geo,t)}addSphere(t){t.center||(t.center=new $.Vector3(0,0,0)),t.radius=t.radius?(0,$.clamp)(t.radius,0,1/0):1.5,t.color=s.CC.color(t.color),this.intersectionShape.sphere.push(new J.Sphere(t.center,t.radius)),rt.drawSphere(this.geo,t.center,t.radius,t.color,t.quality),this.components.push({centroid:new $.Vector3(t.center.x,t.center.y,t.center.z)});var e=this.geo.updateGeoGroup(0);GLShape.updateBoundingFromPoints(this.boundingSphere,this.components,e.vertexArray,e.vertices)}addBox(t){var e,i,r,s=t.dimensions||{w:1,h:1,d:1};e="number"==typeof s.w?{x:s.w,y:0,z:0}:s.w,i="number"==typeof s.h?{x:0,y:s.h,z:0}:s.h,r="number"==typeof s.d?{x:0,y:0,z:s.d}:s.d;var n=t.corner;null==n&&(n=void 0!==t.center?{x:t.center.x-.5*(e.x+i.x+r.x),y:t.center.y-.5*(e.y+i.y+r.y),z:t.center.z-.5*(e.z+i.z+r.z)}:{x:0,y:0,z:0});var a=[{x:n.x,y:n.y,z:n.z},{x:n.x+e.x,y:n.y+e.y,z:n.z+e.z},{x:n.x+i.x,y:n.y+i.y,z:n.z+i.z},{x:n.x+e.x+i.x,y:n.y+e.y+i.y,z:n.z+e.z+i.z},{x:n.x+r.x,y:n.y+r.y,z:n.z+r.z},{x:n.x+e.x+r.x,y:n.y+e.y+r.y,z:n.z+e.z+r.z},{x:n.x+i.x+r.x,y:n.y+i.y+r.y,z:n.z+i.z+r.z},{x:n.x+e.x+i.x+r.x,y:n.y+e.y+i.y+r.y,z:n.z+e.z+i.z+r.z}],o=[],l=[];o.splice(o.length,0,a[0],a[1],a[2],a[3]),l.splice(l.length,0,0,2,1,1,2,3);var h=4;o.splice(o.length,0,a[2],a[3],a[6],a[7]),l.splice(l.length,0,h+0,h+2,h+1,h+1,h+2,h+3),h+=4,o.splice(o.length,0,a[4],a[5],a[0],a[1]),l.splice(l.length,0,h+0,h+2,h+1,h+1,h+2,h+3),h+=4,o.splice(o.length,0,a[6],a[7],a[4],a[5]),l.splice(l.length,0,h+0,h+2,h+1,h+1,h+2,h+3),h+=4,o.splice(o.length,0,a[3],a[1],a[7],a[5]),l.splice(l.length,0,h+0,h+2,h+1,h+1,h+2,h+3),h+=4,o.splice(o.length,0,a[2],a[6],a[0],a[4]),l.splice(l.length,0,h+0,h+2,h+1,h+1,h+2,h+3),h+=4;var c=(0,L.extend)({},t);c.vertexArr=o,c.faceArr=l,c.normalArr=[],GLShape.drawCustom(this,this.geo,c);var d=new $.Vector3;this.components.push({centroid:d.addVectors(a[0],a[7]).multiplyScalar(.5)});var u=this.geo.updateGeoGroup(0);GLShape.updateBoundingFromPoints(this.boundingSphere,this.components,u.vertexArray,u.vertices)}addCylinder(t){var e,i;e=t.start?new $.Vector3(t.start.x||0,t.start.y||0,t.start.z||0):new $.Vector3(0,0,0),t.end?void 0===(i=new $.Vector3(t.end.x,t.end.y||0,t.end.z||0)).x&&(i.x=3):i=new $.Vector3(0,0,0);var r=t.radius||.1,n=s.CC.color(t.color);this.intersectionShape.cylinder.push(new J.Cylinder(e,i,r)),rt.drawCylinder(this.geo,e,i,r,n,t.fromCap,t.toCap);var a=new $.Vector3;this.components.push({centroid:a.addVectors(e,i).multiplyScalar(.5)});var o=this.geo.updateGeoGroup(0);GLShape.updateBoundingFromPoints(this.boundingSphere,this.components,o.vertexArray,o.vertices)}addDashedCylinder(t){var e,i;t.dashLength=t.dashLength||.25,t.gapLength=t.gapLength||.25,e=t.start?new $.Vector3(t.start.x||0,t.start.y||0,t.start.z||0):new $.Vector3(0,0,0),t.end?void 0===(i=new $.Vector3(t.end.x,t.end.y||0,t.end.z||0)).x&&(i.x=3):i=new $.Vector3(3,0,0);for(var r=t.radius||.1,n=s.CC.color(t.color),a=Math.sqrt(Math.pow(e.x-i.x,2)+Math.pow(e.y-i.y,2)+Math.pow(e.z-i.z,2)),o=a/(t.gapLength+t.dashLength),l=new $.Vector3(t.start.x||0,t.start.y||0,t.start.z||0),h=new $.Vector3(t.end.x,t.end.y||0,t.end.z||0),c=new $.Vector3((i.x-e.x)/(a/t.gapLength),(i.y-e.y)/(a/t.gapLength),(i.z-e.z)/(a/t.gapLength)),d=new $.Vector3((i.x-e.x)/(a/t.dashLength),(i.y-e.y)/(a/t.dashLength),(i.z-e.z)/(a/t.dashLength)),u=0;u<o;u++)h=new $.Vector3(l.x+d.x,l.y+d.y,l.z+d.z),this.intersectionShape.cylinder.push(new J.Cylinder(l,h,r)),rt.drawCylinder(this.geo,l,h,r,n,t.fromCap,t.toCap),l=new $.Vector3(h.x+c.x,h.y+c.y,h.z+c.z);var f=new $.Vector3;this.components.push({centroid:f.addVectors(e,i).multiplyScalar(.5)});var p=this.geo.updateGeoGroup(0);GLShape.updateBoundingFromPoints(this.boundingSphere,this.components,p.vertexArray,p.vertices)}addCurve(t){t.points=t.points||[],t.smooth=t.smooth||10,void 0===t.fromCap&&(t.fromCap=2),void 0===t.toCap&&(t.toCap=2);var e=nt(t.points,t.smooth);if(e.length<3)console.log("Too few points in addCurve");else{var i=t.radius||.1,r=s.CC.color(t.color),n=0,a=e.length-1,o=e[0].distanceTo(e[1]),l=Math.ceil(2*i/o);if(t.toArrow){let t={start:e[a-=l],end:e[e.length-1],radius:i,color:r,mid:1e-4};this.addArrow(t)}if(t.fromArrow){let t={start:e[n+=l],end:e[0],radius:i,color:r,mid:1e-4};this.addArrow(t)}for(var h=Math.ceil(e.length/2),c={radius:i,color:r,fromCap:2,toCap:2},d=n;d<a;d++)c.start=e[d],c.end=e[d+1],c.fromCap=2,c.toCap=2,d<h?(c.fromCap=2,c.toCap=0):d>h?(c.fromCap=0,c.toCap=2):(c.fromCap=2,c.toCap=2),this.addCylinder(c)}}addLine(t){var e,i;e=t.start?new $.Vector3(t.start.x||0,t.start.y||0,t.start.z||0):new $.Vector3(0,0,0),t.end?void 0===(i=new $.Vector3(t.end.x,t.end.y||0,t.end.z||0)).x&&(i.x=3):i=new $.Vector3(3,0,0);var r=this.geo.updateGeoGroup(2),s=r.vertices,n=3*s,a=r.vertexArray;a[n]=e.x,a[n+1]=e.y,a[n+2]=e.z,a[n+3]=i.x,a[n+4]=i.y,a[n+5]=i.z,r.vertices+=2;var o=r.lineArray,l=r.lineidx;o[l]=s,o[l+1]=s+1,r.lineidx+=2;var h=new $.Vector3;this.components.push({centroid:h.addVectors(e,i).multiplyScalar(.5)}),r=this.geo.updateGeoGroup(0),GLShape.updateBoundingFromPoints(this.boundingSphere,this.components,r.vertexArray,r.vertices)}addArrow(t){if(t.start?t.start=new $.Vector3(t.start.x||0,t.start.y||0,t.start.z||0):t.start=new $.Vector3(0,0,0),t.dir instanceof $.Vector3&&"number"==typeof t.length){var e=t.dir.clone().multiplyScalar(t.length).add(t.start);t.end=e}else t.end?(t.end=new $.Vector3(t.end.x,t.end.y||0,t.end.z||0),void 0===t.end.x&&(t.end.x=3)):t.end=new $.Vector3(3,0,0);t.radius=t.radius||.1,t.radiusRatio=t.radiusRatio||1.618034,t.mid=0<t.mid&&t.mid<1?t.mid:.618034,GLShape.drawArrow(this,this.geo,t);var i=new $.Vector3;this.components.push({centroid:i.addVectors(t.start,t.end).multiplyScalar(.5)});var r=this.geo.updateGeoGroup(0);GLShape.updateBoundingFromPoints(this.boundingSphere,this.components,r.vertexArray,r.vertices)}static distance_from(t,e){return Math.sqrt(Math.pow(t.x-e.x,2)+Math.pow(t.y-e.y,2)+Math.pow(t.z-e.z,2))}static inSelectedRegion(t,e,i){for(var r=0;r<e.length;r++)if(GLShape.distance_from(e[r],t)<=i)return!0;return!1}addIsosurface(t,e,i,r){var s,n,a=void 0!==e.isoval&&"number"==typeof e.isoval?e.isoval:0,o=!!e.voxel,l=void 0===e.smoothness?1:e.smoothness,h=t.size.x,c=t.size.y,d=t.size.z,u=new Int16Array(h*c*d),f=t.data;for(s=0,n=u.length;s<n;++s)u[s]=-1;var p=new Uint8Array(h*c*d);for(s=0,n=f.length;s<n;++s){(a>=0?f[s]-a:a-f[s])>0&&(p[s]|=GLShape.ISDONE)}var g=[],m=[];MarchingCube.march(p,g,m,{fulltable:!0,voxel:o,unitCube:t.unit,origin:t.origin,matrix:t.matrix,nX:h,nY:c,nZ:d}),!o&&l>0&&MarchingCube.laplacianSmooth(l,g,m);var v=[],_=[],y=[];if(e.selectedRegion&&void 0===e.coords&&(e.coords=e.selectedRegion),void 0===e.coords&&void 0!==e.selection&&(r?e.coords=r.selectedAtoms(e.selection):console.log("addIsosurface needs viewer is selection provided.")),void 0!==e.coords){var b=e.coords[0].x,x=e.coords[0].y,w=e.coords[0].z,A=e.coords[0].x,C=e.coords[0].y,S=e.coords[0].z;for(let t=0;t<e.coords.length;t++)e.coords[t].x>b?b=e.coords[t].x:e.coords[t].x<A&&(A=e.coords[t].x),e.coords[t].y>x?x=e.coords[t].y:e.coords[t].y<C&&(C=e.coords[t].y),e.coords[t].z>w?w=e.coords[t].z:e.coords[t].z<S&&(S=e.coords[t].z);var M=2;void 0!==e.radius&&(M=e.radius),void 0!==e.selectedOffset&&(M=e.selectedOffset),void 0!==e.seldist&&(M=e.seldist),A-=M,b+=M,C-=M,x+=M,S-=M,w+=M;for(let t=0;t<g.length;t++)g[t].x>A&&g[t].x<b&&g[t].y>C&&g[t].y<x&&g[t].z>S&&g[t].z<w&&GLShape.inSelectedRegion(g[t],e.coords,M)?(v.push(_.length),_.push(g[t])):v.push(-1);for(let t=0;t+2<m.length;t+=3)-1!==v[m[t]]&&-1!==v[m[t+1]]&&-1!==v[m[t+2]]&&(y.push(m[t]-(m[t]-v[m[t]])),y.push(m[t+1]-(m[t+1]-v[m[t+1]])),y.push(m[t+2]-(m[t+2]-v[m[t+2]])));g=_,m=y}GLShape.drawCustom(this,this.geo,{vertexArr:g,faceArr:m,normalArr:[],clickable:e.clickable,hoverable:e.hoverable}),this.updateStyle(e);var z=new $.Vector3(t.origin.x,t.origin.y,t.origin.z),T=new $.Vector3(t.size.x*t.unit.x,t.size.y*t.unit.y,t.size.z*t.unit.z),E=new $.Vector3(0,0,0),L=z.clone(),F=z.clone().add(T);for(let t=0;t<g.length;t++)E.add(g[t]),L.max(g[t]),F.min(g[t]);E.divideScalar(g.length);var I=E.distanceTo(F),O=E.distanceTo(L);this.boundingSphere.center=E,this.boundingSphere.radius=Math.max(I,O),"function"==typeof i&&i()}addVolumetricData(t,e,i){t=new st.VolumeData(t,e),this.addIsosurface(t,i)}finalize(){return GLShape.finalizeGeo(this.geo),this.geo.initTypedArrays(),this.geo}globj(t){if(this.renderedShapeObj&&(t.remove(this.renderedShapeObj),this.renderedShapeObj=null),!this.hidden){GLShape.finalizeGeo(this.geo),this.geo.initTypedArrays(),this.wireframe&&this.geo.setUpWireframe(),void 0!==this.color&&GLShape.updateColor(this.geo,this.color),this.shapeObj=new n.Object3D;var e=null;e=this.side==n.DoubleSide?new n.MeshDoubleLambertMaterial({wireframe:this.wireframe,side:this.side,transparent:this.opacity<1,opacity:this.opacity,wireframeLinewidth:this.linewidth,vertexColors:n.Coloring.VertexColors}):new n.MeshLambertMaterial({wireframe:this.wireframe,side:this.side,transparent:this.opacity<1,opacity:this.opacity,wireframeLinewidth:this.linewidth,vertexColors:n.Coloring.VertexColors});var i=new n.Mesh(this.geo,e);if(this.shapeObj.add(i),this.linegeo&&this.linegeo.vertices>0){var r=new n.LineBasicMaterial({linewidth:this.linewidth,color:this.color}),s=new n.Line(this.linegeo,r,n.LineStyle.LinePieces);this.shapeObj.add(s)}this.renderedShapeObj=this.shapeObj.clone(),t.add(this.renderedShapeObj)}}removegl(t){this.renderedShapeObj&&(void 0!==this.renderedShapeObj.geometry&&this.renderedShapeObj.geometry.dispose(),void 0!==this.renderedShapeObj.material&&this.renderedShapeObj.material.dispose(),t.remove(this.renderedShapeObj),this.renderedShapeObj=null),this.shapeObj=null}get position(){return this.boundingSphere.center}get x(){return this.boundingSphere.center.x}get y(){return this.boundingSphere.center.y}get z(){return this.boundingSphere.center.z}}function wt(t){if(t.vertexArr.length<64e3)return[t];var e=[{vertexArr:[],normalArr:[],faceArr:[]}];t.colorArr&&(e.colorArr=[]);var i=[],r=[],s=0,n=t.faceArr;for(let o=0,l=n.length;o<l;o+=3){let l=e[s];for(let e=0;e<3;e++){var a=n[o+e];i[a]!==s&&(i[a]=s,r[a]=l.vertexArr.length,l.vertexArr.push(t.vertexArr[a]),t.normalArr&&t.normalArr[a]&&l.normalArr.push(t.normalArr[a]),t.colorArr&&t.colorArr[a]&&l.colorArr.push(t.colorArr[a])),l.faceArr.push(r[a])}l.vertexArr.length>=64e3&&(e.push({vertexArr:[],normalArr:[],faceArr:[]}),t.colorArr&&(e.colorArr=[]),s++)}return e}GLShape.ISDONE=2,GLShape.drawCustom=function(t,e,i){var r=i,n=r.vertexArr,a=r.faceArr;0!==n.length&&0!==a.length||console.warn("Error adding custom shape component: No vertices and/or face indices supplied!");var o=i.color;void 0===o&&(o=t.color),o=s.CC.color(o);for(var l=wt(r),h=0,c=l.length;h<c;h++)GLShape.addCustomGeo(t,e,l[h],l[h].colorArr?l[h].colorArr:o,i.clickable)};class GLVolumetricRender{static interpolateArray(t,e){function i(t,e,i){return t+(e-t)*i}var r=[],s=(t.length-1)/(e-1);r[0]=t[0];for(var n=1;n<e-1;n++){var a=n*s,o=Math.floor(a),l=Math.ceil(a),h=a-o;r[n]=i(t[o],t[l],h)}return r[e-1]=t[t.length-1],r}constructor(t,e,i){this.hidden=!1,this.boundingSphere=new J.Sphere,this.renderedShapeObj=null,this.shapeObj=null,this.subsamples=5,this.data=null,this.transferfunctionbuffer=[],this.min=0,this.max=0,e=e||{};var r=Object.assign([],e.transferfn);this.subsamples=e.subsamples||5;var n,a,o,l,h,c,d,u,f,p;r.forEach((function(t){t.value=parseFloat(t.value)})),r.sort((function(t,e){return t.value-e.value})),this.min=r[0].value,0==r.length&&r.push(r[0]),this.max=r[r.length-1].value;for(let t=0;t<r.length-1;t++)if(o=s.CC.color(r[t].color),l=s.CC.color(r[t+1].color),f=r[t].opacity,p=r[t+1].opacity,(n=Math.floor(256*(r[t].value-this.min)/(this.max-this.min)))!=(a=Math.floor(256*(r[t+1].value-this.min)/(this.max-this.min)))){h=GLVolumetricRender.interpolateArray([255*o.r,255*l.r],a-n),c=GLVolumetricRender.interpolateArray([255*o.g,255*l.g],a-n),d=GLVolumetricRender.interpolateArray([255*o.b,255*l.b],a-n),u=GLVolumetricRender.interpolateArray([255*f,255*p],a-n);for(let t=0;t<h.length;t++)this.transferfunctionbuffer.push(h[t]),this.transferfunctionbuffer.push(c[t]),this.transferfunctionbuffer.push(d[t]),this.transferfunctionbuffer.push(u[t])}if(this.transferfunctionbuffer=new Uint8ClampedArray(this.transferfunctionbuffer),t.matrix){let e=new $.Vector3(0,0,0),i=new $.Vector3(t.size.x,t.size.y,t.size.z),r=new $.Vector3(1,1,1);e.applyMatrix4(t.matrix),i.applyMatrix4(t.matrix),r.applyMatrix4(t.matrix).sub(e),this.extent=[[e.x,e.y,e.z],[i.x,i.y,i.z]];for(let e=1;e<7;e++)i.x=1&e?t.size.x:0,i.y=2&e?t.size.y:0,i.z=4&e?t.size.z:0,i.applyMatrix4(t.matrix),this.extent[0][0]=Math.min(this.extent[0][0],i.x),this.extent[0][1]=Math.min(this.extent[0][1],i.y),this.extent[0][2]=Math.min(this.extent[0][2],i.z),this.extent[1][0]=Math.max(this.extent[1][0],i.x),this.extent[1][1]=Math.max(this.extent[1][1],i.y),this.extent[1][2]=Math.max(this.extent[1][2],i.z);let s=i.x-e.x,n=i.y-e.y,a=i.z-e.z;this.maxdepth=Math.sqrt(s*s+n*n+a*a),this.minunit=Math.min(Math.min(r.x,r.y),r.z),this.texmatrix=(new $.Matrix4).identity().scale({x:t.size.x,y:t.size.y,z:t.size.z}),this.texmatrix=this.texmatrix.multiplyMatrices(t.matrix,this.texmatrix),this.texmatrix=this.texmatrix.getInverse(this.texmatrix)}else{this.texmatrix=(new $.Matrix4).identity();let e=t.unit.x*t.size.x,i=t.unit.y*t.size.y,r=t.unit.z*t.size.z;this.texmatrix.makeTranslation(-t.origin.x/e,-t.origin.y/i,-t.origin.z/r),this.texmatrix.scale({x:1/e,y:1/i,z:1/r}),this.minunit=Math.min(Math.min(t.unit.x,t.unit.y),t.unit.z),this.extent=[[t.origin.x,t.origin.y,t.origin.z],[t.origin.x+e,t.origin.y+i,t.origin.z+r]],this.maxdepth=Math.sqrt(e*e+i*i+r*r)}var g=new GLShape({});if(g.addBox({corner:{x:this.extent[0][0],y:this.extent[0][1],z:this.extent[0][2]},dimensions:{w:this.extent[1][0]-this.extent[0][0],h:this.extent[1][1]-this.extent[0][1],d:this.extent[1][2]-this.extent[0][2]}}),this.geo=g.finalize(),this.boundingSphere.center=new $.Vector3((this.extent[0][0]+this.extent[1][0])/2,(this.extent[0][1]+this.extent[1][1])/2,(this.extent[0][2]+this.extent[1][2])/2),this.boundingSphere.radius=this.maxdepth/2,void 0===e.coords&&void 0!==e.selection&&(i?e.coords=i.selectedAtoms(e.selection):console.log("Need to provide viewer to volumetric renderer if selection specified.")),void 0!==e.coords&&void 0!==e.seldist){let i=new Uint8Array(t.data.length),r=e.seldist,s=r*r;for(let n=0,a=e.coords.length;n<a;n++){let a=e.coords[n],o=a.x-r,l=a.y-r,h=a.z-r,c=a.x+r,d=a.y+r,u=a.z+r;if(t.getIndex(o,l,h)>=0||t.getIndex(c,d,u)>=0)for(let e=o;e<c;e+=this.minunit)for(let r=l;r<d;r+=this.minunit)for(let n=h;n<u;n+=this.minunit){let o=t.getIndex(e,r,n);if(o>=0&&!i[o]){(e-a.x)*(e-a.x)+(r-a.y)*(r-a.y)+(n-a.z)*(n-a.z)<s&&(i[o]=1)}}}for(let e=0,r=t.data.length;e<r;e++)0==i[e]&&(t.data[e]=1/0)}this.data=t}globj(t){if(this.renderedShapeObj&&(t.remove(this.renderedShapeObj),this.renderedShapeObj=null),!this.hidden){this.shapeObj=new n.Object3D;var e,i=new n.Texture(this.data,!0),r=new n.Texture(this.transferfunctionbuffer,!1);i.needsUpdate=!0,r.needsUpdate=!0,r.flipY=!1,e=new n.VolumetricMaterial({transferfn:r,transfermin:this.min,transfermax:this.max,map:i,extent:this.extent,maxdepth:this.maxdepth,texmatrix:this.texmatrix,unit:this.minunit,subsamples:this.subsamples});var s=new n.Mesh(this.geo,e);this.shapeObj.add(s),this.renderedShapeObj=this.shapeObj.clone(),t.add(this.renderedShapeObj)}}removegl(t){this.renderedShapeObj&&(void 0!==this.renderedShapeObj.geometry&&this.renderedShapeObj.geometry.dispose(),void 0!==this.renderedShapeObj.material&&this.renderedShapeObj.material.dispose(),t.remove(this.renderedShapeObj),this.renderedShapeObj=null),this.shapeObj=null}get position(){return this.boundingSphere.center}get x(){return this.boundingSphere.center.x}get y(){return this.boundingSphere.center.y}get z(){return this.boundingSphere.center.z}}i(620);function At(t,e="utf8"){return new TextDecoder(e).decode(t)}const Ct=new TextEncoder;const St=(()=>{const t=new Uint8Array(4);return!((new Uint32Array(t.buffer)[0]=1)&t[0])})(),Mt={int8:globalThis.Int8Array,uint8:globalThis.Uint8Array,int16:globalThis.Int16Array,uint16:globalThis.Uint16Array,int32:globalThis.Int32Array,uint32:globalThis.Uint32Array,uint64:globalThis.BigUint64Array,int64:globalThis.BigInt64Array,float32:globalThis.Float32Array,float64:globalThis.Float64Array};class IOBuffer{constructor(t=8192,e={}){let i=!1;"number"==typeof t?t=new ArrayBuffer(t):(i=!0,this.lastWrittenByte=t.byteLength);const r=e.offset?e.offset>>>0:0,s=t.byteLength-r;let n=r;(ArrayBuffer.isView(t)||t instanceof IOBuffer)&&(t.byteLength!==t.buffer.byteLength&&(n=t.byteOffset+r),t=t.buffer),this.lastWrittenByte=i?s:0,this.buffer=t,this.length=s,this.byteLength=s,this.byteOffset=n,this.offset=0,this.littleEndian=!0,this._data=new DataView(this.buffer,n,s),this._mark=0,this._marks=[]}available(t=1){return this.offset+t<=this.length}isLittleEndian(){return this.littleEndian}setLittleEndian(){return this.littleEndian=!0,this}isBigEndian(){return!this.littleEndian}setBigEndian(){return this.littleEndian=!1,this}skip(t=1){return this.offset+=t,this}back(t=1){return this.offset-=t,this}seek(t){return this.offset=t,this}mark(){return this._mark=this.offset,this}reset(){return this.offset=this._mark,this}pushMark(){return this._marks.push(this.offset),this}popMark(){const t=this._marks.pop();if(void 0===t)throw new Error("Mark stack empty");return this.seek(t),this}rewind(){return this.offset=0,this}ensureAvailable(t=1){if(!this.available(t)){const e=2*(this.offset+t),i=new Uint8Array(e);i.set(new Uint8Array(this.buffer)),this.buffer=i.buffer,this.length=this.byteLength=e,this._data=new DataView(this.buffer)}return this}readBoolean(){return 0!==this.readUint8()}readInt8(){return this._data.getInt8(this.offset++)}readUint8(){return this._data.getUint8(this.offset++)}readByte(){return this.readUint8()}readBytes(t=1){return this.readArray(t,"uint8")}readArray(t,e){const i=Mt[e].BYTES_PER_ELEMENT*t,r=this.byteOffset+this.offset,s=this.buffer.slice(r,r+i);if(this.littleEndian===St&&"uint8"!==e&&"int8"!==e){const t=new Uint8Array(this.buffer.slice(r,r+i));t.reverse();const s=new Mt[e](t.buffer);return this.offset+=i,s.reverse(),s}const n=new Mt[e](s);return this.offset+=i,n}readInt16(){const t=this._data.getInt16(this.offset,this.littleEndian);return this.offset+=2,t}readUint16(){const t=this._data.getUint16(this.offset,this.littleEndian);return this.offset+=2,t}readInt32(){const t=this._data.getInt32(this.offset,this.littleEndian);return this.offset+=4,t}readUint32(){const t=this._data.getUint32(this.offset,this.littleEndian);return this.offset+=4,t}readFloat32(){const t=this._data.getFloat32(this.offset,this.littleEndian);return this.offset+=4,t}readFloat64(){const t=this._data.getFloat64(this.offset,this.littleEndian);return this.offset+=8,t}readBigInt64(){const t=this._data.getBigInt64(this.offset,this.littleEndian);return this.offset+=8,t}readBigUint64(){const t=this._data.getBigUint64(this.offset,this.littleEndian);return this.offset+=8,t}readChar(){return String.fromCharCode(this.readInt8())}readChars(t=1){let e="";for(let i=0;i<t;i++)e+=this.readChar();return e}readUtf8(t=1){return At(this.readBytes(t))}decodeText(t=1,e="utf-8"){return At(this.readBytes(t),e)}writeBoolean(t){return this.writeUint8(t?255:0),this}writeInt8(t){return this.ensureAvailable(1),this._data.setInt8(this.offset++,t),this._updateLastWrittenByte(),this}writeUint8(t){return this.ensureAvailable(1),this._data.setUint8(this.offset++,t),this._updateLastWrittenByte(),this}writeByte(t){return this.writeUint8(t)}writeBytes(t){this.ensureAvailable(t.length);for(let e=0;e<t.length;e++)this._data.setUint8(this.offset++,t[e]);return this._updateLastWrittenByte(),this}writeInt16(t){return this.ensureAvailable(2),this._data.setInt16(this.offset,t,this.littleEndian),this.offset+=2,this._updateLastWrittenByte(),this}writeUint16(t){return this.ensureAvailable(2),this._data.setUint16(this.offset,t,this.littleEndian),this.offset+=2,this._updateLastWrittenByte(),this}writeInt32(t){return this.ensureAvailable(4),this._data.setInt32(this.offset,t,this.littleEndian),this.offset+=4,this._updateLastWrittenByte(),this}writeUint32(t){return this.ensureAvailable(4),this._data.setUint32(this.offset,t,this.littleEndian),this.offset+=4,this._updateLastWrittenByte(),this}writeFloat32(t){return this.ensureAvailable(4),this._data.setFloat32(this.offset,t,this.littleEndian),this.offset+=4,this._updateLastWrittenByte(),this}writeFloat64(t){return this.ensureAvailable(8),this._data.setFloat64(this.offset,t,this.littleEndian),this.offset+=8,this._updateLastWrittenByte(),this}writeBigInt64(t){return this.ensureAvailable(8),this._data.setBigInt64(this.offset,t,this.littleEndian),this.offset+=8,this._updateLastWrittenByte(),this}writeBigUint64(t){return this.ensureAvailable(8),this._data.setBigUint64(this.offset,t,this.littleEndian),this.offset+=8,this._updateLastWrittenByte(),this}writeChar(t){return this.writeUint8(t.charCodeAt(0))}writeChars(t){for(let e=0;e<t.length;e++)this.writeUint8(t.charCodeAt(e));return this}writeUtf8(t){return this.writeBytes(function(t){return Ct.encode(t)}(t))}toArray(){return new Uint8Array(this.buffer,this.byteOffset,this.lastWrittenByte)}_updateLastWrittenByte(){this.offset>this.lastWrittenByte&&(this.lastWrittenByte=this.offset)}}const zt=1,Tt=2,Et=3,Lt=4,Ft=5,It=6;function Ot(t){switch(Number(t)){case zt:return"byte";case Tt:return"char";case Et:return"short";case Lt:return"int";case Ft:return"float";case It:return"double";default:return"undefined"}}function Dt(t){switch(Number(t)){case zt:case Tt:return 1;case Et:return 2;case Lt:case Ft:return 4;case It:return 8;default:return-1}}function kt(t){switch(String(t)){case"byte":return zt;case"char":return Tt;case"short":return Et;case"int":return Lt;case"float":return Ft;case"double":return It;default:return-1}}function Rt(t,e){if(1!==t){const i=new Array(t);for(let r=0;r<t;r++)i[r]=e();return i}return e()}function Pt(t,e,i){switch(e){case zt:return Array.from(t.readBytes(i));case Tt:return function(t){if(0===t.charCodeAt(t.length-1))return t.substring(0,t.length-1);return t}(t.readChars(i));case Et:return Rt(i,t.readInt16.bind(t));case Lt:return Rt(i,t.readInt32.bind(t));case Ft:return Rt(i,t.readFloat32.bind(t));case It:return Rt(i,t.readFloat64.bind(t));default:throw new Error(`non valid type ${e}`)}}function Ut(t,e){if(t)throw new TypeError(`Not a valid NetCDF v3.x file: ${e}`)}function Bt(t){t.offset%4!=0&&t.skip(4-t.offset%4)}function Nt(t){const e=t.readUint32(),i=t.readChars(e);return Bt(t),i}function Gt(t,e){const i={version:e},r={length:t.readUint32()},s=function(t){const e={};let i,r;const s=t.readUint32();let n;if(0===s)return Ut(0!==t.readUint32(),"wrong empty tag for list of dimensions"),[];{Ut(10!==s,"wrong tag for list of dimensions");const e=t.readUint32();n=new Array(e);for(let s=0;s<e;s++){const e=Nt(t),a=t.readUint32();0===a&&(i=s,r=e),n[s]={name:e,size:a}}}void 0!==i&&(e.recordId=i);void 0!==r&&(e.recordName=r);return e.dimensions=n,e}(t);Array.isArray(s)||(r.id=s.recordId,r.name=s.recordName,i.dimensions=s.dimensions),i.globalAttributes=Vt(t);const n=function(t,e,i){const r=t.readUint32();let s,n=0;if(0===r)return Ut(0!==t.readUint32(),"wrong empty tag for list of variables"),[];{Ut(11!==r,"wrong tag for list of variables");const a=t.readUint32();s=new Array(a);for(let r=0;r<a;r++){const a=Nt(t),o=t.readUint32(),l=new Array(o);for(let e=0;e<o;e++)l[e]=t.readUint32();const h=Vt(t),c=t.readUint32();Ut(c<1&&c>6,`non valid type ${c}`);const d=t.readUint32();let u=t.readUint32();2===i&&(Ut(u>0,"offsets larger than 4GB not supported"),u=t.readUint32());let f=!1;void 0!==e&&l[0]===e&&(n+=d,f=!0),s[r]={name:a,dimensions:l,attributes:h,type:Ot(c),size:d,offset:u,record:f}}}return{variables:s,recordStep:n}}(t,r?.id,e);return Array.isArray(n)||(i.variables=n.variables,r.recordStep=n.recordStep),i.recordDimension=r,i}function Vt(t){const e=t.readUint32();let i;if(0===e)return Ut(0!==t.readUint32(),"wrong empty tag for list of attributes"),[];{Ut(12!==e,"wrong tag for list of attributes");const r=t.readUint32();i=new Array(r);for(let e=0;e<r;e++){const r=Nt(t),s=t.readUint32();Ut(s<1||s>6,`non valid type ${s}`);const n=t.readUint32(),a=Pt(t,s,n);Bt(t),i[e]={name:r,type:Ot(s),value:a}}}return i}function jt(){const t=[];t.push("DIMENSIONS");for(const e of this.dimensions)t.push(`  ${e.name.padEnd(30)} = size: ${e.size}`);t.push(""),t.push("GLOBAL ATTRIBUTES");for(const e of this.globalAttributes)t.push(`  ${e.name.padEnd(30)} = ${e.value}`);const e=JSON.parse(JSON.stringify(this.variables));t.push(""),t.push("VARIABLES:");for(const i of e){i.value=this.getDataVariable(i);let e=JSON.stringify(i.value);e.length>50&&(e=e.substring(0,50)),isNaN(i.value.length)||(e+=` (length: ${i.value.length})`),t.push(`  ${i.name.padEnd(30)} = ${e}`)}return t.join("\n")}class NetCDFReader{constructor(t){this.toString=jt;const e=new IOBuffer(t);e.setBigEndian(),Ut("CDF"!==e.readChars(3),"should start with CDF");const i=e.readByte();Ut(i>2,"unknown version"),this.header=Gt(e,i),this.buffer=e}get version(){return 1===this.header.version?"classic format":"64-bit offset format"}get recordDimension(){return this.header.recordDimension}get dimensions(){return this.header.dimensions}get globalAttributes(){return this.header.globalAttributes}getAttribute(t){const e=this.globalAttributes.find((e=>e.name===t));return e?e.value:null}getDataVariableAsString(t){const e=this.getDataVariable(t);return e?e.join(""):null}get variables(){return this.header.variables}getDataVariable(t){let e;if(e="string"==typeof t?this.header.variables.find((e=>e.name===t)):t,void 0===e)throw new Error("Not a valid NetCDF v3.x file: variable not found");return this.buffer.seek(e.offset),e.record?function(t,e,i){const r=kt(e.type),s=e.size?e.size/Dt(r):1,n=i.length,a=new Array(n),o=i.recordStep;if(!o)throw new Error("recordDimension.recordStep is undefined");for(let e=0;e<n;e++){const i=t.offset;a[e]=Pt(t,r,s),t.seek(i+o)}return a}(this.buffer,e,this.header.recordDimension):function(t,e){const i=kt(e.type),r=e.size/Dt(i),s=new Array(r);for(let e=0;e<r;e++)s[e]=Pt(t,i,1);return s}(this.buffer,e)}dataVariableExists(t){return void 0!==this.header.variables.find((e=>e.name===t))}attributeExists(t){return void 0!==this.globalAttributes.find((e=>e.name===t))}}class GLModel{static sameObj(t,e){return t&&e?JSON.stringify(t)==JSON.stringify(e):t==e}constructor(t,e){this.atoms=[],this.frames=[],this.box=null,this.atomdfs=null,this.id=0,this.hidden=!1,this.molObj=null,this.renderedMolObj=null,this.lastColors=null,this.modelData={},this.modelDatas=null,this.idMatrix=new $.Matrix4,this.dontDuplicateAtoms=!0,this.defaultColor=s.elementColors.defaultColor,this.defaultStickRadius=.25,this.options=e||{},this.ElementColors=this.options.defaultcolors?this.options.defaultcolors:s.elementColors.defaultColors,this.defaultSphereRadius=this.options.defaultSphereRadius?this.options.defaultSphereRadius:1.5,this.defaultCartoonQuality=this.options.cartoonQuality?this.options.cartoonQuality:10,this.id=t}getRadiusFromStyle(t,e){var i=this.defaultSphereRadius;if(void 0!==e.radius)i=e.radius;else if(GLModel.vdwRadii[t.elem])i=GLModel.vdwRadii[t.elem];else if(t.elem.length>1){let e=t.elem;e=e[0].toUpperCase()+e[1].toLowerCase(),GLModel.vdwRadii[e]&&(i=GLModel.vdwRadii[e])}return void 0!==e.scale&&(i*=e.scale),i}drawAtomCross(t,e){if(t.style.cross){var i=t.style.cross;if(!i.hidden){var r=i.linewidth||GLModel.defaultlineWidth;e[r]||(e[r]=new n.Geometry);var s=e[r].updateGeoGroup(6),a=this.getRadiusFromStyle(t,i),o=[[a,0,0],[-a,0,0],[0,a,0],[0,-a,0],[0,0,a],[0,0,-a]],l=t.clickable||t.hoverable;l&&void 0===t.intersectionShape&&(t.intersectionShape={sphere:[],cylinder:[],line:[]});for(var h=(0,L.getColorFromStyle)(t,i),c=s.vertexArray,d=s.colorArray,u=0;u<6;u++){var f=3*s.vertices;if(s.vertices++,c[f]=t.x+o[u][0],c[f+1]=t.y+o[u][1],c[f+2]=t.z+o[u][2],d[f]=h.r,d[f+1]=h.g,d[f+2]=h.b,l){var p=new $.Vector3(o[u][0],o[u][1],o[u][2]);p.multiplyScalar(.1),p.set(p.x+t.x,p.y+t.y,p.z+t.z),t.intersectionShape.line.push(p)}}}}}getGoodCross(t,e,i,r){for(var s=null,n=-1,a=0,o=t.bonds.length;a<o;a++)if(t.bonds[a]!=e.index){let e=t.bonds[a],o=this.atoms[e],h=new $.Vector3(o.x,o.y,o.z).clone();h.sub(i);let c=h.clone();c.cross(r);var l=c.lengthSq();if(l>n&&(s=c,(n=l)>.1))return s}return s}getSideBondV(t,e,i){var r,s,n,a,o=new $.Vector3(t.x,t.y,t.z),l=new $.Vector3(e.x,e.y,e.z).clone(),h=null;if(l.sub(o),1===t.bonds.length)1===e.bonds.length?(h=l.clone(),Math.abs(h.x)>1e-4?h.y+=1:h.x+=1):(r=(i+1)%e.bonds.length,s=e.bonds[r],(n=this.atoms[s]).index==t.index&&(r=(r+1)%e.bonds.length,s=e.bonds[r],n=this.atoms[s]),(a=new $.Vector3(n.x,n.y,n.z).clone()).sub(o),(h=a.clone()).cross(l));else if((h=this.getGoodCross(t,e,o,l)).lengthSq()<.01){var c=this.getGoodCross(e,t,o,l);null!=c&&(h=c)}return h.lengthSq()<.01&&(h=l.clone(),Math.abs(h.x)>1e-4?h.y+=1:h.x+=1),h.cross(l),h.normalize(),h}addLine(t,e,i,r,s,n){t[i]=r.x,t[i+1]=r.y,t[i+2]=r.z,e[i]=n.r,e[i+1]=n.g,e[i+2]=n.b,t[i+3]=s.x,t[i+4]=s.y,t[i+5]=s.z,e[i+3]=n.r,e[i+4]=n.g,e[i+5]=n.b}drawBondLines(t,e,i){if(t.style.line){var r=t.style.line;if(!r.hidden){var a,o,l,h,c=r.linewidth||GLModel.defaultlineWidth;i[c]||(i[c]=new n.Geometry);for(var d=i[c].updateGeoGroup(6*t.bonds.length),u=d.vertexArray,f=d.colorArray,p=0;p<t.bonds.length;p++){var g=e[t.bonds[p]];if(g.style.line&&!(t.index>=g.index)){var m=new $.Vector3(t.x,t.y,t.z),v=new $.Vector3(g.x,g.y,g.z),_=m.clone().add(v).multiplyScalar(.5),y=!1,b=t.clickable||t.hoverable,x=g.clickable||g.hoverable;(b||x)&&(b&&(void 0===t.intersectionShape&&(t.intersectionShape={sphere:[],cylinder:[],line:[],triangle:[]}),t.intersectionShape.line.push(m),t.intersectionShape.line.push(_)),x&&(void 0===g.intersectionShape&&(g.intersectionShape={sphere:[],cylinder:[],line:[],triangle:[]}),g.intersectionShape.line.push(_),g.intersectionShape.line.push(v)));var w=(0,L.getColorFromStyle)(t,t.style.line),A=(0,L.getColorFromStyle)(g,g.style.line);if(t.bondStyles&&t.bondStyles[p]){var C=t.bondStyles[p];if(!C.iswire)continue;C.singleBond&&(y=!0),void 0!==C.color1&&(w=s.CC.color(C.color1)),void 0!==C.color2&&(A=s.CC.color(C.color2))}var S,M,z=3*d.vertices;if(t.bondOrder[p]>1&&t.bondOrder[p]<4&&!y){var T=this.getSideBondV(t,g,p),E=v.clone();E.sub(m),2==t.bondOrder[p]?(T.multiplyScalar(.1),(a=m.clone()).add(T),(o=m.clone()).sub(T),(l=a.clone()).add(E),(h=o.clone()).add(E),w==A?(d.vertices+=4,this.addLine(u,f,z,a,l,w),this.addLine(u,f,z+6,o,h,w)):(d.vertices+=8,E.multiplyScalar(.5),(S=a.clone()).add(E),(M=o.clone()).add(E),this.addLine(u,f,z,a,S,w),this.addLine(u,f,z+6,S,l,A),this.addLine(u,f,z+12,o,M,w),this.addLine(u,f,z+18,M,h,A))):3==t.bondOrder[p]&&(T.multiplyScalar(.1),(a=m.clone()).add(T),(o=m.clone()).sub(T),(l=a.clone()).add(E),(h=o.clone()).add(E),w==A?(d.vertices+=6,this.addLine(u,f,z,m,v,w),this.addLine(u,f,z+6,a,l,w),this.addLine(u,f,z+12,o,h,w)):(d.vertices+=12,E.multiplyScalar(.5),(S=a.clone()).add(E),(M=o.clone()).add(E),this.addLine(u,f,z,m,_,w),this.addLine(u,f,z+6,_,v,A),this.addLine(u,f,z+12,a,S,w),this.addLine(u,f,z+18,S,l,A),this.addLine(u,f,z+24,o,M,w),this.addLine(u,f,z+30,M,h,A)))}else w==A?(d.vertices+=2,this.addLine(u,f,z,m,v,w)):(d.vertices+=4,this.addLine(u,f,z,m,_,w),this.addLine(u,f,z+6,_,v,A))}}}}}drawAtomSphere(t,e){if(t.style.sphere){var i=t.style.sphere;if(!i.hidden){var r=(0,L.getColorFromStyle)(t,i),s=this.getRadiusFromStyle(t,i);if((!0===t.clickable||t.hoverable)&&void 0!==t.intersectionShape){var n=new $.Vector3(t.x,t.y,t.z);t.intersectionShape.sphere.push(new J.Sphere(n,s))}rt.drawSphere(e,t,s,r)}}}drawAtomClickSphere(t){if(t.style.clicksphere){var e=t.style.clicksphere;if(!e.hidden){var i=this.getRadiusFromStyle(t,e);if((!0===t.clickable||t.hoverable)&&void 0!==t.intersectionShape){var r=new $.Vector3(t.x,t.y,t.z);t.intersectionShape.sphere.push(new J.Sphere(r,i))}}}}drawAtomInstanced(t,e){if(t.style.sphere){var i=t.style.sphere;if(!i.hidden){var r=this.getRadiusFromStyle(t,i),s=(0,L.getColorFromStyle)(t,i),n=e.updateGeoGroup(1),a=n.vertices,o=3*a,l=n.vertexArray,h=n.colorArray,c=n.radiusArray;if(l[o]=t.x,l[o+1]=t.y,l[o+2]=t.z,h[o]=s.r,h[o+1]=s.g,h[o+2]=s.b,c[a]=r,(!0===t.clickable||t.hoverable)&&void 0!==t.intersectionShape){var d=new $.Vector3(t.x,t.y,t.z);t.intersectionShape.sphere.push(new J.Sphere(d,r))}n.vertices+=1}}}drawSphereImposter(t,e,i,r){var s,n=t.updateGeoGroup(4),a=n.vertices,o=3*a,l=n.vertexArray,h=n.colorArray;for(s=0;s<4;s++)l[o+3*s]=e.x,l[o+3*s+1]=e.y,l[o+3*s+2]=e.z;var c=n.normalArray;for(s=0;s<4;s++)h[o+3*s]=r.r,h[o+3*s+1]=r.g,h[o+3*s+2]=r.b;c[o+0]=-i,c[o+1]=i,c[o+2]=0,c[o+3]=-i,c[o+4]=-i,c[o+5]=0,c[o+6]=i,c[o+7]=-i,c[o+8]=0,c[o+9]=i,c[o+10]=i,c[o+11]=0,n.vertices+=4;var d=n.faceArray,u=n.faceidx;d[u+0]=a,d[u+1]=a+1,d[u+2]=a+2,d[u+3]=a+2,d[u+4]=a+3,d[u+5]=a,n.faceidx+=6}drawAtomImposter(t,e){if(t.style.sphere){var i=t.style.sphere;if(!i.hidden){var r=this.getRadiusFromStyle(t,i),s=(0,L.getColorFromStyle)(t,i);if((!0===t.clickable||t.hoverable)&&void 0!==t.intersectionShape){var n=new $.Vector3(t.x,t.y,t.z);t.intersectionShape.sphere.push(new J.Sphere(n,r))}this.drawSphereImposter(e,t,r,s)}}}calculateDashes(t,e,i,r,s){var n=Math.sqrt(Math.pow(t.x-e.x,2)+Math.pow(t.y-e.y,2)+Math.pow(t.z-e.z,2));i=Math.max(i,0),s=Math.max(s,0)+2*i,(r=Math.max(r,.001))+s>n&&(r=n,s=0);var a,o=Math.floor((n-r)/(r+s))+1;s=(n-o*r)/o;for(var l=new $.Vector3(t.x,t.y,t.z),h=new $.Vector3((e.x-t.x)/(n/s),(e.y-t.y)/(n/s),(e.z-t.z)/(n/s)),c=new $.Vector3((e.x-t.x)/(n/r),(e.y-t.y)/(n/r),(e.z-t.z)/(n/r)),d=[],u=0;u<o;u++)a=new $.Vector3(l.x+c.x,l.y+c.y,l.z+c.z),d.push({from:l,to:a}),l=new $.Vector3(a.x+h.x,a.y+h.y,a.z+h.z);return d}static drawStickImposter(t,e,i,r,s,n=0,a=0){for(var o,l=t.updateGeoGroup(4),h=l.vertices,c=3*h,d=l.vertexArray,u=l.colorArray,f=l.radiusArray,p=l.normalArray,g=s.r,m=s.g,v=s.b,_=c,y=0;y<4;y++)d[_]=e.x,p[_]=i.x,u[_]=g,d[++_]=e.y,p[_]=i.y,u[_]=m,d[++_]=e.z,p[_]=i.z,u[_]=y<2?v:(o=void 0,0==(o=-v)&&(o=-1e-4),o),_++;l.vertices+=4,f[h]=-r,f[h+1]=r,f[h+2]=-r,f[h+3]=r;var b=l.faceArray,x=l.faceidx;b[x+0]=h,b[x+1]=h+1,b[x+2]=h+2,b[x+3]=h+2,b[x+4]=h+3,b[x+5]=h,l.faceidx+=6}drawBondSticks(t,e,i){var r,n;if(t.style.stick){var a=t.style.stick;if(!a.hidden){var o,l,h,c,d,u,f,p,g,m,v,_,y,b,x=a.radius||this.defaultStickRadius,w=a.doubleBondScaling||.4,A=a.tripleBondScaling||.25,C=(null===(r=a.dashedBondConfig)||void 0===r?void 0:r.dashLength)||.1,S=(null===(n=a.dashedBondConfig)||void 0===n?void 0:n.gapLength)||.25,M=x,z=a.singleBonds||!1,T=a.dashedBonds||!1,E=0,F=0,I=(0,L.getColorFromStyle)(t,a);!t.capDrawn&&t.bonds.length<4&&(E=2);var O=t=>{var e=i.imposter?GLModel.drawStickImposter:rt.drawCylinder;return!T&&t>=1?e:(t,i,r,s,n,a=0,o=0,l=.1,h=.25)=>{this.calculateDashes(i,r,s,l,h).forEach((i=>{e(t,i.from,i.to,s,n,a,o)}))}};for(h=0;h<t.bonds.length;h++){var D=O(t.bondOrder[h]),k=e[t.bonds[h]];if(_=y=b=null,t.index<k.index){var R=k.style;if(!R.stick||R.stick.hidden)continue;var P=(0,L.getColorFromStyle)(k,R.stick);if(M=x,c=z,t.bondStyles&&t.bondStyles[h]){if((d=t.bondStyles[h]).iswire)continue;d.radius&&(M=d.radius),d.singleBond&&(c=!0),void 0!==d.color1&&(I=s.CC.color(d.color1)),void 0!==d.color2&&(P=s.CC.color(d.color2))}var U=new $.Vector3(t.x,t.y,t.z),B=new $.Vector3(k.x,k.y,k.z);if(t.bondOrder[h]<=1||c||t.bondOrder[h]>3){if(t.bondOrder[h]<1&&(M*=t.bondOrder[h]),!k.capDrawn&&k.bonds.length<4&&(F=2),I!=P?(_=(new $.Vector3).addVectors(U,B).multiplyScalar(.5),D(i,U,_,M,I,E,0,C,S),D(i,_,B,M,P,0,F,C,S)):D(i,U,B,M,I,E,F,C,S),o=t.clickable||t.hoverable,l=k.clickable||k.hoverable,o||l){if(_||(_=(new $.Vector3).addVectors(U,B).multiplyScalar(.5)),o){var N=new J.Cylinder(U,_,M),G=new J.Sphere(U,M);t.intersectionShape.cylinder.push(N),t.intersectionShape.sphere.push(G)}if(l){var V=new J.Cylinder(B,_,M),j=new J.Sphere(B,M);k.intersectionShape.cylinder.push(V),k.intersectionShape.sphere.push(j)}}}else if(t.bondOrder[h]>1){var H=0,W=0;M!=x&&(H=2,W=2);var q,Y,Z,X,K,Q=B.clone(),tt=null;Q.sub(U),tt=this.getSideBondV(t,k,h),2==t.bondOrder[h]?(q=M*w,tt.multiplyScalar(1.5*q),(Y=U.clone()).add(tt),(Z=U.clone()).sub(tt),(X=Y.clone()).add(Q),(K=Z.clone()).add(Q),I!=P?(_=(new $.Vector3).addVectors(Y,X).multiplyScalar(.5),y=(new $.Vector3).addVectors(Z,K).multiplyScalar(.5),D(i,Y,_,q,I,H,0),D(i,_,X,q,P,0,W),D(i,Z,y,q,I,H,0),D(i,y,K,q,P,0,W)):(D(i,Y,X,q,I,H,W),D(i,Z,K,q,I,H,W)),o=t.clickable||t.hoverable,l=k.clickable||k.hoverable,(o||l)&&(_||(_=(new $.Vector3).addVectors(Y,X).multiplyScalar(.5)),y||(y=(new $.Vector3).addVectors(Z,K).multiplyScalar(.5)),o&&(u=new J.Cylinder(Y,_,q),f=new J.Cylinder(Z,y,q),t.intersectionShape.cylinder.push(u),t.intersectionShape.cylinder.push(f)),l&&(g=new J.Cylinder(X,_,q),m=new J.Cylinder(K,y,q),k.intersectionShape.cylinder.push(g),k.intersectionShape.cylinder.push(m)))):3==t.bondOrder[h]&&(q=M*A,tt.cross(Q),tt.normalize(),tt.multiplyScalar(3*q),(Y=U.clone()).add(tt),(Z=U.clone()).sub(tt),(X=Y.clone()).add(Q),(K=Z.clone()).add(Q),I!=P?(_=(new $.Vector3).addVectors(Y,X).multiplyScalar(.5),y=(new $.Vector3).addVectors(Z,K).multiplyScalar(.5),b=(new $.Vector3).addVectors(U,B).multiplyScalar(.5),D(i,Y,_,q,I,H,0),D(i,_,X,q,P,0,W),D(i,U,b,q,I,E,0),D(i,b,B,q,P,0,F),D(i,Z,y,q,I,H,0),D(i,y,K,q,P,0,W)):(D(i,Y,X,q,I,H,W),D(i,U,B,q,I,E,F),D(i,Z,K,q,I,H,W)),o=t.clickable||t.hoverable,l=k.clickable||k.hoverable,(o||l)&&(_||(_=(new $.Vector3).addVectors(Y,X).multiplyScalar(.5)),y||(y=(new $.Vector3).addVectors(Z,K).multiplyScalar(.5)),b||(b=(new $.Vector3).addVectors(U,B).multiplyScalar(.5)),o&&(u=new J.Cylinder(Y.clone(),_.clone(),q),f=new J.Cylinder(Z.clone(),y.clone(),q),p=new J.Cylinder(U.clone(),b.clone(),q),t.intersectionShape.cylinder.push(u),t.intersectionShape.cylinder.push(f),t.intersectionShape.cylinder.push(p)),l&&(g=new J.Cylinder(X.clone(),_.clone(),q),m=new J.Cylinder(K.clone(),y.clone(),q),v=new J.Cylinder(B.clone(),b.clone(),q),k.intersectionShape.cylinder.push(g),k.intersectionShape.cylinder.push(m),k.intersectionShape.cylinder.push(v))))}}}var et=!1,it=0,st=!1;for(h=0;h<t.bonds.length;h++)c=z,t.bondStyles&&t.bondStyles[h]&&((d=t.bondStyles[h]).singleBond&&(c=!0),d.radius&&d.radius!=x&&(st=!0)),(c||1==t.bondOrder[h])&&it++;st?it>0&&(et=!0):0==it&&(t.bonds.length>0||a.showNonBonded)&&(et=!0),et&&(M=x,i.imposter?this.drawSphereImposter(i.sphereGeometry,t,M,I):rt.drawSphere(i,t,M,I))}}}createMolObj(t,e){e=e||{};var i,r,a,o,l=new n.Object3D,h=[],c={},d={},u=this.drawAtomSphere,f=null,p=null;e.supportsImposters?(u=this.drawAtomImposter,(f=new n.Geometry(!0)).imposter=!0,(p=new n.Geometry(!0,!0)).imposter=!0,p.sphereGeometry=new n.Geometry(!0),p.sphereGeometry.imposter=!0,p.drawnCaps={}):e.supportsAIA?(u=this.drawAtomInstanced,(f=new n.Geometry(!1,!0,!0)).instanced=!0,p=new n.Geometry(!0)):(f=new n.Geometry(!0),p=new n.Geometry(!0));var g,m={},v=[Number.POSITIVE_INFINITY,Number.NEGATIVE_INFINITY];for(i=0,a=t.length;i<a;i++){var _=t[i];if(_&&_.style){for(r in(_.clickable||_.hoverable)&&void 0===_.intersectionShape&&(_.intersectionShape={sphere:[],cylinder:[],line:[],triangle:[]}),o={line:void 0,cross:void 0,stick:void 0,sphere:void 0})_.style[r]?_.style[r].opacity?o[r]=parseFloat(_.style[r].opacity):o[r]=1:o[r]=void 0,m[r]?null!=o[r]&&m[r]!=o[r]&&(console.log("Warning: "+r+" opacity is ambiguous"),m[r]=1):m[r]=o[r];u.call(this,_,f),this.drawAtomClickSphere(_),this.drawAtomCross(_,d),this.drawBondLines(_,t,c),this.drawBondSticks(_,t,p),void 0===_.style.cartoon||_.style.cartoon.hidden||("spectrum"!==_.style.cartoon.color||"number"!=typeof _.resi||_.hetflag||(_.resi<v[0]&&(v[0]=_.resi),_.resi>v[1]&&(v[1]=_.resi)),h.push(_))}}if(h.length>0&&xt(l,h,v,this.defaultCartoonQuality),f&&f.vertices>0){f.initTypedArrays();var y=null,b=null;f.imposter?y=new n.SphereImposterMaterial({ambient:0,vertexColors:!0,reflectivity:0}):f.instanced?(b=new n.Geometry(!0),rt.drawSphere(b,{x:0,y:0,z:0},1,new s.Color(.5,.5,.5)),b.initTypedArrays(),y=new n.InstancedMaterial({sphereMaterial:new n.MeshLambertMaterial({ambient:0,vertexColors:!0,reflectivity:0}),sphere:b})):y=new n.MeshLambertMaterial({ambient:0,vertexColors:!0,reflectivity:0}),m.sphere<1&&m.sphere>=0&&(y.transparent=!0,y.opacity=m.sphere),b=new n.Mesh(f,y),l.add(b)}if(p.vertices>0){var x=null,w=null,A=p.sphereGeometry;A&&void 0!==A.vertices&&0!=A.vertices||(A=null),p.initTypedArrays(),A&&A.initTypedArrays();var C={ambient:0,vertexColors:!0,reflectivity:0};p.imposter?(x=new n.StickImposterMaterial(C),w=new n.SphereImposterMaterial(C)):(x=new n.MeshLambertMaterial(C),w=new n.MeshLambertMaterial(C),x.wireframe&&(p.setUpWireframe(),A&&A.setUpWireframe())),m.stick<1&&m.stick>=0&&(x.transparent=!0,x.opacity=m.stick,w.transparent=!0,w.opacity=m.stick);var S=new n.Mesh(p,x);if(l.add(S),A){var M=new n.Mesh(A,w);l.add(M)}}for(i in c)if(c.hasOwnProperty(i)){g=i;var z=new n.LineBasicMaterial({linewidth:g,vertexColors:!0});m.line<1&&m.line>=0&&(z.transparent=!0,z.opacity=m.line),c[i].initTypedArrays();var T=new n.Line(c[i],z,n.LineStyle.LinePieces);l.add(T)}for(i in d)if(d.hasOwnProperty(i)){g=i;var E=new n.LineBasicMaterial({linewidth:g,vertexColors:!0});m.cross<1&&m.cross>=0&&(E.transparent=!0,E.opacity=m.cross),d[i].initTypedArrays();var L=new n.Line(d[i],E,n.LineStyle.LinePieces);l.add(L)}if(this.dontDuplicateAtoms&&this.modelData.symmetries&&this.modelData.symmetries.length>0){var F,I=new n.Object3D;for(F=0;F<this.modelData.symmetries.length;F++){var O=new n.Object3D;(O=l.clone()).matrix.copy(this.modelData.symmetries[F]),O.matrixAutoUpdate=!1,I.add(O)}return I}return l}getInternalState(){return{atoms:this.atoms,frames:this.frames}}setInternalState(t){this.atoms=t.atoms,this.frames=t.frames,this.molObj=null}getCrystData(){if(this.modelData.cryst){if(!this.modelData.cryst.matrix){const t=this.modelData.cryst;this.modelData.cryst.matrix=(0,$.conversionMatrix3)(t.a,t.b,t.c,t.alpha,t.beta,t.gamma)}return this.modelData.cryst}return null}setCrystData(t,e,i,r,s,n){t=t||1,e=e||1,i=i||1,r=r||90,s=s||90,n=n||90;const a=(0,$.conversionMatrix3)(t,e,i,r,s,n);this.modelData.cryst={a:t,b:e,c:i,alpha:r,beta:s,gamma:n,matrix:a}}setCrystMatrix(t){t=t||new $.Matrix3(1,0,0,0,1,0,0,0,1),this.modelData.cryst={matrix:t}}getSymmetries(){return void 0===this.modelData.symmetries&&(this.modelData.symmetries=[this.idMatrix]),this.modelData.symmetries}setSymmetries(t){this.modelData.symmetries=void 0===t?[this.idMatrix]:t}getID(){return this.id}getNumFrames(){return null!=this.frames.numFrames?this.frames.numFrames:this.frames.length}adjustCoord(t,e,i,r){var s=e-t;return s<-i?e+r:s>i?e-r:e}adjustCoordinatesToBox(){if(this.box&&this.atomdfs)for(var t=this.box[0],e=this.box[1],i=this.box[2],r=.9*t,s=.9*e,n=.9*i,a=0;a<this.atomdfs.length;a++)for(var o=this.atomdfs[a],l=1;l<o.length;l++){var h=this.atoms[o[l][0]],c=this.atoms[o[l][1]];h.x=this.adjustCoord(c.x,h.x,r,t),h.y=this.adjustCoord(c.y,h.y,s,e),h.z=this.adjustCoord(c.z,h.z,n,i)}}setFrame(t,e){var i=this.getNumFrames();let r=this;return new Promise((function(s,n){if(0==i&&s(),(t<0||t>=i)&&(t=i-1),null!=r.frames.url){var a=r.frames.url;(0,L.getbin)(a+"/traj/frame/"+t+"/"+r.frames.path,void 0,"POST",void 0).then((function(t){for(var e=new Float32Array(t,44),i=0,n=0;n<r.atoms.length;n++)r.atoms[n].x=e[i++],r.atoms[n].y=e[i++],r.atoms[n].z=e[i++];r.box&&r.atomdfs&&r.adjustCoordinatesToBox(),s()})).catch(n)}else r.atoms=r.frames[t],s();r.molObj=null,r.modelDatas&&t<r.modelDatas.length&&(r.modelData=r.modelDatas[t],r.unitCellObjects&&e&&(e.removeUnitCell(r),e.addUnitCell(r)))}))}addFrame(t){this.frames.push(t)}vibrate(t=10,e=1,i=!1,r,s){var n=0,a=t;i&&(n=-t,a=t),void 0!==this.frames&&void 0!==this.frames.origIndex?this.setFrame(this.frames.origIndex):this.setFrame(0),n<a&&(this.frames=[]),i&&(this.frames.origIndex=t);for(var o=n;o<a;o++){var l=[],h=this.frames.length;if(0!=o||s){for(var c=0;c<this.atoms.length;c++){var d=(0,L.getAtomProperty)(this.atoms[c],"dx"),u=(0,L.getAtomProperty)(this.atoms[c],"dy"),f=(0,L.getAtomProperty)(this.atoms[c],"dz"),p=new $.Vector3(d,u,f),g=new $.Vector3(this.atoms[c].x,this.atoms[c].y,this.atoms[c].z),m=o*e/t;p.multiplyScalar(m),g.add(p);var v={};for(var _ in this.atoms[c])v[_]=this.atoms[c][_];if(v.x=g.x,v.y=g.y,v.z=g.z,l.push(v),r&&s){var y=(0,L.extend)({},s),b=new $.Vector3(d,u,f);if(b.multiplyScalar(e),b.add(g),y.start=g,y.end=b,y.frame=h,!y.color){var x=v.style.sphere;x||(x=v.style.stick),x||(x=v.style.line),y.color=(0,L.getColorFromStyle)(v,x)}r.addArrow(y)}}this.frames.push(l)}else this.frames.push(this.atoms)}}setAtomDefaults(t){for(let e=0;e<t.length;e++){let i=t[e];i&&(i.style=i.style||(0,L.deepCopy)(GLModel.defaultAtomStyle),i.color=i.color||this.ElementColors[i.elem]||this.defaultColor,i.model=this.id,(i.clickable||i.hoverable)&&(i.intersectionShape={sphere:[],cylinder:[],line:[],triangle:[]}))}}addMolData(t,e,i={}){var r=GLModel.parseMolData(t,e,i);this.dontDuplicateAtoms=!i.duplicateAssemblyAtoms;var s=r.modelData;if(s&&(Array.isArray(s)?(this.modelData=s[0],i.frames&&(this.modelDatas=s)):this.modelData=s),r.box?this.box=r.box:this.box=null,0==this.frames.length){for(let t=0;t<r.length;t++)0!=r[t].length&&this.frames.push(r[t]);this.frames[0]&&(this.atoms=this.frames[0])}else if(i.frames)for(let t=0;t<r.length;t++)this.frames.push(r[t]);else for(var n=0;n<r.length;n++)this.addAtoms(r[n]);for(let t=0;t<this.frames.length;t++)this.setAtomDefaults(this.frames[t]);i.vibrate&&i.vibrate.frames&&i.vibrate.amplitude&&this.vibrate(i.vibrate.frames,i.vibrate.amplitude),i.style&&this.setStyle({},i.style)}setDontDuplicateAtoms(t){this.dontDuplicateAtoms=t}setModelData(t){this.modelData=t}propertyMatches(t,e){if(t==e)return!0;if("string"==typeof e&&"number"==typeof t){var i=e.match(/(-?\d+)\s*-\s*(-?\d+)/);if(i){var r=parseInt(i[1]),s=parseInt(i[2]);if(i&&t>=r&&t<=s)return!0}}return!1}static deepCopyAndCache(t,e){if("object"!=typeof t||null==t)return t;if(t.__cache_created)return t;const i={};for(const r in t){const s=t[r];if(Array.isArray(s)){i[r]=[];for(let t=0;t<s.length;t++)i[r].push(GLModel.deepCopyAndCache(s[t],e))}else i[r]="object"==typeof s&&"properties"!=r&&"model"!=r?GLModel.deepCopyAndCache(s,e):s;if("and"==r||"or"==r){const t=[];for(const s of i[r]){const i=new Set;for(const t of e.selectedAtoms(s))i.add(t.index);t.push(i)}if("and"==r){const e=function(t,e){const i=new Set;for(const r of e)t.has(r)&&i.add(r);return i};let s=new Set(t[0]);for(const i of t.splice(1))s=e(s,i);i[r].__cached_results=s}else if("or"==r){const e=new Set;for(const i of t)for(const t of i)e.add(t);i[r].__cached_results=e}}}return i.__cache_created=!0,i}atomIsSelected(t,e){if(void 0===e)return!0;var i=!!e.invert,r=!0;for(var s in e)if("and"==s||"or"==s||"not"==s){if("not"==s){if(this.atomIsSelected(t,e[s])){r=!1;break}}else if(void 0===e[s].__cached_results&&(e=GLModel.deepCopyAndCache(e,this)),!(r=e[s].__cached_results.has(t.index)))break}else if("predicate"===s){if(!e.predicate(t)){r=!1;break}}else if("properties"==s&&t[s]){for(var n in e.properties)if(!n.startsWith("__cache")){if(void 0===t.properties[n]){r=!1;break}if(t.properties[n]!=e.properties[n]){r=!1;break}}}else if(e.hasOwnProperty(s)&&!GLModel.ignoredKeys.has(s)&&!s.startsWith("__cache")){if(void 0===t[s]){r=!1;break}var a=!1;if("bonds"===s){if(e[s]!=t.bonds.length){r=!1;break}}else if(Array.isArray(e[s])){var o=e[s],l=t[s];for(let t=0;t<o.length;t++)if(this.propertyMatches(l,o[t])){a=!0;break}if(!a){r=!1;break}}else{let i=e[s];if(!this.propertyMatches(t[s],i)){r=!1;break}}}return i?!r:r}static squaredDistance(t,e){var i=e.x-t.x,r=e.y-t.y,s=e.z-t.z;return i*i+r*r+s*s}expandAtomList(t,e){if(e<=0)return t;for(var i=(0,L.getExtent)(t,void 0),r=[[],[],[]],s=0;s<3;s++)r[0][s]=i[0][s]-e,r[1][s]=i[1][s]+e,r[2][s]=i[2][s];var n=[];for(let t=0;t<this.atoms.length;t++){var a=this.atoms[t].x,o=this.atoms[t].y,l=this.atoms[t].z;a>=r[0][0]&&a<=r[1][0]&&o>=r[0][1]&&o<=r[1][1]&&l>=r[0][2]&&l<=r[1][2]&&(a>=i[0][0]&&a<=i[1][0]&&o>=i[0][1]&&o<=i[1][1]&&l>=i[0][2]&&l<=i[1][2]||n.push(this.atoms[t]))}return n}static getFloat(t){return"number"==typeof t?t:parseFloat(t)}selectedAtoms(t,e){var i=[];t=GLModel.deepCopyAndCache(t||{},this),e||(e=this.atoms);for(var r=e.length,s=0;s<r;s++){var n=e[s];n&&this.atomIsSelected(n,t)&&i.push(n)}if(t.hasOwnProperty("expand")){const e=GLModel.getFloat(t.expand);let r=this.expandAtomList(i,e),s=i.length;const n=e*e;for(let t=0;t<r.length;t++)for(let e=0;e<s;e++){var a=GLModel.squaredDistance(r[t],i[e]);a<n&&a>0&&i.push(r[t])}}if(t.hasOwnProperty("within")&&t.within.hasOwnProperty("sel")&&t.within.hasOwnProperty("distance")){var o=this.selectedAtoms(t.within.sel,this.atoms),l={};const e=GLModel.getFloat(t.within.distance),r=e*e;for(let t=0;t<o.length;t++)for(let e=0;e<i.length;e++){let s=GLModel.squaredDistance(o[t],i[e]);s<r&&s>0&&(l[e]=1)}var h=[];if(t.within.invert)for(let t=0;t<i.length;t++)l[t]||h.push(i[t]);else for(let t in l)h.push(i[t]);i=h}if(t.hasOwnProperty("byres")){var c={},d=[],u=[];for(let t=0;t<i.length;t++){let e=i[t];var f=e.chain,p=e.resi;if(void 0===c[f]&&(c[f]={}),e.hasOwnProperty("resi")&&void 0===c[f][p])for(c[f][p]=!0,u.push(e);u.length>0;)if(e=u.pop(),f=e.chain,p=e.resi,void 0===d[e.index]){d[e.index]=!0;for(var g=0;g<e.bonds.length;g++){var m=this.atoms[e.bonds[g]];void 0===d[m.index]&&m.hasOwnProperty("resi")&&m.chain==f&&m.resi==p&&(u.push(m),i.push(m))}}}}return i}addAtoms(t){this.molObj=null;var e,i=this.atoms.length,r=[];for(e=0;e<t.length;e++)void 0===t[e].index&&(t[e].index=e),void 0===t[e].serial&&(t[e].serial=e),r[t[e].index]=i+e;for(e=0;e<t.length;e++){var s=t[e],n=r[s.index],a=(0,L.extend)({},s);a.index=n,a.bonds=[],a.bondOrder=[],a.model=this.id,a.style=a.style||(0,L.deepCopy)(GLModel.defaultAtomStyle),void 0===a.color&&(a.color=this.ElementColors[a.elem]||this.defaultColor);for(var o=s.bonds?s.bonds.length:0,l=0;l<o;l++){var h=r[s.bonds[l]];void 0!==h&&(a.bonds.push(h),a.bondOrder.push(s.bondOrder?s.bondOrder[l]:1))}this.atoms.push(a)}}assignBonds(){(0,u.assignBonds)(this.atoms,{assignBonds:!0})}removeAtoms(t){this.molObj=null;var e,i=[];for(e=0;e<t.length;e++)i[t[e].index]=!0;var r=[];for(e=0;e<this.atoms.length;e++){var s=this.atoms[e];i[s.index]||r.push(s)}this.atoms=[],this.addAtoms(r)}setStyle(t,e,i){void 0===e&&void 0===i&&(e=t,t={}),"string"==typeof e&&(e=(0,L.specStringToObject)(e));var r=!1,s=this,n=function(n){var a=s.selectedAtoms(t,n);for(let t=0;t<n.length;t++)n[t]&&(n[t].capDrawn=!1);for(let t=0;t<a.length;t++){r=!0,(a[t].clickable||a[t].hoverable)&&(a[t].intersectionShape={sphere:[],cylinder:[],line:[],triangle:[]}),i||(a[t].style={});for(let i in e)e.hasOwnProperty(i)&&(a[t].style[i]=a[t].style[i]||{},Object.assign(a[t].style[i],e[i]))}};if(void 0!==t.frame&&t.frame<this.frames.length){let e=t.frame;e<0&&(e=this.frames.length+e),n(this.frames[e])}else{n(this.atoms);for(var a=0;a<this.frames.length;a++)this.frames[a]!==this.atoms&&n(this.frames[a])}r&&(this.molObj=null)}setClickable(t,e,i){if(e=!!e,null!==(i=(0,L.makeFunction)(i))){var r=this.selectedAtoms(t,this.atoms),s=r.length;for(let t=0;t<s;t++)r[t].intersectionShape={sphere:[],cylinder:[],line:[],triangle:[]},r[t].clickable=e,i&&(r[t].callback=i);s>0&&(this.molObj=null)}else console.log("Callback is not a function")}setHoverable(t,e,i,r){if(e=!!e,i=(0,L.makeFunction)(i),r=(0,L.makeFunction)(r),null!==i)if(null!==r){var s=this.selectedAtoms(t,this.atoms),n=s.length;for(let t=0;t<n;t++)s[t].intersectionShape={sphere:[],cylinder:[],line:[],triangle:[]},s[t].hoverable=e,i&&(s[t].hover_callback=i),r&&(s[t].unhover_callback=r);n>0&&(this.molObj=null)}else console.log("Unhover_callback is not a function");else console.log("Hover_callback is not a function")}enableContextMenu(t,e){var i;e=!!e;var r=this.selectedAtoms(t,this.atoms),s=r.length;for(i=0;i<s;i++)r[i].intersectionShape={sphere:[],cylinder:[],line:[],triangle:[]},r[i].contextMenuEnabled=e;s>0&&(this.molObj=null)}setColorByElement(t,e){if(null===this.molObj||!GLModel.sameObj(e,this.lastColors)){this.lastColors=e;var i=this.selectedAtoms(t,i);i.length>0&&(this.molObj=null);for(var r=0;r<i.length;r++){var s=i[r];void 0!==e[s.elem]&&(s.color=e[s.elem])}}}setColorByProperty(t,e,i,s){var n,a,o=this.selectedAtoms(t,o);for(this.lastColors=null,o.length>0&&(this.molObj=null),"string"==typeof i&&void 0!==r.Gradient.builtinGradients[i]&&(i=new r.Gradient.builtinGradients[i]),s||(s=i.range()),s||(s=(0,L.getPropertyRange)(o,e)),n=0;n<o.length;n++){a=o[n],null!=(0,L.getAtomProperty)(a,e)&&(a.color=i.valueToHex(parseFloat(a.properties[e]),s))}}setColorByFunction(t,e){var i=this.selectedAtoms(t,i);if("function"==typeof e){this.lastColors=null,i.length>0&&(this.molObj=null);for(let t=0;t<i.length;t++){let r=i[t];r.color=e(r)}}}toCDObject(t=!1){var e={a:[],b:[]};t&&(e.s=[]);for(let r=0;r<this.atoms.length;r++){let s={},n=this.atoms[r];if(s.x=n.x,s.y=n.y,s.z=n.z,"C"!=n.elem&&(s.l=n.elem),t){for(var i=0;i<e.s.length&&JSON.stringify(n.style)!==JSON.stringify(e.s[i]);)i++;i===e.s.length&&e.s.push(n.style),0!==i&&(s.s=i)}e.a.push(s);for(let t=0;t<n.bonds.length;t++){let i=r,s=n.bonds[t];if(i>=s)continue;let a={b:i,e:s},o=n.bondOrder[t];1!=o&&(a.o=o),e.b.push(a)}}return e}globj(t,e){(null===this.molObj||e.regen)&&(this.molObj=this.createMolObj(this.atoms,e),this.renderedMolObj&&(t.remove(this.renderedMolObj),this.renderedMolObj=null),this.renderedMolObj=this.molObj.clone(),this.hidden&&(this.renderedMolObj.setVisible(!1),this.molObj.setVisible(!1)),t.add(this.renderedMolObj))}exportVRML(){return this.createMolObj(this.atoms,{supportsImposters:!1,supportsAIA:!1}).vrml()}removegl(t){this.renderedMolObj&&(void 0!==this.renderedMolObj.geometry&&this.renderedMolObj.geometry.dispose(),void 0!==this.renderedMolObj.material&&this.renderedMolObj.material.dispose(),t.remove(this.renderedMolObj),this.renderedMolObj=null),this.molObj=null}hide(){this.hidden=!0,this.renderedMolObj&&this.renderedMolObj.setVisible(!1),this.molObj&&this.molObj.setVisible(!1)}show(){this.hidden=!1,this.renderedMolObj&&this.renderedMolObj.setVisible(!0),this.molObj&&this.molObj.setVisible(!0)}addPropertyLabels(t,e,i,r){for(var s=this.selectedAtoms(e,s),n=(0,L.deepCopy)(r),a=0;a<s.length;a++){var o=s[a],l=null;void 0!==o[t]?l=String(o[t]):void 0!==o.properties[t]&&(l=String(o.properties[t])),null!=l&&(n.position=o,i.addLabel(l,n))}}addResLabels(t,e,i,r=!1){var s=[],n=function(r,n){for(var a=r.selectedAtoms(t,a),o={},l=0;l<a.length;l++){var h=a[l],c=h.chain,d=h.resn+""+h.resi;o[c]||(o[c]={}),o[c][d]||(o[c][d]=[]),o[c][d].push(h)}var u=(0,L.deepCopy)(i);for(let t in o)if(o.hasOwnProperty(t)){var f=o[t];for(let t in f)if(f.hasOwnProperty(t)){let i=f[t],r=new $.Vector3(0,0,0);for(let t=0;t<i.length;t++){let e=i[t];r.x+=e.x,r.y+=e.y,r.z+=e.z}r.divideScalar(i.length),u.position=r,u.frame=n;let a=e.addLabel(t,u,void 0,!0);s.push(a)}}};if(r){var a=this.getNumFrames();let t=this.atoms;for(let t=0;t<a;t++)this.frames[t]&&(this.atoms=this.frames[t],n(this,t));this.atoms=t}else n(this);return s}setupDFS(){this.atomdfs=[];var t=this,e=new Int8Array(this.atoms.length);e.fill(0);for(var i=function(r,s,n){n.push([r,s]);var a=t.atoms[r];e[r]=1;for(var o=0;o<a.bonds.length;o++){var l=a.bonds[o];t.atoms[l]&&!e[l]&&i(l,r,n)}},r=0;r<this.atoms.length;r++){if(this.atoms[r]&&!e[r]){var s=[];i(r,-1,s),this.atomdfs.push(s)}}}setCoordinatesFromURL(t,e){this.frames=[];var i=this;return this.box&&this.setupDFS(),t.startsWith("http")||(t="http://"+t),(0,L.get)(t+"/traj/numframes/"+e,(function(r){if(!isNaN(parseInt(r)))return i.frames.push(i.atoms),i.frames.numFrames=r,i.frames.url=t,i.frames.path=e,i.setFrame(0)}))}setCoordinates(t,e){if(!t)return[];if(/\.gz$/.test(e=e||"")){e=e.replace(/\.gz$/,"");try{t=(0,L.inflateString)(t)}catch(t){console.log(t)}}if({mdcrd:"",inpcrd:"",pdb:"",netcdf:"",array:""}.hasOwnProperty(e)){this.frames=[];for(var i=this.atoms.length,r=GLModel.parseCrd(t,e),s=0;s<r.length;){for(var n=[],a=0;a<i;a++){var o={};for(var l in this.atoms[a])o[l]=this.atoms[a][l];n[a]=o,n[a].x=r[s++],n[a].y=r[s++],n[a].z=r[s++]}this.frames.push(n)}return this.atoms=this.frames[0],this.frames}return[]}addAtomSpecs(t){}static parseCrd(t,e){var i=[],r=0;if("pdb"==e)for(var s=t.indexOf("\nATOM");-1!=s;){for(;"\nATOM"==t.slice(s,s+5)||"\nHETATM"==t.slice(s,s+7);)i[r++]=parseFloat(t.slice(s+31,s+39)),i[r++]=parseFloat(t.slice(s+39,s+47)),i[r++]=parseFloat(t.slice(s+47,s+55)),s=t.indexOf("\n",s+54),"\nTER"==t.slice(s,s+4)&&(s=t.indexOf("\n",s+5));s=t.indexOf("\nATOM",s)}else if("netcdf"==e){var n=new NetCDFReader(t);i=[].concat.apply([],n.getDataVariable("coordinates"))}else{if("array"==e||Array.isArray(t))return t.flat(2);{let r=t.indexOf("\n");"inpcrd"==e&&(r=t.indexOf("\n",r+1)),i=(t=t.slice(r+1)).match(/\S+/g).map(parseFloat)}}return i}static parseMolData(t,e="",i){if(!t)return[];if(/\.gz$/.test(e)){e=e.replace(/\.gz$/,"");try{t=e.match(/bcif/i)?(0,L.inflateString)(t,!1):(0,L.inflateString)(t)}catch(t){console.log(t)}}return void 0===K[e]&&(e=e.split(".").pop(),void 0===K[e]&&(console.log("Unknown format: "+e),e=t instanceof Uint8Array?"bcif":t.match(/^@<TRIPOS>MOLECULE/gm)?"mol2":t.match(/^data_/gm)&&t.match(/^loop_/gm)?"cif":t.match(/^HETATM/gm)||t.match(/^ATOM/gm)?"pdb":t.match(/ITEM: TIMESTEP/gm)?"lammpstrj":t.match(/^.*\n.*\n.\s*(\d+)\s+(\d+)/gm)?"sdf":t.match(/^%VERSION\s+VERSION_STAMP/gm)?"prmtop":"xyz",console.log("Best guess: "+e))),(0,K[e])(t,i)}}GLModel.defaultAtomStyle={line:{}},GLModel.defaultlineWidth=1,GLModel.vdwRadii={H:1.2,He:1.4,Li:1.82,Be:1.53,B:1.92,C:1.7,N:1.55,O:1.52,F:1.47,Ne:1.54,Na:2.27,Mg:1.73,Al:1.84,Si:2.1,P:1.8,S:1.8,Cl:1.75,Ar:1.88,K:2.75,Ca:2.31,Ni:1.63,Cu:1.4,Zn:1.39,Ga:1.87,Ge:2.11,As:1.85,Se:1.9,Br:1.85,Kr:2.02,Rb:3.03,Sr:2.49,Pd:1.63,Ag:1.72,Cd:1.58,In:1.93,Sn:2.17,Sb:2.06,Te:2.06,I:1.98,Xe:2.16,Cs:3.43,Ba:2.68,Pt:1.75,Au:1.66,Hg:1.55,Tl:1.96,Pb:2.02,Bi:2.07,Po:1.97,At:2.02,Rn:2.2,Fr:3.48,Ra:2.83,U:1.86},GLModel.ignoredKeys=new Set(["props","invert","model","frame","byres","expand","within","and","or","not"]);var Ht=i(111);const Wt=16;class GLViewer{getWidth(){let t=this.container,e=t.offsetWidth;if(0==e&&"none"===t.style.display){let i=t.style.position,r=t.style.visibility;t.style.display="block",t.style.visibility="hidden",t.style.position="absolute",e=t.offsetWidth,t.style.display="none",t.style.visibility=r,t.style.position=i}return e}getHeight(){let t=this.container,e=t.offsetHeight;if(0==e&&"none"===t.style.display){let i=t.style.position,r=t.style.visibility;t.style.display="block",t.style.visibility="hidden",t.style.position="absolute",e=t.offsetHeight,t.style.display="none",t.style.visibility=r,t.style.position=i}return e}setupRenderer(){this.renderer=new n.Renderer({antialias:this.config.antialias,preserveDrawingBuffer:!0,premultipliedAlpha:!1,id:this.config.id,row:this.config.row,col:this.config.col,rows:this.config.rows,cols:this.config.cols,canvas:this.config.canvas,containerWidth:this.WIDTH,containerHeight:this.HEIGHT,ambientOcclusion:this.config.ambientOcclusion,outline:this.config.outline}),this.renderer.domElement.style.width="100%",this.renderer.domElement.style.height="100%",this.renderer.domElement.style.padding="0",this.renderer.domElement.style.position="absolute",this.renderer.domElement.style.top="0px",this.renderer.domElement.style.left="0px",this.renderer.domElement.style.zIndex="0"}initializeScene(){this.scene=new n.Scene,this.scene.fog=new n.Fog(this.bgColor,100,200),this.modelGroup=new n.Object3D,this.rotationGroup=new n.Object3D,this.rotationGroup.useQuaternion=!0,this.rotationGroup.quaternion=new $.Quaternion(0,0,0,1),this.rotationGroup.add(this.modelGroup),this.scene.add(this.rotationGroup);var t=new n.Light(16777215);t.position=new $.Vector3(.2,.2,1).normalize(),t.intensity=1,this.scene.add(t)}_handleLostContext(t){const e=function(t){const e=t.getBoundingClientRect();return!(e.right<0||e.bottom<0||e.top>(window.innerHeight||document.documentElement.clientHeight)||e.left>(window.innerWidth||document.documentElement.clientWidth))};if(e(this.container)){let t=0;for(let i of document.getElementsByTagName("canvas"))if(e(i)&&null!=i._3dmol_viewer&&(i._3dmol_viewer.resize(),t+=1,t>=Wt))break}}initContainer(t){this.container=t,this.WIDTH=this.getWidth(),this.HEIGHT=this.getHeight(),this.ASPECT=this.renderer.getAspect(this.WIDTH,this.HEIGHT),this.renderer.setSize(this.WIDTH,this.HEIGHT),this.container.append(this.renderer.domElement),this.glDOM=this.renderer.domElement,this.glDOM._3dmol_viewer=this,this.glDOM.addEventListener("webglcontextlost",this._handleLostContext.bind(this)),this.nomouse||(this.glDOM.addEventListener("mousedown",this._handleMouseDown.bind(this),{passive:!1}),this.glDOM.addEventListener("touchstart",this._handleMouseDown.bind(this),{passive:!1}),this.glDOM.addEventListener("wheel",this._handleMouseScroll.bind(this),{passive:!1}),this.glDOM.addEventListener("mousemove",this._handleMouseMove.bind(this),{passive:!1}),this.glDOM.addEventListener("touchmove",this._handleMouseMove.bind(this),{passive:!1}),this.glDOM.addEventListener("contextmenu",this._handleContextMenu.bind(this),{passive:!1}))}decAnim(){this.animated--,this.animated<0&&(this.animated=0)}incAnim(){this.animated++}nextSurfID(){var t=0;for(let i in this.surfaces)if(this.surfaces.hasOwnProperty(i)){var e=parseInt(i);isNaN(e)||e>t&&(t=e)}return t+1}setSlabAndFog(){let t=this.camera.position.z-this.rotationGroup.position.z;t<1&&(t=1),this.camera.near=t+this.slabNear,!this.camera.ortho&&this.camera.near<1&&(this.camera.near=1),this.camera.far=t+this.slabFar,this.camera.near+1>this.camera.far&&(this.camera.far=this.camera.near+1),this.camera.fov=this.fov,this.camera.right=t*Math.tan(Math.PI/180*this.fov),this.camera.left=-this.camera.right,this.camera.top=this.camera.right/this.ASPECT,this.camera.bottom=-this.camera.top,this.camera.updateProjectionMatrix(),this.scene.fog.near=this.camera.near+this.fogStart*(this.camera.far-this.camera.near),this.scene.fog.far=this.camera.far,this.config.disableFog&&(this.scene.fog.near=this.scene.fog.far)}show(t){if(this.renderer.setViewport(),this.scene&&(this.setSlabAndFog(),this.renderer.render(this.scene,this.camera),this.viewChangeCallback&&this.viewChangeCallback(this._viewer.getView()),!t&&this.linkedViewers.length>0))for(var e=this._viewer.getView(),i=0;i<this.linkedViewers.length;i++){this.linkedViewers[i].setView(e,!0)}}updateClickables(){this.clickables.splice(0,this.clickables.length),this.hoverables.splice(0,this.hoverables.length),this.contextMenuEnabledObjects.splice(0,this.contextMenuEnabledObjects.length);for(let t=0,e=this.models.length;t<e;t++){let e=this.models[t];if(e){let t=e.selectedAtoms({clickable:!0}),i=e.selectedAtoms({hoverable:!0}),r=e.selectedAtoms({contextMenuEnabled:!0});for(let t=0;t<i.length;t++)this.hoverables.push(i[t]);for(let e=0;e<t.length;e++)this.clickables.push(t[e]);for(let t=0;t<r.length;t++)this.contextMenuEnabledObjects.push(r[t])}}for(let t=0,e=this.shapes.length;t<e;t++){let e=this.shapes[t];e&&e.clickable&&this.clickables.push(e),e&&e.hoverable&&this.hoverables.push(e),e&&e.contextMenuEnabled&&this.contextMenuEnabledObjects.push(e)}}handleClickSelection(t,e,i){let r=this.targetedObjects(t,e,this.clickables);if(r.length){var s=r[0].clickable;if(void 0!==s.callback&&("function"!=typeof s.callback&&(s.callback=(0,L.makeFunction)(s.callback)),"function"==typeof s.callback)){3===this.mouseButton&&this.contextMenuEnabledObjects.includes(s)&&this.userContextMenuHandler||s.callback(s,this._viewer,i,this.container,r)}}}canvasOffset(){let t=this.glDOM,e=t.getBoundingClientRect(),i=t.ownerDocument,r=i.documentElement,s=i.defaultView;return{top:e.top+s.pageYOffset-r.clientTop,left:e.left+s.pageXOffset-r.clientLeft}}setHover(t,e,i){this.current_hover!=t&&(this.current_hover&&("function"!=typeof this.current_hover.unhover_callback&&(this.current_hover.unhover_callback=(0,L.makeFunction)(this.current_hover.unhover_callback)),this.current_hover.unhover_callback(this.current_hover,this._viewer,e,this.container,i)),this.current_hover=t,t&&void 0!==t.hover_callback&&("function"!=typeof t.hover_callback&&(t.hover_callback=(0,L.makeFunction)(t.hover_callback)),"function"==typeof t.hover_callback&&t.hover_callback(t,this._viewer,e,this.container,i)))}handleHoverSelection(t,e,i){if(0==this.hoverables.length)return;let r=this.targetedObjects(t,e,this.hoverables);if(r.length){var s=r[0].clickable;this.setHover(s,i,r),this.current_hover=s}else this.setHover(null)}handleHoverContinue(t,e){let i=this.targetedObjects(t,e,this.hoverables);0!=i.length&&void 0!==i[0]||this.setHover(null),void 0!==i[0]&&i[0].clickable!==this.current_hover&&this.setHover(null)}closeEnoughForClick(t,{allowTolerance:e=t.targetTouches,tolerance:i=5}={}){const r=this.getX(t),s=this.getY(t);if(e){const t=Math.abs(r-this.mouseStartX),e=Math.abs(s-this.mouseStartY);return t<=i&&e<=i}return r===this.mouseStartX&&s===this.mouseStartY}calcTouchDistance(t){var e=t.targetTouches[0].pageX-t.targetTouches[1].pageX,i=t.targetTouches[0].pageY-t.targetTouches[1].pageY;return Math.hypot(e,i)}getX(t){var e=t.pageX;return null==e&&(e=t.pageX),t.targetTouches&&t.targetTouches[0]?e=t.targetTouches[0].pageX:t.changedTouches&&t.changedTouches[0]&&(e=t.changedTouches[0].pageX),e}getY(t){var e=t.pageY;return null==e&&(e=t.pageY),t.targetTouches&&t.targetTouches[0]?e=t.targetTouches[0].pageY:t.changedTouches&&t.changedTouches[0]&&(e=t.changedTouches[0].pageY),e}isInViewer(t,e){if(null!=this.viewers){var i=this.WIDTH/this.cols,r=this.HEIGHT/this.rows,s=this.canvasOffset(),n=t-s.left,a=e-s.top,o=this.rows-Math.floor(a/r)-1,l=Math.floor(n/i);if(o!=this.row||l!=this.col)return!1}return!0}adjustZoomToLimits(t){if(this.config.lowerZoomLimit&&this.config.lowerZoomLimit>0){let e=this.CAMERA_Z-this.config.lowerZoomLimit;t>e&&(t=e)}if(this.config.upperZoomLimit&&this.config.upperZoomLimit>0){let e=this.CAMERA_Z-this.config.upperZoomLimit;t<e&&(t=e)}return t>this.CAMERA_Z-1&&(t=this.CAMERA_Z-1),t}static slerp(t,e,i){if(1==i)return e.clone();if(0==i)return t.clone();let r=t.x*e.x+t.y*e.y+t.z*e.z+t.w*e.w;if(r>.9995){let r=new $.Quaternion(t.x+i*(e.x-t.x),t.y+i*(e.y-t.y),t.z+i*(e.z-t.z),t.w+i*(e.w-t.w));return r.normalize(),r}r<0&&(e=e.clone().multiplyScalar(-1),r=-r),r>1?r=1:r<-1&&(r=-1);var s=Math.acos(r)*i,n=e.clone();n.sub(t.clone().multiplyScalar(r)),n.normalize();var a=Math.cos(s),o=Math.sin(s),l=new $.Quaternion(t.x*a+n.x*o,t.y*a+n.y*o,t.z*a+n.z*o,t.w*a+n.w*o);return l.normalize(),l}constructor(t,e={}){if(this.nomouse=!1,this.glDOM=null,this.models=[],this.surfaces={},this.shapes=[],this.labels=[],this.clickables=[],this.hoverables=[],this.contextMenuEnabledObjects=[],this.current_hover=null,this.hoverDuration=500,this.longTouchDuration=1e3,this.viewer_frame=0,this.viewChangeCallback=null,this.stateChangeCallback=null,this.NEAR=1,this.FAR=800,this.CAMERA_Z=150,this.fov=20,this.linkedViewers=[],this.renderer=null,this.control_all=!1,this.scene=null,this.rotationGroup=null,this.modelGroup=null,this.fogStart=.4,this.slabNear=-50,this.slabFar=50,this.cq=new $.Quaternion(0,0,0,1),this.dq=new $.Quaternion(0,0,0,1),this.animated=0,this.animationTimers=new Set,this.isDragging=!1,this.mouseStartX=0,this.mouseStartY=0,this.touchDistanceStart=0,this.touchHold=!1,this.currentModelPos=0,this.cz=0,this.cslabNear=0,this.cslabFar=0,this.userContextMenuHandler=null,this.config=e,this.callback=this.config.callback,this.defaultcolors=this.config.defaultcolors,this.defaultcolors||(this.defaultcolors=s.elementColors.defaultColors),this.nomouse=Boolean(this.config.nomouse),this.bgColor=0,this.config.backgroundColor=this.config.backgroundColor||"#ffffff",void 0!==this.config.backgroundColor&&(this.bgColor=s.CC.color(this.config.backgroundColor).getHex()),this.config.backgroundAlpha=null==this.config.backgroundAlpha?1:this.config.backgroundAlpha,this.camerax=0,void 0!==this.config.camerax&&(this.camerax="string"==typeof this.config.camerax?parseFloat(this.config.camerax):this.config.camerax),this._viewer=this,this.container=t,null!=this.config.hoverDuration&&(this.hoverDuration=this.config.hoverDuration),void 0===this.config.antialias&&(this.config.antialias=!0),void 0===this.config.cartoonQuality&&(this.config.cartoonQuality=10),this.WIDTH=this.getWidth(),this.HEIGHT=this.getHeight(),this.setupRenderer(),this.row=null==this.config.row?0:this.config.row,this.col=null==this.config.col?0:this.config.col,this.cols=this.config.cols,this.rows=this.config.rows,this.viewers=this.config.viewers,this.control_all=this.config.control_all,this.ASPECT=this.renderer.getAspect(this.WIDTH,this.HEIGHT),this.camera=new n.Camera(this.fov,this.ASPECT,this.NEAR,this.FAR,this.config.orthographic),this.camera.position=new $.Vector3(this.camerax,0,this.CAMERA_Z),this.lookingAt=new $.Vector3,this.camera.lookAt(this.lookingAt),this.raycaster=new n.Raycaster(new $.Vector3(0,0,0),new $.Vector3(0,0,0)),this.projector=new n.Projector,this.initializeScene(),this.renderer.setClearColorHex(this.bgColor,this.config.backgroundAlpha),this.scene.fog.color=s.CC.color(this.bgColor),document.body.addEventListener("mouseup",this._handleMouseUp.bind(this)),document.body.addEventListener("touchend",this._handleMouseUp.bind(this)),this.initContainer(this.container),this.config.style&&this.setViewStyle(this.config),window.addEventListener("resize",this.resize.bind(this)),void 0!==window.ResizeObserver&&(this.divwatcher=new window.ResizeObserver(this.resize.bind(this)),this.divwatcher.observe(this.container)),void 0!==window.IntersectionObserver){let t=(t,e)=>{t.forEach((t=>{t.isIntersecting&&this.resize()}))};this.intwatcher=new window.IntersectionObserver(t),this.intwatcher.observe(this.container)}try{"function"==typeof this.callback&&this.callback(this)}catch(t){console.log("error with glviewer callback: "+t)}}targetedObjects(t,e,i){var r={x:t,y:e,z:-1};return Array.isArray(i)||(i=this.selectedAtoms(i)),0==i.length?[]:(this.raycaster.setFromCamera(r,this.camera),this.raycaster.intersectObjects(this.modelGroup,i))}modelToScreen(t){let e=!1;Array.isArray(t)||(t=[t],e=!0);let i=this.renderer.getXRatio(),r=this.renderer.getYRatio(),s=this.col,n=this.row,a=s*(this.WIDTH/i),o=(r-n-1)*(this.HEIGHT/r),l=[],h=this.canvasOffset();return t.forEach((t=>{let e=new $.Vector3(t.x,t.y,t.z);e.applyMatrix4(this.modelGroup.matrixWorld),this.projector.projectVector(e,this.camera);let s=this.WIDTH/i*(e.x+1)/2+h.left+a,n=-this.HEIGHT/r*(e.y-1)/2+h.top+o;l.push({x:s,y:n})})),e&&(l=l[0]),l}screenOffsetToModel(t,e,i){var r=t/this.WIDTH,s=e/this.HEIGHT,n=void 0===i?this.rotationGroup.position.z:i,a=this.rotationGroup.quaternion,o=new $.Vector3(0,0,n);return this.projector.projectVector(o,this.camera),o.x+=2*r,o.y-=2*s,this.projector.unprojectVector(o,this.camera),o.z=0,o.applyQuaternion(a),o}screenToModelDistance(t,e){let i=this.canvasOffset(),r=new $.Vector3(e.x,e.y,e.z);r.applyMatrix4(this.modelGroup.matrixWorld);let s=r.clone();this.projector.projectVector(r,this.camera);let n=new $.Vector3(2*(t.x-i.left)/this.WIDTH-1,2*(t.y-i.top)/-this.HEIGHT+1,r.z);return this.projector.unprojectVector(n,this.camera),n.distanceTo(s)}setViewChangeCallback(t){"function"!=typeof t&&null!=t||(this.viewChangeCallback=t)}setStateChangeCallback(t){"function"!=typeof t&&null!=t||(this.stateChangeCallback=t)}getConfig(){return this.config}setConfig(t){this.config=t,t.ambientOcclusion&&this.renderer.enableAmbientOcclusion(t.ambientOcclusion)}getInternalState(){var t={models:[],surfaces:[],shapes:[],labels:[]};for(let e=0;e<this.models.length;e++)this.models[e]&&(t.models[e]=this.models[e].getInternalState());return t}setInternalState(t){this.clear();var e=t.models;for(let t=0;t<e.length;t++)e[t]&&(this.models[t]=new GLModel(t),this.models[t].setInternalState(e[t]));this.render()}setZoomLimits(t,e){void 0!==t&&(this.config.lowerZoomLimit=t),e&&(this.config.upperZoomLimit=e),this.rotationGroup.position.z=this.adjustZoomToLimits(this.rotationGroup.position.z),this.show()}setCameraParameters(t){void 0!==t.fov&&(this.fov=t.fov,this.camera.fov=this.fov),void 0!==t.z&&(this.CAMERA_Z=t.z,this.camera.z=this.CAMERA_Z),void 0!==t.orthographic&&(this.camera.ortho=t.orthographic),this.setSlabAndFog()}_handleMouseDown(t){if(t.preventDefault(),!this.scene)return;var e=this.getX(t),i=this.getY(t);if(void 0===e)return;this.isDragging=!0,this.mouseButton=t.which,this.mouseStartX=e,this.mouseStartY=i,this.touchHold=!0,this.touchDistanceStart=0,t.targetTouches&&2==t.targetTouches.length&&(this.touchDistanceStart=this.calcTouchDistance(t)),this.cq=this.rotationGroup.quaternion.clone(),this.cz=this.rotationGroup.position.z,this.currentModelPos=this.modelGroup.position.clone(),this.cslabNear=this.slabNear,this.cslabFar=this.slabFar;let r=this;t.targetTouches&&1===t.targetTouches.length&&(this.longTouchTimeout=setTimeout((function(){if(1==r.touchHold){r.glDOM=r.renderer.domElement;const e=t.targetTouches[0],i=new PointerEvent("contextmenu",Object.assign(Object.assign({},t),{pageX:e.pageX,pageY:e.pageY,screenX:e.screenX,screenY:e.screenY,clientX:e.clientX,clientY:e.clientY}));r.glDOM.dispatchEvent(i)}}),this.longTouchDuration))}_handleMouseUp(t){if(this.touchHold=!1,this.isDragging&&this.scene){var e=this.getX(t),i=this.getY(t);if(this.closeEnoughForClick(t)&&this.isInViewer(e,i)){let r=this.mouseXY(e,i);this.handleClickSelection(r.x,r.y,t)}}this.isDragging=!1}_handleMouseScroll(t){if(t.preventDefault(),this.scene){var e=this.getX(t),i=this.getY(t);if(void 0!==e&&(this.control_all||this.isInViewer(e,i))){var r=.85*(this.CAMERA_Z-this.rotationGroup.position.z),s=1;if(t.ctrlKey&&(s=-1),t.detail)this.rotationGroup.position.z+=s*r*t.detail/10;else if(t.wheelDelta){let e=600*t.wheelDelta/(t.wheelDelta+600);this.rotationGroup.position.z-=s*r*e/400}this.rotationGroup.position.z=this.adjustZoomToLimits(this.rotationGroup.position.z),this.show()}}}pngURI(){return this.getCanvas().toDataURL("image/png")}apngURI(t){let e=this;return t=t||1,new Promise((function(i){let r=0,s=e.viewChangeCallback,n=[],a=[],o=Date.now();e.viewChangeCallback=function(){a.push(Date.now()-o),o=Date.now(),n.push(new Promise((t=>{e.getCanvas().toBlob((function(e){e.arrayBuffer().then(t)}),"image/png")}))),r+=1,r==t&&(e.viewChangeCallback=s,Promise.all(n).then((t=>{let r=[];for(let e=0;e<t.length;e++){let i=(0,Ht.decode)(t[e]);r.push((0,Ht.toRGBA8)(i)[0])}let s=e.getCanvas().width,n=e.getCanvas().height,o=(0,Ht.encode)(r,s,n,0,a),l=new Blob([o],{type:"image/png"}),h=new FileReader;h.onload=function(t){i(t.target.result)},h.readAsDataURL(l)})))}}))}getCanvas(){return this.glDOM}getRenderer(){return this.renderer}setHoverDuration(t){this.hoverDuration=t}mouseXY(t,e){let i=this.canvasOffset(),r=this.renderer.getXRatio(),s=this.renderer.getYRatio(),n=this.col,a=this.row,o=n*(this.WIDTH/r),l=(s-a-1)*(this.HEIGHT/s);return{x:(t-i.left-o)/(this.WIDTH/r)*2-1,y:-(e-i.top-l)/(this.HEIGHT/s)*2+1}}_handleMouseMove(t){clearTimeout(this.hoverTimeout),t.preventDefault();let e=this.getX(t),i=this.getY(t);if(void 0===e)return;let r=this.renderer.getXRatio(),s=this.renderer.getYRatio(),n=this.mouseXY(e,i),a=this;null!==this.current_hover&&this.handleHoverContinue(n.x,n.y);var o=0;if((this.control_all||this.isInViewer(e,i))&&this.scene&&(this.hoverables.length>0&&(this.hoverTimeout=setTimeout((function(){a.handleHoverSelection(n.x,n.y,t)}),this.hoverDuration)),this.isDragging)){t.targetTouches&&(t.targetTouches.length>1||1===t.targetTouches.length&&!this.closeEnoughForClick(t))&&clearTimeout(this.longTouchTimeout);var l=(e-this.mouseStartX)/this.WIDTH,h=(i-this.mouseStartY)/this.HEIGHT;if(0!=this.touchDistanceStart&&t.targetTouches&&2==t.targetTouches.length)o=2,h=2*(this.calcTouchDistance(t)-this.touchDistanceStart)/(this.WIDTH+this.HEIGHT);else t.targetTouches&&3==t.targetTouches.length&&(o=1);l*=r,h*=s;var c,d=Math.hypot(l,h);if(3==o||3==this.mouseButton&&t.ctrlKey)this.slabNear=this.cslabNear+100*l,this.slabFar=this.cslabFar-100*h;else if(2==o||3==this.mouseButton||t.shiftKey)(c=.85*(this.CAMERA_Z-this.rotationGroup.position.z))<80&&(c=80),this.rotationGroup.position.z=this.cz+h*c,this.rotationGroup.position.z=this.adjustZoomToLimits(this.rotationGroup.position.z);else if(1==o||2==this.mouseButton||t.ctrlKey){var u=this.screenOffsetToModel(r*(e-this.mouseStartX),s*(i-this.mouseStartY));this.modelGroup.position.addVectors(this.currentModelPos,u)}else if((0===o||1==this.mouseButton)&&0!==d){var f=Math.sin(d*Math.PI)/d;this.dq.x=Math.cos(d*Math.PI),this.dq.y=0,this.dq.z=f*l,this.dq.w=-f*h,this.rotationGroup.quaternion.set(1,0,0,0),this.rotationGroup.quaternion.multiply(this.dq),this.rotationGroup.quaternion.multiply(this.cq)}this.show()}}_handleContextMenu(t){if(t.preventDefault(),this.closeEnoughForClick(t)){var e=this.mouseStartX,i=this.mouseStartY,r=this.canvasOffset();let n=this.mouseXY(e,i),a=n.x,o=n.y,l=this.targetedObjects(a,o,this.contextMenuEnabledObjects);var s=null;l.length&&(s=l[0].clickable);r=this.canvasOffset(),e=this.mouseStartX-r.left,i=this.mouseStartY-r.top;this.userContextMenuHandler&&(this.userContextMenuHandler(s,e,i,l,t),this.isDragging=!1)}}setContainer(t){let e=(0,L.getElement)(t)||this.container;return this.initContainer(e),this}setBackgroundColor(t,e){(void 0===e||e<0||e>1)&&(e=1);var i=s.CC.color(t);return this.scene.fog.color=i,this.bgColor=i.getHex(),this.renderer.setClearColorHex(i.getHex(),e),this.show(),this}setProjection(t){this.camera.ortho="orthographic"===t,this.setSlabAndFog()}setViewStyle(t){if((t=t||{}).style=t.style||"",t.style.includes("outline")?this.renderer.enableOutline(t):this.renderer.disableOutline(),t.style.includes("ambientOcclusion")){var e={};t.strength&&(e.strength=t.strength),t.radius&&(e.radius=t.radius),this.renderer.enableAmbientOcclusion(e)}else this.renderer.disableAmbientOcclusion();return this}updateSize(){this.renderer.setSize(this.WIDTH,this.HEIGHT),this.ASPECT=this.renderer.getAspect(this.WIDTH,this.HEIGHT),this.renderer.setSize(this.WIDTH,this.HEIGHT),this.camera.aspect=this.ASPECT,this.camera.updateProjectionMatrix()}setWidth(t){return this.WIDTH=t||this.WIDTH,this.updateSize(),this}setHeight(t){return this.HEIGHT=t||this.HEIGHT,this.updateSize(),this}resize(){this.WIDTH=this.getWidth(),this.HEIGHT=this.getHeight();let t=!1;if(this.renderer.isLost()&&this.WIDTH>0&&this.HEIGHT>0){let e=!1,i=this.container.querySelector("canvas");i&&i!=this.renderer.getCanvas()?this.config.canvas=i:(i.remove(),this.config&&null!=this.config.canvas&&(delete this.config.canvas,e=!0)),this.setupRenderer(),this.initContainer(this.container),this.renderer.setClearColorHex(this.bgColor,this.config.backgroundAlpha),t=!0,e&&(this.config.canvas=this.renderer.getCanvas())}if(0==this.WIDTH||0==this.HEIGHT?this.animated&&this._viewer.pauseAnimate():this.animated&&this._viewer.resumeAnimate(),this.updateSize(),t){let t=this.renderer.supportedExtensions();if(t.regen=!0,this.viewers)for(let e=0,i=this.viewers.length;e<i;e++)for(let i=0,r=this.viewers[e].length;i<r;i++)this.viewers[e][i].render(null,t);this._viewer.render(null,t)}else this.show();return this}getModel(t){return void 0===t?0==this.models.length?null:this.models[this.models.length-1]:t instanceof GLModel?t:t in this.models?this.models[t]:0==this.models.length?null:this.models[this.models.length-1]}spin(t,e=1,i=!1){if(clearInterval(this.spinInterval),void 0===t&&(t="y"),"boolean"==typeof t){if(!t)return;t="y"}Array.isArray(t)&&(t={x:t[0],y:t[1],z:t[2]});var r=this;this.spinInterval=setInterval((function(){!r.getCanvas().isConnected&&r.renderer.isLost()&&clearInterval(r.spinInterval),(!i||r.container.checkVisibility&&r.container.checkVisibility())&&r.rotate(1*e,t)}),25)}animateMotion(t,e,i,r,s,n){var a=Math.ceil(t/20);a<1&&(a=1),this.incAnim();var o={mpos:this.modelGroup.position.clone(),rz:this.rotationGroup.position.z,rot:this.rotationGroup.quaternion.clone(),cam:this.lookingAt.clone()};if(e){let t=new Array(a);for(let e=0;e<a;e++){let l=(e+1)/a,h={mpos:o.mpos,rz:o.rz,rot:o.rot};h.mpos=i.clone().sub(o.mpos).multiplyScalar(l).add(o.mpos),h.rz=o.rz+l*(r-o.rz),h.rot=GLViewer.slerp(o.rot,s,l),h.cam=n.clone().sub(o.cam).multiplyScalar(l).add(o.cam),t[e]=h}let e=0,l=this,h=function(){var i=t[e];e+=1,l.modelGroup.position=i.mpos,l.rotationGroup.position.z=i.rz,l.rotationGroup.quaternion=i.rot,l.camera.lookAt(i.cam),e<t.length?setTimeout(h,20):l.decAnim(),l.show()};setTimeout(h,20)}else{var l={};let t=1/a;if(i&&(l.mpos=i.clone().sub(o.mpos).multiplyScalar(t)),void 0!==r&&null!=r&&(l.rz=t*(r-o.rz)),s){var h=GLViewer.slerp(o.rot,s,t);l.rot=o.rot.clone().inverse().multiply(h)}n&&(l.cam=n.clone().sub(o.cam).multiplyScalar(t));let e=0,c=this,d=function(){e+=1,l.mpos&&c.modelGroup.position.add(l.mpos),l.rz&&(c.rotationGroup.position.z+=l.rz),l.rot&&c.rotationGroup.quaternion.multiply(l.rot),l.cam&&(c.lookingAt.add(l.cam),c.camera.lookAt(c.lookingAt)),e<a?setTimeout(d,20):c.decAnim(),c.show()};setTimeout(d,20)}}rotate(t,e="y",i=0,r=!1){if("x"==e?e={x:1,y:0,z:0}:"y"==e?e={x:0,y:1,z:0}:"z"==e&&(e={x:0,y:0,z:1}),"vx"==e?e={vx:1,vy:0,vz:0}:"vy"==e?e={vx:0,vy:1,vz:0}:"vz"==e&&(e={vx:0,vy:0,vz:1}),void 0!==e.vx){var s=new $.Vector3(e.vx,e.vy,e.vz);s.applyQuaternion(this.rotationGroup.quaternion),e={x:s.x,y:s.y,z:s.z}}var n=function(t){var i,r,s,n=Math.sin(t/2),a=Math.cos(t/2);return i=e.x*n,r=e.y*n,s=e.z*n,new $.Quaternion(i,r,s,a).normalize()}(Math.PI*t/180);if(i){var a=(new $.Quaternion).copy(this.rotationGroup.quaternion).multiply(n);this.animateMotion(i,r,this.modelGroup.position,this.rotationGroup.position.z,a,this.lookingAt)}else this.rotationGroup.quaternion.multiply(n),this.show();return this}surfacesFinished(){for(var t in this.surfaces)if(!this.surfaces[t][0].done)return!1;return!0}getView(){if(!this.modelGroup)return[0,0,0,0,0,0,0,1];var t=this.modelGroup.position,e=this.rotationGroup.quaternion;return[t.x,t.y,t.z,this.rotationGroup.position.z,e.x,e.y,e.z,e.w]}setView(t,e){return void 0!==t&&(t instanceof Array||8!==t.length)&&this.modelGroup&&this.rotationGroup?(this.modelGroup.position.x=t[0],this.modelGroup.position.y=t[1],this.modelGroup.position.z=t[2],this.rotationGroup.position.z=t[3],this.rotationGroup.quaternion.x=t[4],this.rotationGroup.quaternion.y=t[5],this.rotationGroup.quaternion.z=t[6],this.rotationGroup.quaternion.w=t[7],void 0!==t[8]&&(this.rotationGroup.position.x=t[8],this.rotationGroup.position.y=t[9]),this.show(e),this):this}render(t,e){this.renderer.setViewport(),this.updateClickables();var i,r,s=this.getView();for(this.stateChangeCallback&&this.stateChangeCallback(this.getInternalState()),e||(e=this.renderer.supportedExtensions()),i=0;i<this.models.length;i++)this.models[i]&&this.models[i].globj(this.modelGroup,e);for(i=0;i<this.shapes.length;i++)this.shapes[i]&&(void 0===this.shapes[i].frame||this.viewer_frame<0||this.shapes[i].frame<0||this.shapes[i].frame==this.viewer_frame?this.shapes[i].globj(this.modelGroup,e):this.shapes[i].removegl(this.modelGroup));for(i=0;i<this.labels.length;i++)e.regen&&(this.labels[i].dispose(),this.modelGroup.remove(this.labels[i].sprite),this.labels[i].setContext(),this.modelGroup.add(this.labels[i].sprite)),this.labels[i]&&void 0!==this.labels[i].frame&&this.labels[i].frame>=0&&(this.modelGroup.remove(this.labels[i].sprite),(this.viewer_frame<0||this.labels[i].frame==this.viewer_frame)&&this.modelGroup.add(this.labels[i].sprite));for(i in this.surfaces)if(this.surfaces.hasOwnProperty(i)){var a=this.surfaces[i];for(r=0;r<a.length;r++)if(a.hasOwnProperty(r)){var o=a[r].geo;if(!a[r].finished||e.regen){o.verticesNeedUpdate=!0,o.elementsNeedUpdate=!0,o.normalsNeedUpdate=!0,o.colorsNeedUpdate=!0,o.buffersNeedUpdate=!0,a[r].mat.needsUpdate=!0,a[r].done&&(a[r].finished=!0),a[r].lastGL&&this.modelGroup.remove(a[r].lastGL);var l=null;if(l=a[r].mat instanceof n.LineBasicMaterial?new n.Line(o,a[r].mat):new n.Mesh(o,a[r].mat),a[r].mat.transparent&&0==a[r].mat.opacity?l.visible=!1:l.visible=!0,a[r].symmetries.length>1||1==a[r].symmetries.length&&!a[r].symmetries[r].isIdentity()){var h,c=new n.Object3D;for(h=0;h<a[r].symmetries.length;h++){var d=l.clone();d.matrix=a[r].symmetries[h],d.matrixAutoUpdate=!1,c.add(d)}a[r].lastGL=c,this.modelGroup.add(c)}else a[r].lastGL=l,this.modelGroup.add(l)}}}return this.setView(s),"function"==typeof t&&t(this),this}getModelList(t){let e=[];if(void 0===t||void 0===t.model)for(let t=0;t<this.models.length;t++)this.models[t]&&e.push(this.models[t]);else{let r=t.model;Array.isArray(r)||(r=[r]);for(let t=0;t<r.length;t++)if("number"==typeof r[t]){var i=r[t];i<0&&(i+=this.models.length),e.push(this.models[i])}else e.push(r[t])}return e}getAtomsFromSel(t){var e=[];void 0===t&&(t={});var i=this.getModelList(t);for(let r=0;r<i.length;r++)e=e.concat(i[r].selectedAtoms(t));return e}atomIsSelected(t,e){void 0===e&&(e={});for(var i=this.getModelList(e),r=0;r<i.length;r++)if(i[r].atomIsSelected(t,e))return!0;return!1}selectedAtoms(t){return this.getAtomsFromSel(t)}getUniqueValues(t,e){void 0===e&&(e={});var i=this.getAtomsFromSel(e),r={};for(var s in i){if(i[s].hasOwnProperty(t))r[i[s][t]]=!0}return Object.keys(r)}pdbData(t){for(var e=this.getAtomsFromSel(t),i="",r=0,s=e.length;r<s;++r)i+=e[r].pdbline+"\n";return i}zoom(t=2,e=0,i=!1){var r=(this.CAMERA_Z-this.rotationGroup.position.z)/t,s=this.CAMERA_Z-r;return e>0?this.animateMotion(e,i,this.modelGroup.position,this.adjustZoomToLimits(s),this.rotationGroup.quaternion,this.lookingAt):(this.rotationGroup.position.z=this.adjustZoomToLimits(s),this.show()),this}translate(t,e,i=0,r=!1){var s=t/this.WIDTH,n=e/this.HEIGHT,a=new $.Vector3(0,0,-this.CAMERA_Z);this.projector.projectVector(a,this.camera),a.x-=s,a.y-=n,this.projector.unprojectVector(a,this.camera),a.z=0;var o=this.lookingAt.clone().add(a);return i>0?this.animateMotion(i,r,this.modelGroup.position,this.rotationGroup.position.z,this.rotationGroup.quaternion,o):(this.lookingAt=o,this.camera.lookAt(this.lookingAt),this.show()),this}translateScene(t,e,i=0,r=!1){var s=this.screenOffsetToModel(t,e),n=this.modelGroup.position.clone().add(s);return i>0?this.animateMotion(i,r,this.modelGroup.position,this.rotationGroup.position.z,this.rotationGroup.quaternion,this.lookingAt):(this.modelGroup.position=n,this.show()),this}fitSlab(t){t=t||{};var e=this.getAtomsFromSel(t),i=(0,L.getExtent)(e),r=i[1][0]-i[0][0],s=i[1][1]-i[0][1],n=i[1][2]-i[0][2],a=Math.hypot(r,s,n);return a<5&&(a=5),this.slabNear=-a/1.9,this.slabFar=a/2,this}center(t={},e=0,i=!1){var r,s,n=this.getAtomsFromSel(t),a=(0,L.getExtent)(n);(0,L.isEmptyObject)(t)?(this.shapes.forEach((t=>{if(t&&t.boundingSphere&&t.boundingSphere.center){var e=t.boundingSphere.center,i=t.boundingSphere.radius;i>0?(n.push(new $.Vector3(e.x+i,e.y,e.z)),n.push(new $.Vector3(e.x-i,e.y,e.z)),n.push(new $.Vector3(e.x,e.y+i,e.z)),n.push(new $.Vector3(e.x,e.y-i,e.z)),n.push(new $.Vector3(e.x,e.y,e.z+i)),n.push(new $.Vector3(e.x,e.y,e.z-i))):n.push(e)}})),a=(0,L.getExtent)(n),r=n,s=a):(r=this.getAtomsFromSel({}),s=(0,L.getExtent)(r));var o=new $.Vector3(a[2][0],a[2][1],a[2][2]),l=s[1][0]-s[0][0],h=s[1][1]-s[0][1],c=s[1][2]-s[0][2],d=Math.hypot(l,h,c);d<5&&(d=5),this.slabNear=-d/1.9,this.slabFar=d/2,l=a[1][0]-a[0][0],h=a[1][1]-a[0][1],c=a[1][2]-a[0][2],(d=Math.hypot(l,h,c))<5&&(d=5);for(var u=25,f=0;f<n.length;f++)if(n[f]){var p=o.distanceToSquared(n[f]);p>u&&(u=p)}d=2*Math.sqrt(u);var g=o.clone().multiplyScalar(-1);return e>0?this.animateMotion(e,i,g,this.rotationGroup.position.z,this.rotationGroup.quaternion,this.lookingAt):(this.modelGroup.position=g,this.show()),this}zoomTo(t={},e=0,i=!1){let r=this.getAtomsFromSel(t),s=(0,L.getExtent)(r),n=s;if((0,L.isEmptyObject)(t)){let t=r&&r.length;if(this.shapes.forEach((t=>{if(t&&t.boundingSphere)if(t.boundingSphere.box){let e=t.boundingSphere.box;r.push(new $.Vector3(e.min.x,e.min.y,e.min.z)),r.push(new $.Vector3(e.max.x,e.max.y,e.max.z))}else if(t.boundingSphere.center){var e=t.boundingSphere.center,i=t.boundingSphere.radius;i>0?(r.push(new $.Vector3(e.x+i,e.y,e.z)),r.push(new $.Vector3(e.x-i,e.y,e.z)),r.push(new $.Vector3(e.x,e.y+i,e.z)),r.push(new $.Vector3(e.x,e.y-i,e.z)),r.push(new $.Vector3(e.x,e.y,e.z+i)),r.push(new $.Vector3(e.x,e.y,e.z-i))):r.push(e)}})),n=(0,L.getExtent)(r),!t)for(let t=0;t<3;t++)s[2][t]=(n[0][t]+n[1][t])/2}else{let t=this.getAtomsFromSel({});n=(0,L.getExtent)(t)}var a=new $.Vector3(s[2][0],s[2][1],s[2][2]),o=n[1][0]-n[0][0],l=n[1][1]-n[0][1],h=n[1][2]-n[0][2],c=Math.hypot(o,l,h);c<5&&(c=5),this.slabNear=-c/1.9,this.slabFar=c/2,0===Object.keys(t).length&&(this.slabNear=Math.min(2*-c,-50),this.slabFar=Math.max(2*c,50));var d=this.config.minimumZoomToDistance||5;o=s[1][0]-s[0][0],l=s[1][1]-s[0][1],h=s[1][2]-s[0][2],(c=Math.hypot(o,l,h))<d&&(c=d);for(var u=d*d,f=0;f<r.length;f++)if(r[f]){var p=a.distanceToSquared(r[f]);p>u&&(u=p)}c=2*Math.sqrt(u);var g=a.clone().multiplyScalar(-1),m=-(.5*c/Math.tan(Math.PI/180*this.camera.fov/2)-this.CAMERA_Z);return m=this.adjustZoomToLimits(m),e>0?this.animateMotion(e,i,g,m,this.rotationGroup.quaternion,this.lookingAt):(this.modelGroup.position=g,this.rotationGroup.position.z=m,this.show()),this}setSlab(t,e){this.slabNear=t,this.slabFar=e}getSlab(){return{near:this.slabNear,far:this.slabFar}}addLabel(t,e={},i,r=!1){if(i){var s=(0,L.getExtent)(this.getAtomsFromSel(i));e.position={x:s[2][0],y:s[2][1],z:s[2][2]}}var n=new Label(t,e);return n.setContext(),this.modelGroup.add(n.sprite),this.labels.push(n),r||this.show(),n}addResLabels(t,e,i=!1){let r=this.labels.length;return this.applyToModels("addResLabels",t,this,e,i),this.show(),this.labels.slice(r)}addPropertyLabels(t,e,i){return this.applyToModels("addPropertyLabels",t,e,this,i),this.show(),this}removeLabel(t){for(var e=0;e<this.labels.length;e++)if(this.labels[e]==t){this.labels.splice(e,1),t.dispose(),this.modelGroup.remove(t.sprite);break}return this.show(),this}removeAllLabels(){for(var t=0;t<this.labels.length;t++)this.labels[t]&&this.labels[t].sprite&&this.modelGroup.remove(this.labels[t].sprite);return this.labels.splice(0,this.labels.length),this.show(),this}hideAllLabels(){for(var t=0;t<this.labels.length;t++)this.labels[t]&&this.labels[t].hide();return this.show(),this}showAllLabels(){for(var t=0;t<this.labels.length;t++)this.labels[t]&&this.labels[t].show();return this.show(),this}setLabelStyle(t,e){return this.modelGroup.remove(t.sprite),t.dispose(),t.stylespec=e,t.setContext(),this.modelGroup.add(t.sprite),this.show(),t}setLabelText(t,e){return this.modelGroup.remove(t.sprite),t.dispose(),t.text=e,t.setContext(),this.modelGroup.add(t.sprite),this.show(),t}addShape(t){var e=new GLShape(t=t||{});return e.shapePosition=this.shapes.length,this.shapes.push(e),e}removeShape(t){if(!t)return this;for(t.removegl(this.modelGroup),delete this.shapes[t.shapePosition];this.shapes.length>0&&void 0===this.shapes[this.shapes.length-1];)this.shapes.pop();return this}removeAllShapes(){for(var t=0;t<this.shapes.length;t++){var e=this.shapes[t];e&&e.removegl(this.modelGroup)}return this.shapes.splice(0,this.shapes.length),this}getSelectionCenter(t){if(t.hasOwnProperty("x")&&t.hasOwnProperty("y")&&t.hasOwnProperty("z"))return t;var e=this.getAtomsFromSel(t);if(0==e.length)return{x:0,y:0,z:0};var i=(0,L.getExtent)(e);return{x:i[0][0]+(i[1][0]-i[0][0])/2,y:i[0][1]+(i[1][1]-i[0][1])/2,z:i[0][2]+(i[1][2]-i[0][2])/2}}addSphere(t){(t=t||{}).center=this.getSelectionCenter(t.center);var e=new GLShape(t);return e.shapePosition=this.shapes.length,e.addSphere(t),this.shapes.push(e),e.finalize(),e}addBox(t={}){null!=t.corner&&(t.corner=this.getSelectionCenter(t.corner)),null!=t.center&&(t.center=this.getSelectionCenter(t.center));var e=new GLShape(t);return e.shapePosition=this.shapes.length,e.addBox(t),this.shapes.push(e),e.finalize(),e}addArrow(t={}){t.start=this.getSelectionCenter(t.start),t.end=this.getSelectionCenter(t.end);var e=new GLShape(t);return e.shapePosition=this.shapes.length,e.addArrow(t),this.shapes.push(e),e.finalize(),e}addCylinder(t={}){t.start=this.getSelectionCenter(t.start),t.end=this.getSelectionCenter(t.end);var e=new GLShape(t);return e.shapePosition=this.shapes.length,t.dashed?e.addDashedCylinder(t):e.addCylinder(t),this.shapes.push(e),e.finalize(),e}addCurve(t={}){var e=new GLShape(t);return e.shapePosition=this.shapes.length,e.addCurve(t),this.shapes.push(e),e.finalize(),e}addLine(t={}){t.start=this.getSelectionCenter(t.start),t.end=this.getSelectionCenter(t.end),t.wireframe=!0;var e=new GLShape(t);return e.shapePosition=this.shapes.length,t.dashed?e=this.addLineDashed(t,e):e.addLine(t),this.shapes.push(e),e.finalize(),e}addUnitCell(t,e){t=this.getModel(t),(e=e||{alabel:"a",blabel:"b",clabel:"c"}).box=e.box||{},e.astyle=e.astyle||{color:"red",radius:.1,midpos:-1},e.bstyle=e.bstyle||{color:"green",radius:.1,midpos:-1},e.cstyle=e.cstyle||{color:"blue",radius:.1,midpos:-1},e.alabelstyle=e.alabelstyle||{fontColor:"red",showBackground:!1,alignment:"center",inFront:!1},e.blabelstyle=e.blabelstyle||{fontColor:"green",showBackground:!1,alignment:"center",inFront:!1},e.clabelstyle=e.clabelstyle||{fontColor:"blue",showBackground:!1,alignment:"center",inFront:!1},t.unitCellObjects&&this.removeUnitCell(t),t.unitCellObjects={shapes:[],labels:[]};var i=t.getCrystData(),r=null;if(i){if(i.matrix)r=i.matrix;else{var s,n,a,o=i.a,l=i.b,h=i.c,c=i.alpha,d=i.beta,u=i.gamma;c=c*Math.PI/180,d=d*Math.PI/180,u=u*Math.PI/180,s=Math.cos(d),n=(Math.cos(c)-Math.cos(d)*Math.cos(u))/Math.sin(u),a=Math.sqrt(Math.max(0,1-s*s-n*n)),r=new $.Matrix3(o,l*Math.cos(u),h*s,0,l*Math.sin(u),h*n,0,0,h*a)}var f=[new $.Vector3(0,0,0),new $.Vector3(1,0,0),new $.Vector3(0,1,0),new $.Vector3(0,0,1),new $.Vector3(1,1,0),new $.Vector3(0,1,1),new $.Vector3(1,0,1),new $.Vector3(1,1,1)];if(i.matrix4)for(let t=0;t<f.length;t++)i.size&&f[t].multiplyVectors(f[t],i.size),f[t]=f[t].applyMatrix4(i.matrix4);else for(let t=0;t<f.length;t++)f[t]=f[t].applyMatrix3(r);if(e.box&&!e.box.hidden){e.box.wireframe=!0;var p=new GLShape(e.box);p.shapePosition=this.shapes.length,p.addLine({start:f[0],end:f[1]}),p.addLine({start:f[0],end:f[2]}),p.addLine({start:f[1],end:f[4]}),p.addLine({start:f[2],end:f[4]}),p.addLine({start:f[0],end:f[3]}),p.addLine({start:f[3],end:f[5]}),p.addLine({start:f[2],end:f[5]}),p.addLine({start:f[1],end:f[6]}),p.addLine({start:f[4],end:f[7]}),p.addLine({start:f[6],end:f[7]}),p.addLine({start:f[3],end:f[6]}),p.addLine({start:f[5],end:f[7]}),this.shapes.push(p),t.unitCellObjects.shapes.push(p),p.finalize()}if(!e.astyle.hidden){e.astyle.start=f[0],e.astyle.end=f[1];let i=this.addArrow(e.astyle);t.unitCellObjects.shapes.push(i)}if(!e.bstyle.hidden){e.bstyle.start=f[0],e.bstyle.end=f[2];let i=this.addArrow(e.bstyle);t.unitCellObjects.shapes.push(i)}if(!e.cstyle.hidden){e.cstyle.start=f[0],e.cstyle.end=f[3];let i=this.addArrow(e.cstyle);t.unitCellObjects.shapes.push(i)}if(e.alabel){e.alabelstyle.position=f[1];let i=this.addLabel(e.alabel,e.alabelstyle);t.unitCellObjects.labels.push(i)}if(e.blabel){e.blabelstyle.position=f[2];let i=this.addLabel(e.blabel,e.blabelstyle);t.unitCellObjects.labels.push(i)}if(e.clabel){e.clabelstyle.position=f[3];let i=this.addLabel(e.clabel,e.clabelstyle);t.unitCellObjects.labels.push(i)}}}removeUnitCell(t){if((t=this.getModel(t)).unitCellObjects){let e=this;t.unitCellObjects.shapes.forEach((function(t){e.removeShape(t)})),t.unitCellObjects.labels.forEach((function(t){e.removeLabel(t)}))}delete t.unitCellObjects}replicateUnitCell(t=3,e=t,i=e,r,s,n){let a=(r=this.getModel(r)).getCrystData();if(a){const o=r.selectedAtoms({}),l=a.matrix;let h=function(t){return t%2==0?-t/2:Math.ceil(t/2)};t<=1&&e<=1&&i<=1&&(n=!0,t=e=i=3);let c=function(t,e,i){return!1};if(n){const t=(new $.Matrix3).getInverse3(l);c=function(e,i,r){let s=new $.Vector3(e,i,r).applyMatrix3(t);return!(s.x>-1e-4&&s.x<1.0001&&s.y>-1e-4&&s.y<1.0001&&s.z>-1e-4&&s.z<1.0001)}}for(let s=0;s<t;s++)for(let t=0;t<e;t++)for(let e=0;e<i;e++){if(0==s&&0==t&&0==e)continue;let i=new $.Vector3(h(s),h(t),h(e));i.applyMatrix3(l);let n=[];for(let t=0;t<o.length;t++){let e=o[t].x+i.x,r=o[t].y+i.y,s=o[t].z+i.z;if(c(e,r,s))continue;let a={};for(let e in o[t])a[e]=o[t][e];a.x=e,a.y=r,a.z=s,n.push(a)}r.addAtoms(n)}s&&r.assignBonds()}}addLineDashed(t,e){var i,r;t.dashLength=t.dashLength||.5,t.gapLength=t.gapLength||.5,i=t.start?new $.Vector3(t.start.x||0,t.start.y||0,t.start.z||0):new $.Vector3(0,0,0),r=t.end?new $.Vector3(t.end.x,t.end.y||0,t.end.z||0):new $.Vector3(0,0,0);var s,n,a,o=new $.Vector3,l=new $.Vector3,h=new $.Vector3,c=i.clone(),d=0;for(o.subVectors(r,i),s=o.length(),o.normalize(),l=o.clone(),h=o.clone(),l.multiplyScalar(t.dashLength),h.multiplyScalar(t.gapLength),n=l.length(),a=h.length();d<s;){if(d+n>s){t.start=i,t.end=r,e.addLine(t);break}c.addVectors(i,l),t.start=i,t.end=c,e.addLine(t),i=c.clone(),d+=n,c.addVectors(i,h),i=c.clone(),d+=a}return e.finalize(),e}addCustom(t){var e=new GLShape(t=t||{});return e.shapePosition=this.shapes.length,e.addCustom(t),this.shapes.push(e),e.finalize(),e}addVolumetricData(t,e,i={}){var r=new st.VolumeData(t,e);return i.hasOwnProperty("transferfn")?this.addVolumetricRender(r,i):this.addIsosurface(r,i)}addIsosurface(t,e={},i){var r=new GLShape(e);return r.shapePosition=this.shapes.length,r.addIsosurface(t,e,i,this),this.shapes.push(r),r}addVolumetricRender(t,e){var i=new GLVolumetricRender(t,e=e||{},this);return i.shapePosition=this.shapes.length,this.shapes.push(i),i}hasVolumetricRender(){return this.renderer.supportsVolumetric()}enableFog(t){t?this.scene.fog=new n.Fog(this.bgColor,100,200):(this.config.disableFog=!0,this.show())}setFrame(t){this.viewer_frame=t;let e=this;return new Promise((function(i){var r=e.models.map((function(i){return i.setFrame(t,e)}));Promise.all(r).then((function(){i()}))}))}getFrame(){return this.viewer_frame}getNumFrames(){var t=0;for(let e=0;e<this.models.length;e++)this.models[e].getNumFrames()>t&&(t=this.models[e].getNumFrames());for(let e=0;e<this.shapes.length;e++)this.shapes[e].frame&&this.shapes[e].frame>=t&&(t=this.shapes[e].frame+1);for(let e=0;e<this.labels.length;e++)this.labels[e].frame&&this.labels[e].frame>=t&&(t=this.labels[e].frame+1);return t}animate(t){this.incAnim();var e=100,i="forward",r=1/0;(t=t||{}).interval&&(e=t.interval),t.loop&&(i=t.loop),t.reps&&(r=t.reps);var s=this.getNumFrames(),n=this,a=0;t.startFrame&&(a=t.startFrame%s);var o=1;t.step&&(r/=o=t.step);var l,h,c=0,d=s*r,u=new Date,f=function(t){u=new Date,"forward"==t?n.setFrame(a).then((function(){a=(a+o)%s,l()})):"backward"==t?n.setFrame(s-1-a).then((function(){a=(a+o)%s,l()})):n.setFrame(a).then((function(){o*=(a+=o)%(s-1)==0?-1:1,l()}))};return l=function(){if(n.render(),n.getCanvas().isConnected)if(++c>=d||!n.isAnimated())h.cancel(),n.animationTimers.delete(h),n.decAnim();else{var t=e-((new Date).getTime()-u.getTime());t=t>0?t:0,n.animationTimers.delete(h),h=new L.PausableTimer(f,t,i),n.animationTimers.add(h)}else n.stopAnimate()},h=new L.PausableTimer(f,0,i),this.animationTimers.add(h),this}stopAnimate(){return this.animated=0,this.animationTimers.forEach((function(t){t.cancel()})),this.animationTimers=new Set,this}pauseAnimate(){return this.animationTimers.forEach((function(t){t.pause()})),this}resumeAnimate(){return this.animationTimers.forEach((function(t){t.resume()})),this}isAnimated(){return this.animated>0}getModelOpt(t){return t&&!t.defaultcolors?(t.defaultcolors=this.defaultcolors,t.cartoonQuality=t.cartoonQuality||this.config.cartoonQuality):void 0===t&&(t={defaultcolors:this.defaultcolors,cartoonQuality:this.config.cartoonQuality}),t}addModel(t,e="",i){i=this.getModelOpt(i);var r=new GLModel(this.models.length,i);return r.addMolData(t,e,i),this.models.push(r),r}addModels(t,e,i){(i=this.getModelOpt(i)).multimodel=!0,i.frames=!0;for(var r=GLModel.parseMolData(t,e,i),s=0;s<r.length;s++){var n=new GLModel(this.models.length,i);n.setAtomDefaults(r[s]),n.addFrame(r[s]),n.setFrame(0),r.modelData&&n.setModelData(r.modelData[s]),n.setDontDuplicateAtoms(!i.duplicateAssemblyAtoms),this.models.push(n)}return this.models}addModelsAsFrames(t,e,i){(i=this.getModelOpt(i)).multimodel=!0,i.frames=!0;var r=new GLModel(this.models.length,i);return r.addMolData(t,e,i),this.models.push(r),r}addAsOneMolecule(t,e,i){(i=this.getModelOpt(i)).multimodel=!0,i.onemol=!0;var r=new GLModel(this.models.length,i);return r.addMolData(t,e,i),this.models.push(r),r}removeModel(t){if(t=this.getModel(t)){for(t.removegl(this.modelGroup),delete this.models[t.getID()];this.models.length>0&&void 0===this.models[this.models.length-1];)this.models.pop();return this}}removeAllModels(){for(var t=0;t<this.models.length;t++){var e=this.models[t];e&&e.removegl(this.modelGroup)}return this.models.splice(0,this.models.length),this}exportJSON(t,e){var i={};return i.m=void 0===e?this.models.map((function(e){return e.toCDObject(t)})):[this.models[e].toCDObject()],JSON.stringify(i)}exportVRML(){var t=this.modelGroup;this.applyToModels("removegl",this.modelGroup),this.modelGroup=new n.Object3D,this.render(null,{supportsImposters:!1,supportsAIA:!1,regen:!0});var e="#VRML V2.0 utf8\n"+this.modelGroup.vrml()+"\n";return this.applyToModels("removegl",this.modelGroup),this.modelGroup=t,e}createModelFrom(t,e=!1){for(var i=new GLModel(this.models.length,this.defaultcolors),r=0;r<this.models.length;r++)if(this.models[r]){var s=this.models[r].selectedAtoms(t);i.addAtoms(s),e&&this.models[r].removeAtoms(s)}return this.models.push(i),i}applyToModels(t,e,i,r,s,n,a){for(var o=this.getModelList(e),l=0;l<o.length;l++)o[l][t](e,i,r,s,n,a)}setStyle(t,e){return void 0===e&&(e=t,t={}),this.applyToModels("setStyle",t,e,!1),this}addStyle(t,e){return void 0===e&&(e=t,t={}),this.applyToModels("setStyle",t,e,!0),this}setClickable(t,e,i){return this.applyToModels("setClickable",t,e,i),this}setHoverable(t,e,i,r){return this.applyToModels("setHoverable",t,e,i,r),this}enableContextMenu(t,e){return this.applyToModels("enableContextMenu",t,e),this}vibrate(t,e,i,r){return this.applyToModels("vibrate",t,e,i,this,r),this}setColorByProperty(t,e,i,r){return this.applyToModels("setColorByProperty",t,e,i,r),this}setColorByElement(t,e){return this.applyToModels("setColorByElement",t,e),this}static getAtomsWithin(t,e){var i=[];for(let s=0;s<t.length;s++){var r=t[s];void 0!==r&&(r.x<e[0][0]||r.x>e[1][0]||r.y<e[0][1]||r.y>e[1][1]||r.z<e[0][2]||r.z>e[1][2]||i.push(r))}return i}static volume(t){return(t[1][0]-t[0][0])*(t[1][1]-t[0][1])*(t[1][2]-t[0][2])}carveUpExtent(t,e,i){let r=[],s={};for(let t=0,i=e.length;t<i;t++)s[e[t].index]=t;let n=function(t){let e=[];for(let i=0,r=t.length;i<r;i++)t[i].index in s&&e.push(s[t[i].index]);return e},a=function(t){let e=[];return e[0]=[t[0][0],t[0][1],t[0][2]],e[1]=[t[1][0],t[1][1],t[1][2]],e},o=function(t){if(GLViewer.volume(t)<GLViewer.maxVolume)return[t];var e,i=t[1][0]-t[0][0],r=t[1][1]-t[0][1],s=t[1][2]-t[0][2];e=i>r&&i>s?0:r>i&&r>s?1:2;var n=a(t),l=a(t),h=(t[1][e]-t[0][e])/2+t[0][e];n[1][e]=h,l[0][e]=h;var c=o(n),d=o(l);return c.concat(d)},l=o(t);for(let t=0,s=l.length;t<s;t++){let s=a(l[t]);s[0][0]-=6,s[0][1]-=6,s[0][2]-=6,s[1][0]+=6,s[1][1]+=6,s[1][2]+=6;let o=GLViewer.getAtomsWithin(e,s),h=GLViewer.getAtomsWithin(i,l[t]);r.push({extent:l[t],atoms:n(o),toshow:n(h)})}return r}static generateSurfaceMesh(t,e,i){var r=new n.Geometry(!0),a=r.updateGeoGroup(0),o=[];for(let e=0,i=t.length;e<i;e++){var l=t[e];l&&(void 0!==l.surfaceColor?o[e]=l.surfaceColor:l.color&&(o[e]=s.CC.color(l.color)))}var h=a.vertexArray,c=e.vertices;for(let t=0,e=c.length;t<e;t++){let e=3*a.vertices;h[e]=c[t].x,h[e+1]=c[t].y,h[e+2]=c[t].z,a.vertices++}var d=a.colorArray;let u=a.atomArray;if(i.voldata&&i.volscheme){var f=i.volscheme,p=i.voldata,g=f.range()||[-1,1];for(let e=0,i=c.length;e<i;e++){let i=c[e].atomid,r=p.getVal(c[e].x,c[e].y,c[e].z),n=s.CC.color(f.valueToHex(r,g)),a=3*e;d[a]=n.r,d[a+1]=n.g,d[a+2]=n.b,u[e]=t[i]}}else if(o.length>0)for(let e=0,i=c.length;e<i;e++){let i=c[e].atomid,r=3*e;d[r]=o[i].r,d[r+1]=o[i].g,d[r+2]=o[i].b,u[e]=t[i]}var m=e.faces;a.faceidx=m.length,r.initTypedArrays();var v,_,y,b,x=a.vertexArray,w=a.normalArray;for(let t=0,e=m.length;t<e;t+=3){var A=3*m[t],C=3*m[t+1],S=3*m[t+2];v=new $.Vector3(x[A],x[A+1],x[A+2]),_=new $.Vector3(x[C],x[C+1],x[C+2]),(y=new $.Vector3(x[S],x[S+1],x[S+2])).subVectors(y,_),v.subVectors(v,_),y.cross(v),(b=y).normalize(),w[A]+=b.x,w[C]+=b.x,w[S]+=b.x,w[A+1]+=b.y,w[C+1]+=b.y,w[S+1]+=b.y,w[A+2]+=b.z,w[C+2]+=b.z,w[S+2]+=b.z}return a.faceArray=new Uint16Array(m),new n.Mesh(r,i)}static generateMeshSyncHelper(t,e,i,r,s,n){var a=new ProteinSurface;return a.initparm(e,1!==t,n),a.fillvoxels(s,i),a.buildboundary(),t!=Q.SES&&t!=Q.MS||(a.fastdistancemap(),a.boundingatom(!1),a.fillvoxelswaals(s,i)),a.marchingcube(t),a.getFacesAndVertices(r)}static getMatWithStyle(t){let e=null;for(var i in e=t.onesided?new n.MeshLambertMaterial:new n.MeshDoubleLambertMaterial,e.vertexColors=n.Coloring.VertexColors,t)"color"===i||"map"===i||t.hasOwnProperty(i)&&(e[i]=t[i]);return void 0!==t.opacity&&(1===t.opacity?e.transparent=!1:e.transparent=!0),e}addMesh(t){var e={geo:t.geometry,mat:t.material,done:!0,finished:!1},i=this.nextSurfID();return this.surfaces[i]=[e],i}static shallowCopy(t){var e=[];let i=t.length;for(let r=0;r<i;r++)e[r]=(0,L.extend)({},t[r]);return e}addSurface(t,e={},i={},s,a,o){let l=this.nextSurfID(),h=null,c=this,d=Q.VDW;"string"==typeof t?void 0!==GLViewer.surfaceTypeMap[t.toUpperCase()]?d=GLViewer.surfaceTypeMap[t]:console.log("Surface type : "+t+" is not recognized"):"number"==typeof t&&(d=t);var u=null,f=null,p=GLViewer.shallowCopy(this.getAtomsFromSel(i));u=s?GLViewer.shallowCopy(this.getAtomsFromSel(s)):p,(0,L.adjustVolumeStyle)(e);var g,m=!1;for(g=0;g<this.models.length;g++)if(this.models[g]){var v=this.models[g].getSymmetries();if(v.length>1||1==v.length&&!v[0].isIdentity()){m=!0;break}}var _=function(t,i,s){var n;f=a?GLViewer.shallowCopy(c.getAtomsFromSel(a)):s;var o=(0,L.getExtent)(s,!0);if(e.map&&e.map.prop){var u=e.map.prop;let t=(0,r.getGradient)(e.map.scheme||e.map.gradient||new r.Gradient.RWB),i=t.range();i||(i=(0,L.getPropertyRange)(s,u)),e.colorscheme={prop:u,gradient:t}}for(let t=0,r=i.length;t<r;t++)(n=i[t]).surfaceColor=(0,L.getColorFromStyle)(n,e);var p=GLViewer.volume(o),g=c.carveUpExtent(o,i,s);if(f&&f.length&&f.length>0){var m=(0,L.getExtent)(f,!0);g.sort((function(t,e){var i=function(t,e){var i=t.extent,r=i[1][0]-i[0][0],s=i[1][1]-i[0][1],n=i[1][2]-i[0][2],a=r-e[2][0];a*=a;var o=s-e[2][1];o*=o;var l=n-e[2][2];return a+o+(l*=l)};return i(t,m)-i(e,m)}))}var v=[];for(let t=0,e=i.length;t<e;t++)n=i[t],v[t]={x:n.x,y:n.y,z:n.z,serial:t,elem:n.elem};if(!!tt){var _=function(e){return new Promise((function(r){for(var s=GLViewer.generateMeshSyncHelper(d,g[e].extent,g[e].atoms,g[e].toshow,v,p),n=wt({vertexArr:s.vertices,faceArr:s.faces}),a=0,o=n.length;a<o;a++){s={vertices:n[a].vertexArr,faces:n[a].faceArr};var l=GLViewer.generateSurfaceMesh(i,s,h);(0,L.mergeGeos)(t.geo,l)}c.render(),r()}))},y=[];for(let t=0;t<g.length;t++)y.push(_(t));return Promise.all(y).then((function(){return t.done=!0,Promise.resolve(l)}))}var b=[];d<0&&(d=0);for(let t=0,e=GLViewer.numWorkers;t<e;t++){var x=new Worker($3Dmol.SurfaceWorker);b.push(x),x.postMessage({type:-1,atoms:v,volume:p})}return new Promise((function(e,r){var s=0,n=function(){b&&b.length&&b.forEach((function(t){t&&t.terminate&&t.terminate()}))},a=function(r){for(var a=wt({vertexArr:r.data.vertices,faceArr:r.data.faces}),o=0,d=a.length;o<d;o++){var u={vertices:a[o].vertexArr,faces:a[o].faceArr},f=GLViewer.generateSurfaceMesh(i,u,h);(0,L.mergeGeos)(t.geo,f)}c.render(),++s==g.length&&(t.done=!0,n(),e(l))},o=function(t){n(),console.log(t.message+" ("+t.filename+":"+t.lineno+")"),r(t)};for(let t=0;t<g.length;t++){var u=b[t%b.length];u.onmessage=a,u.onerror=o,u.postMessage({type:d,expandedExtent:g[t].extent,extendedAtoms:g[t].atoms,atomsToShow:g[t].toshow})}}))};e=e||{},h=GLViewer.getMatWithStyle(e);var y=[];y.style=e,y.atomsel=i,y.allsel=s,y.focus=a;var b=null;if(m){var x={},w={};for(g=0;g<this.models.length;g++)x[g]=[],w[g]=[];for(g=0;g<u.length;g++)x[u[g].model].push(u[g]);for(g=0;g<p.length;g++)w[p[g].model].push(p[g]);var A=[];for(g=0;g<this.models.length;g++)w[g].length>0&&(y.push({geo:new n.Geometry(!0),mat:h,done:!1,finished:!1,symmetries:this.models[g].getSymmetries()}),A.push(_(y[y.length-1],x[g],w[g])));b=Promise.all(A)}else y.push({geo:new n.Geometry(!0),mat:h,done:!1,finished:!1,symmetries:[new $.Matrix4]}),b=_(y[y.length-1],u,p);return this.surfaces[l]=y,b.surfid=l,o&&"function"==typeof o?(b.then((function(t){o(t)})),l):b}setSurfaceMaterialStyle(t,e){if((0,L.adjustVolumeStyle)(e),this.surfaces[t]){var i=this.surfaces[t];for(let t=0;t<i.length;t++){var r=i[t].mat=GLViewer.getMatWithStyle(e);if(i[t].mat.side=n.FrontSide,e.color){i[t].mat.color=s.CC.color(e.color),i[t].geo.colorsNeedUpdate=!0;const r=s.CC.color(e.color);i[t].geo.setColor(r)}else if(r.voldata&&r.volscheme){const e=r.volscheme,n=r.voldata,a=s.CC,o=e.range()||[-1,1];i[t].geo.setColors((function(t,i,r){let s=n.getVal(t,i,r);return a.color(e.valueToHex(s,o))}))}else{i[t].geo.colorsNeedUpdate=!0;for(let r of i[t].geo.geometryGroups)for(let t=0;t<r.vertices;t++){let i=(0,L.getColorFromStyle)(r.atomArray[t],e),s=3*t;r.colorArray[s]=i.r,r.colorArray[s+1]=i.g,r.colorArray[s+2]=i.b}}i[t].finished=!1}}return this}getSurface(t){return this.surfaces[t]}removeSurface(t){for(var e=this.surfaces[t],i=0;i<e.length;i++)e[i]&&e[i].lastGL&&(void 0!==e[i].geo&&e[i].geo.dispose(),void 0!==e[i].mat&&e[i].mat.dispose(),this.modelGroup.remove(e[i].lastGL));return delete this.surfaces[t],this.show(),this}removeAllSurfaces(){for(var t in this.surfaces)if(this.surfaces.hasOwnProperty(t)){for(var e=this.surfaces[t],i=0;i<e.length;i++)e[i]&&e[i].lastGL&&(void 0!==e[i].geo&&e[i].geo.dispose(),void 0!==e[i].mat&&e[i].mat.dispose(),this.modelGroup.remove(e[i].lastGL));delete this.surfaces[t]}return this.show(),this}jmolMoveTo(){var t=this.modelGroup.position,e="center { "+-t.x+" "+-t.y+" "+-t.z+" }; ",i=this.rotationGroup.quaternion;return e+="moveto .5 quaternion { "+i.x+" "+i.y+" "+i.z+" "+i.w+" };"}clear(){return this.removeAllSurfaces(),this.removeAllModels(),this.removeAllLabels(),this.removeAllShapes(),this.show(),this}mapAtomProperties(t,e){e=e||{};var i=this.getAtomsFromSel(e);if("function"==typeof t)for(let e=0,r=i.length;e<r;e++){t(i[e])}else for(let e=0,n=i.length;e<n;e++){var r=i[e];for(let e=0,i=t.length;e<i;e++){let i=t[e];if(i.props)for(var s in i.props)i.props.hasOwnProperty(s)&&this.atomIsSelected(r,i)&&(r.properties||(r.properties={}),r.properties[s]=i.props[s])}}return this}linkViewer(t){return this.linkedViewers.push(t),this}getPerceivedDistance(){return this.CAMERA_Z-this.rotationGroup.position.z}setPerceivedDistance(t){this.rotationGroup.position.z=this.CAMERA_Z-t}setAutoEyeSeparation(t,e){var i=this.getPerceivedDistance();return e||(e=5),t||this.camera.position.x>0?this.camera.position.x=i*Math.tan(Math.PI/180*e):this.camera.position.x=-i*Math.tan(Math.PI/180*e),this.camera.lookAt(new $.Vector3(0,0,this.rotationGroup.position.z)),this.camera.position.x}setDefaultCartoonQuality(t){this.config.cartoonQuality=t}}function qt(t,e){if(t=(0,L.getElement)(t)){e=e||{};try{return new GLViewer(t,e)}catch(t){throw"error creating viewer: "+t}}}function Yt(t,e={},i={}){if(t=(0,L.getElement)(t)){var r=[],s=document.createElement("canvas");i.rows=e.rows,i.cols=e.cols,i.control_all=null!=e.control_all&&e.control_all,t.appendChild(s);try{for(var n=0;n<e.rows;n++){for(var a=[],o=0;o<e.cols;o++){i.row=n,i.col=o,i.canvas=s,i.viewers=r,i.control_all=e.control_all;var l=qt(t,(0,L.extend)({},i));a.push(l)}r.unshift(a)}}catch(t){throw"error creating viewer grid: "+t}return r}}function Zt(t){var e=this;if(t=(0,L.getElement)(t)){var i=Yt(t,{rows:1,cols:2,control_all:!0});this.glviewer1=i[0][0],this.glviewer2=i[0][1],this.glviewer1.setAutoEyeSeparation(!1),this.glviewer2.setAutoEyeSeparation(!0),this.glviewer1.linkViewer(this.glviewer2),this.glviewer2.linkViewer(this.glviewer1);for(var r=Object.getOwnPropertyNames(this.glviewer1.__proto__).filter((function(t){return"function"==typeof e.glviewer1[t]})),s=0;s<r.length;s++)this[r[s]]=function(t){return function(){return[this.glviewer1[t].apply(this.glviewer1,arguments),this.glviewer2[t].apply(this.glviewer2,arguments)]}}(r[s]);this.setCoordinates=function(t,e,i){for(var r=0;r<t.length;r++)t[r].setCoordinates(e,i)},this.surfacesFinished=function(){return this.glviewer1.surfacesFinished()&&this.glviewer2.surfacesFinished()},this.isAnimated=function(){return this.glviewer1.isAnimated()||this.glviewer2.isAnimated()},this.render=function(t){this.glviewer1.render(),this.glviewer2.render(),t&&t(this)},this.getCanvas=function(){return this.glviewer1.getCanvas()}}}GLViewer.numWorkers=4,GLViewer.maxVolume=64e3,GLViewer.surfaceTypeMap={VDW:Q.VDW,MS:Q.MS,SAS:Q.SAS,SES:Q.SES};var Xt=!1,Kt=!1,Qt={};function $t(t,e){var i,r,n;if(null!=document.querySelector(".viewer_3Dmoljs")&&(Xt=!0),Xt){Kt=!0,t=null!=t?t:null;var a=0;document.querySelectorAll(".viewer_3Dmoljs").forEach((o=>{var l=[],h=[],c="";"static"==o.style.position&&(o.style.position="relative");var d=null;if(n=null,o.dataset.pdb)l.push("https://files.rcsb.org/view/"+o.dataset.pdb+".pdb"),h.push("pdb");else if(o.dataset.cid)h.push("sdf"),l.push("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/"+o.dataset.cid+"/SDF?record_type=3d");else if(o.dataset.href||o.dataset.url){if(c=o.dataset.href?o.dataset.href:o.dataset.url,l.push(c),"gz"==(n=c.substring(c.lastIndexOf(".")+1))){let t=c.substring(0,c.lastIndexOf(".")).lastIndexOf(".");n=c.substring(t+1)}h.push(n);var u=c.substring(c.lastIndexOf("/")+1,c.lastIndexOf("."));"/"==u&&(u=c.substring(c.lastIndexOf("/")+1)),o.dataset[h[h.length-1]]=u}let f=o.dataset;for(i in f)"pdb"===i.substring(0,3)&&"pdb"!==i?(l.push("https://files.rcsb.org/view/"+f[i]+".pdb"),h.push("pdb")):"href"===i.substring(0,4)&&"href"!==i?(c=f[i],l.push(c),h.push(c.substring(c.lastIndexOf(".")+1))):"cid"===i.substring(0,3)&&"cid"!==i&&(l.push("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/"+f[i]+"/SDF?record_type=3d"),h.push("sdf"));var p={};o.dataset.options&&(p=(0,L.specStringToObject)(o.dataset.options));var g=s.CC.color(o.dataset.backgroundcolor),m=o.dataset.backgroundalpha;m=null==m?1:parseFloat(m);var v={line:{}};o.dataset.style&&(v=(0,L.specStringToObject)(o.dataset.style));var _={};o.dataset.select&&(_=(0,L.specStringToObject)(o.dataset.select));var y=[],b=[],x=[],w={},A=null,C=o.dataset,S=/style(.+)/,M=/surface(.*)/,z=/labelres(.*)/,T=[];for(r in C)Object.prototype.hasOwnProperty.call(C,r)&&T.push(r);for(T.sort(),i=0;i<T.length;i++){r=T[i];var E,F,I,O=S.exec(r);O&&(E="select"+O[1],F=(0,L.specStringToObject)(C[E]),I=(0,L.specStringToObject)(C[r]),y.push([F,I])),(O=M.exec(r))&&(E="select"+O[1],F=(0,L.specStringToObject)(C[E]),I=(0,L.specStringToObject)(C[r]),b.push([F,I])),(O=z.exec(r))&&(E="select"+O[1],F=(0,L.specStringToObject)(C[E]),I=(0,L.specStringToObject)(C[r]),x.push([F,I])),"zoomto"==r&&(w=(0,L.specStringToObject)(C[r])),"spin"==r&&(A=(0,L.specStringToObject)(C[r]))}var D=function(t){for(t.setStyle(_,v),d&&d.createSelectionAndStyle(_,v),i=0;i<y.length;i++){let e=y[i][0]||{},r=y[i][1]||{line:{}};t.setStyle(e,r),d&&d.createSelectionAndStyle(_,v)}for(i=0;i<b.length;i++){let e=b[i][0]||{},r=b[i][1]||{};d?t.addSurface(Q.VDW,r,e,e).then((t=>{d.loadSurface("VDW",e,r,t)})):t.addSurface(Q.VDW,r,e,e)}for(i=0;i<x.length;i++){let e=x[i][0]||{},r=x[i][1]||{};t.addResLabels(e,r)}t.render(),t.zoomTo(w),A&&t.spin(A.axis,A.speed)};let k=t;try{var R=(0,L.specStringToObject)(o.dataset.config)||{};void 0===R.backgroundColor&&(R.backgroundColor=g),void 0===R.backgroundAlpha&&(R.backgroundAlpha=m),null==k?k=Qt[o.id||a++]=qt(o,R):(k.setBackgroundColor(g,m),k.setConfig(R),d&&d.initiateUI()),o.dataset.ui&&$3Dmol.StateManager&&(d=new $3Dmol.StateManager(k))}catch(t){console.log(t),o.textContent="WebGL appears to be disabled."}if(0!=l.length){let t=0,i=((r,s)=>function(n){c=l[t];var a=r.dataset.type||r.dataset.datatype||h[t];if(s.addModel(n,a,p),d){var 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y=3*p;l[M=p<<2]=e[y],l[M+1]=e[y+1],l[M+2]=e[y+2],l[M+3]=255,-1!=m&&e[y]==m&&e[y+1]==v&&e[y+2]==_&&(l[M+3]=0)}if(16==d)for(p=0;p<n;p++)y=6*p,l[M=p<<2]=e[y],l[M+1]=e[y+2],l[M+2]=e[y+4],l[M+3]=255,-1!=m&&u(e,y)==m&&u(e,y+2)==v&&u(e,y+4)==_&&(l[M+3]=0)}else if(3==c){var b=s.tabs.PLTE,x=s.tabs.tRNS,w=x?x.length:0;if(1==d)for(var A=0;A<r;A++){var C=A*o,S=A*i;for(p=0;p<i;p++){var M=S+p<<2,z=3*(T=e[C+(p>>3)]>>7-(7&p)&1);l[M]=b[z],l[M+1]=b[z+1],l[M+2]=b[z+2],l[M+3]=T<w?x[T]:255}}if(2==d)for(A=0;A<r;A++)for(C=A*o,S=A*i,p=0;p<i;p++)M=S+p<<2,z=3*(T=e[C+(p>>2)]>>6-((3&p)<<1)&3),l[M]=b[z],l[M+1]=b[z+1],l[M+2]=b[z+2],l[M+3]=T<w?x[T]:255;if(4==d)for(A=0;A<r;A++)for(C=A*o,S=A*i,p=0;p<i;p++)M=S+p<<2,z=3*(T=e[C+(p>>1)]>>4-((1&p)<<2)&15),l[M]=b[z],l[M+1]=b[z+1],l[M+2]=b[z+2],l[M+3]=T<w?x[T]:255;if(8==d)for(p=0;p<n;p++){var T;M=p<<2,z=3*(T=e[p]),l[M]=b[z],l[M+1]=b[z+1],l[M+2]=b[z+2],l[M+3]=T<w?x[T]:255}}else if(4==c){if(8==d)for(p=0;p<n;p++){M=p<<2;var E=e[L=p<<1];l[M]=E,l[M+1]=E,l[M+2]=E,l[M+3]=e[L+1]}if(16==d)for(p=0;p<n;p++){var L;M=p<<2,E=e[L=p<<2],l[M]=E,l[M+1]=E,l[M+2]=E,l[M+3]=e[L+2]}}else if(0==c){if(m=s.tabs.tRNS?s.tabs.tRNS:-1,1==d)for(p=0;p<n;p++){var F=(E=255*(e[p>>3]>>7-(7&p)&1))==255*m?0:255;h[p]=F<<24|E<<16|E<<8|E}if(2==d)for(p=0;p<n;p++)F=(E=85*(e[p>>2]>>6-((3&p)<<1)&3))==85*m?0:255,h[p]=F<<24|E<<16|E<<8|E;if(4==d)for(p=0;p<n;p++)F=(E=17*(e[p>>1]>>4-((1&p)<<2)&15))==17*m?0:255,h[p]=F<<24|E<<16|E<<8|E;if(8==d)for(p=0;p<n;p++)F=(E=e[p])==m?0:255,h[p]=F<<24|E<<16|E<<8|E;if(16==d)for(p=0;p<n;p++)E=e[p<<1],F=u(e,p<<1)==m?0:255,h[p]=F<<24|E<<16|E<<8|E}return l},t.decode=function(e){for(var i,r=new Uint8Array(e),s=8,n=t._bin,a=n.readUshort,o=n.readUint,l={tabs:{},frames:[]},h=new Uint8Array(r.length),c=0,d=0,u=[137,80,78,71,13,10,26,10],f=0;f<8;f++)if(r[f]!=u[f])throw"The input is not a PNG file!";for(;s<r.length;){var p=n.readUint(r,s);s+=4;var g=n.readASCII(r,s,4);if(s+=4,"IHDR"==g)t.decode._IHDR(r,s,l);else if("IDAT"==g){for(f=0;f<p;f++)h[c+f]=r[s+f];c+=p}else if("acTL"==g)l.tabs[g]={num_frames:o(r,s),num_plays:o(r,s+4)},i=new Uint8Array(r.length);else if("fcTL"==g){0!=d&&((C=l.frames[l.frames.length-1]).data=t.decode._decompress(l,i.slice(0,d),C.rect.width,C.rect.height),d=0);var m={x:o(r,s+12),y:o(r,s+16),width:o(r,s+4),height:o(r,s+8)},v=a(r,s+22);v=a(r,s+20)/(0==v?100:v);var _={rect:m,delay:Math.round(1e3*v),dispose:r[s+24],blend:r[s+25]};l.frames.push(_)}else if("fdAT"==g){for(f=0;f<p-4;f++)i[d+f]=r[s+f+4];d+=p-4}else if("pHYs"==g)l.tabs[g]=[n.readUint(r,s),n.readUint(r,s+4),r[s+8]];else if("cHRM"==g)for(l.tabs[g]=[],f=0;f<8;f++)l.tabs[g].push(n.readUint(r,s+4*f));else if("tEXt"==g){null==l.tabs[g]&&(l.tabs[g]={});var y=n.nextZero(r,s),b=n.readASCII(r,s,y-s),x=n.readASCII(r,y+1,s+p-y-1);l.tabs[g][b]=x}else if("iTXt"==g){null==l.tabs[g]&&(l.tabs[g]={}),y=0;var w=s;y=n.nextZero(r,w),b=n.readASCII(r,w,y-w),r[w=y+1],r[w+1],w+=2,y=n.nextZero(r,w),n.readASCII(r,w,y-w),w=y+1,y=n.nextZero(r,w),n.readUTF8(r,w,y-w),w=y+1,x=n.readUTF8(r,w,p-(w-s)),l.tabs[g][b]=x}else if("PLTE"==g)l.tabs[g]=n.readBytes(r,s,p);else if("hIST"==g){var A=l.tabs.PLTE.length/3;for(l.tabs[g]=[],f=0;f<A;f++)l.tabs[g].push(a(r,s+2*f))}else if("tRNS"==g)3==l.ctype?l.tabs[g]=n.readBytes(r,s,p):0==l.ctype?l.tabs[g]=a(r,s):2==l.ctype&&(l.tabs[g]=[a(r,s),a(r,s+2),a(r,s+4)]);else if("gAMA"==g)l.tabs[g]=n.readUint(r,s)/1e5;else if("sRGB"==g)l.tabs[g]=r[s];else if("bKGD"==g)0==l.ctype||4==l.ctype?l.tabs[g]=[a(r,s)]:2==l.ctype||6==l.ctype?l.tabs[g]=[a(r,s),a(r,s+2),a(r,s+4)]:3==l.ctype&&(l.tabs[g]=r[s]);else if("IEND"==g){var C;0!=d&&((C=l.frames[l.frames.length-1]).data=t.decode._decompress(l,i.slice(0,d),C.rect.width,C.rect.height),d=0),l.data=t.decode._decompress(l,h,l.width,l.height);break}s+=p,n.readUint(r,s),s+=4}return delete l.compress,delete l.interlace,delete l.filter,l},t.decode._decompress=function(e,i,r,s){return 0==e.compress&&(i=t.decode._inflate(i)),0==e.interlace?i=t.decode._filterZero(i,e,0,r,s):1==e.interlace&&(i=t.decode._readInterlace(i,e)),i},t.decode._inflate=function(t){return e.inflate(t)},t.decode._readInterlace=function(e,i){for(var r=i.width,s=i.height,n=t.decode._getBPP(i),a=n>>3,o=Math.ceil(r*n/8),l=new Uint8Array(s*o),h=0,c=[0,0,4,0,2,0,1],d=[0,4,0,2,0,1,0],u=[8,8,8,4,4,2,2],f=[8,8,4,4,2,2,1],p=0;p<7;){for(var g=u[p],m=f[p],v=0,_=0,y=c[p];y<s;)y+=g,_++;for(var b=d[p];b<r;)b+=m,v++;var x=Math.ceil(v*n/8);t.decode._filterZero(e,i,h,v,_);for(var w=0,A=c[p];A<s;){for(var C=d[p],S=h+w*x<<3;C<r;){var M;if(1==n&&(M=(M=e[S>>3])>>7-(7&S)&1,l[A*o+(C>>3)]|=M<<7-(3&C)),2==n&&(M=(M=e[S>>3])>>6-(7&S)&3,l[A*o+(C>>2)]|=M<<6-((3&C)<<1)),4==n&&(M=(M=e[S>>3])>>4-(7&S)&15,l[A*o+(C>>1)]|=M<<4-((1&C)<<2)),n>=8)for(var z=A*o+C*a,T=0;T<a;T++)l[z+T]=e[(S>>3)+T];S+=n,C+=m}w++,A+=g}v*_!=0&&(h+=_*(1+x)),p+=1}return l},t.decode._getBPP=function(t){return[1,null,3,1,2,null,4][t.ctype]*t.depth},t.decode._filterZero=function(e,i,r,s,n){var a=t.decode._getBPP(i),o=Math.ceil(s*a/8),l=t.decode._paeth;a=Math.ceil(a/8);for(var h=0;h<n;h++){var c=r+h*o,d=c+h+1,u=e[d-1];if(0==u)for(var f=0;f<o;f++)e[c+f]=e[d+f];else if(1==u){for(f=0;f<a;f++)e[c+f]=e[d+f];for(f=a;f<o;f++)e[c+f]=e[d+f]+e[c+f-a]&255}else if(0==h){for(f=0;f<a;f++)e[c+f]=e[d+f];if(2==u)for(f=a;f<o;f++)e[c+f]=255&e[d+f];if(3==u)for(f=a;f<o;f++)e[c+f]=e[d+f]+(e[c+f-a]>>1)&255;if(4==u)for(f=a;f<o;f++)e[c+f]=e[d+f]+l(e[c+f-a],0,0)&255}else{if(2==u)for(f=0;f<o;f++)e[c+f]=e[d+f]+e[c+f-o]&255;if(3==u){for(f=0;f<a;f++)e[c+f]=e[d+f]+(e[c+f-o]>>1)&255;for(f=a;f<o;f++)e[c+f]=e[d+f]+(e[c+f-o]+e[c+f-a]>>1)&255}if(4==u){for(f=0;f<a;f++)e[c+f]=e[d+f]+l(0,e[c+f-o],0)&255;for(f=a;f<o;f++)e[c+f]=e[d+f]+l(e[c+f-a],e[c+f-o],e[c+f-a-o])&255}}}return e},t.decode._paeth=function(t,e,i){var r=t+e-i,s=Math.abs(r-t),n=Math.abs(r-e),a=Math.abs(r-i);return s<=n&&s<=a?t:n<=a?e:i},t.decode._IHDR=function(e,i,r){var s=t._bin;r.width=s.readUint(e,i),i+=4,r.height=s.readUint(e,i),i+=4,r.depth=e[i],i++,r.ctype=e[i],i++,r.compress=e[i],i++,r.filter=e[i],i++,r.interlace=e[i],i++},t._bin={nextZero:function(t,e){for(;0!=t[e];)e++;return e},readUshort:function(t,e){return t[e]<<8|t[e+1]},writeUshort:function(t,e,i){t[e]=i>>8&255,t[e+1]=255&i},readUint:function(t,e){return 16777216*t[e]+(t[e+1]<<16|t[e+2]<<8|t[e+3])},writeUint:function(t,e,i){t[e]=i>>24&255,t[e+1]=i>>16&255,t[e+2]=i>>8&255,t[e+3]=255&i},readASCII:function(t,e,i){for(var r="",s=0;s<i;s++)r+=String.fromCharCode(t[e+s]);return r},writeASCII:function(t,e,i){for(var r=0;r<i.length;r++)t[e+r]=i.charCodeAt(r)},readBytes:function(t,e,i){for(var r=[],s=0;s<i;s++)r.push(t[e+s]);return r},pad:function(t){return t.length<2?"0"+t:t},readUTF8:function(e,i,r){for(var s,n="",a=0;a<r;a++)n+="%"+t._bin.pad(e[i+a].toString(16));try{s=decodeURIComponent(n)}catch(s){return t._bin.readASCII(e,i,r)}return s}},t._copyTile=function(t,e,i,r,s,n,a,o,l){for(var h=Math.min(e,s),c=Math.min(i,n),d=0,u=0,f=0;f<c;f++)for(var p=0;p<h;p++)if(a>=0&&o>=0?(d=f*e+p<<2,u=(o+f)*s+a+p<<2):(d=(-o+f)*e-a+p<<2,u=f*s+p<<2),0==l)r[u]=t[d],r[u+1]=t[d+1],r[u+2]=t[d+2],r[u+3]=t[d+3];else if(1==l){var g=t[d+3]*(1/255),m=t[d]*g,v=t[d+1]*g,_=t[d+2]*g,y=r[u+3]*(1/255),b=r[u]*y,x=r[u+1]*y,w=r[u+2]*y,A=1-g,C=g+y*A,S=0==C?0:1/C;r[u+3]=255*C,r[u+0]=(m+b*A)*S,r[u+1]=(v+x*A)*S,r[u+2]=(_+w*A)*S}else if(2==l)g=t[d+3],m=t[d],v=t[d+1],_=t[d+2],y=r[u+3],b=r[u],x=r[u+1],w=r[u+2],g==y&&m==b&&v==x&&_==w?(r[u]=0,r[u+1]=0,r[u+2]=0,r[u+3]=0):(r[u]=m,r[u+1]=v,r[u+2]=_,r[u+3]=g);else if(3==l){if(g=t[d+3],m=t[d],v=t[d+1],_=t[d+2],y=r[u+3],b=r[u],x=r[u+1],w=r[u+2],g==y&&m==b&&v==x&&_==w)continue;if(g<220&&y>20)return!1}return!0},t.encode=function(e,i,r,s,n,a){null==s&&(s=0),null==a&&(a=!1);for(var o=new Uint8Array(e[0].byteLength*e.length+100),l=[137,80,78,71,13,10,26,10],h=0;h<8;h++)o[h]=l[h];var c=8,d=t._bin,u=t.crc.crc,f=d.writeUint,p=d.writeUshort,g=d.writeASCII,m=t.encode.compressPNG(e,i,r,s,a);f(o,c,13),g(o,c+=4,"IHDR"),f(o,c+=4,i),f(o,c+=4,r),o[c+=4]=m.depth,o[++c]=m.ctype,o[++c]=0,o[++c]=0,o[++c]=0,f(o,++c,u(o,c-17,17)),f(o,c+=4,1),g(o,c+=4,"sRGB"),o[c+=4]=1,f(o,++c,u(o,c-5,5)),c+=4;var v=e.length>1;if(v&&(f(o,c,8),g(o,c+=4,"acTL"),f(o,c+=4,e.length),f(o,c+=4,0),f(o,c+=4,u(o,c-12,12)),c+=4),3==m.ctype){for(f(o,c,3*(M=m.plte.length)),g(o,c+=4,"PLTE"),c+=4,h=0;h<M;h++){var _=3*h,y=m.plte[h],b=255&y,x=y>>8&255,w=y>>16&255;o[c+_+0]=b,o[c+_+1]=x,o[c+_+2]=w}if(f(o,c+=3*M,u(o,c-3*M-4,3*M+4)),c+=4,m.gotAlpha){for(f(o,c,M),g(o,c+=4,"tRNS"),c+=4,h=0;h<M;h++)o[c+h]=m.plte[h]>>24&255;f(o,c+=M,u(o,c-M-4,M+4)),c+=4}}for(var A=0,C=0;C<m.frames.length;C++){var S=m.frames[C];v&&(f(o,c,26),g(o,c+=4,"fcTL"),f(o,c+=4,A++),f(o,c+=4,S.rect.width),f(o,c+=4,S.rect.height),f(o,c+=4,S.rect.x),f(o,c+=4,S.rect.y),p(o,c+=4,n[C]),p(o,c+=2,1e3),o[c+=2]=S.dispose,o[++c]=S.blend,f(o,++c,u(o,c-30,30)),c+=4);var M,z=S.cimg;f(o,c,(M=z.length)+(0==C?0:4));var T=c+=4;for(g(o,c,0==C?"IDAT":"fdAT"),c+=4,0!=C&&(f(o,c,A++),c+=4),h=0;h<M;h++)o[c+h]=z[h];f(o,c+=M,u(o,T,c-T)),c+=4}return f(o,c,0),g(o,c+=4,"IEND"),f(o,c+=4,u(o,c-4,4)),c+=4,o.buffer.slice(0,c)},t.encode.compressPNG=function(e,i,r,s,n){for(var a=t.encode.compress(e,i,r,s,!1,n),o=0;o<e.length;o++){var l=a.frames[o],h=(l.rect.width,l.rect.height),c=l.bpl,d=l.bpp,u=new Uint8Array(h*c+h);l.cimg=t.encode._filterZero(l.img,h,d,c,u)}return a},t.encode.compress=function(e,i,r,s,n,a){null==a&&(a=!1);for(var o=6,l=8,h=4,c=255,d=0;d<e.length;d++)for(var u=new Uint8Array(e[d]),f=u.length,p=0;p<f;p+=4)c&=u[p+3];var g=255!=c,m={},v=[];if(0!=e.length&&(m[0]=0,v.push(0),0!=s&&s--),0!=s){var _=t.quantize(e,s,n);for(e=_.bufs,p=0;p<_.plte.length;p++)null==m[b=_.plte[p].est.rgba]&&(m[b]=v.length,v.push(b))}else for(d=0;d<e.length;d++){var y=new Uint32Array(e[d]);for(f=y.length,p=0;p<f;p++){var b=y[p];if((p<i||b!=y[p-1]&&b!=y[p-i])&&null==m[b]&&(m[b]=v.length,v.push(b),v.length>=300))break}}var x=!!g&&n,w=v.length;w<=256&&0==a&&(l=w<=2?1:w<=4?2:w<=16?4:8,n&&(l=8),g=!0);var A=[];for(d=0;d<e.length;d++){var C=new Uint8Array(e[d]),S=new Uint32Array(C.buffer),M=0,z=0,T=i,E=r,L=0;if(0!=d&&!x){for(var F=n||1==d||2==A[A.length-2].dispose?1:2,I=0,O=1e9,D=0;D<F;D++){for(var k=new Uint8Array(e[d-1-D]),R=new Uint32Array(e[d-1-D]),P=i,U=r,B=-1,N=-1,G=0;G<r;G++)for(var V=0;V<i;V++)S[p=G*i+V]!=R[p]&&(V<P&&(P=V),V>B&&(B=V),G<U&&(U=G),G>N&&(N=G));var j=-1==B?1:(B-P+1)*(N-U+1);j<O&&(O=j,I=D,-1==B?(M=z=0,T=E=1):(M=P,z=U,T=B-P+1,E=N-U+1))}k=new Uint8Array(e[d-1-I]),1==I&&(A[A.length-1].dispose=2);var H=new Uint8Array(T*E*4);new Uint32Array(H.buffer),t._copyTile(k,i,r,H,T,E,-M,-z,0),t._copyTile(C,i,r,H,T,E,-M,-z,3)?(t._copyTile(C,i,r,H,T,E,-M,-z,2),L=1):(t._copyTile(C,i,r,H,T,E,-M,-z,0),L=0),C=H,S=new Uint32Array(C.buffer)}var W=4*T;if(w<=256&&0==a){for(W=Math.ceil(l*T/8),H=new Uint8Array(W*E),G=0;G<E;G++){p=G*W;var q=G*T;if(8==l)for(V=0;V<T;V++)H[p+V]=m[S[q+V]];else if(4==l)for(V=0;V<T;V++)H[p+(V>>1)]|=m[S[q+V]]<<4-4*(1&V);else if(2==l)for(V=0;V<T;V++)H[p+(V>>2)]|=m[S[q+V]]<<6-2*(3&V);else if(1==l)for(V=0;V<T;V++)H[p+(V>>3)]|=m[S[q+V]]<<7-1*(7&V)}C=H,o=3,h=1}else if(0==g&&1==e.length){H=new Uint8Array(T*E*3);var Y=T*E;for(p=0;p<Y;p++){var Z=3*p,X=4*p;H[Z]=C[X],H[Z+1]=C[X+1],H[Z+2]=C[X+2]}C=H,o=2,h=3,W=3*T}A.push({rect:{x:M,y:z,width:T,height:E},img:C,bpl:W,bpp:h,blend:L,dispose:x?1:0})}return{ctype:o,depth:l,plte:v,gotAlpha:g,frames:A}},t.encode._filterZero=function(i,r,s,n,a){for(var o=[],l=0;l<5;l++)if(!(r*n>5e5)||2!=l&&3!=l&&4!=l){for(var h=0;h<r;h++)t.encode._filterLine(a,i,h,n,s,l);if(o.push(e.deflate(a)),1==s)break}for(var c,d=1e9,u=0;u<o.length;u++)o[u].length<d&&(c=u,d=o[u].length);return o[c]},t.encode._filterLine=function(e,i,r,s,n,a){var o=r*s,l=o+r,h=t.decode._paeth;if(e[l]=a,l++,0==a)for(var c=0;c<s;c++)e[l+c]=i[o+c];else if(1==a){for(c=0;c<n;c++)e[l+c]=i[o+c];for(c=n;c<s;c++)e[l+c]=i[o+c]-i[o+c-n]+256&255}else if(0==r){for(c=0;c<n;c++)e[l+c]=i[o+c];if(2==a)for(c=n;c<s;c++)e[l+c]=i[o+c];if(3==a)for(c=n;c<s;c++)e[l+c]=i[o+c]-(i[o+c-n]>>1)+256&255;if(4==a)for(c=n;c<s;c++)e[l+c]=i[o+c]-h(i[o+c-n],0,0)+256&255}else{if(2==a)for(c=0;c<s;c++)e[l+c]=i[o+c]+256-i[o+c-s]&255;if(3==a){for(c=0;c<n;c++)e[l+c]=i[o+c]+256-(i[o+c-s]>>1)&255;for(c=n;c<s;c++)e[l+c]=i[o+c]+256-(i[o+c-s]+i[o+c-n]>>1)&255}if(4==a){for(c=0;c<n;c++)e[l+c]=i[o+c]+256-h(0,i[o+c-s],0)&255;for(c=n;c<s;c++)e[l+c]=i[o+c]+256-h(i[o+c-n],i[o+c-s],i[o+c-n-s])&255}}},t.crc={table:function(){for(var t=new Uint32Array(256),e=0;e<256;e++){for(var i=e,r=0;r<8;r++)1&i?i=3988292384^i>>>1:i>>>=1;t[e]=i}return t}(),update:function(e,i,r,s){for(var n=0;n<s;n++)e=t.crc.table[255&(e^i[r+n])]^e>>>8;return e},crc:function(e,i,r){return 4294967295^t.crc.update(4294967295,e,i,r)}},t.quantize=function(e,i,r){for(var s=[],n=0,a=0;a<e.length;a++)s.push(t.encode.alphaMul(new Uint8Array(e[a]),r)),n+=e[a].byteLength;var o=new Uint8Array(n),l=new Uint32Array(o.buffer),h=0;for(a=0;a<s.length;a++){for(var c=s[a],d=c.length,u=0;u<d;u++)o[h+u]=c[u];h+=d}var f={i0:0,i1:o.length,bst:null,est:null,tdst:0,left:null,right:null};f.bst=t.quantize.stats(o,f.i0,f.i1),f.est=t.quantize.estats(f.bst);for(var p=[f];p.length<i;){var g=0,m=0;for(a=0;a<p.length;a++)p[a].est.L>g&&(g=p[a].est.L,m=a);if(g<.001)break;var v=p[m],_=t.quantize.splitPixels(o,l,v.i0,v.i1,v.est.e,v.est.eMq255),y={i0:v.i0,i1:_,bst:null,est:null,tdst:0,left:null,right:null};y.bst=t.quantize.stats(o,y.i0,y.i1),y.est=t.quantize.estats(y.bst);var b={i0:_,i1:v.i1,bst:null,est:null,tdst:0,left:null,right:null};for(b.bst={R:[],m:[],N:v.bst.N-y.bst.N},a=0;a<16;a++)b.bst.R[a]=v.bst.R[a]-y.bst.R[a];for(a=0;a<4;a++)b.bst.m[a]=v.bst.m[a]-y.bst.m[a];b.est=t.quantize.estats(b.bst),v.left=y,v.right=b,p[m]=y,p.push(b)}p.sort((function(t,e){return e.bst.N-t.bst.N}));for(var x=0;x<s.length;x++){var w=t.quantize.planeDst,A=new Uint8Array(s[x].buffer),C=new Uint32Array(s[x].buffer),S=A.length;for(a=0;a<S;a+=4){for(var M=A[a]*(1/255),z=A[a+1]*(1/255),T=A[a+2]*(1/255),E=A[a+3]*(1/255),L=f;L.left;)L=w(L.est,M,z,T,E)<=0?L.left:L.right;C[a>>2]=L.est.rgba}s[x]=C.buffer}return{bufs:s,plte:p}},t.quantize.getNearest=function(e,i,r,s,n){if(null==e.left)return e.tdst=t.quantize.dist(e.est.q,i,r,s,n),e;var a=t.quantize.planeDst(e.est,i,r,s,n),o=e.left,l=e.right;a>0&&(o=e.right,l=e.left);var h=t.quantize.getNearest(o,i,r,s,n);if(h.tdst<=a*a)return h;var c=t.quantize.getNearest(l,i,r,s,n);return c.tdst<h.tdst?c:h},t.quantize.planeDst=function(t,e,i,r,s){var n=t.e;return n[0]*e+n[1]*i+n[2]*r+n[3]*s-t.eMq},t.quantize.dist=function(t,e,i,r,s){var n=e-t[0],a=i-t[1],o=r-t[2],l=s-t[3];return n*n+a*a+o*o+l*l},t.quantize.splitPixels=function(e,i,r,s,n,a){var o=t.quantize.vecDot;for(s-=4;r<s;){for(;o(e,r,n)<=a;)r+=4;for(;o(e,s,n)>a;)s-=4;if(r>=s)break;var l=i[r>>2];i[r>>2]=i[s>>2],i[s>>2]=l,r+=4,s-=4}for(;o(e,r,n)>a;)r-=4;return r+4},t.quantize.vecDot=function(t,e,i){return t[e]*i[0]+t[e+1]*i[1]+t[e+2]*i[2]+t[e+3]*i[3]},t.quantize.stats=function(t,e,i){for(var r=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],s=[0,0,0,0],n=i-e>>2,a=e;a<i;a+=4){var o=t[a]*(1/255),l=t[a+1]*(1/255),h=t[a+2]*(1/255),c=t[a+3]*(1/255);s[0]+=o,s[1]+=l,s[2]+=h,s[3]+=c,r[0]+=o*o,r[1]+=o*l,r[2]+=o*h,r[3]+=o*c,r[5]+=l*l,r[6]+=l*h,r[7]+=l*c,r[10]+=h*h,r[11]+=h*c,r[15]+=c*c}return r[4]=r[1],r[8]=r[2],r[12]=r[3],r[9]=r[6],r[13]=r[7],r[14]=r[11],{R:r,m:s,N:n}},t.quantize.estats=function(e){var i=e.R,r=e.m,s=e.N,n=r[0],a=r[1],o=r[2],l=r[3],h=0==s?0:1/s,c=[i[0]-n*n*h,i[1]-n*a*h,i[2]-n*o*h,i[3]-n*l*h,i[4]-a*n*h,i[5]-a*a*h,i[6]-a*o*h,i[7]-a*l*h,i[8]-o*n*h,i[9]-o*a*h,i[10]-o*o*h,i[11]-o*l*h,i[12]-l*n*h,i[13]-l*a*h,i[14]-l*o*h,i[15]-l*l*h],d=c,u=t.M4,f=[.5,.5,.5,.5],p=0,g=0;if(0!=s)for(var m=0;m<10&&(f=u.multVec(d,f),g=Math.sqrt(u.dot(f,f)),f=u.sml(1/g,f),!(Math.abs(g-p)<1e-9));m++)p=g;var v=[n*h,a*h,o*h,l*h],_=u.dot(u.sml(255,v),f),y=v[3]<.001?0:1/v[3];return{Cov:c,q:v,e:f,L:p,eMq255:_,eMq:u.dot(f,v),rgba:(Math.round(255*v[3])<<24|Math.round(255*v[2]*y)<<16|Math.round(255*v[1]*y)<<8|Math.round(255*v[0]*y))>>>0}},t.M4={multVec:function(t,e){return[t[0]*e[0]+t[1]*e[1]+t[2]*e[2]+t[3]*e[3],t[4]*e[0]+t[5]*e[1]+t[6]*e[2]+t[7]*e[3],t[8]*e[0]+t[9]*e[1]+t[10]*e[2]+t[11]*e[3],t[12]*e[0]+t[13]*e[1]+t[14]*e[2]+t[15]*e[3]]},dot:function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]+t[3]*e[3]},sml:function(t,e){return[t*e[0],t*e[1],t*e[2],t*e[3]]}},t.encode.alphaMul=function(t,e){for(var i=new 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t.insert=0,4===e?(y(t,!0),0===t.strm.avail_out?3:4):(t.strstart>t.block_start&&(y(t,!1),t.strm.avail_out),1)})),new M(4,4,8,4,C),new M(4,5,16,8,C),new M(4,6,32,32,C),new M(4,4,16,16,S),new M(8,16,32,32,S),new M(8,16,128,128,S),new M(8,32,128,256,S),new M(32,128,258,1024,S),new M(32,258,258,4096,S)],e.deflateInit=function(t,e){return L(t,e,8,15,8,0)},e.deflateInit2=L,e.deflateReset=E,e.deflateResetKeep=T,e.deflateSetHeader=function(t,e){return t&&t.state?2!==t.state.wrap?h:(t.state.gzhead=e,0):h},e.deflate=function(t,e){var i,s,a,l;if(!t||!t.state||e>5||e<0)return t?g(t,h):h;if(s=t.state,!t.output||!t.input&&0!==t.avail_in||s.status===p&&4!==e)return 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t;m--,_+=u[p++]<<y,y+=8}_>>>=L,y-=L,i.back+=L}if(_>>>=z,y-=z,i.back+=z,i.length=E,0===T){i.mode=26;break}if(32&T){i.back=-1,i.mode=h;break}if(64&T){t.msg="invalid literal/length code",i.mode=c;break}i.extra=15&T,i.mode=22;case 22:if(i.extra){for(R=i.extra;y<R;){if(0===m)break t;m--,_+=u[p++]<<y,y+=8}i.length+=_&(1<<i.extra)-1,_>>>=i.extra,y-=i.extra,i.back+=i.extra}i.was=i.length,i.mode=23;case 23:for(;T=(P=i.distcode[_&(1<<i.distbits)-1])>>>16&255,E=65535&P,!((z=P>>>24)<=y);){if(0===m)break t;m--,_+=u[p++]<<y,y+=8}if(!(240&T)){for(L=z,F=T,I=E;T=(P=i.distcode[I+((_&(1<<L+F)-1)>>L)])>>>16&255,E=65535&P,!(L+(z=P>>>24)<=y);){if(0===m)break t;m--,_+=u[p++]<<y,y+=8}_>>>=L,y-=L,i.back+=L}if(_>>>=z,y-=z,i.back+=z,64&T){t.msg="invalid distance code",i.mode=c;break}i.offset=E,i.extra=15&T,i.mode=24;case 24:if(i.extra){for(R=i.extra;y<R;){if(0===m)break t;m--,_+=u[p++]<<y,y+=8}i.offset+=_&(1<<i.extra)-1,_>>>=i.extra,y-=i.extra,i.back+=i.extra}if(i.offset>i.dmax){t.msg="invalid distance too far 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t.next_out=g,t.avail_out=v,t.next_in=p,t.avail_in=m,i.hold=_,i.bits=y,(i.wsize||A!==t.avail_out&&i.mode<c&&(i.mode<27||4!==e))&&x(t,t.output,t.next_out,A-t.avail_out)?(i.mode=31,-4):(w-=t.avail_in,A-=t.avail_out,t.total_in+=w,t.total_out+=A,i.total+=A,i.wrap&&A&&(t.adler=i.check=i.flags?n(i.check,f,A,t.next_out-A):s(i.check,f,A,t.next_out-A)),t.data_type=i.bits+(i.last?64:0)+(i.mode===h?128:0)+(20===i.mode||15===i.mode?256:0),(0===w&&0===A||4===e)&&0===D&&(D=-5),D)},e.inflateEnd=function(t){if(!t||!t.state)return l;var e=t.state;return e.window&&(e.window=null),t.state=null,0},e.inflateGetHeader=function(t,e){var i;return t&&t.state&&2&(i=t.state).wrap?(i.head=e,e.done=!1,0):l},e.inflateSetDictionary=function(t,e){var i,r=e.length;return t&&t.state?0!==(i=t.state).wrap&&11!==i.mode?l:11===i.mode&&s(1,e,r,0)!==i.check?-3:x(t,e,r,r)?(i.mode=31,-4):(i.havedict=1,0):l},e.inflateInfo="pako inflate (from Nodeca project)"},358:(t,e,i)=>{"use strict";var r=i(981),s=15,n=[3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258,0,0],a=[16,16,16,16,16,16,16,16,17,17,17,17,18,18,18,18,19,19,19,19,20,20,20,20,21,21,21,21,16,72,78],o=[1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577,0,0],l=[16,16,16,16,17,17,18,18,19,19,20,20,21,21,22,22,23,23,24,24,25,25,26,26,27,27,28,28,29,29,64,64];t.exports=function(t,e,i,h,c,d,u,f){var p,g,m,v,_,y,b,x,w,A=f.bits,C=0,S=0,M=0,z=0,T=0,E=0,L=0,F=0,I=0,O=0,D=null,k=0,R=new r.Buf16(16),P=new r.Buf16(16),U=null,B=0;for(C=0;C<=s;C++)R[C]=0;for(S=0;S<h;S++)R[e[i+S]]++;for(T=A,z=s;z>=1&&0===R[z];z--);if(T>z&&(T=z),0===z)return c[d++]=20971520,c[d++]=20971520,f.bits=1,0;for(M=1;M<z&&0===R[M];M++);for(T<M&&(T=M),F=1,C=1;C<=s;C++)if(F<<=1,(F-=R[C])<0)return-1;if(F>0&&(0===t||1!==z))return-1;for(P[1]=0,C=1;C<s;C++)P[C+1]=P[C]+R[C];for(S=0;S<h;S++)0!==e[i+S]&&(u[P[e[i+S]]++]=S);if(0===t?(D=U=u,y=19):1===t?(D=n,k-=257,U=a,B-=257,y=256):(D=o,U=l,y=-1),O=0,S=0,C=M,_=d,E=T,L=0,m=-1,v=(I=1<<T)-1,1===t&&I>852||2===t&&I>592)return 1;for(;;){b=C-L,u[S]<y?(x=0,w=u[S]):u[S]>y?(x=U[B+u[S]],w=D[k+u[S]]):(x=96,w=0),p=1<<C-L,M=g=1<<E;do{c[_+(O>>L)+(g-=p)]=b<<24|x<<16|w}while(0!==g);for(p=1<<C-1;O&p;)p>>=1;if(0!==p?(O&=p-1,O+=p):O=0,S++,0==--R[C]){if(C===z)break;C=e[i+u[S]]}if(C>T&&(O&v)!==m){for(0===L&&(L=T),_+=M,F=1<<(E=C-L);E+L<z&&!((F-=R[E+L])<=0);)E++,F<<=1;if(I+=1<<E,1===t&&I>852||2===t&&I>592)return 1;c[m=O&v]=T<<24|E<<16|_-d}}return 0!==O&&(c[_+O]=C-L<<24|64<<16),f.bits=T,0}},834:t=>{"use strict";t.exports={2:"need dictionary",1:"stream end",0:"","-1":"file error","-2":"stream error","-3":"data error","-4":"insufficient 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e,i,r,s=t.w_size;e=t.hash_size,r=e;do{i=t.head[--r],t.head[r]=i>=s?i-s:0}while(--e);e=s,r=e;do{i=t.prev[--r],t.prev[r]=i>=s?i-s:0}while(--e)};let Ct=(t,e,i)=>(e<<t.hash_shift^i)&t.hash_mask;const St=t=>{const e=t.state;let i=e.pending;i>t.avail_out&&(i=t.avail_out),0!==i&&(t.output.set(e.pending_buf.subarray(e.pending_out,e.pending_out+i),t.next_out),t.next_out+=i,e.pending_out+=i,t.total_out+=i,t.avail_out-=i,e.pending-=i,0===e.pending&&(e.pending_out=0))},Mt=(t,e)=>{Z(t,t.block_start>=0?t.block_start:-1,t.strstart-t.block_start,e),t.block_start=t.strstart,St(t.strm)},zt=(t,e)=>{t.pending_buf[t.pending++]=e},Tt=(t,e)=>{t.pending_buf[t.pending++]=e>>>8&255,t.pending_buf[t.pending++]=255&e},Et=(t,e,i,r)=>{let s=t.avail_in;return s>r&&(s=r),0===s?0:(t.avail_in-=s,e.set(t.input.subarray(t.next_in,t.next_in+s),i),1===t.state.wrap?t.adler=G(t.adler,e,s,i):2===t.state.wrap&&(t.adler=j(t.adler,e,s,i)),t.next_in+=s,t.total_in+=s,s)},Lt=(t,e)=>{let i,r,s=t.max_chain_length,n=t.strstart,a=t.prev_length,o=t.nice_match;const l=t.strstart>t.w_size-mt?t.strstart-(t.w_size-mt):0,h=t.window,c=t.w_mask,d=t.prev,u=t.strstart+gt;let f=h[n+a-1],p=h[n+a];t.prev_length>=t.good_match&&(s>>=2),o>t.lookahead&&(o=t.lookahead);do{if(i=e,h[i+a]===p&&h[i+a-1]===f&&h[i]===h[n]&&h[++i]===h[n+1]){n+=2,i++;do{}while(h[++n]===h[++i]&&h[++n]===h[++i]&&h[++n]===h[++i]&&h[++n]===h[++i]&&h[++n]===h[++i]&&h[++n]===h[++i]&&h[++n]===h[++i]&&h[++n]===h[++i]&&n<u);if(r=gt-(u-n),n=u-gt,r>a){if(t.match_start=e,a=r,r>=o)break;f=h[n+a-1],p=h[n+a]}}}while((e=d[e&c])>l&&0!=--s);return a<=t.lookahead?a:t.lookahead},Ft=t=>{const e=t.w_size;let i,r,s;do{if(r=t.window_size-t.lookahead-t.strstart,t.strstart>=e+(e-mt)&&(t.window.set(t.window.subarray(e,e+e-r),0),t.match_start-=e,t.strstart-=e,t.block_start-=e,t.insert>t.strstart&&(t.insert=t.strstart),At(t),r+=e),0===t.strm.avail_in)break;if(i=Et(t.strm,t.window,t.strstart+t.lookahead,r),t.lookahead+=i,t.lookahead+t.insert>=3)for(s=t.strstart-t.insert,t.ins_h=t.window[s],t.ins_h=Ct(t,t.ins_h,t.window[s+1]);t.insert&&(t.ins_h=Ct(t,t.ins_h,t.window[s+3-1]),t.prev[s&t.w_mask]=t.head[t.ins_h],t.head[t.ins_h]=s,s++,t.insert--,!(t.lookahead+t.insert<3)););}while(t.lookahead<mt&&0!==t.strm.avail_in)},It=(t,e)=>{let i,r,s,n=t.pending_buf_size-5>t.w_size?t.w_size:t.pending_buf_size-5,a=0,o=t.strm.avail_in;do{if(i=65535,s=t.bi_valid+42>>3,t.strm.avail_out<s)break;if(s=t.strm.avail_out-s,r=t.strstart-t.block_start,i>r+t.strm.avail_in&&(i=r+t.strm.avail_in),i>s&&(i=s),i<n&&(0===i&&e!==tt||e===Q||i!==r+t.strm.avail_in))break;a=e===tt&&i===r+t.strm.avail_in?1:0,Y(t,0,0,a),t.pending_buf[t.pending-4]=i,t.pending_buf[t.pending-3]=i>>8,t.pending_buf[t.pending-2]=~i,t.pending_buf[t.pending-1]=~i>>8,St(t.strm),r&&(r>i&&(r=i),t.strm.output.set(t.window.subarray(t.block_start,t.block_start+r),t.strm.next_out),t.strm.next_out+=r,t.strm.avail_out-=r,t.strm.total_out+=r,t.block_start+=r,i-=r),i&&(Et(t.strm,t.strm.output,t.strm.next_out,i),t.strm.next_out+=i,t.strm.avail_out-=i,t.strm.total_out+=i)}while(0===a);return o-=t.strm.avail_in,o&&(o>=t.w_size?(t.matches=2,t.window.set(t.strm.input.subarray(t.strm.next_in-t.w_size,t.strm.next_in),0),t.strstart=t.w_size,t.insert=t.strstart):(t.window_size-t.strstart<=o&&(t.strstart-=t.w_size,t.window.set(t.window.subarray(t.w_size,t.w_size+t.strstart),0),t.matches<2&&t.matches++,t.insert>t.strstart&&(t.insert=t.strstart)),t.window.set(t.strm.input.subarray(t.strm.next_in-o,t.strm.next_in),t.strstart),t.strstart+=o,t.insert+=o>t.w_size-t.insert?t.w_size-t.insert:o),t.block_start=t.strstart),t.high_water<t.strstart&&(t.high_water=t.strstart),a?4:e!==Q&&e!==tt&&0===t.strm.avail_in&&t.strstart===t.block_start?2:(s=t.window_size-t.strstart,t.strm.avail_in>s&&t.block_start>=t.w_size&&(t.block_start-=t.w_size,t.strstart-=t.w_size,t.window.set(t.window.subarray(t.w_size,t.w_size+t.strstart),0),t.matches<2&&t.matches++,s+=t.w_size,t.insert>t.strstart&&(t.insert=t.strstart)),s>t.strm.avail_in&&(s=t.strm.avail_in),s&&(Et(t.strm,t.window,t.strstart,s),t.strstart+=s,t.insert+=s>t.w_size-t.insert?t.w_size-t.insert:s),t.high_water<t.strstart&&(t.high_water=t.strstart),s=t.bi_valid+42>>3,s=t.pending_buf_size-s>65535?65535:t.pending_buf_size-s,n=s>t.w_size?t.w_size:s,r=t.strstart-t.block_start,(r>=n||(r||e===tt)&&e!==Q&&0===t.strm.avail_in&&r<=s)&&(i=r>s?s:r,a=e===tt&&0===t.strm.avail_in&&i===r?1:0,Y(t,t.block_start,i,a),t.block_start+=i,St(t.strm)),a?3:1)},Ot=(t,e)=>{let i,r;for(;;){if(t.lookahead<mt){if(Ft(t),t.lookahead<mt&&e===Q)return 1;if(0===t.lookahead)break}if(i=0,t.lookahead>=3&&(t.ins_h=Ct(t,t.ins_h,t.window[t.strstart+3-1]),i=t.prev[t.strstart&t.w_mask]=t.head[t.ins_h],t.head[t.ins_h]=t.strstart),0!==i&&t.strstart-i<=t.w_size-mt&&(t.match_length=Lt(t,i)),t.match_length>=3)if(r=X(t,t.strstart-t.match_start,t.match_length-3),t.lookahead-=t.match_length,t.match_length<=t.max_lazy_match&&t.lookahead>=3){t.match_length--;do{t.strstart++,t.ins_h=Ct(t,t.ins_h,t.window[t.strstart+3-1]),i=t.prev[t.strstart&t.w_mask]=t.head[t.ins_h],t.head[t.ins_h]=t.strstart}while(0!=--t.match_length);t.strstart++}else t.strstart+=t.match_length,t.match_length=0,t.ins_h=t.window[t.strstart],t.ins_h=Ct(t,t.ins_h,t.window[t.strstart+1]);else r=X(t,0,t.window[t.strstart]),t.lookahead--,t.strstart++;if(r&&(Mt(t,!1),0===t.strm.avail_out))return 1}return t.insert=t.strstart<2?t.strstart:2,e===tt?(Mt(t,!0),0===t.strm.avail_out?3:4):t.sym_next&&(Mt(t,!1),0===t.strm.avail_out)?1:2},Dt=(t,e)=>{let i,r,s;for(;;){if(t.lookahead<mt){if(Ft(t),t.lookahead<mt&&e===Q)return 1;if(0===t.lookahead)break}if(i=0,t.lookahead>=3&&(t.ins_h=Ct(t,t.ins_h,t.window[t.strstart+3-1]),i=t.prev[t.strstart&t.w_mask]=t.head[t.ins_h],t.head[t.ins_h]=t.strstart),t.prev_length=t.match_length,t.prev_match=t.match_start,t.match_length=2,0!==i&&t.prev_length<t.max_lazy_match&&t.strstart-i<=t.w_size-mt&&(t.match_length=Lt(t,i),t.match_length<=5&&(t.strategy===lt||3===t.match_length&&t.strstart-t.match_start>4096)&&(t.match_length=2)),t.prev_length>=3&&t.match_length<=t.prev_length){s=t.strstart+t.lookahead-3,r=X(t,t.strstart-1-t.prev_match,t.prev_length-3),t.lookahead-=t.prev_length-1,t.prev_length-=2;do{++t.strstart<=s&&(t.ins_h=Ct(t,t.ins_h,t.window[t.strstart+3-1]),i=t.prev[t.strstart&t.w_mask]=t.head[t.ins_h],t.head[t.ins_h]=t.strstart)}while(0!=--t.prev_length);if(t.match_available=0,t.match_length=2,t.strstart++,r&&(Mt(t,!1),0===t.strm.avail_out))return 1}else if(t.match_available){if(r=X(t,0,t.window[t.strstart-1]),r&&Mt(t,!1),t.strstart++,t.lookahead--,0===t.strm.avail_out)return 1}else t.match_available=1,t.strstart++,t.lookahead--}return t.match_available&&(r=X(t,0,t.window[t.strstart-1]),t.match_available=0),t.insert=t.strstart<2?t.strstart:2,e===tt?(Mt(t,!0),0===t.strm.avail_out?3:4):t.sym_next&&(Mt(t,!1),0===t.strm.avail_out)?1:2};function kt(t,e,i,r,s){this.good_length=t,this.max_lazy=e,this.nice_length=i,this.max_chain=r,this.func=s}const Rt=[new kt(0,0,0,0,It),new kt(4,4,8,4,Ot),new kt(4,5,16,8,Ot),new kt(4,6,32,32,Ot),new kt(4,4,16,16,Dt),new kt(8,16,32,32,Dt),new kt(8,16,128,128,Dt),new kt(8,32,128,256,Dt),new kt(32,128,258,1024,Dt),new kt(32,258,258,4096,Dt)];function Pt(){this.strm=null,this.status=0,this.pending_buf=null,this.pending_buf_size=0,this.pending_out=0,this.pending=0,this.wrap=0,this.gzhead=null,this.gzindex=0,this.method=pt,this.last_flush=-1,this.w_size=0,this.w_bits=0,this.w_mask=0,this.window=null,this.window_size=0,this.prev=null,this.head=null,this.ins_h=0,this.hash_size=0,this.hash_bits=0,this.hash_mask=0,this.hash_shift=0,this.block_start=0,this.match_length=0,this.prev_match=0,this.match_available=0,this.strstart=0,this.match_start=0,this.lookahead=0,this.prev_length=0,this.max_chain_length=0,this.max_lazy_match=0,this.level=0,this.strategy=0,this.good_match=0,this.nice_match=0,this.dyn_ltree=new Uint16Array(1146),this.dyn_dtree=new Uint16Array(122),this.bl_tree=new Uint16Array(78),wt(this.dyn_ltree),wt(this.dyn_dtree),wt(this.bl_tree),this.l_desc=null,this.d_desc=null,this.bl_desc=null,this.bl_count=new Uint16Array(16),this.heap=new Uint16Array(573),wt(this.heap),this.heap_len=0,this.heap_max=0,this.depth=new Uint16Array(573),wt(this.depth),this.sym_buf=0,this.lit_bufsize=0,this.sym_next=0,this.sym_end=0,this.opt_len=0,this.static_len=0,this.matches=0,this.insert=0,this.bi_buf=0,this.bi_valid=0}const Ut=t=>{if(!t)return 1;const e=t.state;return!e||e.strm!==t||e.status!==vt&&57!==e.status&&69!==e.status&&73!==e.status&&91!==e.status&&103!==e.status&&e.status!==_t&&e.status!==yt?1:0},Bt=t=>{if(Ut(t))return bt(t,st);t.total_in=t.total_out=0,t.data_type=ft;const e=t.state;return e.pending=0,e.pending_out=0,e.wrap<0&&(e.wrap=-e.wrap),e.status=2===e.wrap?57:e.wrap?vt:_t,t.adler=2===e.wrap?0:1,e.last_flush=-2,q(e),it},Nt=t=>{const e=Bt(t);var i;return e===it&&((i=t.state).window_size=2*i.w_size,wt(i.head),i.max_lazy_match=Rt[i.level].max_lazy,i.good_match=Rt[i.level].good_length,i.nice_match=Rt[i.level].nice_length,i.max_chain_length=Rt[i.level].max_chain,i.strstart=0,i.block_start=0,i.lookahead=0,i.insert=0,i.match_length=i.prev_length=2,i.match_available=0,i.ins_h=0),e},Gt=(t,e,i,r,s,n)=>{if(!t)return st;let a=1;if(e===ot&&(e=6),r<0?(a=0,r=-r):r>15&&(a=2,r-=16),s<1||s>9||i!==pt||r<8||r>15||e<0||e>9||n<0||n>dt||8===r&&1!==a)return bt(t,st);8===r&&(r=9);const o=new Pt;return t.state=o,o.strm=t,o.status=vt,o.wrap=a,o.gzhead=null,o.w_bits=r,o.w_size=1<<o.w_bits,o.w_mask=o.w_size-1,o.hash_bits=s+7,o.hash_size=1<<o.hash_bits,o.hash_mask=o.hash_size-1,o.hash_shift=~~((o.hash_bits+3-1)/3),o.window=new Uint8Array(2*o.w_size),o.head=new Uint16Array(o.hash_size),o.prev=new Uint16Array(o.w_size),o.lit_bufsize=1<<s+6,o.pending_buf_size=4*o.lit_bufsize,o.pending_buf=new Uint8Array(o.pending_buf_size),o.sym_buf=o.lit_bufsize,o.sym_end=3*(o.lit_bufsize-1),o.level=e,o.strategy=n,o.method=i,Nt(t)};var Vt={deflateInit:(t,e)=>Gt(t,e,pt,15,8,ut),deflateInit2:Gt,deflateReset:Nt,deflateResetKeep:Bt,deflateSetHeader:(t,e)=>Ut(t)||2!==t.state.wrap?st:(t.state.gzhead=e,it),deflate:(t,e)=>{if(Ut(t)||e>et||e<0)return t?bt(t,st):st;const i=t.state;if(!t.output||0!==t.avail_in&&!t.input||i.status===yt&&e!==tt)return bt(t,0===t.avail_out?at:st);const r=i.last_flush;if(i.last_flush=e,0!==i.pending){if(St(t),0===t.avail_out)return i.last_flush=-1,it}else if(0===t.avail_in&&xt(e)<=xt(r)&&e!==tt)return bt(t,at);if(i.status===yt&&0!==t.avail_in)return bt(t,at);if(i.status===vt&&0===i.wrap&&(i.status=_t),i.status===vt){let e=pt+(i.w_bits-8<<4)<<8,r=-1;if(r=i.strategy>=ht||i.level<2?0:i.level<6?1:6===i.level?2:3,e|=r<<6,0!==i.strstart&&(e|=32),e+=31-e%31,Tt(i,e),0!==i.strstart&&(Tt(i,t.adler>>>16),Tt(i,65535&t.adler)),t.adler=1,i.status=_t,St(t),0!==i.pending)return i.last_flush=-1,it}if(57===i.status)if(t.adler=0,zt(i,31),zt(i,139),zt(i,8),i.gzhead)zt(i,(i.gzhead.text?1:0)+(i.gzhead.hcrc?2:0)+(i.gzhead.extra?4:0)+(i.gzhead.name?8:0)+(i.gzhead.comment?16:0)),zt(i,255&i.gzhead.time),zt(i,i.gzhead.time>>8&255),zt(i,i.gzhead.time>>16&255),zt(i,i.gzhead.time>>24&255),zt(i,9===i.level?2:i.strategy>=ht||i.level<2?4:0),zt(i,255&i.gzhead.os),i.gzhead.extra&&i.gzhead.extra.length&&(zt(i,255&i.gzhead.extra.length),zt(i,i.gzhead.extra.length>>8&255)),i.gzhead.hcrc&&(t.adler=j(t.adler,i.pending_buf,i.pending,0)),i.gzindex=0,i.status=69;else if(zt(i,0),zt(i,0),zt(i,0),zt(i,0),zt(i,0),zt(i,9===i.level?2:i.strategy>=ht||i.level<2?4:0),zt(i,3),i.status=_t,St(t),0!==i.pending)return i.last_flush=-1,it;if(69===i.status){if(i.gzhead.extra){let e=i.pending,r=(65535&i.gzhead.extra.length)-i.gzindex;for(;i.pending+r>i.pending_buf_size;){let s=i.pending_buf_size-i.pending;if(i.pending_buf.set(i.gzhead.extra.subarray(i.gzindex,i.gzindex+s),i.pending),i.pending=i.pending_buf_size,i.gzhead.hcrc&&i.pending>e&&(t.adler=j(t.adler,i.pending_buf,i.pending-e,e)),i.gzindex+=s,St(t),0!==i.pending)return i.last_flush=-1,it;e=0,r-=s}let s=new Uint8Array(i.gzhead.extra);i.pending_buf.set(s.subarray(i.gzindex,i.gzindex+r),i.pending),i.pending+=r,i.gzhead.hcrc&&i.pending>e&&(t.adler=j(t.adler,i.pending_buf,i.pending-e,e)),i.gzindex=0}i.status=73}if(73===i.status){if(i.gzhead.name){let e,r=i.pending;do{if(i.pending===i.pending_buf_size){if(i.gzhead.hcrc&&i.pending>r&&(t.adler=j(t.adler,i.pending_buf,i.pending-r,r)),St(t),0!==i.pending)return i.last_flush=-1,it;r=0}e=i.gzindex<i.gzhead.name.length?255&i.gzhead.name.charCodeAt(i.gzindex++):0,zt(i,e)}while(0!==e);i.gzhead.hcrc&&i.pending>r&&(t.adler=j(t.adler,i.pending_buf,i.pending-r,r)),i.gzindex=0}i.status=91}if(91===i.status){if(i.gzhead.comment){let e,r=i.pending;do{if(i.pending===i.pending_buf_size){if(i.gzhead.hcrc&&i.pending>r&&(t.adler=j(t.adler,i.pending_buf,i.pending-r,r)),St(t),0!==i.pending)return i.last_flush=-1,it;r=0}e=i.gzindex<i.gzhead.comment.length?255&i.gzhead.comment.charCodeAt(i.gzindex++):0,zt(i,e)}while(0!==e);i.gzhead.hcrc&&i.pending>r&&(t.adler=j(t.adler,i.pending_buf,i.pending-r,r))}i.status=103}if(103===i.status){if(i.gzhead.hcrc){if(i.pending+2>i.pending_buf_size&&(St(t),0!==i.pending))return i.last_flush=-1,it;zt(i,255&t.adler),zt(i,t.adler>>8&255),t.adler=0}if(i.status=_t,St(t),0!==i.pending)return i.last_flush=-1,it}if(0!==t.avail_in||0!==i.lookahead||e!==Q&&i.status!==yt){let r=0===i.level?It(i,e):i.strategy===ht?((t,e)=>{let i;for(;;){if(0===t.lookahead&&(Ft(t),0===t.lookahead)){if(e===Q)return 1;break}if(t.match_length=0,i=X(t,0,t.window[t.strstart]),t.lookahead--,t.strstart++,i&&(Mt(t,!1),0===t.strm.avail_out))return 1}return 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Uint8Array([16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15]);if(Ge(t)||!t.output||!t.input&&0!==t.avail_in)return Se;i=t.state,i.mode===Ie&&(i.mode=Oe),a=t.next_out,s=t.output,l=t.avail_out,n=t.next_in,r=t.input,o=t.avail_in,h=i.hold,c=i.bits,d=o,u=l,A=we;t:for(;;)switch(i.mode){case Le:if(0===i.wrap){i.mode=Oe;break}for(;c<16;){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}if(2&i.wrap&&35615===h){0===i.wbits&&(i.wbits=15),i.check=0,S[0]=255&h,S[1]=h>>>8&255,i.check=j(i.check,S,2,0),h=0,c=0,i.mode=16181;break}if(i.head&&(i.head.done=!1),!(1&i.wrap)||(((255&h)<<8)+(h>>8))%31){t.msg="incorrect header check",i.mode=Ue;break}if((15&h)!==Ee){t.msg="unknown compression method",i.mode=Ue;break}if(h>>>=4,c-=4,w=8+(15&h),0===i.wbits&&(i.wbits=w),w>15||w>i.wbits){t.msg="invalid window size",i.mode=Ue;break}i.dmax=1<<i.wbits,i.flags=0,t.adler=i.check=1,i.mode=512&h?16189:Ie,h=0,c=0;break;case 16181:for(;c<16;){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}if(i.flags=h,(255&i.flags)!==Ee){t.msg="unknown 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t;s.set(r.subarray(n,n+f),a),o-=f,n+=f,l-=f,a+=f,i.length-=f;break}i.mode=Ie;break;case 16196:for(;c<14;){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}if(i.nlen=257+(31&h),h>>>=5,c-=5,i.ndist=1+(31&h),h>>>=5,c-=5,i.ncode=4+(15&h),h>>>=4,c-=4,i.nlen>286||i.ndist>30){t.msg="too many length or distance symbols",i.mode=Ue;break}i.have=0,i.mode=16197;case 16197:for(;i.have<i.ncode;){for(;c<3;){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}i.lens[T[i.have++]]=7&h,h>>>=3,c-=3}for(;i.have<19;)i.lens[T[i.have++]]=0;if(i.lencode=i.lendyn,i.lenbits=7,M={bits:i.lenbits},A=_e(0,i.lens,0,19,i.lencode,0,i.work,M),i.lenbits=M.bits,A){t.msg="invalid code lengths set",i.mode=Ue;break}i.have=0,i.mode=16198;case 16198:for(;i.have<i.nlen+i.ndist;){for(;C=i.lencode[h&(1<<i.lenbits)-1],m=C>>>24,v=C>>>16&255,_=65535&C,!(m<=c);){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}if(_<16)h>>>=m,c-=m,i.lens[i.have++]=_;else{if(16===_){for(z=m+2;c<z;){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}if(h>>>=m,c-=m,0===i.have){t.msg="invalid bit 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Re:if(o>=6&&l>=258){t.next_out=a,t.avail_out=l,t.next_in=n,t.avail_in=o,i.hold=h,i.bits=c,ue(t,u),a=t.next_out,s=t.output,l=t.avail_out,n=t.next_in,r=t.input,o=t.avail_in,h=i.hold,c=i.bits,i.mode===Ie&&(i.back=-1);break}for(i.back=0;C=i.lencode[h&(1<<i.lenbits)-1],m=C>>>24,v=C>>>16&255,_=65535&C,!(m<=c);){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}if(v&&!(240&v)){for(y=m,b=v,x=_;C=i.lencode[x+((h&(1<<y+b)-1)>>y)],m=C>>>24,v=C>>>16&255,_=65535&C,!(y+m<=c);){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}h>>>=y,c-=y,i.back+=y}if(h>>>=m,c-=m,i.back+=m,i.length=_,0===v){i.mode=16205;break}if(32&v){i.back=-1,i.mode=Ie;break}if(64&v){t.msg="invalid literal/length code",i.mode=Ue;break}i.extra=15&v,i.mode=16201;case 16201:if(i.extra){for(z=i.extra;c<z;){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}i.length+=h&(1<<i.extra)-1,h>>>=i.extra,c-=i.extra,i.back+=i.extra}i.was=i.length,i.mode=16202;case 16202:for(;C=i.distcode[h&(1<<i.distbits)-1],m=C>>>24,v=C>>>16&255,_=65535&C,!(m<=c);){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}if(!(240&v)){for(y=m,b=v,x=_;C=i.distcode[x+((h&(1<<y+b)-1)>>y)],m=C>>>24,v=C>>>16&255,_=65535&C,!(y+m<=c);){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}h>>>=y,c-=y,i.back+=y}if(h>>>=m,c-=m,i.back+=m,64&v){t.msg="invalid distance code",i.mode=Ue;break}i.offset=_,i.extra=15&v,i.mode=16203;case 16203:if(i.extra){for(z=i.extra;c<z;){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}i.offset+=h&(1<<i.extra)-1,h>>>=i.extra,c-=i.extra,i.back+=i.extra}if(i.offset>i.dmax){t.msg="invalid distance too far back",i.mode=Ue;break}i.mode=16204;case 16204:if(0===l)break t;if(f=u-l,i.offset>f){if(f=i.offset-f,f>i.whave&&i.sane){t.msg="invalid distance too far back",i.mode=Ue;break}f>i.wnext?(f-=i.wnext,p=i.wsize-f):p=i.wnext-f,f>i.length&&(f=i.length),g=i.window}else g=s,p=a-i.offset,f=i.length;f>l&&(f=l),l-=f,i.length-=f;do{s[a++]=g[p++]}while(--f);0===i.length&&(i.mode=Re);break;case 16205:if(0===l)break t;s[a++]=i.length,l--,i.mode=Re;break;case Pe:if(i.wrap){for(;c<32;){if(0===o)break t;o--,h|=r[n++]<<c,c+=8}if(u-=l,t.total_out+=u,i.total+=u,4&i.wrap&&u&&(t.adler=i.check=i.flags?j(i.check,s,u,a-u):G(i.check,s,u,a-u)),u=l,4&i.wrap&&(i.flags?h:Be(h))!==i.check){t.msg="incorrect data check",i.mode=Ue;break}h=0,c=0}i.mode=16207;case 16207:if(i.wrap&&i.flags){for(;c<32;){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}if(4&i.wrap&&h!==(4294967295&i.total)){t.msg="incorrect length check",i.mode=Ue;break}h=0,c=0}i.mode=16208;case 16208:A=Ae;break t;case Ue:A=Me;break t;case 16210:return ze;default:return Se}return 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$e=function(){this.text=0,this.time=0,this.xflags=0,this.os=0,this.extra=null,this.extra_len=0,this.name="",this.comment="",this.hcrc=0,this.done=!1};const Je=Object.prototype.toString,{Z_NO_FLUSH:ti,Z_FINISH:ei,Z_OK:ii,Z_STREAM_END:ri,Z_NEED_DICT:si,Z_STREAM_ERROR:ni,Z_DATA_ERROR:ai,Z_MEM_ERROR:oi}=W;function li(t){this.options=Ht({chunkSize:65536,windowBits:15,to:""},t||{});const e=this.options;e.raw&&e.windowBits>=0&&e.windowBits<16&&(e.windowBits=-e.windowBits,0===e.windowBits&&(e.windowBits=-15)),!(e.windowBits>=0&&e.windowBits<16)||t&&t.windowBits||(e.windowBits+=32),e.windowBits>15&&e.windowBits<48&&(15&e.windowBits||(e.windowBits|=15)),this.err=0,this.msg="",this.ended=!1,this.chunks=[],this.strm=new Qt,this.strm.avail_out=0;let i=Qe.inflateInit2(this.strm,e.windowBits);if(i!==ii)throw new Error(H[i]);if(this.header=new $e,Qe.inflateGetHeader(this.strm,this.header),e.dictionary&&("string"==typeof e.dictionary?e.dictionary=Zt(e.dictionary):"[object 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t=Kt(i.output,i.next_out),e=i.next_out-t,s=Xt(i.output,t);i.next_out=e,i.avail_out=r-e,e&&i.output.set(i.output.subarray(t,t+e),0),this.onData(s)}else this.onData(i.output.length===i.next_out?i.output:i.output.subarray(0,i.next_out));if(n!==ii||0!==o){if(n===ri)return n=Qe.inflateEnd(this.strm),this.onEnd(n),this.ended=!0,!0;if(0===i.avail_in)break}}return!0},li.prototype.onData=function(t){this.chunks.push(t)},li.prototype.onEnd=function(t){t===ii&&("string"===this.options.to?this.result=this.chunks.join(""):this.result=Wt(this.chunks)),this.chunks=[],this.err=t,this.msg=this.strm.msg};var ci={Inflate:li,inflate:hi,inflateRaw:function(t,e){return(e=e||{}).raw=!0,hi(t,e)},ungzip:hi,constants:W};const{Deflate:di,deflate:ui,deflateRaw:fi,gzip:pi}=ce,{Inflate:gi,inflate:mi,inflateRaw:vi,ungzip:_i}=ci;var yi=di,bi=ui,xi=fi,wi=pi,Ai=gi,Ci=mi,Si=vi,Mi=_i,zi=W,Ti={Deflate:di,deflate:ui,deflateRaw:fi,gzip:pi,Inflate:gi,inflate:mi,inflateRaw:vi,ungzip:_i,constants:W}}},__webpack_module_cache__={};function __webpack_require__(t){var e=__webpack_module_cache__[t];if(void 0!==e)return e.exports;var i=__webpack_module_cache__[t]={exports:{}};return __webpack_modules__[t].call(i.exports,i,i.exports,__webpack_require__),i.exports}__webpack_require__.d=(t,e)=>{for(var i in e)__webpack_require__.o(e,i)&&!__webpack_require__.o(t,i)&&Object.defineProperty(t,i,{enumerable:!0,get:e[i]})},__webpack_require__.o=(t,e)=>Object.prototype.hasOwnProperty.call(t,e),__webpack_require__.r=t=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(t,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(t,"__esModule",{value:!0})},__webpack_require__(421),__webpack_require__(185);var __webpack_exports__=__webpack_require__(307);return __webpack_exports__})()));
\ No newline at end of file
diff --git a/assets/variant_effect_inspection_tool/3Dmol-min.js.LICENSE.txt b/assets/variant_effect_inspection_tool/3Dmol-min.js.LICENSE.txt
new file mode 100644
index 0000000..1b2a169
--- /dev/null
+++ b/assets/variant_effect_inspection_tool/3Dmol-min.js.LICENSE.txt
@@ -0,0 +1,90 @@
+3Dmol.js incorporates code from GLmol, Three.js, and jQuery and
+is licensed under a BSD-3-Clause license.
+
+* 3Dmol.js
+	Copyright (c) 2014, University of Pittsburgh and contributors
+	All rights reserved.
+
+	Redistribution and use in source and binary forms, with or without 
+	modification, are permitted provided that the following conditions are met:
+
+	1. Redistributions of source code must retain the above copyright 
+	notice, this list of conditions and the following disclaimer.
+
+	2. Redistributions in binary form must reproduce the above copyright 
+	notice, this list of conditions and the following disclaimer in the 
+	documentation and/or other materials provided with the distribution.
+
+	3. Neither the name of the copyright holder nor the names of its 
+	contributors may be used to endorse or promote products derived from 
+	this software without specific prior written permission.
+
+	THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 
+	"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 
+	LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A 
+	PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+	HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 
+	SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED 
+	TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 
+	PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF 
+	LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 
+	NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 
+	SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+* GLmol
+ GLmol - Molecular Viewer on WebGL/Javascript (0.47)
+  (C) Copyright 2011-2012, biochem_fan
+      License: dual license of MIT or LGPL3
+
+  Contributors:
+    Robert Hanson for parseXYZ, deferred instantiation
+
+
+
+* Three.js 
+     https://github.com/mrdoob/three.js
+
+     Copyright (c) 2010-2012 three.js Authors. All rights reserved.
+
+     Permission is hereby granted, free of charge, to any person obtaining a copy
+     of this software and associated documentation files (the "Software"), to deal
+     in the Software without restriction, including without limitation the rights
+     to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+     copies of the Software, and to permit persons to whom the Software is
+     furnished to do so, subject to the following conditions:
+
+     The above copyright notice and this permission notice shall be included in
+     all copies or substantial portions of the Software.
+
+     THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+     IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+     FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+     AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+     LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+     OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+     THE SOFTWARE.
+
+* jQuery
+     http://jquery.org/
+
+     Copyright (c) 2011 John Resig
+
+     Permission is hereby granted, free of charge, to any person obtaining
+     a copy of this software and associated documentation files (the
+     "Software"), to deal in the Software without restriction, including
+     without limitation the rights to use, copy, modify, merge, publish,
+     distribute, sublicense, and/or sell copies of the Software, and to
+     permit persons to whom the Software is furnished to do so, subject to
+     the following conditions:
+
+     The above copyright notice and this permission notice shall be
+     included in all copies or substantial portions of the Software.
+
+     THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+     EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+     MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+     NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+     LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+     OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+     WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
diff --git a/assets/variant_effect_inspection_tool/viewer_template.html b/assets/variant_effect_inspection_tool/viewer_template.html
new file mode 100644
index 0000000..c1e4f5a
--- /dev/null
+++ b/assets/variant_effect_inspection_tool/viewer_template.html
@@ -0,0 +1,907 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <!--
+  Generated by nf-core/deepmutscan (MIT). This viewer bundles 3Dmol.js (https://3dmol.org) —
+  Copyright (c) 2014 University of Pittsburgh and contributors, licensed BSD-3-Clause. The full
+  license text is distributed with the pipeline as assets/variant_effect_inspection_tool/3Dmol-min.js.LICENSE.txt.
+-->
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <title>nf-core/deepmutscan · variant effect inspection tool · __SAMPLE__</title>
+    <style>
+      :root {
+        --bg: #ffffff;
+        --panel: #f7f8fa;
+        --line: #e6e8ec;
+        --ink: #1c2230;
+        --muted: #6b7280;
+        --accent: #2f6df0;
+        --na: #dddddd;
+        --shadow: 0 1px 2px rgba(16, 24, 40, 0.06), 0 1px 3px rgba(16, 24, 40, 0.1);
+        --mono: ui-monospace, SFMono-Regular, Menlo, Consolas, monospace;
+        --sans: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif;
+      }
+      * {
+        box-sizing: border-box;
+      }
+      html,
+      body {
+        margin: 0;
+        height: 100%;
+      }
+      body {
+        font-family: var(--sans);
+        color: var(--ink);
+        background: var(--bg);
+        font-size: 14px;
+      }
+      header {
+        display: flex;
+        align-items: center;
+        gap: 14px;
+        padding: 14px 22px;
+        border-bottom: 1px solid var(--line);
+      }
+      header .logo {
+        height: 44px;
+        width: auto;
+        display: block;
+      }
+      header .title {
+        color: var(--muted);
+        font-size: 18px;
+        font-weight: 500;
+        border-left: 1px solid var(--line);
+        padding-left: 14px;
+      }
+      header .spacer {
+        flex: 1;
+      }
+      header .sub {
+        color: var(--muted);
+        font-size: 14px;
+      }
+      header .sub strong {
+        color: var(--ink);
+      }
+      .layout {
+        display: grid;
+        grid-template-columns: 288px 1fr 340px;
+        gap: 0;
+        height: calc(100vh - 74px - 92px);
+      }
+      .sidebar,
+      .detail {
+        padding: 16px;
+        overflow: auto;
+      }
+      .sidebar {
+        border-right: 1px solid var(--line);
+      }
+      .detail {
+        border-left: 1px solid var(--line);
+        background: var(--panel);
+      }
+      #stage {
+        position: relative;
+        background: radial-gradient(120% 120% at 50% 0%, #fbfcfe 0%, #f1f3f7 100%);
+      }
+      #viewer {
+        position: absolute;
+        inset: 0;
+      }
+      .group {
+        margin-bottom: 18px;
+      }
+      .group h3 {
+        font-size: 11px;
+        text-transform: uppercase;
+        letter-spacing: 0.08em;
+        color: var(--muted);
+        margin: 0 0 8px;
+      }
+      label.field {
+        display: block;
+        margin-bottom: 10px;
+      }
+      label.field span {
+        display: block;
+        font-size: 12px;
+        color: var(--muted);
+        margin-bottom: 4px;
+      }
+      select,
+      button {
+        font-family: inherit;
+        font-size: 13px;
+        color: var(--ink);
+      }
+      select {
+        width: 100%;
+        padding: 7px 9px;
+        border: 1px solid var(--line);
+        border-radius: 8px;
+        background: #fff;
+      }
+      .btnrow {
+        display: flex;
+        gap: 8px;
+        flex-wrap: wrap;
+      }
+      button {
+        padding: 7px 11px;
+        border: 1px solid var(--line);
+        border-radius: 8px;
+        background: #fff;
+        cursor: pointer;
+        box-shadow: var(--shadow);
+      }
+      button:hover {
+        border-color: #cdd2da;
+      }
+      button.primary {
+        background: var(--accent);
+        border-color: var(--accent);
+        color: #fff;
+      }
+      button.primary:hover {
+        filter: brightness(0.96);
+      }
+      button.on {
+        background: var(--accent);
+        border-color: var(--accent);
+        color: #fff;
+      }
+      .legend {
+        margin-top: 6px;
+      }
+      .legend .bar {
+        height: 12px;
+        border-radius: 6px;
+        border: 1px solid var(--line);
+      }
+      .legend .ticks {
+        display: flex;
+        justify-content: space-between;
+        font-variant-numeric: tabular-nums;
+        color: var(--muted);
+        font-size: 11px;
+        margin-top: 4px;
+      }
+      .legend .desc {
+        color: var(--muted);
+        font-size: 12px;
+        margin-top: 8px;
+        line-height: 1.45;
+      }
+      .legend .na {
+        display: inline-flex;
+        align-items: center;
+        gap: 6px;
+        margin-top: 8px;
+        font-size: 12px;
+        color: var(--muted);
+      }
+      .swatch {
+        width: 12px;
+        height: 12px;
+        border-radius: 3px;
+        border: 1px solid var(--line);
+        display: inline-block;
+      }
+      .detail h2 {
+        font-size: 14px;
+        margin: 0 0 2px;
+      }
+      .detail .pos-sub {
+        color: var(--muted);
+        font-size: 12px;
+        margin-bottom: 12px;
+      }
+      .metrics {
+        display: grid;
+        grid-template-columns: 1fr auto;
+        gap: 6px 10px;
+        margin-bottom: 14px;
+      }
+      .metrics .k {
+        color: var(--muted);
+        font-size: 12px;
+      }
+      .metrics .v {
+        font-variant-numeric: tabular-nums;
+        font-size: 12px;
+      }
+      .subhead {
+        font-size: 11px;
+        text-transform: uppercase;
+        letter-spacing: 0.08em;
+        color: var(--muted);
+        margin: 4px 0 8px;
+      }
+      .vbar {
+        display: grid;
+        grid-template-columns: 22px 1fr 52px;
+        align-items: center;
+        gap: 8px;
+        margin: 1px 0;
+        padding: 2px 3px;
+        border-radius: 6px;
+        cursor: pointer;
+      }
+      .vbar:hover {
+        background: #eef0f4;
+      }
+      .vbar.sel {
+        background: #e8efff;
+        box-shadow: inset 0 0 0 1px var(--accent);
+      }
+      .vbar .aa {
+        font-family: var(--mono);
+        font-weight: 600;
+        text-align: center;
+      }
+      .vbar .track {
+        height: 14px;
+        background: #eef0f4;
+        border-radius: 4px;
+        position: relative;
+        overflow: hidden;
+      }
+      .vbar.sel .track {
+        background: #dbe5ff;
+      }
+      .vbar .fill {
+        position: absolute;
+        top: 0;
+        bottom: 0;
+      }
+      .vbar .num {
+        font-variant-numeric: tabular-nums;
+        font-size: 11px;
+        color: var(--muted);
+        text-align: right;
+      }
+      .empty {
+        color: var(--muted);
+        font-size: 13px;
+        line-height: 1.5;
+      }
+      .swarm {
+        margin-top: 14px;
+        border-top: 1px solid var(--line);
+        padding-top: 12px;
+      }
+      .swarm .shead {
+        display: flex;
+        justify-content: space-between;
+        align-items: baseline;
+        margin-bottom: 8px;
+      }
+      .swarm .shead b {
+        font-size: 12px;
+      }
+      .swarm .plot {
+        margin-bottom: 12px;
+      }
+      .swarm .plot .pt {
+        font-size: 11px;
+        color: var(--ink);
+        margin-bottom: 2px;
+        display: flex;
+        justify-content: space-between;
+      }
+      .swarm .plot .pt .val {
+        font-variant-numeric: tabular-nums;
+        color: var(--muted);
+      }
+      .swarm .plot canvas {
+        display: block;
+        width: 100%;
+        height: 96px;
+        border-radius: 6px;
+        background: #fff;
+        border: 1px solid var(--line);
+      }
+      .swarm .plot .ax {
+        display: flex;
+        justify-content: space-between;
+        font-size: 10px;
+        color: var(--muted);
+        font-variant-numeric: tabular-nums;
+        margin-top: 2px;
+      }
+      .ibtn {
+        display: inline-flex;
+        align-items: center;
+        justify-content: center;
+        width: 14px;
+        height: 14px;
+        border-radius: 50%;
+        border: 1px solid var(--line);
+        background: transparent;
+        color: var(--muted);
+        font:
+          italic 700 10px Georgia,
+          serif;
+        cursor: pointer;
+        padding: 0;
+        line-height: 1;
+        vertical-align: middle;
+        margin-left: 4px;
+      }
+      .ibtn:hover {
+        color: var(--ink);
+        border-color: var(--muted);
+      }
+      .ipop {
+        position: fixed;
+        z-index: 40;
+        max-width: 250px;
+        background: var(--panel);
+        color: var(--ink);
+        border: 1px solid var(--line);
+        border-radius: 8px;
+        padding: 8px 10px;
+        font-size: 12px;
+        line-height: 1.45;
+        box-shadow: 0 6px 22px rgba(0, 0, 0, 0.2);
+      }
+      .hint {
+        color: var(--muted);
+        font-size: 11px;
+        margin-top: 6px;
+      }
+      .reps {
+        display: flex;
+        flex-direction: column;
+        gap: 5px;
+      }
+      .reps label {
+        display: flex;
+        align-items: center;
+        gap: 7px;
+        font-size: 12.5px;
+        color: var(--ink2);
+        cursor: pointer;
+      }
+      .reps label:hover {
+        color: var(--ink);
+      }
+      .reps input {
+        accent-color: var(--accent);
+        margin: 0;
+      }
+      .seqwrap {
+        border-top: 1px solid var(--line);
+        height: 92px;
+        padding: 8px 14px;
+        overflow-x: auto;
+        overflow-y: hidden;
+        white-space: nowrap;
+        background: #fff;
+      }
+      .seqwrap .ruler {
+        height: 14px;
+        position: relative;
+        font-size: 10px;
+        color: var(--muted);
+        font-family: var(--mono);
+      }
+      .seqwrap .ruler span {
+        position: absolute;
+        transform: translateX(-50%);
+      }
+      .seq {
+        font-family: var(--mono);
+        line-height: 1;
+        user-select: none;
+      }
+      .seq .res {
+        display: inline-block;
+        width: 13px;
+        height: 20px;
+        line-height: 20px;
+        text-align: center;
+        border-radius: 3px;
+        color: #11151f;
+        cursor: pointer;
+        font-size: 12px;
+      }
+      .seq .res.sel {
+        outline: 2px solid var(--ink);
+        outline-offset: -1px;
+      }
+      .seq .res.unmapped {
+        opacity: 0.45;
+      }
+    </style>
+  </head>
+  <body>
+    <header>
+      <a
+        href="https://nf-co.re/deepmutscan"
+        target="_blank"
+        rel="noopener noreferrer"
+        aria-label="nf-core/deepmutscan"
+        style="display: inline-block; line-height: 0"
+        ><img
+          class="logo"
+          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"
+          alt="nf-core/deepmutscan"
+      /></a>
+      <span class="title">variant effect inspection tool</span>
+      <span class="spacer"></span>
+      <span class="sub" id="sample-tag"></span>
+    </header>
+
+    <div class="layout">
+      <aside class="sidebar">
+        <div class="group">
+          <h3>Display</h3>
+          <label class="field"
+            ><span>Color residues by</span>
+            <select id="metric"></select
+          ></label>
+          <label class="field"
+            ><span>Representation</span>
+            <select id="rep">
+              <option value="cartoon">Cartoon</option>
+              <option value="surface">Surface</option>
+              <option value="stick">Sticks</option>
+              <option value="sphere">Spheres</option>
+              <option value="line">Lines</option>
+            </select></label
+          >
+          <div class="btnrow">
+            <button id="spin">Spin</button>
+            <button id="reset">Reset view</button>
+            <button id="clearsel">Clear selection</button>
+          </div>
+        </div>
+        <div class="group" id="repgroup" style="display: none">
+          <h3>
+            Replicates
+            <button
+              class="ibtn"
+              type="button"
+              data-desc="Include or exclude individual sequencing replicates to see how much each one drives the fitness estimate. Mean fitness and its SD are recomputed over the selected replicates only. This is exact, not an approximation: each replicate's fitness is derived from its own input/output counts and its own synonymous/stop anchors, so its value does not depend on which other replicates were in the run. The colour scale stays fixed so values remain comparable across selections. DiMSum and mutscan fit their own models across replicates and are not affected by this control."
+            >
+              i
+            </button>
+          </h3>
+          <div class="reps" id="reps"></div>
+          <div class="hint" id="rephint"></div>
+        </div>
+        <div class="group">
+          <h3>Legend</h3>
+          <div class="legend">
+            <div class="bar" id="legbar"></div>
+            <div class="ticks"><span id="legmin"></span><span id="legmid"></span><span id="legmax"></span></div>
+            <div class="desc" id="legdesc"></div>
+            <div class="na"><span class="swatch" style="background: var(--na)"></span> no data / unmapped residue</div>
+          </div>
+        </div>
+        <div class="group">
+          <h3>Export</h3>
+          <div class="btnrow">
+            <button class="primary" id="png">Download high-res PNG</button>
+            <button id="pymol">Download PyMOL (.pml)</button>
+          </div>
+          <div class="hint">
+            PNG renders the current 3D view at 2400&nbsp;px on a white background. The PyMOL script reproduces the
+            current coloring on the structure.
+          </div>
+        </div>
+      </aside>
+
+      <div id="stage"><div id="viewer"></div></div>
+
+      <aside class="detail" id="detail">
+        <div class="empty" id="detail-empty">
+          Click a residue — in the structure or the sequence below — to see every substitution's effect at that
+          position. Click several residues to compare; click a selected one again to deselect.
+        </div>
+        <div id="detail-body" style="display: none"></div>
+      </aside>
+    </div>
+
+    <div class="seqwrap">
+      <div class="ruler" id="ruler"></div>
+      <div class="seq" id="seq"></div>
+    </div>
+
+    <script>
+      /*__THREEDMOL_JS__*/
+    </script>
+    <script>
+      const DATA = /*__DATA_JSON__*/;
+      const PDB  = "__PDB_TEXT__";
+      const GREY = 0xdddddd;
+      const AA = "ACDEFGHIKLMNPQRSTVWY";
+      const METRIC_KEYS = Object.keys(DATA.metrics);
+
+      /* ---------- colour scales ---------- */
+      function lerp(a,b,t){return a+(b-a)*t}
+      function hex2rgb(h){return [(h>>16)&255,(h>>8)&255,h&255]}
+      function rgb2int(r,g,b){return (Math.round(r)<<16)|(Math.round(g)<<8)|Math.round(b)}
+      function rampColor(stops,t){
+        t=Math.max(0,Math.min(1,t));
+        const n=stops.length-1, x=t*n, i=Math.min(n-1,Math.floor(x)), f=x-i;
+        const a=hex2rgb(stops[i]), b=hex2rgb(stops[i+1]);
+        return rgb2int(lerp(a[0],b[0],f),lerp(a[1],b[1],f),lerp(a[2],b[2],f));
+      }
+      const VIRIDIS=[0x440154,0x414487,0x2a788e,0x22a884,0x7ad151,0xfde725];
+      const COOLWARM=[0x3a4cc0,0x6f8fe8,0xc3cfe2,0xf2f2f2,0xf2c0a4,0xe26a52,0xb40426];
+      function plddtColor(v){ if(v>=90)return 0x0053d6; if(v>=70)return 0x65cbf3; if(v>=50)return 0xffdb13; return 0xff7d45; }
+      function metricColor(metric,v){
+        if(v===null||v===undefined)return GREY;
+        const m=DATA.metrics[metric], lo=m.range[0], hi=m.range[1];
+        if(m.type==='plddt')return plddtColor(v);
+        let t=(v-lo)/((hi-lo)||1);
+        if(m.reverse) t=1-t;                       // e.g. fitness: low -> red, high -> blue
+        return m.type==='diverging'?rampColor(COOLWARM,t):rampColor(VIRIDIS,t);
+      }
+      function cssColor(intc){return '#'+intc.toString(16).padStart(6,'0')}
+      function textOn(intc){ const c=hex2rgb(intc); return (0.299*c[0]+0.587*c[1]+0.114*c[2])/255 < 0.55 ? '#fff' : '#11151f'; }
+
+      /* ---------- state ---------- */
+      let METRIC=METRIC_KEYS[0];
+      let REP='cartoon';
+      const selected=new Set();      // positions currently selected in 3D
+      let activePos=null;            // residue whose detail panel is shown
+      let activeVariant=null;        // {pos,mut} whose bee-swarm plots are shown
+      let resiColor={};              // structure resnum -> int colour
+      let initialView=null;
+      const byResnum={}, byPos={};
+      DATA.residues.forEach(r=>{byPos[r.pos]=r; if(r.resnum!=null)byResnum[r.resnum]=r;});
+
+      /* ---------- replicate selection ----------
+         The default fitness estimation never pools replicates: rescaled_fitness_rep<N> comes from
+         that replicate's own counts and its own synonymous/stop anchors, and `mean fitness`/`fitness sd`
+         are just the row-wise mean/SD across those columns. So recomputing them over a subset here is
+         exactly what a pipeline rerun without the excluded replicates would produce. */
+      const REPS=DATA.replicates||[];
+      const selReps=new Set(REPS.map((_,i)=>i));
+
+      // mirrors R's mean(x, na.rm=TRUE) / sd(x, na.rm=TRUE): NA dropped, sample SD, SD undefined for n<2
+      function meanSd(vals){
+        const v=vals.filter(x=>x!=null&&!isNaN(x));
+        if(!v.length) return {mean:null, sd:null};
+        const m=v.reduce((a,b)=>a+b,0)/v.length;
+        if(v.length<2) return {mean:m, sd:null};
+        const s=Math.sqrt(v.reduce((a,b)=>a+(b-m)*(b-m),0)/(v.length-1));
+        return {mean:m, sd:s};
+      }
+
+      // re-derive every fitness-dependent metric for the current replicate subset
+      function recomputeFitness(){
+        if(!REPS.length) return;
+        const sel=[...selReps];
+        DATA.residues.forEach(r=>{
+          const fits=[], sds=[];
+          r.variants.forEach(v=>{
+            if(!v.reps) return;
+            const ms=meanSd(sel.map(i=>v.reps[i]));
+            if('fitness' in v.metrics) v.metrics.fitness=ms.mean;
+            if('fitness_sd' in v.metrics) v.metrics.fitness_sd=ms.sd;
+            if(!v.syn && !v.stop && ms.mean!=null){ fits.push(ms.mean); if(ms.sd!=null) sds.push(ms.sd); }
+          });
+          const mean=a=>a.length?a.reduce((x,y)=>x+y,0)/a.length:null;
+          if('fitness' in r.metrics) r.metrics.fitness=mean(fits);
+          if('fitness_sd' in r.metrics) r.metrics.fitness_sd=mean(sds);
+          if('n_substitutions' in r.metrics) r.metrics.n_substitutions=fits.length||null;
+        });
+      }
+
+      function renderReps(){
+        if(REPS.length<2) return;                       // nothing to choose from
+        const host=document.getElementById('reps');
+        host.innerHTML=REPS.map((id,i)=>
+          `<label><input type="checkbox" data-i="${i}"${selReps.has(i)?' checked':''}>replicate ${id}</label>`
+        ).join('');
+        const n=selReps.size;
+        document.getElementById('rephint').textContent =
+          n===0 ? 'no replicates selected — fitness has no data, the structure is grey'
+          : n===REPS.length ? `all ${n} replicates — mean fitness as computed by the pipeline`
+          : n===1 ? 'single replicate — SD is undefined and shown as no data'
+          : `${n} of ${REPS.length} replicates — mean and SD recomputed`;
+      }
+
+      function buildColorMap(){
+        resiColor={};
+        DATA.residues.forEach(r=>{ if(r.resnum!=null) resiColor[r.resnum]=metricColor(METRIC,r.metrics[METRIC]); });
+      }
+
+      /* ---------- 3Dmol ---------- */
+      const viewer=$3Dmol.createViewer(document.getElementById('viewer'),{backgroundColor:'white'});
+      viewer.addModel(PDB,'pdb');
+      const colorfunc=atom=>(atom.resi in resiColor?resiColor[atom.resi]:GREY);
+
+      function applyStyle(){
+        buildColorMap();
+        viewer.removeAllSurfaces();
+        viewer.removeAllLabels();
+        viewer.setStyle({},{});
+        if(REP==='surface'){
+          // 3Dmol's click picker only tests sphere/stick/line geometry, never the surface mesh — so a lone
+          // surface is not clickable. Render small clickable spheres at the atom centres (hidden inside the
+          // opaque surface): a click passes through the non-pickable surface and hits the sphere behind it.
+          viewer.setStyle({},{sphere:{colorfunc:colorfunc,radius:1.2}});
+          viewer.addSurface($3Dmol.SurfaceType.VDW,{colorfunc:colorfunc,opacity:1.0});
+        }else{
+          const s={};
+          if(REP==='cartoon')s.cartoon={colorfunc:colorfunc};
+          else if(REP==='stick')s.stick={colorfunc:colorfunc,radius:0.22};
+          else if(REP==='sphere')s.sphere={colorfunc:colorfunc,scale:0.32};
+          else s.line={colorfunc:colorfunc};
+          viewer.setStyle({},s);
+        }
+        // highlight every selected residue: sticks + a label
+        selected.forEach(pos=>{
+          const r=byPos[pos]; if(!r||r.resnum==null)return;
+          viewer.addStyle({resi:r.resnum},{stick:{color:0x111827,radius:0.34}});
+          viewer.addResLabels({resi:r.resnum},{font:'sans-serif',fontSize:12,fontColor:'white',
+              backgroundColor:0x111827,backgroundOpacity:0.85,showBackground:true});
+        });
+        // Re-assert atom clickability after (re)building the representation. 3Dmol surface clicks route
+        // through the underlying atoms, so doing this here makes click-select work in every representation
+        // (cartoon, sticks, spheres, lines AND surface) — not only via the sequence track.
+        viewer.setClickable({}, true, onAtomClick);
+        viewer.render();
+      }
+      function onAtomClick(atom){ if(atom && atom.resi in byResnum) toggleResidue(byResnum[atom.resi].pos); }
+
+      /* ---------- legend ---------- */
+      function renderLegend(){
+        const m=DATA.metrics[METRIC], bar=document.getElementById('legbar');
+        let css;
+        if(m.type==='plddt'){
+          css='linear-gradient(90deg,#ff7d45 0%,#ff7d45 25%,#ffdb13 25%,#ffdb13 50%,#65cbf3 50%,#65cbf3 75%,#0053d6 75%)';
+          document.getElementById('legmin').textContent='0';
+          document.getElementById('legmid').textContent='50 · 70 · 90';
+          document.getElementById('legmax').textContent='100';
+        }else{
+          const stops=[]; for(let i=0;i<=10;i++)stops.push(cssColor(metricColor(METRIC,m.range[0]+(m.range[1]-m.range[0])*i/10)));
+          css='linear-gradient(90deg,'+stops.join(',')+')';
+          document.getElementById('legmin').textContent=nfmt(m.range[0]);
+          document.getElementById('legmid').textContent=nfmt((m.range[0]+m.range[1])/2);
+          document.getElementById('legmax').textContent=nfmt(m.range[1]);
+        }
+        bar.style.background=css;
+        document.getElementById('legdesc').textContent=m.desc;
+      }
+      function nfmt(x){return Math.abs(x)>=1000?x.toExponential(1):(Math.round(x*100)/100);}
+
+      /* ---------- sequence track ---------- */
+      function renderSequence(){
+        const seq=document.getElementById('seq'), ruler=document.getElementById('ruler');
+        seq.innerHTML=''; ruler.innerHTML='';
+        DATA.residues.forEach((r,i)=>{
+          const el=document.createElement('span');
+          el.className='res'+(r.resnum==null?' unmapped':'')+(selected.has(r.pos)?' sel':'');
+          el.textContent=r.wt; el.dataset.pos=r.pos;
+          const bg=metricColor(METRIC,r.metrics[METRIC]);
+          el.style.background=cssColor(bg);
+          el.style.color=textOn(bg);              // white letters on dark cells for legibility
+          el.title='pos '+r.pos+' ('+r.wt+')';
+          el.onclick=()=>toggleResidue(r.pos);
+          seq.appendChild(el);
+          if(r.pos%10===0){const t=document.createElement('span');t.textContent=r.pos;t.style.left=(i*13+6.5)+'px';ruler.appendChild(t);}
+        });
+      }
+      function markSequence(){
+        document.querySelectorAll('.seq .res').forEach(e=>e.classList.toggle('sel',selected.has(+e.dataset.pos)));
+      }
+
+      /* ---------- selection / camera ---------- */
+      function toggleResidue(pos){
+        const wasEmpty=selected.size===0;
+        if(selected.has(pos)){
+          // deselect — keep the current camera (do NOT reset the view here)
+          selected.delete(pos);
+          if(activePos===pos){ activePos = selected.size ? [...selected][selected.size-1] : null; activeVariant=null; }
+        }else{
+          selected.add(pos); activePos=pos; activeVariant=null;
+          const r=byPos[pos];
+          if(r&&r.resnum!=null){ viewer.center({resi:r.resnum},400); if(wasEmpty) viewer.zoom(1.15,400); }
+        }
+        markSequence();
+        applyStyle();
+        renderDetail();
+      }
+
+      /* ---------- residue detail ---------- */
+      function fmt(x,d=2){return x===null||x===undefined?'—':(Math.abs(x)>=1000?x.toExponential(1):(Math.round(x*Math.pow(10,d))/Math.pow(10,d)));}
+      function renderDetail(){
+        const empty=document.getElementById('detail-empty'), body=document.getElementById('detail-body');
+        if(activePos===null){ empty.style.display='block'; body.style.display='none'; return; }
+        empty.style.display='none'; body.style.display='block';
+        const r=byPos[activePos], fr=DATA.metrics.fitness.range;
+        let h='<h2>Position '+r.pos+' · '+r.wt+'</h2>';
+        h+='<div class="pos-sub">'+(r.resnum!=null?'structure residue '+r.resnum:'not mapped onto structure')
+           +(selected.size>1?' · '+selected.size+' residues selected':'')+'</div>';
+        h+='<div class="metrics">';
+        for(const k of METRIC_KEYS) h+='<div class="k">'+DATA.metrics[k].label+'</div><div class="v">'+fmt(r.metrics[k])+'</div>';
+        h+='</div>';
+        h+='<div class="subhead">Substitutions — click one for its distribution</div>';
+        if(!r.variants.length) h+='<div class="empty">No measured substitutions at this position.</div>';
+        r.variants.forEach(v=>{
+          const f=v.metrics?v.metrics.fitness:v.fitness, has=f!==null&&f!==undefined;
+          const t=has?Math.max(0,Math.min(1,(f-fr[0])/((fr[1]-fr[0])||1))):0;
+          const col=has?cssColor(metricColor('fitness',f)):'var(--na)';   // respects the fitness colour flip
+          const wpct=has?Math.max(4,t*100):0;
+          const isSel=activeVariant&&activeVariant.pos===r.pos&&activeVariant.mut===v.mut;
+          const lab=v.mut+(v.stop?' (stop)':v.syn?' (syn)':'');
+          h+='<div class="vbar'+(isSel?' sel':'')+'" data-mut="'+v.mut+'">'
+            +'<div class="aa">'+v.mut+'</div>'
+            +'<div class="track"><div class="fill" style="left:0;width:'+wpct+'%;background:'+col+'"></div></div>'
+            +'<div class="num" title="'+lab+'">'+fmt(f)+'</div></div>';
+        });
+        h+='<div id="swarm"></div>';
+        body.innerHTML=h;
+        body.querySelectorAll('.vbar').forEach(el=>{
+          el.onclick=()=>{ const mut=el.dataset.mut;
+            activeVariant=(activeVariant&&activeVariant.pos===r.pos&&activeVariant.mut===mut)?null:{pos:r.pos,mut:mut};
+            renderDetail();
+          };
+        });
+        if(activeVariant&&activeVariant.pos===r.pos) renderSwarm();
+      }
+
+      /* ---------- bee-swarm plots ---------- */
+      function jitter(pos,mutIdx){ const h=((pos*131+mutIdx*97)%211)/211; return h-0.5; }
+      function variantValue(metric,r,v){
+        // variant-scope metrics (e.g. fitness) have a per-substitution value; residue-scope metrics
+        // (coverage, pLDDT, correlation, ...) are per-position and shared by all substitutions there.
+        return DATA.metrics[metric].scope==='variant' ? (v.metrics? v.metrics[metric] : null) : r.metrics[metric];
+      }
+      function renderSwarm(){
+        const host=document.getElementById('swarm'); if(!host)return;
+        const av=activeVariant;
+        let h='<div class="swarm"><div class="shead"><b>'+av.mut+'&thinsp;@&thinsp;'+av.pos
+              +'</b><span class="hint" style="margin:0">one dot per variant</span></div>';
+        for(const k of METRIC_KEYS){
+          const info = DATA.metrics[k].desc
+            ? ' <button class="ibtn" data-desc="'+escAttr(DATA.metrics[k].desc)+'" aria-label="What is '+escAttr(DATA.metrics[k].label)+'?">i</button>'
+            : '';
+          h+='<div class="plot" data-metric="'+k+'"><div class="pt"><span>'+DATA.metrics[k].label+info
+            +'</span><span class="val" data-role="val"></span></div>'
+            +'<canvas></canvas><div class="ax"><span data-role="lo"></span><span data-role="hi"></span></div></div>';
+        }
+        h+='</div>';
+        host.innerHTML=h;
+        host.querySelectorAll('.plot').forEach(p=>drawSwarm(p, p.dataset.metric, av));
+      }
+      function drawSwarm(plotEl, metric, av){
+        const m=DATA.metrics[metric], lo=m.range[0], hi=m.range[1];
+        // population: one point per variant across all positions that has a value for this metric.
+        // Each point keeps its own value (x) and a stable per-substitution jitter (y), so the
+        // highlighted variant is shown at its TRUE location — not forced to the middle.
+        const pts=[]; let sel=null;
+        DATA.residues.forEach(r=>{
+          r.variants.forEach(v=>{
+            const val=variantValue(metric,r,v);
+            if(val===null||val===undefined)return;
+            const p={val, jit:jitter(r.pos, AA.indexOf(v.mut)), sel:(r.pos===av.pos&&v.mut===av.mut)};
+            pts.push(p); if(p.sel) sel=p;
+          });
+        });
+        const cv=plotEl.querySelector('canvas'), dpr=window.devicePixelRatio||1;
+        const cssW=Math.max(220, plotEl.clientWidth||280), cssH=96;
+        cv.width=Math.round(cssW*dpr); cv.height=Math.round(cssH*dpr);
+        const ctx=cv.getContext('2d'); ctx.scale(dpr,dpr);
+        const padX=12, padY=12, W=cssW, H=cssH, plotW=W-2*padX, midY=H/2, band=(H-2*padY)/2;
+        const xOf=val=>padX+plotW*Math.max(0,Math.min(1,(val-lo)/((hi-lo)||1)));
+        const yOf=jit=>midY+jit*2*band;
+        // population dots
+        for(const p of pts){ if(p.sel)continue;
+          ctx.beginPath(); ctx.arc(xOf(p.val), yOf(p.jit), 2.0, 0, 6.2832);
+          ctx.fillStyle=cssColor(metricColor(metric,p.val)); ctx.globalAlpha=0.4; ctx.fill();
+        }
+        ctx.globalAlpha=1;
+        // selected variant: highlight its actual data point (real x AND real jittered y)
+        if(sel){
+          const x=xOf(sel.val), y=yOf(sel.jit);
+          ctx.strokeStyle='rgba(17,24,39,.22)'; ctx.lineWidth=1;
+          ctx.beginPath(); ctx.moveTo(x,padY-4); ctx.lineTo(x,H-padY+4); ctx.stroke();
+          ctx.beginPath(); ctx.arc(x, y, 6.5, 0, 6.2832);
+          ctx.fillStyle=cssColor(metricColor(metric,sel.val)); ctx.fill();
+          ctx.lineWidth=3; ctx.strokeStyle='#fff'; ctx.stroke();
+          ctx.lineWidth=1.5; ctx.strokeStyle='#111827'; ctx.stroke();
+        }
+        plotEl.querySelector('[data-role=lo]').textContent=nfmt(lo);
+        plotEl.querySelector('[data-role=hi]').textContent=nfmt(hi);
+        plotEl.querySelector('[data-role=val]').textContent=sel?nfmt(sel.val):'no value';
+      }
+
+      /* ---------- controls ---------- */
+      function initMetricSelect(){
+        const sel=document.getElementById('metric');
+        for(const k of METRIC_KEYS){ const o=document.createElement('option');o.value=k;o.textContent=DATA.metrics[k].label;sel.appendChild(o); }
+        sel.value=METRIC;
+        sel.onchange=()=>{METRIC=sel.value;refresh();};
+      }
+      function refresh(){renderLegend();renderSequence();applyStyle();}
+      let spinning=false;
+      document.getElementById('rep').onchange=e=>{REP=e.target.value;applyStyle();};
+      document.getElementById('spin').onclick=e=>{spinning=!spinning;viewer.spin(spinning?'y':false);e.target.classList.toggle('on',spinning);};
+      document.getElementById('reset').onclick=()=>{ if(initialView){viewer.setView(initialView);viewer.render();} };
+      document.getElementById('clearsel').onclick=()=>{ selected.clear(); activePos=null; activeVariant=null;
+        markSequence(); applyStyle(); renderDetail(); };  // clears selection but keeps the camera
+      document.getElementById('png').onclick=downloadPNG;
+      document.getElementById('pymol').onclick=downloadPymol;
+
+      function downloadPNG(){
+        const el=document.getElementById('viewer');
+        const ow=el.style.width, oh=el.style.height, op=el.style.position, ol=el.style.left;
+        el.style.position='fixed'; el.style.left='-99999px'; el.style.width='2400px'; el.style.height='1800px';
+        viewer.resize(); viewer.render();
+        const uri=viewer.pngURI();
+        el.style.position=op; el.style.left=ol; el.style.width=ow; el.style.height=oh;
+        viewer.resize(); viewer.render();
+        const a=document.createElement('a'); a.href=uri;
+        a.download='deepmutscan_'+DATA.sample+'_'+METRIC+'.png'; document.body.appendChild(a); a.click(); a.remove();
+      }
+      function downloadPymol(){
+        buildColorMap();
+        const obj=(DATA.sample||'wildtype').replace(/[^A-Za-z0-9_]/g,'_');
+        let py='from pymol import cmd\n';
+        py+='cmd.read_pdbstr("""'+PDB.replace(/"""/g,'')+'""", "'+obj+'")\n';
+        py+='cmd.hide("everything")\ncmd.show("cartoon","'+obj+'")\ncmd.bg_color("white")\n';
+        DATA.residues.forEach(r=>{ if(r.resnum==null)return;
+          const c=hex2rgb((r.resnum in resiColor)?resiColor[r.resnum]:GREY);
+          const nm='dm_'+METRIC+'_'+r.resnum;
+          py+='cmd.set_color("'+nm+'",['+(c[0]/255).toFixed(3)+','+(c[1]/255).toFixed(3)+','+(c[2]/255).toFixed(3)+'])\n';
+          py+='cmd.color("'+nm+'","'+obj+' and resi '+r.resnum+'")\n';
+        });
+        const pml='# nf-core/deepmutscan structure viewer — coloring: '+DATA.metrics[METRIC].label
+          +'\n# Run in PyMOL:  File > Run Script...  (or:  @this_file.pml )\npython\n'+py+'python end\ncmd.orient()\n';
+        const blob=new Blob([pml],{type:'text/plain'});
+        const a=document.createElement('a'); a.href=URL.createObjectURL(blob);
+        a.download='deepmutscan_'+obj+'_'+METRIC+'.pml'; document.body.appendChild(a); a.click(); a.remove();
+        URL.revokeObjectURL(a.href);
+      }
+
+      /* ---------- replicate toggles ---------- */
+      if(REPS.length>1){
+        document.getElementById('repgroup').style.display='';
+        document.getElementById('reps').addEventListener('change',e=>{
+          const cb=e.target.closest('input[type=checkbox]'); if(!cb)return;
+          const i=+cb.dataset.i;
+          if(cb.checked) selReps.add(i); else selReps.delete(i);
+          // an empty selection is a legitimate state: no replicates -> no fitness -> everything grey (NA)
+          recomputeFitness();
+          renderReps();
+          refresh();                                   // legend + sequence + structure colouring
+          renderDetail();                              // detail panel numbers
+          if(activeVariant) renderSwarm();             // bee-swarms
+        });
+        renderReps();
+      }
+
+      /* ---------- boot ---------- */
+      document.getElementById('sample-tag').innerHTML='sample <strong>'+DATA.sample+'</strong> · '+DATA.residues.length+' residues';
+      initMetricSelect();
+      renderLegend(); renderSequence();
+      viewer.zoomTo(); applyStyle();
+      initialView=viewer.getView();
+      renderDetail();
+      window.addEventListener('resize',()=>{viewer.resize();viewer.render(); if(activeVariant)renderSwarm();});
+
+      /* ---------- bee-swarm info-button popovers ---------- */
+      function escAttr(s){return String(s).replace(/&/g,'&amp;').replace(/"/g,'&quot;').replace(/</g,'&lt;');}
+      document.addEventListener('click',e=>{
+        const prev=document.querySelector('.ipop'); if(prev)prev.remove();
+        const b=e.target.closest('.ibtn'); if(!b)return;
+        e.preventDefault(); e.stopPropagation();
+        const pop=document.createElement('div'); pop.className='ipop'; pop.textContent=b.getAttribute('data-desc');
+        document.body.appendChild(pop);
+        const r=b.getBoundingClientRect();
+        pop.style.left=Math.max(8,Math.min(r.left,innerWidth-pop.offsetWidth-8))+'px';
+        pop.style.top=(r.bottom+6+pop.offsetHeight>innerHeight ? r.top-pop.offsetHeight-6 : r.bottom+6)+'px';
+      });
+    </script>
+  </body>
+</html>
diff --git a/conf/base.config b/conf/base.config
index 9aa66a0..98efd62 100644
--- a/conf/base.config
+++ b/conf/base.config
@@ -1,6 +1,6 @@
 /*
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-    nf-core/dmscore Nextflow base config file
+    nf-core/deepmutscan Nextflow base config file
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
     A 'blank slate' config file, appropriate for general use on most high performance
     compute environments. Assumes that all software is installed and available on
@@ -9,13 +9,11 @@
 */
 
 process {
-
-    // TODO nf-core: Check the defaults for all processes
     cpus   = { 1      * task.attempt }
     memory = { 6.GB   * task.attempt }
     time   = { 4.h    * task.attempt }
 
-    errorStrategy = { task.exitStatus in ((130..145) + 104) ? 'retry' : 'finish' }
+    errorStrategy = { task.exitStatus in ((130..145) + 104 + (175..177)) ? 'retry' : 'finish' }
     maxRetries    = 1
     maxErrors     = '-1'
 
@@ -24,7 +22,6 @@ process {
     //        These labels are used and recognised by default in DSL2 files hosted on nf-core/modules.
     //        If possible, it would be nice to keep the same label naming convention when
     //        adding in your local modules too.
-    // TODO nf-core: Customise requirements for specific processes.
     // See https://www.nextflow.io/docs/latest/config.html#config-process-selectors
     withLabel:process_single {
         cpus   = { 1                   }
@@ -49,6 +46,9 @@ process {
     withLabel:process_long {
         time   = { 20.h  * task.attempt }
     }
+    withLabel:process_low_memory {
+        memory = { 1.GB * task.attempt }
+    }
     withLabel:process_high_memory {
         memory = { 200.GB * task.attempt }
     }
@@ -59,4 +59,7 @@ process {
         errorStrategy = 'retry'
         maxRetries    = 2
     }
+    withLabel: process_gpu {
+        accelerator = { workflow.profile.contains('gpu') ? 1 : null }
+    }
 }
diff --git a/conf/containers_conda_lock_files_amd64.config b/conf/containers_conda_lock_files_amd64.config
new file mode 100644
index 0000000..c6847cf
--- /dev/null
+++ b/conf/containers_conda_lock_files_amd64.config
@@ -0,0 +1,28 @@
+process { withName: 'COUNTS_TO_FITNESS' { conda = 'modules/local/fitness/counts_to_fitness/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt' } }
+process { withName: 'DMSANALYSIS_AASEQ' { conda = 'modules/local/dmsanalysis/aa_seq/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt' } }
+process { withName: 'DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES' { conda = 'modules/local/dmsanalysis/error_correction_false_doubles/.conda-lock/linux_amd64-bd-801bac9c074a1f9b_1.txt' } }
+process { withName: 'DMSANALYSIS_ERROR_CORRECTION_WILDTYPE' { conda = 'modules/local/dmsanalysis/error_correction_wildtype/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt' } }
+process { withName: 'DMSANALYSIS_POSSIBLE_MUTATIONS' { conda = 'modules/local/dmsanalysis/possible_mutations/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt' } }
+process { withName: 'DMSANALYSIS_PROCESS_VARIANT_COUNTS' { conda = 'modules/local/dmsanalysis/process_variant_counts/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt' } }
+process { withName: 'EXPDESIGN_FITNESS' { conda = 'modules/local/fitness/fitness_experimental_design/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt' } }
+process { withName: 'FASTQC' { conda = 'modules/nf-core/fastqc/.conda-lock/linux_amd64-bd-b342db7b694c9af4_1.txt' } }
+process { withName: 'FASTQ_CHECK' { conda = 'modules/local/fastq_check/.conda-lock/linux_amd64-bd-b3082ff93d0ccff0_1.txt' } }
+process { withName: 'FIND_SYNONYMOUS_MUTATION' { conda = 'modules/local/fitness/find_synonymous_mutation/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt' } }
+process { withName: 'FITNESS_CALCULATION' { conda = 'modules/local/fitness/fitness_calculation/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt' } }
+process { withName: 'FITNESS_HEATMAP' { conda = 'modules/local/fitness/fitness_heatmap/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt' } }
+process { withName: 'FITNESS_QC' { conda = 'modules/local/fitness/fitness_QC/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt' } }
+process { withName: 'MERGE_ALIGN' { conda = 'modules/local/bamprocessing/merge_align/.conda-lock/linux_amd64-bd-68780a2d1eaef64d_1.txt' } }
+process { withName: 'MERGE_COUNTS' { conda = 'modules/local/fitness/merge_counts/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt' } }
+process { withName: 'MULTIQC' { conda = 'modules/nf-core/multiqc/.conda-lock/linux_amd64-bd-c17fb751507e9dfc_1.txt' } }
+process { withName: 'RUN_DIMSUM' { conda = 'modules/local/fitness/run_dimsum/.conda-lock/linux_amd64-bd-833765ab7b2cf9aa_1.txt' } }
+process { withName: 'RUN_MUTSCAN' { conda = 'modules/local/fitness/run_mutscan/.conda-lock/linux_amd64-bd-c5cefc95c9f1002e_1.txt' } }
+process { withName: 'VARIANTCOUNTING' { conda = 'modules/local/variantcounting/.conda-lock/linux_amd64-bd-f223db6b9aa476d1_1.txt' } }
+process { withName: 'VARIANT_EFFECT_INSPECTION_TOOL' { conda = 'modules/local/structure/variant_effect_inspection_tool/.conda-lock/linux_amd64-bd-cfb38dbf0574e16d_1.txt' } }
+process { withName: 'VISUALIZATION_COUNTS_HEATMAP' { conda = 'modules/local/visualization/counts_heatmap/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt' } }
+process { withName: 'VISUALIZATION_COUNTS_PER_COV' { conda = 'modules/local/visualization/counts_per_cov/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt' } }
+process { withName: 'VISUALIZATION_ERROR_CORRECTION_REPORT' { conda = 'modules/local/visualization/error_correction_report/.conda-lock/linux_amd64-bd-cfb38dbf0574e16d_1.txt' } }
+process { withName: 'VISUALIZATION_GLOBAL_POS_BIASES_COUNTS' { conda = 'modules/local/visualization/global_pos_biases_counts/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt' } }
+process { withName: 'VISUALIZATION_GLOBAL_POS_BIASES_COV' { conda = 'modules/local/visualization/global_pos_biases_cov/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt' } }
+process { withName: 'VISUALIZATION_LOGDIFF' { conda = 'modules/local/visualization/logdiff/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt' } }
+process { withName: 'VISUALIZATION_SEQDEPTH' { conda = 'modules/local/visualization/seqdepth/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt' } }
+process { withName: 'VISUALIZATION_SUMMARY_REPORT' { conda = 'modules/local/visualization/summary_report/.conda-lock/linux_amd64-bd-cfb38dbf0574e16d_1.txt' } }
diff --git a/conf/containers_conda_lock_files_arm64.config b/conf/containers_conda_lock_files_arm64.config
new file mode 100644
index 0000000..a7fdf07
--- /dev/null
+++ b/conf/containers_conda_lock_files_arm64.config
@@ -0,0 +1,27 @@
+process { withName: 'COUNTS_TO_FITNESS' { conda = 'modules/local/fitness/counts_to_fitness/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt' } }
+process { withName: 'DMSANALYSIS_AASEQ' { conda = 'modules/local/dmsanalysis/aa_seq/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt' } }
+process { withName: 'DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES' { conda = 'modules/local/dmsanalysis/error_correction_false_doubles/.conda-lock/linux_arm64-bd-fbcc673012c62cb0_1.txt' } }
+process { withName: 'DMSANALYSIS_ERROR_CORRECTION_WILDTYPE' { conda = 'modules/local/dmsanalysis/error_correction_wildtype/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt' } }
+process { withName: 'DMSANALYSIS_POSSIBLE_MUTATIONS' { conda = 'modules/local/dmsanalysis/possible_mutations/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt' } }
+process { withName: 'DMSANALYSIS_PROCESS_VARIANT_COUNTS' { conda = 'modules/local/dmsanalysis/process_variant_counts/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt' } }
+process { withName: 'EXPDESIGN_FITNESS' { conda = 'modules/local/fitness/fitness_experimental_design/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt' } }
+process { withName: 'FASTQC' { conda = 'modules/nf-core/fastqc/.conda-lock/linux_arm64-bd-7c8a53b986d9a000_1.txt' } }
+process { withName: 'FASTQ_CHECK' { conda = 'modules/local/fastq_check/.conda-lock/linux_arm64-bd-41c3b62452310f82_1.txt' } }
+process { withName: 'FIND_SYNONYMOUS_MUTATION' { conda = 'modules/local/fitness/find_synonymous_mutation/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt' } }
+process { withName: 'FITNESS_CALCULATION' { conda = 'modules/local/fitness/fitness_calculation/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt' } }
+process { withName: 'FITNESS_HEATMAP' { conda = 'modules/local/fitness/fitness_heatmap/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt' } }
+process { withName: 'FITNESS_QC' { conda = 'modules/local/fitness/fitness_QC/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt' } }
+process { withName: 'MERGE_ALIGN' { conda = 'modules/local/bamprocessing/merge_align/.conda-lock/linux_arm64-bd-7b395b04348e2784_1.txt' } }
+process { withName: 'MERGE_COUNTS' { conda = 'modules/local/fitness/merge_counts/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt' } }
+process { withName: 'MULTIQC' { conda = 'modules/nf-core/multiqc/.conda-lock/linux_arm64-bd-5c84a5000a226ab5_1.txt' } }
+process { withName: 'RUN_DIMSUM' { conda = 'modules/local/fitness/run_dimsum/.conda-lock/linux_arm64-bd-8a66e8e1354d6370_1.txt' } }
+process { withName: 'VARIANTCOUNTING' { conda = 'modules/local/variantcounting/.conda-lock/linux_arm64-bd-4cc0c5d536e48053_1.txt' } }
+process { withName: 'VARIANT_EFFECT_INSPECTION_TOOL' { conda = 'modules/local/structure/variant_effect_inspection_tool/.conda-lock/linux_arm64-bd-a15c6f547e2083b8_1.txt' } }
+process { withName: 'VISUALIZATION_COUNTS_HEATMAP' { conda = 'modules/local/visualization/counts_heatmap/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt' } }
+process { withName: 'VISUALIZATION_COUNTS_PER_COV' { conda = 'modules/local/visualization/counts_per_cov/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt' } }
+process { withName: 'VISUALIZATION_ERROR_CORRECTION_REPORT' { conda = 'modules/local/visualization/error_correction_report/.conda-lock/linux_arm64-bd-a15c6f547e2083b8_1.txt' } }
+process { withName: 'VISUALIZATION_GLOBAL_POS_BIASES_COUNTS' { conda = 'modules/local/visualization/global_pos_biases_counts/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt' } }
+process { withName: 'VISUALIZATION_GLOBAL_POS_BIASES_COV' { conda = 'modules/local/visualization/global_pos_biases_cov/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt' } }
+process { withName: 'VISUALIZATION_LOGDIFF' { conda = 'modules/local/visualization/logdiff/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt' } }
+process { withName: 'VISUALIZATION_SEQDEPTH' { conda = 'modules/local/visualization/seqdepth/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt' } }
+process { withName: 'VISUALIZATION_SUMMARY_REPORT' { conda = 'modules/local/visualization/summary_report/.conda-lock/linux_arm64-bd-a15c6f547e2083b8_1.txt' } }
diff --git a/conf/containers_docker_amd64.config b/conf/containers_docker_amd64.config
new file mode 100644
index 0000000..a8416f6
--- /dev/null
+++ b/conf/containers_docker_amd64.config
@@ -0,0 +1,28 @@
+process { withName: 'COUNTS_TO_FITNESS' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7' } }
+process { withName: 'DMSANALYSIS_AASEQ' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7' } }
+process { withName: 'DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-stringr:801bac9c074a1f9b' } }
+process { withName: 'DMSANALYSIS_ERROR_CORRECTION_WILDTYPE' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7' } }
+process { withName: 'DMSANALYSIS_POSSIBLE_MUTATIONS' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7' } }
+process { withName: 'DMSANALYSIS_PROCESS_VARIANT_COUNTS' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7' } }
+process { withName: 'EXPDESIGN_FITNESS' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7' } }
+process { withName: 'FASTQC' { container = 'community.wave.seqera.io/library/fastqc:0.13.0--b342db7b694c9af4' } }
+process { withName: 'FASTQ_CHECK' { container = 'community.wave.seqera.io/library/fq:0.12.0--b3082ff93d0ccff0' } }
+process { withName: 'FIND_SYNONYMOUS_MUTATION' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7' } }
+process { withName: 'FITNESS_CALCULATION' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7' } }
+process { withName: 'FITNESS_HEATMAP' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7' } }
+process { withName: 'FITNESS_QC' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7' } }
+process { withName: 'MERGE_ALIGN' { container = 'community.wave.seqera.io/library/bwa_samtools_vsearch:68780a2d1eaef64d' } }
+process { withName: 'MERGE_COUNTS' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7' } }
+process { withName: 'MULTIQC' { container = 'community.wave.seqera.io/library/multiqc:1.35--c17fb751507e9dfc' } }
+process { withName: 'RUN_DIMSUM' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-dimsum_r-base_r-data.table_pruned:cca13eed371a9d84' } }
+process { withName: 'RUN_MUTSCAN' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base:c5cefc95c9f1002e' } }
+process { withName: 'VARIANTCOUNTING' { container = 'community.wave.seqera.io/library/pysam_biopython_numpy_polars-lts-cpu_pruned:f223db6b9aa476d1' } }
+process { withName: 'VARIANT_EFFECT_INSPECTION_TOOL' { container = 'community.wave.seqera.io/library/numpy_pandas_python:cfb38dbf0574e16d' } }
+process { withName: 'VISUALIZATION_COUNTS_HEATMAP' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7' } }
+process { withName: 'VISUALIZATION_COUNTS_PER_COV' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7' } }
+process { withName: 'VISUALIZATION_ERROR_CORRECTION_REPORT' { container = 'community.wave.seqera.io/library/numpy_pandas_python:cfb38dbf0574e16d' } }
+process { withName: 'VISUALIZATION_GLOBAL_POS_BIASES_COUNTS' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7' } }
+process { withName: 'VISUALIZATION_GLOBAL_POS_BIASES_COV' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7' } }
+process { withName: 'VISUALIZATION_LOGDIFF' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7' } }
+process { withName: 'VISUALIZATION_SEQDEPTH' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7' } }
+process { withName: 'VISUALIZATION_SUMMARY_REPORT' { container = 'community.wave.seqera.io/library/numpy_pandas_python:cfb38dbf0574e16d' } }
diff --git a/conf/containers_docker_arm64.config b/conf/containers_docker_arm64.config
new file mode 100644
index 0000000..8a8cc73
--- /dev/null
+++ b/conf/containers_docker_arm64.config
@@ -0,0 +1,26 @@
+process { withName: 'COUNTS_TO_FITNESS' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb' } }
+process { withName: 'DMSANALYSIS_AASEQ' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb' } }
+process { withName: 'DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-stringr:fbcc673012c62cb0' } }
+process { withName: 'DMSANALYSIS_ERROR_CORRECTION_WILDTYPE' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb' } }
+process { withName: 'DMSANALYSIS_POSSIBLE_MUTATIONS' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb' } }
+process { withName: 'DMSANALYSIS_PROCESS_VARIANT_COUNTS' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb' } }
+process { withName: 'EXPDESIGN_FITNESS' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb' } }
+process { withName: 'FASTQC' { container = 'community.wave.seqera.io/library/fastqc:0.13.0--7c8a53b986d9a000' } }
+process { withName: 'FASTQ_CHECK' { container = 'community.wave.seqera.io/library/fq:0.12.0--41c3b62452310f82' } }
+process { withName: 'FIND_SYNONYMOUS_MUTATION' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb' } }
+process { withName: 'FITNESS_CALCULATION' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb' } }
+process { withName: 'FITNESS_HEATMAP' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb' } }
+process { withName: 'FITNESS_QC' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb' } }
+process { withName: 'MERGE_ALIGN' { container = 'community.wave.seqera.io/library/bwa_samtools_vsearch:7b395b04348e2784' } }
+process { withName: 'MERGE_COUNTS' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb' } }
+process { withName: 'MULTIQC' { container = 'community.wave.seqera.io/library/multiqc:1.35--5c84a5000a226ab5' } }
+process { withName: 'VARIANTCOUNTING' { container = 'community.wave.seqera.io/library/pysam_biopython_numpy_polars-lts-cpu_pruned:4cc0c5d536e48053' } }
+process { withName: 'VARIANT_EFFECT_INSPECTION_TOOL' { container = 'community.wave.seqera.io/library/numpy_pandas_python:a15c6f547e2083b8' } }
+process { withName: 'VISUALIZATION_COUNTS_HEATMAP' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb' } }
+process { withName: 'VISUALIZATION_COUNTS_PER_COV' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb' } }
+process { withName: 'VISUALIZATION_ERROR_CORRECTION_REPORT' { container = 'community.wave.seqera.io/library/numpy_pandas_python:a15c6f547e2083b8' } }
+process { withName: 'VISUALIZATION_GLOBAL_POS_BIASES_COUNTS' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb' } }
+process { withName: 'VISUALIZATION_GLOBAL_POS_BIASES_COV' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb' } }
+process { withName: 'VISUALIZATION_LOGDIFF' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb' } }
+process { withName: 'VISUALIZATION_SEQDEPTH' { container = 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb' } }
+process { withName: 'VISUALIZATION_SUMMARY_REPORT' { container = 'community.wave.seqera.io/library/numpy_pandas_python:a15c6f547e2083b8' } }
diff --git a/conf/containers_singularity_https_amd64.config b/conf/containers_singularity_https_amd64.config
new file mode 100644
index 0000000..02a459b
--- /dev/null
+++ b/conf/containers_singularity_https_amd64.config
@@ -0,0 +1,28 @@
+process { withName: 'COUNTS_TO_FITNESS' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data' } }
+process { withName: 'DMSANALYSIS_AASEQ' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data' } }
+process { withName: 'DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/e5/e5a2e5419dff47f9dbcea8823692f60f9a1b1c1a1ee32ae56eef0ce2989f7b72/data' } }
+process { withName: 'DMSANALYSIS_ERROR_CORRECTION_WILDTYPE' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data' } }
+process { withName: 'DMSANALYSIS_POSSIBLE_MUTATIONS' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data' } }
+process { withName: 'DMSANALYSIS_PROCESS_VARIANT_COUNTS' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data' } }
+process { withName: 'EXPDESIGN_FITNESS' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data' } }
+process { withName: 'FASTQC' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/81/816bda6dd014e810753f642611232443ddeabb2b47f9681ad43f9044816f00ff/data' } }
+process { withName: 'FASTQ_CHECK' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/1e/1edbe701f591caa7f7a930781d0364392706d291016f1028b5b9fe501ec6ef8c/data' } }
+process { withName: 'FIND_SYNONYMOUS_MUTATION' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data' } }
+process { withName: 'FITNESS_CALCULATION' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data' } }
+process { withName: 'FITNESS_HEATMAP' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data' } }
+process { withName: 'FITNESS_QC' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data' } }
+process { withName: 'MERGE_ALIGN' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/1e/1e03db44377380bacb3a5ec57a7e77ad36603863b3f0e821d2268a47981572b1/data' } }
+process { withName: 'MERGE_COUNTS' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data' } }
+process { withName: 'MULTIQC' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/c8/c8e346f4f6080eadf1253505e6ff09ef004454fc18e8d672006fd7b222cc412e/data' } }
+process { withName: 'RUN_DIMSUM' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/92/9298c391f285d7f2c1155f6a519a96c4f7591971780ba4f10569558282f40b6f/data' } }
+process { withName: 'RUN_MUTSCAN' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/0a/0a32a5f637634c064a3caecc77d9e99f4367efd459bc28d1d4971ed5d05b73a7/data' } }
+process { withName: 'VARIANTCOUNTING' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/62/6247bcc77159c0adea94e0fdc439006f94d499e9d32004470fa7e402ede08538/data' } }
+process { withName: 'VARIANT_EFFECT_INSPECTION_TOOL' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/f7/f727259d394cff047a26f3e3fa3a43e1a7a1c9dc51a35e6def7aa90a2085bb7b/data' } }
+process { withName: 'VISUALIZATION_COUNTS_HEATMAP' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data' } }
+process { withName: 'VISUALIZATION_COUNTS_PER_COV' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data' } }
+process { withName: 'VISUALIZATION_ERROR_CORRECTION_REPORT' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/f7/f727259d394cff047a26f3e3fa3a43e1a7a1c9dc51a35e6def7aa90a2085bb7b/data' } }
+process { withName: 'VISUALIZATION_GLOBAL_POS_BIASES_COUNTS' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data' } }
+process { withName: 'VISUALIZATION_GLOBAL_POS_BIASES_COV' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data' } }
+process { withName: 'VISUALIZATION_LOGDIFF' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data' } }
+process { withName: 'VISUALIZATION_SEQDEPTH' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data' } }
+process { withName: 'VISUALIZATION_SUMMARY_REPORT' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/f7/f727259d394cff047a26f3e3fa3a43e1a7a1c9dc51a35e6def7aa90a2085bb7b/data' } }
diff --git a/conf/containers_singularity_https_arm64.config b/conf/containers_singularity_https_arm64.config
new file mode 100644
index 0000000..6738b71
--- /dev/null
+++ b/conf/containers_singularity_https_arm64.config
@@ -0,0 +1,26 @@
+process { withName: 'COUNTS_TO_FITNESS' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data' } }
+process { withName: 'DMSANALYSIS_AASEQ' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data' } }
+process { withName: 'DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/cc/ccd8ca061a33803f1d15bc47bf8e6d87b242ebb66b387cbb5ab02a4c7670f425/data' } }
+process { withName: 'DMSANALYSIS_ERROR_CORRECTION_WILDTYPE' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data' } }
+process { withName: 'DMSANALYSIS_POSSIBLE_MUTATIONS' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data' } }
+process { withName: 'DMSANALYSIS_PROCESS_VARIANT_COUNTS' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data' } }
+process { withName: 'EXPDESIGN_FITNESS' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data' } }
+process { withName: 'FASTQC' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/ad/add047b79c7b7b0f2c0711161f64256ffe51ebf8a6e14fa7adf31877da6425db/data' } }
+process { withName: 'FASTQ_CHECK' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/80/80ba09e7d98be1120310df58960280440253953eaba0da00d71cd6b3f13211f9/data' } }
+process { withName: 'FIND_SYNONYMOUS_MUTATION' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data' } }
+process { withName: 'FITNESS_CALCULATION' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data' } }
+process { withName: 'FITNESS_HEATMAP' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data' } }
+process { withName: 'FITNESS_QC' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data' } }
+process { withName: 'MERGE_ALIGN' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/6f/6f3676079712b1ee571f0a90e69b8d197fcb1ba68085cf475a25cc74bd4cb45a/data' } }
+process { withName: 'MERGE_COUNTS' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data' } }
+process { withName: 'MULTIQC' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/e4/e48aa28aebc881254a499b24c3e1ce77b8df1b85a5432699ed6f72eb17ac7fb5/data' } }
+process { withName: 'VARIANTCOUNTING' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/00/009b2a9e9a23d373d35a9afb3e353449f962bcc5511cb56aa3a286b942b0d514/data' } }
+process { withName: 'VARIANT_EFFECT_INSPECTION_TOOL' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/22/229745e9be125462f1850ac75f13afb0738d30241558908657bfc802c2c6681f/data' } }
+process { withName: 'VISUALIZATION_COUNTS_HEATMAP' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data' } }
+process { withName: 'VISUALIZATION_COUNTS_PER_COV' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data' } }
+process { withName: 'VISUALIZATION_ERROR_CORRECTION_REPORT' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/22/229745e9be125462f1850ac75f13afb0738d30241558908657bfc802c2c6681f/data' } }
+process { withName: 'VISUALIZATION_GLOBAL_POS_BIASES_COUNTS' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data' } }
+process { withName: 'VISUALIZATION_GLOBAL_POS_BIASES_COV' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data' } }
+process { withName: 'VISUALIZATION_LOGDIFF' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data' } }
+process { withName: 'VISUALIZATION_SEQDEPTH' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data' } }
+process { withName: 'VISUALIZATION_SUMMARY_REPORT' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/22/229745e9be125462f1850ac75f13afb0738d30241558908657bfc802c2c6681f/data' } }
diff --git a/conf/containers_singularity_oras_amd64.config b/conf/containers_singularity_oras_amd64.config
new file mode 100644
index 0000000..e45ca59
--- /dev/null
+++ b/conf/containers_singularity_oras_amd64.config
@@ -0,0 +1,27 @@
+process { withName: 'COUNTS_TO_FITNESS' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb' } }
+process { withName: 'DMSANALYSIS_AASEQ' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb' } }
+process { withName: 'DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-stringr:f7ed82e8081ab38d' } }
+process { withName: 'DMSANALYSIS_ERROR_CORRECTION_WILDTYPE' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb' } }
+process { withName: 'DMSANALYSIS_POSSIBLE_MUTATIONS' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb' } }
+process { withName: 'DMSANALYSIS_PROCESS_VARIANT_COUNTS' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb' } }
+process { withName: 'EXPDESIGN_FITNESS' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb' } }
+process { withName: 'FASTQC' { container = 'oras://community.wave.seqera.io/library/fastqc:0.13.0--aadddbed09923ea6' } }
+process { withName: 'FASTQ_CHECK' { container = 'oras://community.wave.seqera.io/library/fq:0.12.0--2eebf4e58a46525e' } }
+process { withName: 'FIND_SYNONYMOUS_MUTATION' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb' } }
+process { withName: 'FITNESS_CALCULATION' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb' } }
+process { withName: 'FITNESS_HEATMAP' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb' } }
+process { withName: 'FITNESS_QC' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb' } }
+process { withName: 'MERGE_ALIGN' { container = 'oras://community.wave.seqera.io/library/bwa_samtools_vsearch:22b018a8e6d18e4f' } }
+process { withName: 'MERGE_COUNTS' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb' } }
+process { withName: 'MULTIQC' { container = 'oras://community.wave.seqera.io/library/multiqc:1.35--c680f2aea25ccec2' } }
+process { withName: 'RUN_MUTSCAN' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base:764a8471b126fb35' } }
+process { withName: 'VARIANTCOUNTING' { container = 'oras://community.wave.seqera.io/library/pysam_biopython_numpy_polars-lts-cpu_pruned:52b881e1947dfdb0' } }
+process { withName: 'VARIANT_EFFECT_INSPECTION_TOOL' { container = 'oras://community.wave.seqera.io/library/numpy_pandas_python:7f16bcac97973062' } }
+process { withName: 'VISUALIZATION_COUNTS_HEATMAP' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb' } }
+process { withName: 'VISUALIZATION_COUNTS_PER_COV' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb' } }
+process { withName: 'VISUALIZATION_ERROR_CORRECTION_REPORT' { container = 'oras://community.wave.seqera.io/library/numpy_pandas_python:7f16bcac97973062' } }
+process { withName: 'VISUALIZATION_GLOBAL_POS_BIASES_COUNTS' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb' } }
+process { withName: 'VISUALIZATION_GLOBAL_POS_BIASES_COV' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb' } }
+process { withName: 'VISUALIZATION_LOGDIFF' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb' } }
+process { withName: 'VISUALIZATION_SEQDEPTH' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb' } }
+process { withName: 'VISUALIZATION_SUMMARY_REPORT' { container = 'oras://community.wave.seqera.io/library/numpy_pandas_python:7f16bcac97973062' } }
diff --git a/conf/containers_singularity_oras_arm64.config b/conf/containers_singularity_oras_arm64.config
new file mode 100644
index 0000000..09983ca
--- /dev/null
+++ b/conf/containers_singularity_oras_arm64.config
@@ -0,0 +1,26 @@
+process { withName: 'COUNTS_TO_FITNESS' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7' } }
+process { withName: 'DMSANALYSIS_AASEQ' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7' } }
+process { withName: 'DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-stringr:1c3d5150b5f34345' } }
+process { withName: 'DMSANALYSIS_ERROR_CORRECTION_WILDTYPE' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7' } }
+process { withName: 'DMSANALYSIS_POSSIBLE_MUTATIONS' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7' } }
+process { withName: 'DMSANALYSIS_PROCESS_VARIANT_COUNTS' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7' } }
+process { withName: 'EXPDESIGN_FITNESS' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7' } }
+process { withName: 'FASTQC' { container = 'oras://community.wave.seqera.io/library/fastqc:0.13.0--f5a404ab2c13e090' } }
+process { withName: 'FASTQ_CHECK' { container = 'oras://community.wave.seqera.io/library/fq:0.12.0--8bfef4392e820de8' } }
+process { withName: 'FIND_SYNONYMOUS_MUTATION' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7' } }
+process { withName: 'FITNESS_CALCULATION' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7' } }
+process { withName: 'FITNESS_HEATMAP' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7' } }
+process { withName: 'FITNESS_QC' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7' } }
+process { withName: 'MERGE_ALIGN' { container = 'oras://community.wave.seqera.io/library/bwa_samtools_vsearch:a79e451fc67cfde0' } }
+process { withName: 'MERGE_COUNTS' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7' } }
+process { withName: 'MULTIQC' { container = 'oras://community.wave.seqera.io/library/multiqc:1.35--c0468833d65b2f81' } }
+process { withName: 'VARIANTCOUNTING' { container = 'oras://community.wave.seqera.io/library/pysam_biopython_numpy_polars-lts-cpu_pruned:9df342605773fef4' } }
+process { withName: 'VARIANT_EFFECT_INSPECTION_TOOL' { container = 'oras://community.wave.seqera.io/library/numpy_pandas_python:648dc1860698d21b' } }
+process { withName: 'VISUALIZATION_COUNTS_HEATMAP' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7' } }
+process { withName: 'VISUALIZATION_COUNTS_PER_COV' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7' } }
+process { withName: 'VISUALIZATION_ERROR_CORRECTION_REPORT' { container = 'oras://community.wave.seqera.io/library/numpy_pandas_python:648dc1860698d21b' } }
+process { withName: 'VISUALIZATION_GLOBAL_POS_BIASES_COUNTS' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7' } }
+process { withName: 'VISUALIZATION_GLOBAL_POS_BIASES_COV' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7' } }
+process { withName: 'VISUALIZATION_LOGDIFF' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7' } }
+process { withName: 'VISUALIZATION_SEQDEPTH' { container = 'oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7' } }
+process { withName: 'VISUALIZATION_SUMMARY_REPORT' { container = 'oras://community.wave.seqera.io/library/numpy_pandas_python:648dc1860698d21b' } }
diff --git a/conf/igenomes.config b/conf/igenomes.config
deleted file mode 100644
index 3f11437..0000000
--- a/conf/igenomes.config
+++ /dev/null
@@ -1,440 +0,0 @@
-/*
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-    Nextflow config file for iGenomes paths
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-    Defines reference genomes using iGenome paths.
-    Can be used by any config that customises the base path using:
-        $params.igenomes_base / --igenomes_base
-----------------------------------------------------------------------------------------
-*/
-
-params {
-    // illumina iGenomes reference file paths
-    genomes {
-        'GRCh37' {
-            fasta       = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Annotation/Genes/genes.bed"
-            readme      = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Annotation/README.txt"
-            mito_name   = "MT"
-            macs_gsize  = "2.7e9"
-            blacklist   = "${projectDir}/assets/blacklists/GRCh37-blacklist.bed"
-        }
-        'GRCh38' {
-            fasta       = "${params.igenomes_base}/Homo_sapiens/NCBI/GRCh38/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Homo_sapiens/NCBI/GRCh38/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Homo_sapiens/NCBI/GRCh38/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Homo_sapiens/NCBI/GRCh38/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Homo_sapiens/NCBI/GRCh38/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Homo_sapiens/NCBI/GRCh38/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Homo_sapiens/NCBI/GRCh38/Annotation/Genes/genes.bed"
-            mito_name   = "chrM"
-            macs_gsize  = "2.7e9"
-            blacklist   = "${projectDir}/assets/blacklists/hg38-blacklist.bed"
-        }
-        'CHM13' {
-            fasta       = "${params.igenomes_base}/Homo_sapiens/UCSC/CHM13/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Homo_sapiens/UCSC/CHM13/Sequence/BWAIndex/"
-            bwamem2     = "${params.igenomes_base}/Homo_sapiens/UCSC/CHM13/Sequence/BWAmem2Index/"
-            gtf         = "${params.igenomes_base}/Homo_sapiens/NCBI/CHM13/Annotation/Genes/genes.gtf"
-            gff         = "ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/009/914/755/GCF_009914755.1_T2T-CHM13v2.0/GCF_009914755.1_T2T-CHM13v2.0_genomic.gff.gz"
-            mito_name   = "chrM"
-        }
-        'GRCm38' {
-            fasta       = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Annotation/Genes/genes.bed"
-            readme      = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Annotation/README.txt"
-            mito_name   = "MT"
-            macs_gsize  = "1.87e9"
-            blacklist   = "${projectDir}/assets/blacklists/GRCm38-blacklist.bed"
-        }
-        'TAIR10' {
-            fasta       = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Annotation/Genes/genes.bed"
-            readme      = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Annotation/README.txt"
-            mito_name   = "Mt"
-        }
-        'EB2' {
-            fasta       = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Annotation/Genes/genes.bed"
-            readme      = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Annotation/README.txt"
-        }
-        'UMD3.1' {
-            fasta       = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Annotation/Genes/genes.bed"
-            readme      = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Annotation/README.txt"
-            mito_name   = "MT"
-        }
-        'WBcel235' {
-            fasta       = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Annotation/Genes/genes.bed"
-            mito_name   = "MtDNA"
-            macs_gsize  = "9e7"
-        }
-        'CanFam3.1' {
-            fasta       = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Annotation/Genes/genes.bed"
-            readme      = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Annotation/README.txt"
-            mito_name   = "MT"
-        }
-        'GRCz10' {
-            fasta       = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Annotation/Genes/genes.bed"
-            mito_name   = "MT"
-        }
-        'BDGP6' {
-            fasta       = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Annotation/Genes/genes.bed"
-            mito_name   = "M"
-            macs_gsize  = "1.2e8"
-        }
-        'EquCab2' {
-            fasta       = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Annotation/Genes/genes.bed"
-            readme      = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Annotation/README.txt"
-            mito_name   = "MT"
-        }
-        'EB1' {
-            fasta       = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Annotation/Genes/genes.bed"
-            readme      = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Annotation/README.txt"
-        }
-        'Galgal4' {
-            fasta       = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Annotation/Genes/genes.bed"
-            mito_name   = "MT"
-        }
-        'Gm01' {
-            fasta       = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Annotation/Genes/genes.bed"
-            readme      = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Annotation/README.txt"
-        }
-        'Mmul_1' {
-            fasta       = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Annotation/Genes/genes.bed"
-            readme      = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Annotation/README.txt"
-            mito_name   = "MT"
-        }
-        'IRGSP-1.0' {
-            fasta       = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Annotation/Genes/genes.bed"
-            mito_name   = "Mt"
-        }
-        'CHIMP2.1.4' {
-            fasta       = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Annotation/Genes/genes.bed"
-            readme      = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Annotation/README.txt"
-            mito_name   = "MT"
-        }
-        'Rnor_5.0' {
-            fasta       = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_5.0/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_5.0/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_5.0/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_5.0/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_5.0/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_5.0/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_5.0/Annotation/Genes/genes.bed"
-            mito_name   = "MT"
-        }
-        'Rnor_6.0' {
-            fasta       = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Annotation/Genes/genes.bed"
-            mito_name   = "MT"
-        }
-        'R64-1-1' {
-            fasta       = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Annotation/Genes/genes.bed"
-            mito_name   = "MT"
-            macs_gsize  = "1.2e7"
-        }
-        'EF2' {
-            fasta       = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Annotation/Genes/genes.bed"
-            readme      = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Annotation/README.txt"
-            mito_name   = "MT"
-            macs_gsize  = "1.21e7"
-        }
-        'Sbi1' {
-            fasta       = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Annotation/Genes/genes.bed"
-            readme      = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Annotation/README.txt"
-        }
-        'Sscrofa10.2' {
-            fasta       = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Annotation/Genes/genes.bed"
-            readme      = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Annotation/README.txt"
-            mito_name   = "MT"
-        }
-        'AGPv3' {
-            fasta       = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Annotation/Genes/genes.bed"
-            mito_name   = "Mt"
-        }
-        'hg38' {
-            fasta       = "${params.igenomes_base}/Homo_sapiens/UCSC/hg38/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Homo_sapiens/UCSC/hg38/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Homo_sapiens/UCSC/hg38/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Homo_sapiens/UCSC/hg38/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Homo_sapiens/UCSC/hg38/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Homo_sapiens/UCSC/hg38/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Homo_sapiens/UCSC/hg38/Annotation/Genes/genes.bed"
-            mito_name   = "chrM"
-            macs_gsize  = "2.7e9"
-            blacklist   = "${projectDir}/assets/blacklists/hg38-blacklist.bed"
-        }
-        'hg19' {
-            fasta       = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Annotation/Genes/genes.bed"
-            readme      = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Annotation/README.txt"
-            mito_name   = "chrM"
-            macs_gsize  = "2.7e9"
-            blacklist   = "${projectDir}/assets/blacklists/hg19-blacklist.bed"
-        }
-        'mm10' {
-            fasta       = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Annotation/Genes/genes.bed"
-            readme      = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Annotation/README.txt"
-            mito_name   = "chrM"
-            macs_gsize  = "1.87e9"
-            blacklist   = "${projectDir}/assets/blacklists/mm10-blacklist.bed"
-        }
-        'bosTau8' {
-            fasta       = "${params.igenomes_base}/Bos_taurus/UCSC/bosTau8/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Bos_taurus/UCSC/bosTau8/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Bos_taurus/UCSC/bosTau8/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Bos_taurus/UCSC/bosTau8/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Bos_taurus/UCSC/bosTau8/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Bos_taurus/UCSC/bosTau8/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Bos_taurus/UCSC/bosTau8/Annotation/Genes/genes.bed"
-            mito_name   = "chrM"
-        }
-        'ce10' {
-            fasta       = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Annotation/Genes/genes.bed"
-            readme      = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Annotation/README.txt"
-            mito_name   = "chrM"
-            macs_gsize  = "9e7"
-        }
-        'canFam3' {
-            fasta       = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Annotation/Genes/genes.bed"
-            readme      = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Annotation/README.txt"
-            mito_name   = "chrM"
-        }
-        'danRer10' {
-            fasta       = "${params.igenomes_base}/Danio_rerio/UCSC/danRer10/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Danio_rerio/UCSC/danRer10/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Danio_rerio/UCSC/danRer10/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Danio_rerio/UCSC/danRer10/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Danio_rerio/UCSC/danRer10/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Danio_rerio/UCSC/danRer10/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Danio_rerio/UCSC/danRer10/Annotation/Genes/genes.bed"
-            mito_name   = "chrM"
-            macs_gsize  = "1.37e9"
-        }
-        'dm6' {
-            fasta       = "${params.igenomes_base}/Drosophila_melanogaster/UCSC/dm6/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Drosophila_melanogaster/UCSC/dm6/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Drosophila_melanogaster/UCSC/dm6/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Drosophila_melanogaster/UCSC/dm6/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Drosophila_melanogaster/UCSC/dm6/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Drosophila_melanogaster/UCSC/dm6/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Drosophila_melanogaster/UCSC/dm6/Annotation/Genes/genes.bed"
-            mito_name   = "chrM"
-            macs_gsize  = "1.2e8"
-        }
-        'equCab2' {
-            fasta       = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Annotation/Genes/genes.bed"
-            readme      = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Annotation/README.txt"
-            mito_name   = "chrM"
-        }
-        'galGal4' {
-            fasta       = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Annotation/Genes/genes.bed"
-            readme      = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Annotation/README.txt"
-            mito_name   = "chrM"
-        }
-        'panTro4' {
-            fasta       = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Annotation/Genes/genes.bed"
-            readme      = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Annotation/README.txt"
-            mito_name   = "chrM"
-        }
-        'rn6' {
-            fasta       = "${params.igenomes_base}/Rattus_norvegicus/UCSC/rn6/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Rattus_norvegicus/UCSC/rn6/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Rattus_norvegicus/UCSC/rn6/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Rattus_norvegicus/UCSC/rn6/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Rattus_norvegicus/UCSC/rn6/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Rattus_norvegicus/UCSC/rn6/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Rattus_norvegicus/UCSC/rn6/Annotation/Genes/genes.bed"
-            mito_name   = "chrM"
-        }
-        'sacCer3' {
-            fasta       = "${params.igenomes_base}/Saccharomyces_cerevisiae/UCSC/sacCer3/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Saccharomyces_cerevisiae/UCSC/sacCer3/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Saccharomyces_cerevisiae/UCSC/sacCer3/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Saccharomyces_cerevisiae/UCSC/sacCer3/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Saccharomyces_cerevisiae/UCSC/sacCer3/Sequence/BismarkIndex/"
-            readme      = "${params.igenomes_base}/Saccharomyces_cerevisiae/UCSC/sacCer3/Annotation/README.txt"
-            mito_name   = "chrM"
-            macs_gsize  = "1.2e7"
-        }
-        'susScr3' {
-            fasta       = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Sequence/WholeGenomeFasta/genome.fa"
-            bwa         = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Sequence/BWAIndex/version0.6.0/"
-            bowtie2     = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Sequence/Bowtie2Index/"
-            star        = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Sequence/STARIndex/"
-            bismark     = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Sequence/BismarkIndex/"
-            gtf         = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Annotation/Genes/genes.gtf"
-            bed12       = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Annotation/Genes/genes.bed"
-            readme      = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Annotation/README.txt"
-            mito_name   = "chrM"
-        }
-    }
-}
diff --git a/conf/igenomes_ignored.config b/conf/igenomes_ignored.config
deleted file mode 100644
index b4034d8..0000000
--- a/conf/igenomes_ignored.config
+++ /dev/null
@@ -1,9 +0,0 @@
-/*
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-    Nextflow config file for iGenomes paths
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-    Empty genomes dictionary to use when igenomes is ignored.
-----------------------------------------------------------------------------------------
-*/
-
-params.genomes = [:]
diff --git a/conf/modules.config b/conf/modules.config
index d203d2b..263307a 100644
--- a/conf/modules.config
+++ b/conf/modules.config
@@ -7,6 +7,9 @@
         ext.args2  = Second set of arguments appended to command in module (multi-tool modules).
         ext.args3  = Third set of arguments appended to command in module (multi-tool modules).
         ext.prefix = File name prefix for output files.
+
+    Output layout: Outputs are published directly into functional directories
+    under `${outdir}/...` without sample-specific or reference parent folders.
 ----------------------------------------------------------------------------------------
 */
 
@@ -20,15 +23,236 @@ process {
 
     withName: FASTQC {
         ext.args = '--quiet'
+        containerOptions = ''
+        publishDir = [
+            path: { "${params.outdir}/fastqc" },
+            mode: params.publish_dir_mode,
+            saveAs: { filename -> filename.equals('versions.yml') ? null : filename }
+        ]
+    }
+
+    withName: 'FASTQ_CHECK' {
+        // The per-library results only feed the start-of-run check and its log message
+        publishDir = [enabled: false]
     }
 
     withName: 'MULTIQC' {
         ext.args   = { params.multiqc_title ? "--title \"$params.multiqc_title\"" : '' }
         publishDir = [
             path: { "${params.outdir}/multiqc" },
+            mode: params.publish_dir_mode
+        ]
+    }
+
+    // --- Reference-derived, merged into intermediate_files ---
+
+    withName: 'BWA_INDEX' {
+        cpus   = 1
+        memory = 1.GB
+        time   = 30.min
+        publishDir = [
+            path: "${params.outdir}/intermediate_files",
             mode: params.publish_dir_mode,
-            saveAs: { filename -> filename.equals('versions.yml') ? null : filename }
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
         ]
     }
 
+    withName: 'DMSANALYSIS_POSSIBLE_MUTATIONS' {
+        publishDir = [
+            path: "${params.outdir}/intermediate_files",
+            mode: params.publish_dir_mode,
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'DMSANALYSIS_AASEQ' {
+        publishDir = [
+            path: "${params.outdir}/intermediate_files",
+            mode: params.publish_dir_mode,
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    // --- Read merging / alignment / filtering ---
+
+    withName: 'MERGE_ALIGN' {
+        // Merges the raw read pairs (vsearch), aligns the merged reads (bwa mem), filters them, and
+        // sorts and indexes them (samtools), streamed in one task. args -> vsearch read-pair merging; args2 -> samtools filter on the
+        // aligned merged reads: drop unmapped, secondary and supplementary records (0x904) and
+        // anything below MAPQ 30. Reads with indels are removed in the module itself.
+        ext.args  = '--fastq_minovlen 10 --fastq_allowmergestagger'
+        ext.args2 = '-F 0x904 -q 30'
+        // Published: the merging statistics, and the sorted, indexed BAM - the one the counter
+        // actually reads, and the only BAM written at all.
+        publishDir = [
+            [
+                path: { "${params.outdir}/intermediate_files/merged_reads" },
+                mode: params.publish_dir_mode,
+                pattern: '*.log'
+            ],
+            [
+                path: { "${params.outdir}/intermediate_files/bam_files/sorted" },
+                mode: params.publish_dir_mode,
+                pattern: '*.sorted.bam*'
+            ]
+        ]
+    }
+
+    // --- Variant counting / processing / error correction (per-replicate meta.id subfolder) ---
+
+    withName: 'VARIANTCOUNTING' {
+      publishDir = [
+        path: { "${params.outdir}/intermediate_files/variant_counts" },
+        mode: params.publish_dir_mode,
+        saveAs: { filename ->
+          if (filename == 'versions.yml') return null
+          "${meta.id}/${filename}"
+        }
+      ]
+    }
+
+    withName: 'DMSANALYSIS_PROCESS_VARIANT_COUNTS' {
+        publishDir = [
+            path: { "${params.outdir}/intermediate_files/processed_variant_counts" },
+            mode: params.publish_dir_mode,
+            saveAs: { filename ->
+                if (filename == 'versions.yml') return null
+                "${meta.id}/${filename}"
+            }
+        ]
+    }
+
+    withName: 'DMSANALYSIS_ERROR_CORRECTION_.*' {
+        publishDir = [
+            path: { "${params.outdir}/intermediate_files/processed_variant_counts" },
+            mode: params.publish_dir_mode,
+            saveAs: { filename ->
+                if (filename == 'versions.yml') return null
+                "${meta.id}/${filename}"
+            }
+        ]
+    }
+
+    // Per-sample visualisations file under library_QC/<sample>/. The negative lookahead excludes the two
+    // run-level reports, which have no per-sample `meta.id` to file under - Nextflow ACCUMULATES
+    // publishDir across matching selectors rather than letting a later `withName` override it, so if this
+    // selector also matched them its `${meta.id}` would still be evaluated (throwing for the EC report,
+    // and mis-filing the summary report under library_QC/run/).
+    withName: /.*VISUALIZATION_(?!ERROR_CORRECTION_REPORT|SUMMARY_REPORT).*/ {
+        publishDir = [
+          path: { "${params.outdir}/library_QC" },
+          mode: params.publish_dir_mode,
+            saveAs: { fn ->
+            if (fn == 'versions.yml') return null
+            "${meta.id}/${fn}"
+          }
+        ]
+      }
+
+    // Run-level, so it has no meta.id to file itself under - published flat under library_QC.
+    withName: 'VISUALIZATION_ERROR_CORRECTION_REPORT' {
+        publishDir = [
+            path: { "${params.outdir}/library_QC" },
+            mode: params.publish_dir_mode,
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    // The all-in-one report is the flagship output - published at the results root, not buried.
+    withName: 'VISUALIZATION_SUMMARY_REPORT' {
+        publishDir = [
+            path: { "${params.outdir}" },
+            mode: params.publish_dir_mode,
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    // --- Fitness and Experimental Design ---
+
+    // Per-library DiMSum input tables: published only when DiMSum runs (they are intermediate otherwise)
+    withName: 'COUNTS_TO_FITNESS' {
+        publishDir = [
+            path: { "${params.outdir}/fitness/DiMSum_results/single_rep_counts" },
+            enabled: params.dimsum.toString() != 'false',
+            mode: params.publish_dir_mode,
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'MERGE_COUNTS' {
+        publishDir = [
+            path: { "${params.outdir}/fitness" },
+            mode: params.publish_dir_mode,
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'EXPDESIGN_FITNESS' {
+        publishDir = [
+            path: "${params.outdir}/fitness",
+            mode: params.publish_dir_mode,
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'FIND_SYNONYMOUS_MUTATION' {
+        publishDir = [
+            path: { "${params.outdir}/fitness" },
+            mode: params.publish_dir_mode,
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'FITNESS_CALCULATION' {
+        publishDir = [
+            path: { "${params.outdir}/fitness/default_results" },
+            mode: params.publish_dir_mode,
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'FITNESS_QC' {
+        publishDir = [
+            path: { "${params.outdir}/fitness/default_results" },
+            mode: params.publish_dir_mode,
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'FITNESS_HEATMAP' {
+        publishDir = [
+            path: { "${params.outdir}/fitness/default_results" },
+            mode: params.publish_dir_mode,
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'RUN_DIMSUM' {
+        // --mixedSubstitutions T: the synonymous wildtype proxy can differ from the reference by one
+        // codon; with DiMSum's default (F), single mutants of the proxy would be discarded as
+        // "mixed substitutions". --fitnessErrorModel F: the error model needs enough well-covered
+        // variants in every replicate, which sparse shotgun libraries (and the test data) lack.
+        ext.args = '--mixedSubstitutions T --fitnessErrorModel F --retainIntermediateFiles T'
+        publishDir = [
+            path: { "${params.outdir}/fitness/DiMSum_results" },
+            mode: params.publish_dir_mode,
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'RUN_MUTSCAN' {
+        publishDir = [
+            path: { "${params.outdir}/fitness/mutscan_results" },
+            mode: params.publish_dir_mode,
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'VARIANT_EFFECT_INSPECTION_TOOL' {
+        publishDir = [
+            path: { "${params.outdir}/variant_effect_inspection_tool" },
+            mode: params.publish_dir_mode,
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
 }
diff --git a/conf/test.config b/conf/test.config
index 8c566af..ce366cc 100644
--- a/conf/test.config
+++ b/conf/test.config
@@ -5,7 +5,7 @@
     Defines input files and everything required to run a fast and simple pipeline test.
 
     Use as follows:
-        nextflow run nf-core/dmscore -profile test,<docker/singularity> --outdir <OUTDIR>
+        nextflow run nf-core/deepmutscan -profile test,<docker/singularity> --outdir <OUTDIR>
 
 ----------------------------------------------------------------------------------------
 */
@@ -13,7 +13,7 @@
 process {
     resourceLimits = [
         cpus: 4,
-        memory: '15.GB',
+        memory: '8.GB',
         time: '1.h'
     ]
 }
@@ -23,8 +23,17 @@ params {
     config_profile_description = 'Minimal test dataset to check pipeline function'
 
     // Input data
-    // TODO nf-core: Specify the paths to your test data on nf-core/test-datasets
-    // TODO nf-core: Give any required params for the test so that command line flags are not needed
-    input  = params.pipelines_testdata_base_path + 'viralrecon/samplesheet/samplesheet_test_illumina_amplicon.csv'// Genome references
-    genome = 'R64-1-1'
+    input = params.pipelines_testdata_base_path + 'deepmutscan/samplesheet/GID1A_test.csv'
+    fasta = params.pipelines_testdata_base_path + 'deepmutscan/testdata/GID1A.fasta'
+    reading_frame = '352-1383'
+    min_counts = 1
+    mutagenesis_type = 'nnk_nns'
+    run_seqdepth = true
+    fitness = true
+    mutscan = true
+    dimsum  = true
+
+    // Precomputed wildtype structure (AlphaFold DB model, GID1A / UniProt Q9MAA7, CC-BY-4.0) so the
+    // interactive variant effect inspection tool runs end-to-end without structure prediction.
+    pdb = params.pipelines_testdata_base_path + 'deepmutscan/testdata/GID1A_AFDB.pdb'
 }
diff --git a/conf/test_full.config b/conf/test_full.config
index b57a75c..ae592d4 100644
--- a/conf/test_full.config
+++ b/conf/test_full.config
@@ -5,7 +5,7 @@
     Defines input files and everything required to run a full size pipeline test.
 
     Use as follows:
-        nextflow run nf-core/dmscore -profile test_full,<docker/singularity> --outdir <OUTDIR>
+        nextflow run nf-core/deepmutscan -profile test_full,<docker/singularity> --outdir <OUTDIR>
 
 ----------------------------------------------------------------------------------------
 */
@@ -15,10 +15,18 @@ params {
     config_profile_description = 'Full test dataset to check pipeline function'
 
     // Input data for full size test
-    // TODO nf-core: Specify the paths to your full test data ( on nf-core/test-datasets or directly in repositories, e.g. SRA)
-    // TODO nf-core: Give any required params for the test so that command line flags are not needed
-    input = params.pipelines_testdata_base_path + 'viralrecon/samplesheet/samplesheet_full_illumina_amplicon.csv'
+    // GluePCA GID1A-GAI shotgun DMS (ENA PRJEB110196): 3 input and 3 output (2500 uM GA3) replicates,
+    // 2x150 bp, ~480M read pairs in total. The samplesheet links the FASTQs on ENA.
+    // The minimal `test` profile uses 50k read-pair subsamples of the same libraries.
+    input = params.pipelines_testdata_base_path + 'deepmutscan/samplesheet/GID1A_full.csv'
+    fasta = params.pipelines_testdata_base_path + 'deepmutscan/testdata/GID1A.fasta'
+    reading_frame    = '352-1383'
+    mutagenesis_type = 'nnk_nns'
+    fitness = true
+    mutscan = true
+    dimsum  = true
 
-    // Genome references
-    genome = 'R64-1-1'
+    // Precomputed wildtype structure (AlphaFold DB model, GID1A / UniProt Q9MAA7, CC-BY-4.0) so the
+    // interactive variant effect inspection tool runs end-to-end without structure prediction.
+    pdb = params.pipelines_testdata_base_path + 'deepmutscan/testdata/GID1A_AFDB.pdb'
 }
diff --git a/docs/CONTRIBUTING.md b/docs/CONTRIBUTING.md
new file mode 100644
index 0000000..6f3e0ac
--- /dev/null
+++ b/docs/CONTRIBUTING.md
@@ -0,0 +1,181 @@
+---
+title: Contributing
+markdownPlugin: checklist
+---
+
+# `nf-core/deepmutscan`: Contributing guidelines
+
+Hi there!
+Thanks for taking an interest in improving nf-core/deepmutscan.
+
+This page describes the recommended nf-core way to contribute to both nf-core/deepmutscan and nf-core pipelines in general, including:
+
+- [General contribution guidelines](#general-contribution-guidelines): common procedures or guides across all nf-core pipelines.
+- [Pipeline-specific contribution guidelines](#pipeline-specific-contribution-guidelines): procedures or guides specific to the development conventions of nf-core/deepmutscan.
+
+> [!NOTE]
+> If you need help using or modifying nf-core/deepmutscan, ask on the nf-core Slack [#deepmutscan](https://nfcore.slack.com/channels/deepmutscan) channel ([join our Slack here](https://nf-co.re/join/slack)).
+
+## General contribution guidelines
+
+### Contribution quick start
+
+To contribute code to any nf-core pipeline:
+
+- [ ] Ensure you have Nextflow, nf-core tools, and nf-test installed. See the [nf-core/tools repository](https://github.com/nf-core/tools) for instructions.
+- [ ] Check whether a GitHub [issue](https://github.com/nf-core/deepmutscan/issues) about your idea already exists. If an issue does not exist, create one so that others are aware you are working on it.
+- [ ] [Fork](https://help.github.com/en/github/getting-started-with-github/fork-a-repo) the [nf-core/deepmutscan repository](https://github.com/nf-core/deepmutscan) to your GitHub account.
+- [ ] Create a branch on your forked repository and make your changes following [pipeline conventions](#pipeline-contribution-conventions) (if applicable).
+- [ ] To fix major bugs, name your branch `patch` and follow the [patch release](#patch-release) process.
+- [ ] Update relevant documentation within the `docs/` folder, use nf-core/tools to update `nextflow_schema.json`, and update `CITATIONS.md`.
+- [ ] Run and/or update tests. See [Testing](#testing) for more information.
+- [ ] [Lint](#lint-tests) your code with nf-core/tools.
+- [ ] Submit a pull request (PR) against the `dev` branch and request a review.
+
+If you are not used to this workflow with Git, see the [GitHub documentation](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests) or [Git resources](https://try.github.io/) for more information.
+
+## Use of AI and LLMs
+
+The nf-core stance on the use of AI and LLMs is that humans are still ultimately responsible for their submitted code, regardless of the tools they use.
+
+If you’re using AI tools, try to stick by these guidelines:
+
+- Keep PRs as small and focused as possible
+- Avoid any unnecessary changes, such as moving or refactoring code (unless that is the explicit intention of the PR)
+- Review all generated code yourself before opening a PR, and ensure that you understand it
+- Engage with the community review process and expect to make revisions
+
+For more detail, see the [blog post](https://nf-co.re/blog/2026/statement-on-ai) for a statement from the nf-core/core team.
+
+### Getting help
+
+For further information and help, see the [nf-core/deepmutscan documentation](https://nf-co.re/deepmutscan/usage) or ask on the nf-core [#deepmutscan](https://nfcore.slack.com/channels/deepmutscan) Slack channel ([join our Slack here](https://nf-co.re/join/slack)).
+
+### GitHub Codespaces
+
+You can contribute to nf-core/deepmutscan without installing a local development environment on your machine by using [GitHub Codespaces](https://github.com/codespaces).
+
+[GitHub Codespaces](https://github.com/codespaces) is an online developer environment that runs in your browser, complete with VS Code and a terminal.
+Most nf-core repositories include a devcontainer configuration, which creates a GitHub Codespaces environment specifically for Nextflow development.
+The environment includes pre-installed nf-core tools, Nextflow, and a few other helpful utilities via a Docker container.
+
+To get started, open the repository in [Codespaces](https://github.com/nf-core/deepmutscan/codespaces).
+
+### Testing
+
+Once you have made your changes, run the pipeline with nf-test to test them locally.
+For additional information, use the `--verbose` flag to view the Nextflow console log output.
+
+```bash
+nf-test test --tag test --profile +docker --verbose
+```
+
+If you have added new functionality, ensure you update the test assertions in the `.nf.test` files in the `tests/` directory.
+Update the snapshots with the following command:
+
+```bash
+nf-test test --tag test --profile +docker --verbose --update-snapshots
+```
+
+When you create a pull request with changes, GitHub Actions will run automatic tests.
+Pull requests are typically reviewed when these tests are passing.
+
+Two types of tests are typically run:
+
+#### Lint tests
+
+nf-core has a [set of guidelines](https://nf-co.re/docs/specifications/overview) which all pipelines must follow.
+To enforce these, run linting with nf-core/tools:
+
+```bash
+nf-core pipelines lint <pipeline_directory>
+```
+
+If you encounter failures or warnings, follow the linked documentation printed to screen.
+For more information about linting tests, see [nf-core/tools API documentation](https://nf-co.re/docs/nf-core-tools/api_reference/latest/pipeline_lint_tests/actions_awsfulltest).
+
+#### Pipeline tests
+
+Each nf-core pipeline should be set up with a minimal set of test data.
+GitHub Actions runs the pipeline on this data to ensure it runs through and exits successfully.
+If there are any failures then the automated tests fail.
+These tests are run with the latest available version of Nextflow and the minimum required version specified in the pipeline code.
+
+### Patch release
+
+> [!WARNING]
+> Only in the unlikely event of a release that contains a critical bug.
+
+- [ ] Create a new branch `patch` on your fork based on `upstream/main` or `upstream/master`.
+- [ ] Fix the bug and use nf-core/tools to bump the version to the next semantic version, for example, `1.2.3` → `1.2.4`.
+- [ ] Open a Pull Request from `patch` directly to `main`/`master` with the changes.
+
+### Pipeline contribution conventions
+
+nf-core semi-standardises how you write code and other contributions to make the nf-core/deepmutscan code and processing logic more understandable for new contributors and to ensure quality.
+
+#### Add a new pipeline step
+
+To contribute a new step to the pipeline, follow the general nf-core coding procedure.
+Please also refer to the [pipeline-specific contribution guidelines](#pipeline-specific-contribution-guidelines):
+
+- [ ] Define the corresponding [input channel](#channel-naming-schemes) into your new process from the expected previous process channel.
+- [ ] Install a module with nf-core/tools, or write a local module (see [default processes resource requirements](#default-processes-resource-requirements)), and add it to the target `<workflow>.nf`.
+- [ ] Define the output channel if needed. Mix the version output channel into `ch_versions` and relevant files into `ch_multiqc`.
+- [ ] Add new or updated parameters to `nextflow.config` with a [default value](#default-parameter-values).
+- [ ] Add new or updated parameters and relevant help text to `nextflow_schema.json` with [nf-core/tools](#default-parameter-values).
+- [ ] Add validation for relevant parameters to the pipeline utilisation section of `utils_nfcore_\_pipeline/main.nf` subworkflow.
+- [ ] Perform local tests to validate that the new code works as expected.
+  - [ ] If applicable, add a new test in the `tests` directory.
+- [ ] Update `usage.md`, `output.md`, and `citation.md` as appropriate.
+- [ ] [Lint](#lint-tests) the code with nf-core/tools.
+- [ ] Update any diagrams or pipeline images as necessary.
+- [ ] Update MultiQC config `assets/multiqc_config.yml` so relevant suffixes, file name cleanup, and module plots are in the appropriate order.
+- [ ] If applicable, create a [MultiQC](https://seqera.io/multiqc/) module.
+- [ ] Add a description of the output files and, if relevant, images from the MultiQC report to `docs/output.md`.
+
+To update the minimum required Nextflow version, see the [Nextflow version bumping](#nextflow-version-bumping) section below. For more information about pipeline contributions, see [pipeline-specific contribution guidelines](#pipeline-specific-contribution-guidelines).
+
+#### Channel naming schemes
+
+Use the following naming schemes for channels to make the channel flow easier to understand:
+
+- Initial process channel: `ch_output_from_<process>`
+- Intermediate and terminal channels: `ch_<previousprocess>_for_<nextprocess>`
+
+#### Default parameter values
+
+Parameters should be initialised and defined with default values within the `params` scope in `nextflow.config`.
+They should also be documented in the pipeline JSON schema.
+
+To update `nextflow_schema.json`, run:
+
+```bash
+nf-core pipelines schema build
+```
+
+The schema builder interface that loads in your browser should automatically update the defaults in the parameter documentation.
+
+#### Default processes resource requirements
+
+If you write a local module, specify a default set of resource requirements for the process.
+
+Sensible defaults for process resource requirements (CPUs, memory, time) should be defined in `conf/base.config`.
+Specify these with generic `withLabel:` selectors, so they can be shared across multiple processes and steps of the pipeline.
+
+nf-core provides a set of standard labels that you should follow where possible, as seen in the [nf-core pipeline template](https://github.com/nf-core/tools/blob/main/nf_core/pipeline-template/conf/base.config).
+These labels define resource defaults for single-core processes, modules that require a GPU, and different levels of multi-core configurations with increasing memory requirements.
+
+Values assigned within these labels can be dynamically passed to a tool using the `${task.cpus}` and `${task.memory}` Nextflow variables in the `script:` block of a module (see an example in the [modules repository](https://github.com/nf-core/modules/blob/bd1b6a40f55933d94b8c9ca94ec8c1ea0eaf4b82/modules/nf-core/samtools/bam2fq/main.nf#L30)).
+
+#### Nextflow version bumping
+
+If you use a new feature from core Nextflow, bump the minimum required Nextflow version in the pipeline with:
+
+```bash
+nf-core pipelines bump-version --nextflow . <min_nf_version>
+```
+
+#### Images and figures guidelines
+
+If you update images or graphics, follow the nf-core [style guidelines](https://nf-co.re/docs/community/brand/workflow-schematics).
diff --git a/docs/README.md b/docs/README.md
index 917d984..657c1b3 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -1,6 +1,6 @@
-# nf-core/dmscore: Documentation
+# nf-core/deepmutscan: Documentation
 
-The nf-core/dmscore documentation is split into the following pages:
+The nf-core/deepmutscan documentation is split into the following pages:
 
 - [Usage](usage.md)
   - An overview of how the pipeline works, how to run it and a description of all of the different command-line flags.
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diff --git a/docs/output.md b/docs/output.md
index ff49a08..75b9f4a 100644
--- a/docs/output.md
+++ b/docs/output.md
@@ -1,18 +1,44 @@
-# nf-core/dmscore: Output
+# nf-core/deepmutscan: Output
 
 ## Introduction
 
-This document describes the output produced by the pipeline. Most of the plots are taken from the MultiQC report, which summarises results at the end of the pipeline.
-
-The directories listed below will be created in the results directory after the pipeline has finished. All paths are relative to the top-level results directory.
-
-<!-- TODO nf-core: Write this documentation describing your workflow's output -->
+This document describes the output produced by the `nf-core/deepmutscan` pipeline.
+
+The directories listed below will be created in the results directory after the pipeline has finished. All paths are relative to the top-level results directory. Library-specific files are named, or placed in subfolders named, after the library identifier `<library>`, built from the samplesheet as `<sample>_<type>_<replicate>_pe` (e.g. `GID1A_input_1_pe`).
+
+```tree title="nf-core/deepmutscan results"
+results/
+├── deepmutscan_report.html              # all-in-one, self-contained run report
+├── fastqc/                              # raw read QC per FASTQ file
+├── multiqc/                             # MultiQC report across all FASTQ files
+├── intermediate_files/                  # reference index, alignments, variant count tables, error correction
+│   ├── bwa/
+│   ├── merged_reads/                    # read-pair merging statistics
+│   ├── bam_files/sorted/                # only the sorted, indexed BAM is published
+│   ├── variant_counts/<library>/
+│   └── processed_variant_counts/<library>/
+├── library_QC/                          # per-library QC plots + run-level error-correction report
+│   └── <library>/
+├── fitness/                             # fitness results, mostly only with --fitness
+│   ├── default_results/
+│   ├── DiMSum_results/                  # only with --dimsum
+│   └── mutscan_results/                 # only with --mutscan
+├── variant_effect_inspection_tool/      # only with --fitness and --pdb
+└── pipeline_info/                       # Nextflow and pipeline run information
+```
 
 ## Pipeline overview
 
-The pipeline is built using [Nextflow](https://www.nextflow.io/) and processes data using the following steps:
+The pipeline is built using [Nextflow](https://www.nextflow.io/) and processes data using the following key steps:
 
-- [FastQC](#fastqc) - Raw read QC- [MultiQC](#multiqc) - Aggregate report describing results and QC from the whole pipeline
+- [FastQC](#fastqc) - Raw read QC
+- [MultiQC](#multiqc) - Aggregate report describing results and QC from the whole pipeline
+- [Intermediate files](#intermediate-files) - Reference index, alignments and variant count tables
+- [Error correction](#error-correction) - Sequencing-error corrected count tables and report
+- [Library QC](#library-qc) - Per-sample visualisations of mutation efficiency, coverage and saturation
+- [Fitness](#fitness) - Merged counts and fitness estimates (optional)
+- [Variant effect inspection tool](#variant-effect-inspection-tool) - Interactive 3D structure viewer (optional)
+- [All-in-one report](#all-in-one-report) - Self-contained HTML report of the whole run
 - [Pipeline information](#pipeline-information) - Report metrics generated during the workflow execution
 
 ### FastQC
@@ -26,7 +52,11 @@ The pipeline is built using [Nextflow](https://www.nextflow.io/) and processes d
 
 </details>
 
-[FastQC](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) gives general quality metrics about your sequenced reads. It provides information about the quality score distribution across your reads, per base sequence content (%A/T/G/C), adapter contamination and overrepresented sequences. For further reading and documentation see the [FastQC help pages](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/).### MultiQC
+[FastQC](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) gives general quality metrics about your sequenced reads. It provides information about the quality score distribution across your reads, per base sequence content (%A/T/G/C), adapter contamination estimates and overrepresented sequences. For further reading and documentation see the [FastQC help pages](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/).
+
+![FastQC report](images/fastqc.png)
+
+### MultiQC
 
 <details markdown="1">
 <summary>Output files</summary>
@@ -38,19 +68,150 @@ The pipeline is built using [Nextflow](https://www.nextflow.io/) and processes d
 
 </details>
 
-[MultiQC](http://multiqc.info) is a visualization tool that generates a single HTML report summarising all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available in the report data directory.
+[MultiQC](http://multiqc.info) is a visualization tool that generates a single HTML report summarising all the sequenced samples in your project. Most of the pipeline QC results are visualised in the all-in-one report and further statistics are available in the report data directory. The pipeline has special steps which also allow the software versions to be reported in the MultiQC output for future traceability. For more information about how to use MultiQC reports, see <http://multiqc.info>.
+
+![MultiQC overview](images/multiqc1.png)
+![MultiQC base-quality summary](images/multiqc2.png)
+![MultiQC GC-content summary](images/multiqc3.png)
+
+### Intermediate files
+
+<details markdown="1">
+<summary>Output files</summary>
+
+- `intermediate_files/`
+  - `aa_seq.txt`: wildtype amino acid sequence translated from `--reading_frame`.
+  - `possible_mutations.csv`: all programmed codon variants per position, as defined by `--mutagenesis_type`; used for library filtering and visualisation.
+  - `bwa/`: BWA index of the reference FASTA.
+  - `merged_reads/<library>.vsearch_mergepairs.log`: read-pair merging statistics from vsearch (pairs merged, reasons for failed merges, fragment lengths, expected errors).
+  - `bam_files/sorted/<library>.sorted.bam` and `.bai`: the coordinate-sorted, indexed alignments of
+    the merged read pairs, after filtering - exactly what the variant counter reads. This is the only
+    BAM the pipeline writes: merging, alignment, filtering and sorting run as one stream.
+  - `variant_counts/<library>/<library>.variant_counts.tsv`: variant count table with one row per observed combination of nucleotide changes, its count, coverage and codon / amino acid annotation. No count threshold is applied here: `--min_counts` acts only on fitness estimation.
+  - `variant_counts/<library>/variant_counts_columns.tsv`: description of every column of the variant count table.
+  - `processed_variant_counts/<library>/`:
+    - `annotated_variantCounts.csv`: variant counts annotated with codon and amino acid changes and counts per coverage.
+    - `variantCounts_filtered_by_library.csv`: single-codon variants that match the programmed library.
+    - `library_completed_variantCounts.csv`: all programmed variants, including unobserved ones (counts 0, `observed` FALSE), used for the LogDiff plots.
+    - `variantCounts_for_heatmaps.csv`: counts and counts per coverage aggregated per position and amino acid, used for the count heatmaps.
+
+</details>
+
+These files document every step from raw reads to variant frequencies.
+
+### Error correction
+
+<details markdown="1">
+<summary>Output files</summary>
+
+- `intermediate_files/processed_variant_counts/<library>/` (unless `--error_correction none`)
+  - `variantCounts_filtered_by_library_error_corrected.csv`, `library_completed_variantCounts_error_corrected.csv`, `variantCounts_for_heatmaps_error_corrected.csv`: corrected versions of the tables above. The corrected counts are used by all downstream steps; in `variantCounts_filtered_by_library_error_corrected.csv`, the original counts are kept in the `counts_raw` and `counts_per_cov_raw` columns.
+  - `seq_error_rate.csv` (`--error_correction false_doubles` only): estimated sequencing error rate for every possible single-nucleotide substitution along the reference.
+- `library_QC/`
+  - `error_correction_report.html`: interactive report of the correction across all libraries of the run.
+
+</details>
+
+The error-correction report shows the single-nucleotide substitution error correlation across samples, raw versus corrected counts, the distribution of per-variant correction magnitudes, positional error bias along the ORF and a searchable per-variant table. Libraries can be selected and deselected in the report, for example to compare input and output libraries.
+
+![Error rate reproducibility](images/seq_error1.png)
+![Raw vs. corrected count frequencies](images/seq_error2.png)
+![Per-sample error profile](images/seq_error3.png)
+
+### Library QC
+
+<details markdown="1">
+<summary>Output files</summary>
+
+- `library_QC/<library>/`
+  - `counts_heatmap.pdf`: variant counts per position (x-axis) and mutant amino acid (y-axis).
+  - `counts_per_cov_heatmap.pdf`: as above, but as counts per sequencing coverage.
+  - `logdiff_plot.pdf`: sorted, log-scale distribution of counts per coverage of all programmed variants, with the log10 ratio of their 90th and 10th percentiles (LogDiff) and the number of variants without counts. Variants without counts (never observed, or corrected to 0) are drawn at half the lowest observed counts per coverage. When they make up more than 10% of the library, the 10th percentile would be this floor, so LogDiff is not reported; the share of variants without counts is shown instead.
+  - `logdiff_varying_bases.pdf`: as above, stratified by the number of changed nucleotides per codon.
+  - `rolling_coverage.pdf`: sliding-window sequencing coverage along the ORF, with the coverage required to observe every amino acid variant `--aimed_cov` times.
+  - `rolling_counts.pdf`: sliding-window variant counts along the ORF, stratified by the number of changed nucleotides per codon.
+  - `rolling_counts_per_cov.pdf`: as above, but as counts per coverage.
+  - `SeqDepth.pdf` and `seqdepth_curve.csv` (with `--run_seqdepth`, default): sequencing-depth rarefaction curve, i.e. the expected fraction of programmed variants that remain detected when the library is subsampled to lower sequencing depths.
+
+</details>
+
+These plots help to judge the success of the mutagenesis (are all positions and amino acids represented?), the evenness of the fragmentation and sequencing coverage along the ORF, and whether a library was sequenced deeply enough.
+
+![Counts per coverage heatmap](images/library_QC_counts_per_cov_heatmap.png)
+![Counts per coverage distribution](images/library_QC_logdiff_plot.png)
+![Counts per coverage distribution by number of changed bases](images/library_QC_logdiff_varying_bases.png)
+![Rolling coverage](images/library_QC_rolling_coverage.png)
+![Rolling counts](images/library_QC_rolling_counts.png)
+![Sequencing depth rarefaction](images/library_QC_SeqDepth.png)
+
+### Fitness
+
+<details markdown="1">
+<summary>Output files</summary>
+
+- `fitness/`
+  - `counts_merged.tsv`: merged variant counts of all `input` and `output` libraries of a sample.
+  - `experimentalDesign.tsv`: experimental design derived from the original samplesheet (DiMSum input format).
+  - `synonymous_wt.txt`: nucleotide sequence of the synonymous variant used as wildtype proxy.
+  - `default_results/`
+    - `fitness_estimation.tsv`: per-variant counts, raw and rescaled fitness per replicate, and mean fitness and standard deviation across replicates.
+    - `fitness_estimation_count_correlation.pdf`: pairwise variant count correlations between all libraries.
+    - `fitness_estimation_fitness_correlation.pdf`: pairwise fitness correlations between replicates.
+    - `fitness_heatmap.pdf`: mean fitness per position (x-axis) and mutant amino acid (y-axis).
+  - `single_rep_counts/<library>_fitness_input.tsv`: per-library count tables in DiMSum input format. These are written for every run, also without `--fitness`.
+  - `DiMSum_results/`
+    - `single_rep_counts/<library>_fitness_input.tsv` (with `--dimsum`): per-library count tables in DiMSum input format.
+    - `dimsum_results/` (with `--dimsum`): the complete [DiMSum](https://github.com/lehner-lab/DiMSum) default output, including its HTML `report.html`, fitness tables (`fitness_singles.txt`, `fitness_doubles.txt`, `fitness_synonymous.txt`, `fitness_wildtype.txt`, `fitness_singles_MaveDB.csv`) and `.RData` objects.
+  - `mutscan_results/` (with `--mutscan`)
+    - `fitness_estimation_mutscan_edgeR.tsv`, `fitness_estimation_mutscan_limma.tsv`: [mutscan](https://github.com/fmicompbio/mutscan) default variant enrichment estimates with edgeR and limma.
+    - `mutscan_counts_corr.pdf`, `mutscan_edgeR_volcano.pdf`, `mutscan_limma_volcano.pdf`: count correlation and volcano plots.
+
+</details>
+
+Fitness is only estimated with `--fitness` and requires at least one `input` and one `output` library per sample. See the [usage documentation](usage.md#8-fitness-estimation-optional) for how fitness is calculated. Fitness values are scaled so that wild-type synonymous variants centre on 0 and stop codon (nonsense) variants on -1.
+
+![Variant count correlations](images/fitness_estimation_count_correlation.png)
+![Fitness correlations](images/fitness_estimation_fitness_correlation.png)
+![Fitness heatmap](images/fitness_heatmap.png)
+
+### Variant effect inspection tool
+
+<details markdown="1">
+<summary>Output files</summary>
+
+- `variant_effect_inspection_tool/` (with `--fitness` and `--pdb`)
+  - `variant_effect_inspection_tool.html`: self-contained, interactive 3D structure viewer.
+  - `structure_data.json`: the per-residue and per-variant data shown in the viewer.
+
+</details>
+
+This tool projects the mean per-residue fitness, coverage, counts, counts per coverage and – when error correction is on – the positional error bias onto the supplied wildtype 3D structure. Selecting a residue shows the effect of each substitution. The file works offline and can be shared as a single file.
+
+![Fitness structure](images/fitness_structure.png)
+
+### All-in-one report
+
+<details markdown="1">
+<summary>Output files</summary>
+
+- `deepmutscan_report.html`: a single, self-contained HTML report of the run.
+
+</details>
+
+The report brings all the important metrics and visual results of a run together in one file; it can be opened in any browser and shared without any accompanying files. Features are a complete `nf-core` run overview with parameters and links to Nextflow's execution report and timeline, interactive sequencing and library QC plots per library, the error-correction report, the fitness results and heatmap (with `--fitness`), the DiMSum and mutscan results (if run), a 3D structure view (with `--pdb`), the MultiQC report, and the citations of all the tools and major dependencies that were used in the run.
+
+![Run overview](images/run_overview.png)
 
-Results generated by MultiQC collate pipeline QC from supported tools e.g. FastQC. The pipeline has special steps which also allow the software versions to be reported in the MultiQC output for future traceability. For more information about how to use MultiQC reports, see <http://multiqc.info>.### Pipeline information
+### Pipeline information
 
 <details markdown="1">
 <summary>Output files</summary>
 
 - `pipeline_info/`
-  - Reports generated by Nextflow: `execution_report.html`, `execution_timeline.html`, `execution_trace.txt` and `pipeline_dag.dot`/`pipeline_dag.svg`.
-  - Reports generated by the pipeline: `pipeline_report.html`, `pipeline_report.txt` and `software_versions.yml`. The `pipeline_report*` files will only be present if the `--email` / `--email_on_fail` parameter's are used when running the pipeline.
-  - Reformatted samplesheet files used as input to the pipeline: `samplesheet.valid.csv`.
-  - Parameters used by the pipeline run: `params.json`.
+  - Reports generated by Nextflow: `execution_report_<timestamp>.html`, `execution_timeline_<timestamp>.html`, `execution_trace_<timestamp>.txt` and `pipeline_dag_<timestamp>.html`.
+  - Reports generated by the pipeline: `pipeline_report.html`, `pipeline_report.txt` and `nf_core_deepmutscan_software_mqc_versions.yml`. The `pipeline_report*` files will only be present if the `--email` / `--email_on_fail` parameters are used when running the pipeline.
+  - Parameters used by the pipeline run: `params_<timestamp>.json`.
 
 </details>
 
-[Nextflow](https://www.nextflow.io/docs/latest/tracing.html) provides excellent functionality for generating various reports relevant to the running and execution of the pipeline. This will allow you to troubleshoot errors with the running of the pipeline, and also provide you with other information such as launch commands, run times and resource usage.
+[Nextflow](https://docs.seqera.io/platform-cloud/reports/overview) provides excellent functionality for generating various reports relevant to the running and execution of the pipeline. This will allow you to troubleshoot errors with the running of the pipeline, and also provide you with other information such as launch commands, run times and resource usage.
diff --git a/docs/usage.md b/docs/usage.md
index e7ae0cb..76a02af 100644
--- a/docs/usage.md
+++ b/docs/usage.md
@@ -1,73 +1,88 @@
-# nf-core/dmscore: Usage
+# nf-core/deepmutscan: Usage
 
-## :warning: Please read this documentation on the nf-core website: [https://nf-co.re/dmscore/usage](https://nf-co.re/dmscore/usage)
+## :warning: Please read this documentation on the nf-core website: [https://nf-co.re/deepmutscan/usage](https://nf-co.re/deepmutscan/usage)
 
 > _Documentation of pipeline parameters is generated automatically from the pipeline schema and can no longer be found in markdown files._
 
 ## Introduction
 
-<!-- TODO nf-core: Add documentation about anything specific to running your pipeline. For general topics, please point to (and add to) the main nf-core website. -->
+**nf-core/deepmutscan** is a workflow for the analysis of deep mutational scanning (DMS) data. It takes short-read next-generation DNA sequencing data of a site-saturation mutagenesis library – typically before (`input`) and after (`output`) a variant selection experiment – and turns them into quality-controlled, error-corrected variant frequencies and, optionally, relative variant fitness estimates.
+
+The pipeline was mainly designed for shotgun sequencing of long open reading frames (ORFs), where the mutated gene is randomly fragmented before sequencing and where resulting variant count signals are sparse, but it processes amplicon (tile) sequencing data in the same way.
+
+This page explains how to prepare the inputs, how to run the pipeline, and what each processing step does and why. It is intended both for new DMS users and for developers who want to understand the exact design choices.
 
 ## Samplesheet input
 
-You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 3 columns, and a header row as shown in the examples below.
+You will need to create a samplesheet with information about the sequencing libraries you would like to analyse before running the pipeline. Use the `--input` parameter to specify its location. It has to be a comma-separated file with 5 columns and a header row, as shown in the example below.
 
 ```bash
 --input '[path to samplesheet file]'
 ```
 
-### Multiple runs of the same sample
-
-The `sample` identifiers have to be the same when you have re-sequenced the same sample more than once e.g. to increase sequencing depth. The pipeline will concatenate the raw reads before performing any downstream analysis. Below is an example for the same sample sequenced across 3 lanes:
-
 ```csv title="samplesheet.csv"
-sample,fastq_1,fastq_2
-CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz
-CONTROL_REP1,AEG588A1_S1_L003_R1_001.fastq.gz,AEG588A1_S1_L003_R2_001.fastq.gz
-CONTROL_REP1,AEG588A1_S1_L004_R1_001.fastq.gz,AEG588A1_S1_L004_R2_001.fastq.gz
+sample,type,replicate,file1,file2
+ORF1,input,1,/reads/input1_R1.fastq.gz,/reads/input1_R2.fastq.gz
+ORF1,input,2,/reads/input2_R1.fastq.gz,/reads/input2_R2.fastq.gz
+ORF1,output,1,/reads/output1_R1.fastq.gz,/reads/output1_R2.fastq.gz
+ORF1,output,2,/reads/output2_R1.fastq.gz,/reads/output2_R2.fastq.gz
 ```
 
-### Full samplesheet
+| Column      | Description                                                                                                                                                                                                              |
+| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `sample`    | Name of the biological sample or experiment (e.g. the gene or condition). Must not contain spaces or `/`. As of v1.0.0, all rows of a samplesheet must have the same `sample`; analyse several samples in separate runs. |
+| `type`      | Role of the library: `input` (before selection), `output` (after selection) or `wildtype` (deep sequencing of the unmutated template, used by `--error_correction wildtype`).                                            |
+| `replicate` | Replicate number (positive integer). For fitness estimation, `input` and `output` libraries are paired by replicate number, so both need the same replicate numbers (e.g. 1, 2, 3).                                      |
+| `file1`     | Full path to the FASTQ file for read 1, with extension `.fastq.gz`, `.fq.gz`, `.fastq` or `.fq`.                                                                                                                         |
+| `file2`     | Full path to the FASTQ file for read 2, with extension `.fastq.gz`, `.fq.gz`, `.fastq` or `.fq`. As of v1.0.0, paired-end reads are required.                                                                            |
 
-The pipeline will auto-detect whether a sample is single- or paired-end using the information provided in the samplesheet. The samplesheet can have as many columns as you desire, however, there is a strict requirement for the first 3 columns to match those defined in the table below.
+Each combination of `sample`, `type` and `replicate` may appear only once. Each library gets an internal identifier `<sample>_<type>_<replicate>_pe`, which is used to name its output files and folders.
 
-A final samplesheet file consisting of both single- and paired-end data may look something like the one below. This is for 6 samples, where `TREATMENT_REP3` has been sequenced twice.
+The samplesheet is checked when the pipeline starts, and the run stops with an error message naming the problem if, for example, a file is missing, a row is duplicated or the replicate numbers of `input` and `output` libraries do not match.
 
-```csv title="samplesheet.csv"
-sample,fastq_1,fastq_2
-CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz
-CONTROL_REP2,AEG588A2_S2_L002_R1_001.fastq.gz,AEG588A2_S2_L002_R2_001.fastq.gz
-CONTROL_REP3,AEG588A3_S3_L002_R1_001.fastq.gz,AEG588A3_S3_L002_R2_001.fastq.gz
-TREATMENT_REP1,AEG588A4_S4_L003_R1_001.fastq.gz,
-TREATMENT_REP2,AEG588A5_S5_L003_R1_001.fastq.gz,
-TREATMENT_REP3,AEG588A6_S6_L003_R1_001.fastq.gz,
-TREATMENT_REP3,AEG588A6_S6_L004_R1_001.fastq.gz,
-```
+The FASTQ files themselves are checked in two steps, before any reads are processed. Problems are collected across all libraries and reported together, so that all faulty files can be replaced at once.
+
+1. **At start-up**, from the first read of each file: a file is empty, is named `.gz` but is not gzip-compressed, or is not in FASTQ format; `file1` and `file2` are the same file, start with the same read, or start with reads of different names; a file is used by more than one library.
+2. **As the first step of the workflow** ([**fq lint**](https://github.com/stjude-rust-labs/fq)), on the complete files: a compressed file is truncated or corrupted (e.g. by an interrupted download or copy), a FASTQ record is malformed, `file1` and `file2` have different numbers of reads, or the read names of `file1` and `file2` do not match record by record (mates not in the same order). All other steps wait until every library has passed.
 
-| Column    | Description                                                                                                                                                                            |
-| --------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `sample`  | Custom sample name. This entry will be identical for multiple sequencing libraries/runs from the same sample. Spaces in sample names are automatically converted to underscores (`_`). |
-| `fastq_1` | Full path to FastQ file for Illumina short reads 1. File has to be gzipped and have the extension ".fastq.gz" or ".fq.gz".                                                             |
-| `fastq_2` | Full path to FastQ file for Illumina short reads 2. File has to be gzipped and have the extension ".fastq.gz" or ".fq.gz".                                                             |
+Mates must be in the same order in `file1` and `file2`, because vsearch pairs reads by position: out-of-order mates would not merge, or merge into chimeric reads that look like double mutants. If both files contain the same reads in a different order (for example, lanes concatenated in a different order), re-pair them before running the pipeline with [`seqkit pair`](https://github.com/shenwei356/seqkit) or BBTools [`repair.sh`](https://github.com/bbushnell/BBTools).
 
-An [example samplesheet](../assets/samplesheet.csv) has been provided with the pipeline.
+## Reference and reading frame
+
+As of v1.0.0, all libraries in one run of `nf-core/deepmutscan` must stem from the same gene. Provide its wildtype sequence as a FASTA file via `--fasta`. We recommend that the reference contains N- and C-terminal flanking sequences of approximately one read length (e.g. from surrounding vector regions amplified during the sequencing library preparation), which helps to align reads more uniformly at the ends of the ORF.
+
+Use `--reading_frame` to give the position of the mutagenised ORF within that sequence, as 1-based, inclusive nucleotide coordinates in the format `start-stop`: `start` is the first nucleotide of the first codon and `stop` the last nucleotide of the last codon to be analysed, so the length of the range must be divisible by three. For example, `--reading_frame 151-450` analyses exactly 100 codons of the reference FASTA.
+
+Use `--mutagenesis_type` to define which codons were programmed at each position (default `nnk`). This determines which observed variants count as library variants for filtering, QC and fitness estimation:
+
+- `nnk`, `nns`, `nnh`, `nnn`: degenerate codons, as used e.g. in nicking mutagenesis
+- `nnk_nns`, `nnk_nns_nnh`: position-dependent degenerate codons, chosen by the third base of the wildtype codon: NNS where it is T, NNH where it is G (`nnk_nns_nnh` only), and NNK otherwise
+- `custom`: a user-defined codon library, provided with `--custom_codon_library` as a `.csv` file. Either give one global comma-separated list of codons without a header (e.g. `AAA,AAC,AAG`), or a position-wise list: a header line containing the word `Position`, followed by one row per codon position with the position and its allowed codons (e.g. `1,ACG,AAA,ACA`).
+
+> [!IMPORTANT]
+> Shotgun sequencing cannot distinguish reads from unmutated plasmids from wildtype-matching fragments of mutated plasmids, so exact wildtype reads are not used as the fitness reference. Instead, the pipeline uses a synonymous variant as a wildtype proxy. We therefore highly recommend that your library design choice includes at least some synonymous wildtype amino acid codons – degenerate NNK/NNS codons do so at almost every position.
 
 ## Running the pipeline
 
-The typical command for running the pipeline is as follows:
+The typical command for running the pipeline (here on a hypothetical gene of exactly 100 codons) is as follows:
 
-```bash
-nextflow run nf-core/dmscore --input ./samplesheet.csv --outdir ./results --genome GRCh37 -profile docker
+```bash title="example_run.sh"
+nextflow run nf-core/deepmutscan \
+   -profile <docker/singularity/.../institute> \
+   --input ./samplesheet.csv \
+   --fasta ./ref.fa \
+   --reading_frame 151-450 \
+   --outdir ./results
 ```
 
-This will launch the pipeline with the `docker` configuration profile. See below for more information about profiles.
+This will launch the pipeline with the `docker` configuration profile (or the profile of your choice, see [below](#-profile)). It performs the default read QC, read merging, alignment, filtering, variant counting, sequencing-error correction and library QC for every library in the samplesheet. Add `--fitness` to also estimate variant fitness from matched input and output libraries, and `--dimsum` and/or `--mutscan` for the additional fitness estimators.
 
 Note that the pipeline will create the following files in your working directory:
 
 ```bash
 work                # Directory containing the nextflow working files
 <OUTDIR>            # Finished results in specified location (defined with --outdir)
-.nextflow_log       # Log file from Nextflow
+.nextflow.log       # Log file from Nextflow
 # Other nextflow hidden files, eg. history of pipeline runs and old logs.
 ```
 
@@ -81,7 +96,7 @@ Pipeline settings can be provided in a `yaml` or `json` file via `-params-file <
 The above pipeline run specified with a params file in yaml format:
 
 ```bash
-nextflow run nf-core/dmscore -profile docker -params-file params.yaml
+nextflow run nf-core/deepmutscan -profile docker -params-file params.yaml
 ```
 
 with:
@@ -89,32 +104,131 @@ with:
 ```yaml title="params.yaml"
 input: './samplesheet.csv'
 outdir: './results/'
-genome: 'GRCh37'
+fasta: './ref.fa'
+reading_frame: '151-450'
+fitness: true
 <...>
 ```
 
 You can also generate such `YAML`/`JSON` files via [nf-core/launch](https://nf-co.re/launch).
 
-### Updating the pipeline
+## Pipeline steps in detail
+
+### 1. Read merging
+
+All paired-end raw reads are first quality-checked with [**FastQC**](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/).
+
+Even the highest-quality sequencing platforms do not have perfect base accuracy. To minimise the effect of base errors, which would otherwise be counted as false mutations, `nf-core/deepmutscan` uses the overlap of each read pair. As base errors on the forward and reverse read are largely independent, [**vsearch --fastq_mergepairs**](https://github.com/torognes/vsearch) converts each raw read pair into a single consensus read with adjusted base qualities. Read pairs that cannot be merged (overlap shorter than 10 bp) are discarded. The merging statistics of every library are saved in `intermediate_files/merged_reads/`.
+
+### 2. Alignment
+
+The merged reads are aligned to the reference with [**BWA-MEM**](https://github.com/lh3/bwa). BWA-MEM is a highly efficient mapping algorithm for reads ≥ 70 bp.
+
+### 3. Filtering
+
+Alignments are filtered with [**samtools view**](https://www.htslib.org/doc/samtools-view.html): only primary alignments (no unmapped, secondary or supplementary records) with a mapping quality ≥ 30 and without insertions, deletions or splice gaps are kept, as indel-containing reads are most likely artefacts in substitution libraries. The filtered alignments are coordinate-sorted and indexed with samtools, and used for variant counting.
+
+For long ORF site-saturation mutagenesis libraries, most aligned shotgun sequencing reads match the reference exactly. These wildtype reads are retained: they are ignored by the variant counter but contribute to the overall sequencing coverage and sequencing-error estimation.
+
+Merging, alignment, filtering, sorting and indexing run as one streamed, multi-threaded task per library (`MERGE_ALIGN`, using its `task.cpus`). The vsearch options (`ext.args`) and the samtools filter flags (`ext.args2`) can be changed in a custom config.
+
+> [!TIP]
+> The merging benefit is largest if the average DNA fragment size matches the read length. For example, libraries sequenced with 150 bp paired-end reads should ideally also be sheared or tagmented to a mean size of about 150 bp.
+
+### 4. Variant counting
+
+Merged reads are screened for base-level mismatches against the reference. `nf-core/deepmutscan` uses a light-weight Python counter (built on [**pysam**](https://pysam.readthedocs.io) and [**polars**](https://pola.rs)) that counts all single, double, triple and higher-order nucleotide changes per read, together with the sequencing coverage of every position. This replaces the GATK `AnalyzeSaturationMutagenesis` tool used in earlier versions; a description of every column is written next to each count table (`variant_counts_columns.tsv`).
+
+Two parameters tune the counter:
+
+- `--base_qual` (default `40`): minimum Phred base quality for a mismatch to be counted, and for a base to contribute to coverage.
+  Note what this threshold implies: `vsearch` recalculates qualities only where the two mates overlap, so a base in the overlap
+  that both mates agree on can exceed the instrument's own maximum, while bases outside the overlap keep their instrument quality.
+  On quality-binned instrument data (NovaSeq bins at Q37 or below) the default of `40` therefore restricts counting to the
+  overlapping, double-observed part of each merged read. Lower it (e.g. `--base_qual 30`) to count the full read length.
+- `--min_flank` (default `2`): minimum distance (in nucleotides) of a mismatch or covered base from the read end. Set to `0` to disable.
+
+### 5. Library annotation and filtering
+
+Variant counts are annotated with their codon and amino acid changes within `--reading_frame`, and filtered for the variants that were programmed by the chosen `--mutagenesis_type`. In addition, a library-completed table lists every programmed variant, including those that were not observed (with a near-zero placeholder count), for the count distribution plots.
+
+### 6. Single-nucleotide variant error correction
+
+Sequencing errors occur at typically low, but position- and substitution-specific rates. With very deep sequencing, they inflate the counts of variants that differ from the wildtype by only a single nucleotide. Variants with two or three nucleotide changes in one codon, however, are hardly affected. Select a correction strategy with `--error_correction`:
+
+- `false_doubles` (**default**): the library only contains single-codon changes, so reads carrying a programmed multi-nucleotide codon variant plus an unprogrammed single-nucleotide change in a nearby codon ("false double mutants") reveal the sequencing error rate of that single-nucleotide change. The error rate is estimated for every single-nucleotide variant from these read-linked observations within `--false_doubles_codon_window` codons (default `40`), and the expected number of erroneous counts is subtracted. No extra data are required. If a library contains too few false double mutants to model their coverage along the read (e.g. very small or shallow libraries), no correction is applied and a warning is written to the log. Two estimators are available via `--false_doubles_method`:
+  - `mle` (**default**): a per-variant maximum-likelihood error rate.
+  - `eb`: an empirical-Bayes estimate that shrinks the per-variant rate towards the mean of its substitution class. It is steadier for sparsely observed variants, e.g. in shallowly sequenced libraries.
+- `wildtype`: subtracts the variant-specific background measured by **additional deep sequencing of the unmutated template** from every library variant. Add these libraries to the samplesheet with `type` set to `wildtype` and the same `sample` name as the libraries they should correct. Exactly one `wildtype` library is supported; the pipeline stops with an error if there is none or more than one. In this mode, the `wildtype` libraries themselves are not included in the library QC and fitness outputs.
+- `none`: no correction; counts are used as observed.
+
+Corrected counts are used for every count-dependent step downstream (count heatmaps, positional QC and fitness). The uncorrected tables are kept next to the corrected ones, and an interactive HTML report shows the effect of the correction across all libraries of the run.
+
+### 7. DMS library quality control
+
+Based on the library-filtered (and error-corrected) counts, `nf-core/deepmutscan` produces per-library visualisations to assess mutation efficiency, coverage and saturation along the ORF:
+
+- heatmaps of variant counts and counts per coverage, per position and amino acid
+- sorted, log-scale distributions of counts per coverage of all programmed variants, also stratified by the number of changed nucleotides, summarised by the log10 ratio of their 90th and 10th percentiles
+- sliding-window profiles of coverage and counts along the ORF (window size set by `--sliding_window_size`, default `10` codons), with a reference line for the coverage needed to observe every amino acid variant `--aimed_cov` times (default `100`)
+- a sequencing-depth rarefaction curve that estimates the fraction of programmed variants recovered at lower sequencing depths (`--run_seqdepth`, on by default)
+
+### 8. Fitness estimation (optional)
+
+With `--fitness`, libraries of each `sample` are grouped into a merged count table of all `input` and `output` replicates. Fitness is then estimated per replicate as the natural logarithm of each variant's output-to-input count ratio, relative to that of a synonymous wildtype proxy variant. As the proxy, the pipeline picks the synonymous variant with two nucleotide changes in one codon (or, if there is none, one nucleotide change) with the highest mean input count. Non-synonymous variants encoding the same amino acid sequence are aggregated. Per-replicate fitness values are linearly rescaled so that the median of synonymous variants is `0` and the median of stop codon variants is `-1` (if one of these anchors is missing, fitness values are only centred), and summarised as the mean and, with more than one replicate, the standard deviation across replicates.
+
+Two parameters control which variants are scored, and how:
+
+- `--min_counts` (default `10`): minimum number of reads a variant needs in every `input` replicate to receive a fitness estimate. Variants with fewer input reads are poorly measured and are excluded. The threshold applies to each codon variant before synonymous codon variants are merged, in the same way for the default estimator, DiMSum and mutscan.
+- `--output_pseudocount` (default `1`): pseudocount added to `output` replicates in which a variant observed in the input was not detected ("dropouts"), so that strongly depleted variants still receive a finite fitness value. Set it to `0` to disable; dropout variants then receive no fitness estimate in that replicate.
+
+Two established statistical frameworks can be run on the same merged counts:
+
+- `--dimsum`: [DiMSum](https://github.com/lehner-lab/DiMSum) fitness estimates (stages 4–5 of DiMSum, without its own read processing), on the same merged count table as the default estimator. The pipeline passes `--min_counts` as `--fitnessMinInputCountAll` and `--output_pseudocount` as `--fitnessDropoutPseudocount`, so DiMSum scores the same variants. By default, DiMSum also runs with:
+  - `--mixedSubstitutions T`: the synonymous wildtype proxy can differ from the reference by one codon. With DiMSum's own default (`F`), single mutants relative to the proxy would be discarded as "mixed substitutions". With `T`, DiMSum also adds up synonymous codon variants before estimating fitness, as the default estimator does.
+  - `--fitnessErrorModel F`: DiMSum's error model needs enough variants that are well covered in every replicate, which sparse shotgun libraries often lack. Without it, DiMSum uses count-based errors only and no replicate normalisation. For deeply sequenced libraries, the error model can be switched on.
+  - `--retainIntermediateFiles T`.
+
+  These options are set with `ext.args` and can be changed in a custom config, for example:
+
+  ```groovy
+  process {
+      withName: 'RUN_DIMSUM' { ext.args = '--mixedSubstitutions T --fitnessErrorModel T --retainIntermediateFiles T' }
+  }
+  ```
+
+  DiMSum's container is currently only available for x86-64 (amd64), so on ARM machines (e.g. Apple Silicon) `--dimsum` runs under amd64 emulation, and the `arm64` profile has no native image for it.
+
+- `--mutscan`: [mutscan](https://github.com/fmicompbio/mutscan) enrichment estimates with edgeR and limma, on the same merged count table. mutscan is currently only packaged for x86-64 (amd64), so on ARM machines (e.g. Apple Silicon) `--mutscan` runs under amd64 emulation, and the `arm64` profile has no native image for it.
+
+### Interactive variant effect inspection tool (`--pdb`)
+
+When `--fitness` is set and a wildtype 3D structure is supplied via `--pdb <structure.pdb>` (an experimental structure, an AlphaFold DB model, or any PDB file matching the wildtype ORF), the pipeline additionally builds a portable, interactive HTML tool. It projects per-residue fitness, coverage, counts, counts per coverage and – when error correction is on – the positional error bias onto the structure; clicking a residue shows the effect of each substitution. Structure prediction is not part of the v1.0.0 release, so a user-specified structure file is required to enable the tool.
+
+### 9. Reporting
+
+`nf-core/deepmutscan` writes a single, self-contained `deepmutscan_report.html` to the top of the results directory. It embeds the library QC plots, the error-correction report, the fitness results (and DiMSum/mutscan output, if run), the [MultiQC](https://multiqc.info) report, links to Nextflow's execution report and timeline, and the citations of all tools that were used, so it can be shared as a single file.
+
+## Updating the pipeline
 
-When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you're running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline:
+When you run the original command above, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use this cached version if available - even if the pipeline has been updated since. To make sure that you are running the latest version of the pipeline, make sure that you regularly update the cached version:
 
 ```bash
-nextflow pull nf-core/dmscore
+nextflow pull nf-core/deepmutscan
 ```
 
-### Reproducibility
+## Reproducibility
 
-It is a good idea to specify the pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you'll be running the same version of the pipeline, even if there have been changes to the code since.
+It is a good idea to specify the pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used consistently. If you keep using the same tag, you'll be running the same version of the pipeline, even if there have been changes to the code since.
 
-First, go to the [nf-core/dmscore releases page](https://github.com/nf-core/dmscore/releases) and find the latest pipeline version - numeric only (eg. `1.3.1`). Then specify this when running the pipeline with `-r` (one hyphen) - eg. `-r 1.3.1`. Of course, you can switch to another version by changing the number after the `-r` flag.
+First, go to the [nf-core/deepmutscan releases page](https://github.com/nf-core/deepmutscan/releases) and find the latest pipeline version - numeric only (eg. `1.0.0`). Then specify this when running the pipeline with `-r` (one hyphen) - eg. `-r 1.0.0`.
 
-This version number will be logged in reports when you run the pipeline, so that you'll know what you used when you look back in the future. For example, at the bottom of the MultiQC reports.
+This version number will also be logged in reports when you run the pipeline, for example at the bottom of the MultiQC reports.
 
-To further assist in reproducibility, you can use share and reuse [parameter files](#running-the-pipeline) to repeat pipeline runs with the same settings without having to write out a command with every single parameter.
+To further assist in reproducibility, you can share and reuse [parameter files](#running-the-pipeline) to repeat pipeline runs with the same settings without having to write out a command with every single parameter.
 
 > [!TIP]
-> If you wish to share such profile (such as upload as supplementary material for academic publications), make sure to NOT include cluster specific paths to files, nor institutional specific profiles.
+> If you wish to share such a profile (e.g. providing it as supplementary material for academic publications), make sure to _not_ include your cluster specific file paths or institutional specific profiles.
 
 ## Core Nextflow arguments
 
@@ -135,7 +249,7 @@ The pipeline also dynamically loads configurations from [https://github.com/nf-c
 Note that multiple profiles can be loaded, for example: `-profile test,docker` - the order of arguments is important!
 They are loaded in sequence, so later profiles can overwrite earlier profiles.
 
-If `-profile` is not specified, the pipeline will run locally and expect all software to be installed and available on the `PATH`. This is _not_ recommended, since it can lead to different results on different machines dependent on the computer environment.
+If `-profile` is not specified, the pipeline will run locally and expect all software to be installed and available on the `PATH`. This is _not_ recommended, since it may lead to varying or even irreproducible results across users' different computer environments.
 
 - `test`
   - A profile with a complete configuration for automated testing
@@ -153,13 +267,13 @@ If `-profile` is not specified, the pipeline will run locally and expect all sof
 - `apptainer`
   - A generic configuration profile to be used with [Apptainer](https://apptainer.org/)
 - `wave`
-  - A generic configuration profile to enable [Wave](https://seqera.io/wave/) containers. Use together with one of the above (requires Nextflow ` 24.03.0-edge` or later).
+  - A generic configuration profile to enable [Wave](https://seqera.io/wave/) containers. Use together with one of the above (requires Nextflow `24.03.0-edge` or later).
 - `conda`
   - A generic configuration profile to be used with [Conda](https://conda.io/docs/). Please only use Conda as a last resort i.e. when it's not possible to run the pipeline with Docker, Singularity, Podman, Shifter, Charliecloud, or Apptainer.
 
 ### `-resume`
 
-Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files' contents as well. For more info about this parameter, see [this blog post](https://www.nextflow.io/blog/2019/demystifying-nextflow-resume.html).
+Specify this when restarting a pipeline. Nextflow will use cached results (from within the `/work` directory) from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files' contents as well. For more info about this parameter, see [this blog post](https://www.nextflow.io/blog/2019/demystifying-nextflow-resume.html).
 
 You can also supply a run name to resume a specific run: `-resume [run-name]`. Use the `nextflow log` command to show previous run names.
 
@@ -173,19 +287,19 @@ Specify the path to a specific config file (this is a core Nextflow command). Se
 
 Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customise the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the pipeline steps, if the job exits with any of the error codes specified [here](https://github.com/nf-core/rnaseq/blob/4c27ef5610c87db00c3c5a3eed10b1d161abf575/conf/base.config#L18) it will automatically be resubmitted with higher resources request (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.
 
-To change the resource requests, please see the [max resources](https://nf-co.re/docs/usage/configuration#max-resources) and [tuning workflow resources](https://nf-co.re/docs/usage/configuration#tuning-workflow-resources) section of the nf-core website.
+To change the resource requests, please see the [max resources](https://nf-co.re/docs/running/configuration/nextflow-for-your-system#set-max-resources) and [customise process resources](https://nf-co.re/docs/running/configuration/nextflow-for-your-system#customize-process-resources) section of the nf-core website.
 
 ### Custom Containers
 
 In some cases, you may wish to change the container or conda environment used by a pipeline steps for a particular tool. By default, nf-core pipelines use containers and software from the [biocontainers](https://biocontainers.pro/) or [bioconda](https://bioconda.github.io/) projects. However, in some cases the pipeline specified version maybe out of date.
 
-To use a different container from the default container or conda environment specified in a pipeline, please see the [updating tool versions](https://nf-co.re/docs/usage/configuration#updating-tool-versions) section of the nf-core website.
+To use a different container from the default container or conda environment specified in a pipeline, please see the [updating tool versions](https://nf-co.re/docs/running/configuration/nextflow-for-your-system#update-tool-versions) section of the nf-core website.
 
 ### Custom Tool Arguments
 
 A pipeline might not always support every possible argument or option of a particular tool used in pipeline. Fortunately, nf-core pipelines provide some freedom to users to insert additional parameters that the pipeline does not include by default.
 
-To learn how to provide additional arguments to a particular tool of the pipeline, please see the [customising tool arguments](https://nf-co.re/docs/usage/configuration#customising-tool-arguments) section of the nf-core website.
+To learn how to provide additional arguments to a particular tool of the pipeline, please see the [customising tool arguments](https://nf-co.re/docs/running/configuration/nextflow-for-your-system#modifying-tool-arguments) section of the nf-core website.
 
 ### nf-core/configs
 
@@ -207,7 +321,7 @@ Some HPC setups also allow you to run nextflow within a cluster job submitted yo
 ## Nextflow memory requirements
 
 In some cases, the Nextflow Java virtual machines can start to request a large amount of memory.
-We recommend adding the following line to your environment to limit this (typically in `~/.bashrc` or `~./bash_profile`):
+We recommend adding the following line to your environment to limit this (typically in `~/.bashrc` or `~/.bash_profile`):
 
 ```bash
 NXF_OPTS='-Xms1g -Xmx4g'
diff --git a/main.nf b/main.nf
index 8c9943a..0ff7bcc 100644
--- a/main.nf
+++ b/main.nf
@@ -1,11 +1,11 @@
 #!/usr/bin/env nextflow
 /*
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-    nf-core/dmscore
+    nf-core/deepmutscan
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-    Github : https://github.com/nf-core/dmscore
-    Website: https://nf-co.re/dmscore
-    Slack  : https://nfcore.slack.com/channels/dmscore
+    Github : https://github.com/nf-core/deepmutscan
+    Website: https://nf-co.re/deepmutscan
+    Slack  : https://nfcore.slack.com/channels/deepmutscan
 ----------------------------------------------------------------------------------------
 */
 
@@ -15,21 +15,9 @@
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 */
 
-include { DMSCORE  } from './workflows/dmscore'
-include { PIPELINE_INITIALISATION } from './subworkflows/local/utils_nfcore_dmscore_pipeline'
-include { PIPELINE_COMPLETION     } from './subworkflows/local/utils_nfcore_dmscore_pipeline'
-include { getGenomeAttribute      } from './subworkflows/local/utils_nfcore_dmscore_pipeline'
-
-/*
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-    GENOME PARAMETER VALUES
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-*/
-
-// TODO nf-core: Remove this line if you don't need a FASTA file
-//   This is an example of how to use getGenomeAttribute() to fetch parameters
-//   from igenomes.config using `--genome`
-params.fasta = getGenomeAttribute('fasta')
+include { DEEPMUTSCAN  } from './workflows/deepmutscan'
+include { PIPELINE_INITIALISATION } from './subworkflows/local/utils_nfcore_deepmutscan_pipeline'
+include { PIPELINE_COMPLETION     } from './subworkflows/local/utils_nfcore_deepmutscan_pipeline'
 
 /*
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -40,7 +28,7 @@ params.fasta = getGenomeAttribute('fasta')
 //
 // WORKFLOW: Run main analysis pipeline depending on type of input
 //
-workflow NFCORE_DMSCORE {
+workflow NFCORE_DEEPMUTSCAN {
 
     take:
     samplesheet // channel: samplesheet read in from --input
@@ -50,12 +38,17 @@ workflow NFCORE_DMSCORE {
     //
     // WORKFLOW: Run pipeline
     //
-    DMSCORE (
-        samplesheet
+    DEEPMUTSCAN (
+        samplesheet,
+        params.multiqc_config,
+        params.multiqc_logo,
+        params.multiqc_methods_description,
+        params.outdir,
     )
     emit:
-    multiqc_report = DMSCORE.out.multiqc_report // channel: /path/to/multiqc_report.html
+    multiqc_report = DEEPMUTSCAN.out.multiqc_report // channel: /path/to/multiqc_report.html
 }
+
 /*
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
     RUN MAIN WORKFLOW
@@ -80,7 +73,7 @@ workflow {
     //
     // WORKFLOW: Run main workflow
     //
-    NFCORE_DMSCORE (
+    NFCORE_DEEPMUTSCAN (
         PIPELINE_INITIALISATION.out.samplesheet
     )
     //
@@ -92,8 +85,7 @@ workflow {
         params.plaintext_email,
         params.outdir,
         params.monochrome_logs,
-        params.hook_url,
-        NFCORE_DMSCORE.out.multiqc_report
+        NFCORE_DEEPMUTSCAN.out.multiqc_report
     )
 }
 
diff --git a/modules.json b/modules.json
index 910602b..cc0b49c 100644
--- a/modules.json
+++ b/modules.json
@@ -1,18 +1,23 @@
 {
-    "name": "nf-core/dmscore",
-    "homePage": "https://github.com/nf-core/dmscore",
+    "name": "nf-core/deepmutscan",
+    "homePage": "https://github.com/nf-core/deepmutscan",
     "repos": {
         "https://github.com/nf-core/modules.git": {
             "modules": {
                 "nf-core": {
+                    "bwa/index": {
+                        "branch": "master",
+                        "git_sha": "6d46786420b4d7bc88eba026eb389c0c5535d120",
+                        "installed_by": ["modules"]
+                    },
                     "fastqc": {
                         "branch": "master",
-                        "git_sha": "dc94b6ee04a05ddb9f7ae050712ff30a13149164",
+                        "git_sha": "18eb358065b06f2d3419e37383b002fe7db3da02",
                         "installed_by": ["modules"]
                     },
                     "multiqc": {
                         "branch": "master",
-                        "git_sha": "cf17ca47590cc578dfb47db1c2a44ef86f89976d",
+                        "git_sha": "98403d15b0e50edae1f3fec5eae5e24982f1fade",
                         "installed_by": ["modules"]
                     }
                 }
@@ -21,17 +26,17 @@
                 "nf-core": {
                     "utils_nextflow_pipeline": {
                         "branch": "master",
-                        "git_sha": "c2b22d85f30a706a3073387f30380704fcae013b",
+                        "git_sha": "6ef220fd9252b42fb3c50348bbdbcb6246730686",
                         "installed_by": ["subworkflows"]
                     },
                     "utils_nfcore_pipeline": {
                         "branch": "master",
-                        "git_sha": "51ae5406a030d4da1e49e4dab49756844fdd6c7a",
+                        "git_sha": "6ef220fd9252b42fb3c50348bbdbcb6246730686",
                         "installed_by": ["subworkflows"]
                     },
                     "utils_nfschema_plugin": {
                         "branch": "master",
-                        "git_sha": "2fd2cd6d0e7b273747f32e465fdc6bcc3ae0814e",
+                        "git_sha": "a7b27fd25bfa8dcc07d299e88bd790585901a436",
                         "installed_by": ["subworkflows"]
                     }
                 }
diff --git a/modules/local/bamprocessing/merge_align/.conda-lock/linux_amd64-bd-68780a2d1eaef64d_1.txt b/modules/local/bamprocessing/merge_align/.conda-lock/linux_amd64-bd-68780a2d1eaef64d_1.txt
new file mode 100644
index 0000000..703ea92
--- /dev/null
+++ b/modules/local/bamprocessing/merge_align/.conda-lock/linux_amd64-bd-68780a2d1eaef64d_1.txt
@@ -0,0 +1,397 @@
+version: 6
+environments:
+  default:
+    channels:
+    - url: https://conda.anaconda.org/conda-forge/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/bioconda/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
+      linux-64:
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bwa-0.7.19-h577a1d6_1.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_10.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.9-hebe6cf0_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.10.23-hbd8a1cb_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/htslib-1.24-ha79157c_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-79.1-py314hb486520_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-h7cc23a3_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.22.2-hbc21106_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.23.0-ha042cf0_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.26-h351257e_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20260512-pl5321h373387f_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-h280c20c_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-16.2.0-ha9f2e26_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-16.2.0-he0feb66_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.3-hb03c661_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.70.0-hc5e5977_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libpsl-0.23.1-h7fe8442_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-h6154650_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-16.2.0-h934c35e_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.38-h280c20c_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.2-h25fd6f3_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.6-hdb14827_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.6.5-h781a0a9_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/perl-5.44.0-1_h7cc23a3_perl5.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/procps-ng-4.0.6-h18c060e_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/samtools-1.21-h96c455f_1.tar.bz2
+      - conda: https://conda.anaconda.org/bioconda/linux-64/vsearch-2.30.0-hd6d6fdc_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/which-2.25-h171cf75_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.2-h25fd6f3_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_7.conda
+packages:
+- conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+  build_number: 20
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diff --git a/modules/local/bamprocessing/merge_align/.conda-lock/linux_arm64-bd-7b395b04348e2784_1.txt b/modules/local/bamprocessing/merge_align/.conda-lock/linux_arm64-bd-7b395b04348e2784_1.txt
new file mode 100644
index 0000000..0aaee60
--- /dev/null
+++ b/modules/local/bamprocessing/merge_align/.conda-lock/linux_arm64-bd-7b395b04348e2784_1.txt
@@ -0,0 +1,368 @@
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diff --git a/modules/local/bamprocessing/merge_align/environment.yml b/modules/local/bamprocessing/merge_align/environment.yml
new file mode 100644
index 0000000..0e199ab
--- /dev/null
+++ b/modules/local/bamprocessing/merge_align/environment.yml
@@ -0,0 +1,7 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bwa=0.7.19
+  - bioconda::samtools=1.21
+  - bioconda::vsearch=2.30.0
diff --git a/modules/local/bamprocessing/merge_align/main.nf b/modules/local/bamprocessing/merge_align/main.nf
new file mode 100644
index 0000000..27da122
--- /dev/null
+++ b/modules/local/bamprocessing/merge_align/main.nf
@@ -0,0 +1,87 @@
+
+process MERGE_ALIGN {
+    tag "$meta.id"
+    label 'process_medium'
+
+    conda "${moduleDir}/environment.yml"
+    container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/1e/1e03db44377380bacb3a5ec57a7e77ad36603863b3f0e821d2268a47981572b1/data'
+        : 'community.wave.seqera.io/library/bwa_samtools_vsearch:68780a2d1eaef64d'}"
+
+    input:
+    tuple val(meta), path(reads)
+    tuple val(meta2), path(index)
+
+    output:
+    tuple val(meta), path("*.sorted.bam"), path("*.sorted.bam.bai"), emit: bam
+    tuple val(meta), path("*.vsearch_mergepairs.log"), emit: log
+    tuple val("${task.process}"), val('bwa'), eval('bwa 2>&1 | sed -n "s/^Version: //p"'), topic: versions, emit: versions_bwa
+    tuple val("${task.process}"), val('samtools'), eval("samtools version | sed '1!d;s/.* //'"), topic: versions, emit: versions_samtools
+    tuple val("${task.process}"), val('vsearch'), eval("vsearch --version 2>&1 | head -n1 | sed 's/vsearch v//; s/_.*//'"), topic: versions, emit: versions_vsearch
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    def args = task.ext.args ?: ''
+    def args2 = task.ext.args2 ?: ''
+    def prefix = task.ext.prefix ?: "${meta.id}"
+    """
+    # The BWA index prefix, resolved with a glob and bash parameter expansion instead of `find`.
+    # Seqera Containers builds the Docker image of a conda spec on an Ubuntu base but the
+    # Singularity image on an Amazon Linux 2023 one, and that base ships no findutils, so the usual
+    # `find -L ./ -name "*.amb"` fails with exit 127 under -profile singularity. Globbing needs no
+    # external binary. The leading ./ is kept so the prefix, which bwa records in the BAM's @PG
+    # header, stays exactly what find returned.
+    INDEX=''
+    for amb in ./${index}/*.amb; do
+        if [ -e "\$amb" ]; then INDEX="\${amb%.amb}"; fi
+    done
+    if [ -z "\$INDEX" ]; then
+        echo "No BWA index (*.amb) found in './${index}'" >&2
+        exit 1
+    fi
+
+    # Merge the overlapping mates of each raw read pair into one consensus read, so the overlap is
+    # counted once and its bases carry a recalculated quality. Merging comes first, before any
+    # alignment: the reads are aligned once (as merged reads), and the filter below acts on exactly
+    # the alignments the counter reads. Pairs that cannot be merged are discarded.
+    #
+    # The merged reads are streamed into bwa mem rather than written out (vsearch cannot gzip its
+    # output, and the merged FASTQ is ~27 GB per full-size library), and the alignments are filtered
+    # on the fly: no unmapped, secondary or supplementary records and MAPQ >= 30 (ext.args2), and no
+    # insertions, deletions or splice gaps. Without that filter, supplementary alignments of
+    # chimeric merged reads get counted twice, low-MAPQ hits get counted at all, and a merged read
+    # carrying a deletion is reported as whatever substitution it also happens to carry
+    # (gatk_formating keeps only X events). Wildtype reads are kept: the error correction needs them.
+    # The filtered alignments are coordinate-sorted and indexed in the same pipe: an unsorted BAM is
+    # about 3.5 times larger, and writing it out for a separate sort and index task only costs time.
+    vsearch --fastq_mergepairs ${reads[0]} \\
+        --reverse ${reads[1]} \\
+        --fastqout - \\
+        --threads $task.cpus \\
+        $args \\
+        2> ${prefix}.vsearch_mergepairs.log \\
+        | bwa mem -t $task.cpus \$INDEX - \\
+        | samtools view -h $args2 - \\
+        | awk '/^@/ || \$6 !~ /[IDN]/' \\
+        | samtools sort --threads $task.cpus -o ${prefix}.sorted.bam##idx##${prefix}.sorted.bam.bai --write-index -
+
+    # vsearch's first log line reports the host's RAM and core count, so the file differs
+    # between machines and the nf-test snapshot can never match across them. Drop that line and
+    # keep the statistics, which are reproducible; the version is reported through the versions
+    # topic anyway.
+    sed '1d' ${prefix}.vsearch_mergepairs.log > mergepairs.stats && mv mergepairs.stats ${prefix}.vsearch_mergepairs.log
+
+    # vsearch's merging statistics, also in the task log
+    cat ${prefix}.vsearch_mergepairs.log >&2
+    """
+
+    stub:
+    def prefix = task.ext.prefix ?: "${meta.id}"
+    """
+    touch ${prefix}.sorted.bam
+    touch ${prefix}.sorted.bam.bai
+    touch ${prefix}.vsearch_mergepairs.log
+    """
+}
diff --git a/modules/local/bamprocessing/merge_align/meta.yml b/modules/local/bamprocessing/merge_align/meta.yml
new file mode 100644
index 0000000..1e2e1e2
--- /dev/null
+++ b/modules/local/bamprocessing/merge_align/meta.yml
@@ -0,0 +1,130 @@
+name: "merge_align"
+description: Merges the overlapping mates of raw paired-end reads into consensus reads using VSEARCH, aligns the merged reads to the reference using BWA-MEM, filters the alignments with samtools (no unmapped, secondary, supplementary, low mapping-quality or indel-containing alignments), and coordinate-sorts and indexes them, streamed in one step.
+keywords:
+  - paired-end merging
+  - alignment
+  - bwa
+  - vsearch
+  - samtools
+  - dms
+tools:
+  - "samtools":
+      description: "Tools for manipulating next-generation sequencing data"
+      homepage: "http://www.htslib.org/"
+      documentation: "http://www.htslib.org/doc/samtools.html"
+      tool_dev_url: "https://github.com/samtools/samtools"
+      doi: "10.1093/bioinformatics/btp352"
+      licence: ["MIT/Expat"]
+  - "vsearch":
+      description: "Versatile open-source tool for metagenomics"
+      homepage: "https://github.com/torognes/vsearch"
+      documentation: "https://github.com/torognes/vsearch/blob/master/README.md"
+      doi: "10.7717/peerj.2584"
+      licence: ["GPL-3.0-or-later", "BSD-2-Clause"]
+  - "bwa":
+      description: "Burrow-Wheeler Aligner for short-read alignment"
+      homepage: "http://bio-bwa.sourceforge.net/"
+      documentation: "http://bio-bwa.sourceforge.net/bwa.shtml"
+      doi: "10.1093/bioinformatics/btp324"
+      licence: ["GPL-3.0-or-later"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', single_end:false ]`
+    - reads:
+        type: file
+        description: Raw forward and reverse FASTQ files of one paired-end library
+        pattern: "*.{fastq,fq}{,.gz}"
+  - - meta2:
+        type: map
+        description: Groovy Map containing reference information
+    - index:
+        type: directory
+        description: BWA genome index files, as produced by BWA_INDEX
+        pattern: "bwa"
+
+output:
+  - bam:
+      - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. `[ id:'test', single_end:false ]`
+      - "*.sorted.bam":
+          type: file
+          description: Coordinate-sorted BAM file of the merged, aligned and filtered reads
+          pattern: "*.sorted.bam"
+      - "*.sorted.bam.bai":
+          type: file
+          description: BAM index
+          pattern: "*.sorted.bam.bai"
+  - log:
+      - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+      - "*.vsearch_mergepairs.log":
+          type: file
+          description: vsearch merging statistics (pairs merged, reasons for failed merges, fragment lengths, expected errors)
+          pattern: "*.vsearch_mergepairs.log"
+  - versions_bwa:
+      - ${task.process}:
+          type: string
+          description: The name of the process
+      - bwa:
+          type: string
+          description: The name of the tool
+      - 'bwa 2>&1 | sed -n "s/^Version: //p"':
+          type: eval
+          description: The expression to obtain the version of the tool
+  - versions_samtools:
+      - ${task.process}:
+          type: string
+          description: The name of the process
+      - samtools:
+          type: string
+          description: The name of the tool
+      - "samtools version | sed '1!d;s/.* //'":
+          type: eval
+          description: The expression to obtain the version of the tool
+  - versions_vsearch:
+      - ${task.process}:
+          type: string
+          description: The name of the process
+      - vsearch:
+          type: string
+          description: The name of the tool
+      - "vsearch --version 2>&1 | head -n1 | sed 's/vsearch v//; s/_.*//'":
+          type: eval
+          description: The expression to obtain the version of the tool
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+containers:
+  docker:
+    linux/arm64:
+      name: community.wave.seqera.io/library/bwa_samtools_vsearch:7b395b04348e2784
+      build_id: bd-7b395b04348e2784_1
+      scan_id: sc-e65dd0586cbc897a_1
+    linux/amd64:
+      name: community.wave.seqera.io/library/bwa_samtools_vsearch:68780a2d1eaef64d
+      build_id: bd-68780a2d1eaef64d_1
+      scan_id: sc-698b2de0eed7923f_1
+  singularity:
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/bwa_samtools_vsearch:a79e451fc67cfde0
+      build_id: bd-a79e451fc67cfde0_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/6f/6f3676079712b1ee571f0a90e69b8d197fcb1ba68085cf475a25cc74bd4cb45a/data
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/bwa_samtools_vsearch:22b018a8e6d18e4f
+      build_id: bd-22b018a8e6d18e4f_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/1e/1e03db44377380bacb3a5ec57a7e77ad36603863b3f0e821d2268a47981572b1/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/bamprocessing/merge_align/.conda-lock/linux_amd64-bd-68780a2d1eaef64d_1.txt
+    linux/arm64:
+      lock_file: modules/local/bamprocessing/merge_align/.conda-lock/linux_arm64-bd-7b395b04348e2784_1.txt
diff --git a/modules/local/dmsanalysis/aa_seq/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt b/modules/local/dmsanalysis/aa_seq/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
new file mode 100644
index 0000000..be8d7e8
--- /dev/null
+++ b/modules/local/dmsanalysis/aa_seq/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
@@ -0,0 +1,2942 @@
+version: 6
+environments:
+  default:
+    channels:
+    - url: https://conda.anaconda.org/conda-forge/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
+      linux-64:
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+  license_family: BSD
+  size: 601301
+  timestamp: 1786599621503
diff --git a/modules/local/dmsanalysis/aa_seq/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt b/modules/local/dmsanalysis/aa_seq/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
new file mode 100644
index 0000000..10e5993
--- /dev/null
+++ b/modules/local/dmsanalysis/aa_seq/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
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diff --git a/modules/local/dmsanalysis/aa_seq/environment.yml b/modules/local/dmsanalysis/aa_seq/environment.yml
new file mode 100644
index 0000000..5f5785d
--- /dev/null
+++ b/modules/local/dmsanalysis/aa_seq/environment.yml
@@ -0,0 +1,19 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+# Shared by 17 R modules (all except RUN_MUTSCAN, RUN_DIMSUM and the false-doubles error correction).
+# Keep the copies identical:
+# the one container image these modules use is built from exactly this list.
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/dmsanalysis/aa_seq/main.nf b/modules/local/dmsanalysis/aa_seq/main.nf
new file mode 100644
index 0000000..ad20999
--- /dev/null
+++ b/modules/local/dmsanalysis/aa_seq/main.nf
@@ -0,0 +1,31 @@
+process DMSANALYSIS_AASEQ {
+    tag "amino_acid_sequence"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7'}"
+
+    input:
+    tuple val(meta), path(wt_seq)
+    val pos_range
+
+    output:
+    tuple val(meta), path("aa_seq.txt"), emit: aa_seq
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'aa_seq.R'
+
+    stub:
+    """
+    touch aa_seq.txt
+    echo "DMSANALYSIS_AASEQ:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+    """
+}
diff --git a/modules/local/dmsanalysis/aa_seq/meta.yml b/modules/local/dmsanalysis/aa_seq/meta.yml
new file mode 100644
index 0000000..7f6a023
--- /dev/null
+++ b/modules/local/dmsanalysis/aa_seq/meta.yml
@@ -0,0 +1,72 @@
+name: "dmsanalysis_aaseq"
+description: Translates a wild-type DNA sequence into an amino acid sequence based on a provided coding region (ORF) range.
+keywords:
+  - translation
+  - amino acid
+  - dna
+  - dms
+  - biostrings
+tools:
+  - "bioconductor-biostrings":
+      description: "Memory efficient string containers, string matching algorithms, and other utilities, for fast manipulation of large biological sequences or sets of sequences"
+      homepage: "https://bioconductor.org/packages/Biostrings"
+      documentation: "https://bioconductor.org/packages/release/bioc/manuals/Biostrings/man/Biostrings.pdf"
+      licence: ["Artistic-2.0"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'ref' ]`
+    - wt_seq:
+        type: file
+        description: FASTA file containing the wild-type DNA reference sequence
+        pattern: "*.{fasta,fa}"
+  - - pos_range:
+        type: string
+        description: Start and stop codon positions in the format 'start-stop', e.g., '352-1383'
+
+output:
+  - aa_seq:
+      - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+      - aa_seq.txt:
+          type: file
+          description: Text file containing the translated amino acid sequence
+          pattern: "aa_seq.txt"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+containers:
+  docker:
+    linux/amd64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7
+      build_id: bd-32f1af0a83a018e7_1
+      scan_id: sc-337510c2c910aa1c_1
+    linux/arm64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb
+      build_id: bd-880bbdecb19543bb_1
+      scan_id: sc-09c9d098a09068b9_1
+  singularity:
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7
+      build_id: bd-db46247d9810b3e7_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb
+      build_id: bd-7202e5d19667bddb_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/dmsanalysis/aa_seq/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
+    linux/arm64:
+      lock_file: modules/local/dmsanalysis/aa_seq/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
diff --git a/modules/local/dmsanalysis/aa_seq/templates/aa_seq.R b/modules/local/dmsanalysis/aa_seq/templates/aa_seq.R
new file mode 100644
index 0000000..7c2e8e5
--- /dev/null
+++ b/modules/local/dmsanalysis/aa_seq/templates/aa_seq.R
@@ -0,0 +1,65 @@
+#!/usr/bin/env Rscript
+
+# input: wildtype-seq, start&stopp pos.
+# output: amino acid sequence within the start-stop frame (.txt)
+
+# Load necessary libraries
+suppressMessages(library(Biostrings))
+
+# Define the function
+aa_seq <- function(wt_seq_input, pos_range, output_file) {
+  # Parse the start and stop positions from the input format "start-stop"
+  positions <- unlist(strsplit(pos_range, "-"))
+  start_pos <- as.numeric(positions[1])
+  stop_pos <- as.numeric(positions[2])
+
+  # Check if the input is a file or a string
+  if (file.exists(wt_seq_input)) {
+    # If it's a file, read the sequence from the fasta file
+    seq_data <- readDNAStringSet(filepath = wt_seq_input)
+    wt_seq <- seq_data[[1]]  # Extract the sequence
+  } else {
+    # Otherwise, treat the input as a sequence string
+    wt_seq <- DNAString(wt_seq_input)
+  }
+
+  # Extract the sequence between start and stop codons
+  coding_seq <- subseq(wt_seq, start = start_pos, end = stop_pos)
+
+  # Translate the coding sequence into an amino acid sequence
+  aa_seq <- translate(coding_seq)
+
+  # Write the amino acid sequence to a .txt file
+  write(as.character(aa_seq), file = output_file)
+}
+
+
+#####
+# run function
+#####
+aa_seq(
+  wt_seq_input = "$wt_seq",
+  "$pos_range",
+  "aa_seq.txt"
+  )
+
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+Biostrings_version <- as.character(packageVersion("Biostrings"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(Biostrings_version) == 0) Biostrings_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-Biostrings:', Biostrings_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/dmsanalysis/error_correction_false_doubles/.conda-lock/linux_amd64-bd-801bac9c074a1f9b_1.txt b/modules/local/dmsanalysis/error_correction_false_doubles/.conda-lock/linux_amd64-bd-801bac9c074a1f9b_1.txt
new file mode 100644
index 0000000..8f8a4a5
--- /dev/null
+++ b/modules/local/dmsanalysis/error_correction_false_doubles/.conda-lock/linux_amd64-bd-801bac9c074a1f9b_1.txt
@@ -0,0 +1,1399 @@
+version: 6
+environments:
+  default:
+    channels:
+    - url: https://conda.anaconda.org/conda-forge/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
+      linux-64:
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.46.1-default_hfdba357_103.conda
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-biocgenerics-0.56.0-r45hdfd78af_2.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-biostrings-2.78.0-r45h01b2380_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-iranges-2.44.0-r45h01b2380_1.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-s4vectors-0.48.1-r45h262fe30_0.conda
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diff --git a/modules/local/dmsanalysis/error_correction_false_doubles/.conda-lock/linux_arm64-bd-fbcc673012c62cb0_1.txt b/modules/local/dmsanalysis/error_correction_false_doubles/.conda-lock/linux_arm64-bd-fbcc673012c62cb0_1.txt
new file mode 100644
index 0000000..81ba8a2
--- /dev/null
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diff --git a/modules/local/dmsanalysis/error_correction_false_doubles/environment.yml b/modules/local/dmsanalysis/error_correction_false_doubles/environment.yml
new file mode 100644
index 0000000..c5618ea
--- /dev/null
+++ b/modules/local/dmsanalysis/error_correction_false_doubles/environment.yml
@@ -0,0 +1,11 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+# The false-doubles error-rate model has its own image, so it can be reused outside deepmutscan.
+# Keep this list to what error_correction_false_doubles.R loads.
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-stringr=1.6.0
diff --git a/modules/local/dmsanalysis/error_correction_false_doubles/main.nf b/modules/local/dmsanalysis/error_correction_false_doubles/main.nf
new file mode 100644
index 0000000..c1316f9
--- /dev/null
+++ b/modules/local/dmsanalysis/error_correction_false_doubles/main.nf
@@ -0,0 +1,42 @@
+process DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES {
+    tag "$meta.id"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/e5/e5a2e5419dff47f9dbcea8823692f60f9a1b1c1a1ee32ae56eef0ce2989f7b72/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-stringr:801bac9c074a1f9b'}"
+
+    input:
+    tuple val(meta), path(raw_counts), path(filtered), path(completed)
+    path wt_fasta
+    path aa_seq
+    val method
+    val codon_window
+
+    output:
+    tuple val(meta),
+        path("variantCounts_filtered_by_library_error_corrected.csv"),
+        path("variantCounts_for_heatmaps_error_corrected.csv"),
+        path("library_completed_variantCounts_error_corrected.csv"),
+        emit: corrected
+    tuple val(meta), path("seq_error_rate.csv"), emit: seq_error_rate
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'error_correction_false_doubles.R'
+
+    stub:
+    """
+    touch variantCounts_filtered_by_library_error_corrected.csv
+    touch variantCounts_for_heatmaps_error_corrected.csv
+    touch library_completed_variantCounts_error_corrected.csv
+    touch seq_error_rate.csv
+    echo "DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+    """
+}
diff --git a/modules/local/dmsanalysis/error_correction_false_doubles/meta.yml b/modules/local/dmsanalysis/error_correction_false_doubles/meta.yml
new file mode 100644
index 0000000..780956f
--- /dev/null
+++ b/modules/local/dmsanalysis/error_correction_false_doubles/meta.yml
@@ -0,0 +1,105 @@
+name: "dmsanalysis_error_correction_false_doubles"
+description: "Sequencing-error correction of single-codon variant counts using the false-double-mutant model: because the library only contains single-codon changes, observed multi-codon variants are treated as errors and used to estimate and subtract the per-nucleotide error rate."
+keywords:
+  - deep mutational scanning
+  - error correction
+  - false doubles
+  - variant counting
+tools:
+  - "r-base":
+      description: "The R programming language (base only)"
+      homepage: "https://www.r-project.org/"
+      licence: ["GPL-2.0-or-later"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', sample:'ORF1', type:'input', replicate:1 ]`
+    - raw_counts:
+        type: file
+        description: Raw (pre-library-filter) variant count table from VARIANTCOUNTING.
+        pattern: "*.variant_counts.tsv"
+    - filtered:
+        type: file
+        description: Library-filtered single-codon variant counts (annotated CSV).
+        pattern: "*.csv"
+    - completed:
+        type: file
+        description: Completed library table (all possible variants) to also correct.
+        pattern: "*.csv"
+  - - wt_fasta:
+        type: file
+        description: Reference FASTA the reads were aligned to; base_mut positions are in its frame.
+        pattern: "*.{fa,fasta}"
+  - - aa_seq:
+        type: file
+        description: Reference amino-acid sequence (one line).
+  - - method:
+        type: string
+        description: Estimator for the false-doubles error rate, `mle` or `eb`.
+  - - codon_window:
+        type: integer
+        description: Codon window within which false double mutants are collected for a variant.
+
+output:
+  - corrected:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - variantCounts_filtered_by_library_error_corrected.csv:
+          type: file
+          description: Library-filtered counts with added `counts_corrected` / `counts_per_cov_corrected` columns.
+          pattern: "variantCounts_filtered_by_library_error_corrected.csv"
+      - variantCounts_for_heatmaps_error_corrected.csv:
+          type: file
+          description: Amino-acid-level heatmap table built from corrected counts (canonical `total_counts` columns).
+          pattern: "variantCounts_for_heatmaps_error_corrected.csv"
+      - library_completed_variantCounts_error_corrected.csv:
+          type: file
+          description: Completed library table with corrected counts propagated (for logdiff etc.).
+          pattern: "library_completed_variantCounts_error_corrected.csv"
+  - seq_error_rate:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - seq_error_rate.csv:
+          type: file
+          description: Observed frequency and estimated sequencing-error rate per single-nucleotide variant.
+          pattern: "seq_error_rate.csv"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+maintainers:
+  - "@BenjaminWehnert1008"
+containers:
+  docker:
+    linux/amd64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-stringr:801bac9c074a1f9b
+      build_id: bd-801bac9c074a1f9b_1
+      scan_id: sc-bb0508c32fbdbbf3_1
+    linux/arm64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-stringr:fbcc673012c62cb0
+      build_id: bd-fbcc673012c62cb0_1
+      scan_id: sc-c0377bfc5c678ddc_1
+  singularity:
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-stringr:f7ed82e8081ab38d
+      build_id: bd-f7ed82e8081ab38d_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/e5/e5a2e5419dff47f9dbcea8823692f60f9a1b1c1a1ee32ae56eef0ce2989f7b72/data
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-stringr:1c3d5150b5f34345
+      build_id: bd-1c3d5150b5f34345_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/cc/ccd8ca061a33803f1d15bc47bf8e6d87b242ebb66b387cbb5ab02a4c7670f425/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/dmsanalysis/error_correction_false_doubles/.conda-lock/linux_amd64-bd-801bac9c074a1f9b_1.txt
+    linux/arm64:
+      lock_file: modules/local/dmsanalysis/error_correction_false_doubles/.conda-lock/linux_arm64-bd-fbcc673012c62cb0_1.txt
diff --git a/modules/local/dmsanalysis/error_correction_false_doubles/templates/error_correction_false_doubles.R b/modules/local/dmsanalysis/error_correction_false_doubles/templates/error_correction_false_doubles.R
new file mode 100644
index 0000000..59fda5c
--- /dev/null
+++ b/modules/local/dmsanalysis/error_correction_false_doubles/templates/error_correction_false_doubles.R
@@ -0,0 +1,807 @@
+#!/usr/bin/env Rscript
+
+## false-double mutant codon sequencing-error correction in nf-core/deepmutscan (nucleotide level).
+##
+## Two estimators, ported verbatim from the original analysis scripts:
+##   * MLE (default): per-SNV maximum-likelihood error rate e = F / m, applied per variant.
+##   * EB:            pool F and m across SNVs, then a Gamma-Poisson empirical-Bayes fit with
+##                    six-category shrinkage -> posterior-mean rate e_EB.
+## `$method` (mle|eb) selects which one runs. Everything except I/O plumbing and the dispatch is
+## kept exactly as written.
+##
+## Nextflow template: an R `\$` is a literal dollar; backslashes meant for R are doubled.
+
+suppressMessages({
+  library(Biostrings)
+  library(stringr)
+})
+
+## -------------------------------------------------------------------------------------------------
+## MLE estimator (verbatim)
+## -------------------------------------------------------------------------------------------------
+seq_error_correct_by_false_doubles_MLE <- function(wt_path, input_count_path_raw, input_count_path_processed, output_file_path, seq_error_rate_path, codon_window){
+
+  ## load data (nucleotide-level counts)
+
+  # WT sequence
+  wt.seq <- readDNAStringSet(wt_path)
+
+  # for the seq error rates, build a vector to be filled
+  seq.error.names <- c()
+  for(i in 1:nchar(wt.seq)){
+    tmp.wt <- strsplit(as.character(wt.seq), "")[[1]][i]
+    seq.error.names <- c(seq.error.names, paste0(i, ":", tmp.wt, ">", c("A", "T", "G", "C"))[-match(tmp.wt, c("A", "T", "G", "C"))])
+  }
+  seq.error.rate <- matrix(0, nrow = length(seq.error.names), ncol = 2)
+  rownames(seq.error.rate) <- seq.error.names
+  colnames(seq.error.rate) <- c("1nt counts/coverage", "1nt false counts/coverage")
+
+  # Set the column names
+  colnames <- c("counts", "cov", "mean_length_variant_reads", "varying_bases",
+                "base_mut", "varying_codons", "codon_mut", "aa_mut", "pos_mut")
+  input.counts.raw <- read.table(input_count_path_raw, sep = "\\t", header = FALSE, fill = TRUE, col.names = colnames)
+  input.counts.processed <- read.csv(input_count_path_processed)
+
+  ## append key columns
+  input.counts.processed\$counts_corrected <- input.counts.processed\$counts
+  input.counts.processed\$counts_per_cov_corrected <- input.counts.processed\$counts_per_cov
+
+  ## calculate some things upfront
+
+  ### highest accessible codon
+  max.codon <- max(as.numeric(str_split_fixed(input.counts.raw\$codon_mut, ":", 2)[,1]), na.rm = T)
+
+  ### total distribution of false double mutant coverage decay (%) vs. codon-codon distance
+  cat("Estimating the distance-dependent coverage decay of false double mutants...\\n")
+  all.false.doubles <- input.counts.raw
+  all.false.doubles <- all.false.doubles[which(all.false.doubles[,"varying_bases"] >= 3 & all.false.doubles[,"varying_bases"] < 5 & all.false.doubles[,"varying_codons"] == 2),,drop = F]
+  all.false.doubles\$codon_dist <- vapply(regmatches(all.false.doubles[,"codon_mut"], gregexpr("\\\\d+(?=:)", all.false.doubles[,"codon_mut"], perl = TRUE)), function(z) abs(diff(as.integer(z))), integer(1))
+
+  ### additional filters:
+
+  #### remove outlier double codon mutants with very high counts
+  all.false.doubles <- all.false.doubles[which(all.false.doubles\$counts <= quantile(all.false.doubles\$counts, c(0.995))),]
+
+  #### only keep 2+1 nt and 3+1 nt double codon mutants
+  all.false.doubles.nt <- all.false.doubles\$codon_mut
+  all.false.doubles.nt <- str_split_fixed(all.false.doubles.nt, ", ", 2)
+  all.false.doubles.nt[,1] <- str_split_fixed(all.false.doubles.nt[,1], ":", 2)[,2]
+  all.false.doubles.nt[,2] <- str_split_fixed(all.false.doubles.nt[,2], ":", 2)[,2]
+  classify_double_codon_variant <- function(x) {
+    parts <- strsplit(x, ">", fixed = TRUE)
+    n_changes <- vapply(parts, function(p) {
+      if (length(p) != 2L || nchar(p[1]) != nchar(p[2])) {
+        return(NA_integer_)
+      }
+      sum(strsplit(p[1], "")[[1]] != strsplit(p[2], "")[[1]])
+    }, integer(1))
+    labels <- c(1, 2, 3)
+    ifelse(n_changes %in% 1:3,
+           labels[n_changes],
+           ifelse(n_changes == 0L, "no change", NA_character_))
+  }
+  all.false.doubles.nt[,1] <- classify_double_codon_variant(all.false.doubles.nt[,1])
+  all.false.doubles.nt[,2] <- classify_double_codon_variant(all.false.doubles.nt[,2])
+  if(length(which(all.false.doubles.nt[,1] == 2 & all.false.doubles.nt[,2] == 2) != 0)){
+    all.false.doubles <- all.false.doubles[-which(all.false.doubles.nt[,1] == 2 & all.false.doubles.nt[,2] == 2),]
+    all.false.doubles.nt <- all.false.doubles.nt[-which(all.false.doubles.nt[,1] == 2 & all.false.doubles.nt[,2] == 2),]
+  }
+  if(length(which(all.false.doubles.nt[,1] == 1 & all.false.doubles.nt[,2] == 1) != 0)){
+    all.false.doubles <- all.false.doubles[-which(all.false.doubles.nt[,1] == 1 & all.false.doubles.nt[,2] == 1),]
+    all.false.doubles.nt <- all.false.doubles.nt[-which(all.false.doubles.nt[,1] == 1 & all.false.doubles.nt[,2] == 1),]
+  }
+  if(length(which(c(all.false.doubles.nt[,1] == 3 & all.false.doubles.nt[,2] == 2) | c(all.false.doubles.nt[,1] == 2 & all.false.doubles.nt[,2] == 3)) != 0)){
+    all.false.doubles <- all.false.doubles[-which(c(all.false.doubles.nt[,1] == 3 & all.false.doubles.nt[,2] == 2) | c(all.false.doubles.nt[,1] == 2 & all.false.doubles.nt[,2] == 3)),]
+    all.false.doubles.nt <- all.false.doubles.nt[-which(c(all.false.doubles.nt[,1] == 3 & all.false.doubles.nt[,2] == 2) | c(all.false.doubles.nt[,1] == 2 & all.false.doubles.nt[,2] == 3)),]
+  }
+  if(length(which(is.na(all.false.doubles.nt[,1]) == T | is.na(all.false.doubles.nt[,2]) == T) != 0)){
+    all.false.doubles <- all.false.doubles[-which(is.na(all.false.doubles.nt[,1]) == T | is.na(all.false.doubles.nt[,2]) == T),]
+    all.false.doubles.nt <- all.false.doubles.nt[-which(is.na(all.false.doubles.nt[,1]) == T | is.na(all.false.doubles.nt[,2]) == T),]
+  }
+
+  all.false.doubles.codons <- str_split_fixed(all.false.doubles\$codon_mut, ", ", 2)
+  all.false.doubles.codons[,1] <- as.integer(sub(":.*", "", all.false.doubles.codons[,1]))
+  all.false.doubles.codons[,2] <- as.integer(sub(":.*", "", all.false.doubles.codons[,2]))
+  median.high.conf.coverage.per.pos <- rep(NA, max.codon)
+  names(median.high.conf.coverage.per.pos) <- 1:max.codon
+  for(i in 1:length(median.high.conf.coverage.per.pos)){
+    median.high.conf.coverage.per.pos[i] <- median(input.counts.processed[which(names(median.high.conf.coverage.per.pos)[i] == str_split_fixed(input.counts.processed\$codon_mut, ":", 2)[,1]),"cov"])
+  }
+  all.false.doubles.codons[,1] <- median.high.conf.coverage.per.pos[match(all.false.doubles.codons[,1], names(median.high.conf.coverage.per.pos))]
+  all.false.doubles.codons[,2] <- median.high.conf.coverage.per.pos[match(all.false.doubles.codons[,2], names(median.high.conf.coverage.per.pos))]
+  all.false.doubles\$max_cov_possible <- as.numeric(apply(all.false.doubles.codons, 1, min))
+  all.false.doubles\$perc_cov_to_max <- 100 * all.false.doubles\$cov / all.false.doubles\$max_cov_possible
+
+  ## deepmutscan guard (not in the verbatim script): a run can legitimately contain NO observed false
+  ## double mutants -- a small, shallow or tightly-filtered library, or the nf-core test dataset, where
+  ## every variant is single-codon. The verbatim code then hands lm() an empty frame and dies with
+  ## "0 (non-NA) cases". With no false doubles anywhere there is also no evidence of codon-level
+  ## sequencing error to subtract, so the correct answer is a no-op: corrected == raw. The quadratic
+  ## needs >= 3 distinct codon distances to be estimable at all, so require that too.
+  .usable <- is.finite(log(all.false.doubles\$perc_cov_to_max))
+  if (nrow(all.false.doubles) == 0 || length(unique(all.false.doubles\$codon_dist[.usable])) < 3) {
+    cat("WARNING: only", sum(.usable), "usable false double mutant(s) found - the distance-dependent\\n",
+        "coverage decay is not estimable, so NO sequencing-error correction is applied and the\\n",
+        "corrected counts are identical to the raw counts.\\n")
+    write.csv(seq.error.rate, file = seq_error_rate_path, row.names = T)
+    write.csv(input.counts.processed, file = output_file_path, row.names = F)
+    return(invisible(NULL))
+  }
+
+  ## keep the full distance range before dropping rows, so the lookup below stays long enough
+  .max_dist <- max(c(all.false.doubles\$codon_dist, codon_window), na.rm = TRUE)
+  ## fit on the finite rows only - lm() errors on the -Inf that log() gives a zero-coverage double
+  all.false.doubles <- all.false.doubles[.usable, , drop = FALSE]
+
+  fit <- lm(log(all.false.doubles\$perc_cov_to_max) ~ all.false.doubles\$codon_dist + I(all.false.doubles\$codon_dist^2))
+  all.false.doubles\$pred_cov_perc <- exp(predict(fit))
+
+  ### convert this into a look-up table: "% max. possible coverage" depending on codon-codon distance
+  dual.codon.coverage.estimate <- matrix(NA, nrow = .max_dist, ncol = 2)
+  colnames(dual.codon.coverage.estimate) <- c("Codon distance", "Estimate % max. possible coverage")
+  dual.codon.coverage.estimate[,"Codon distance"] <- 1:nrow(dual.codon.coverage.estimate)
+  ## deepmutscan fix (deliberate deviation from the original script): fill the coverage-decay
+  ## lookup from the fitted quadratic evaluated at EVERY integer codon distance, instead of match()ing
+  ## only the distances that happen to have an observed double. `pred_cov_perc = exp(predict(fit))`
+  ## depends solely on codon_dist, so this is bit-identical to the original at every observed distance
+  ## and merely fills the previously-NA gaps -- those NAs otherwise crash MLE and leave NA corrected
+  ## counts in EB (a distance with no observed double, e.g. around codon 288 on the GID1A min_counts=10
+  ## data).
+  .dccd <- dual.codon.coverage.estimate[,"Codon distance"]
+  .cf <- coef(fit)
+  dual.codon.coverage.estimate[,"Estimate % max. possible coverage"] <- exp(.cf[1] + .cf[2] * .dccd + .cf[3] * .dccd^2)
+
+  ## Process the count file, only look at single nucleotide variants
+  cat("Sequencing error correction of raw counts...\\n")
+  for (i in grep("[,]", input.counts.processed[,"base_mut"], invert = T)){
+
+    ### algorithm:
+    ### 1.) isolate 1nt variant of interest
+    ### 2.) look for 2/3nt hits in a pre-specified window of codons upstream or downstream
+    ### -> if there are zero false double mutants: skip / set correction factor to zero
+    ### -> if there are any false double mutants: continue
+    ### 3.) generate a table for ALL possible 2/3nt variants in the selected window:
+    ### true high-conf. variant count | true high-conf. variant coverage | false double variant count (often 0 or 1) | false double variant coverage
+    ### 4.) Maximum likelihood estimation (MLE) over all events
+    ### 5.) Apply correction factor
+
+    ## 1.) isolate 1nt variant of interest
+    tmp.single <- input.counts.processed[i,"base_mut"]
+    seq.error.rate[tmp.single, "1nt counts/coverage"] <- input.counts.processed[i,"counts_per_cov"]
+
+    ## 2.) look for 2/3nt hits in up to 40 codons (120 bp) upstream or downstream
+    tmp.false.doubles <- all.false.doubles[grep(paste0("(?<!\\\\d)",input.counts.processed[i,"codon_mut"]), all.false.doubles[,"codon_mut"], perl = T),,drop = F]
+
+    ## set and enforce distance threshold
+    tmp.ref.codon <- as.numeric(strsplit(input.counts.processed[i,"codon_mut"] , ":")[[1]][1])
+    tmp.min.from.ref <- max(c(1, tmp.ref.codon - c(codon_window - 1)))
+    tmp.max.from.ref <- min(c(max.codon, tmp.ref.codon + c(codon_window - 1)))
+    tmp.false.doubles <- tmp.false.doubles[which(tmp.false.doubles\$codon_dist < codon_window),,drop = F]
+
+    ## 3.) generate a table for ALL possible 2/3nt variants in the selected window
+    tmp.summary.table.entries <- input.counts.processed
+    tmp.summary.table.entries <- tmp.summary.table.entries[which(as.numeric(str_split_fixed(tmp.summary.table.entries\$codon_mut, ":", 2)[,1]) != tmp.ref.codon),]
+    tmp.summary.table.entries <- tmp.summary.table.entries[which(as.numeric(str_split_fixed(tmp.summary.table.entries\$codon_mut, ":", 2)[,1]) >= tmp.min.from.ref &
+                                                                   as.numeric(str_split_fixed(tmp.summary.table.entries\$codon_mut, ":", 2)[,1]) <= tmp.max.from.ref),]
+    tmp.summary.table.entries <- tmp.summary.table.entries[which(tmp.summary.table.entries\$varying_bases > 1),]
+
+    ### fill the table
+    tmp.summary.table <- matrix(NA, ncol = 5, nrow = nrow(tmp.summary.table.entries))
+    colnames(tmp.summary.table) <- c("codon distance",
+                                     "true high-conf. variant count",
+                                     "true high-conf. variant coverage",
+                                     "false double variant count",
+                                     "false double variant coverage")
+    rownames(tmp.summary.table) <- paste0(c(tmp.summary.table.entries\$codon_mut),", ", c(tmp.summary.table.entries\$base_mut))
+
+    tmp.summary.table[,"codon distance"] <- abs(as.numeric(str_split_fixed(tmp.summary.table.entries\$codon_mut, ":", 2)[,1]) - tmp.ref.codon)
+    tmp.summary.table[,"true high-conf. variant count"] <- tmp.summary.table.entries\$counts
+    tmp.summary.table[,"true high-conf. variant coverage"] <- tmp.summary.table.entries\$cov
+    tmp.summary.table[,"false double variant count"] <- 0
+
+    ## input the actual hits
+    tmp.double.codon <- str_split_fixed(tmp.false.doubles\$codon_mut, ", ", 2)
+    tmp.match1 <- match(tmp.double.codon[,1], str_split_fixed(rownames(tmp.summary.table), ", ", 2)[,1])
+    tmp.match1[is.na(tmp.match1)] <- 0
+    tmp.match2 <- match(tmp.double.codon[,2], str_split_fixed(rownames(tmp.summary.table), ", ", 2)[,1])
+    tmp.match2[is.na(tmp.match2)] <- 0
+    tmp.match <- c(tmp.match1 + tmp.match2)
+
+    ### remove those false doubles for which there are no matched true high confidence calls
+    if(any(tmp.match == 0)){
+      tmp.false.doubles <- tmp.false.doubles[-which(tmp.match == 0),,drop = F]
+      tmp.match <- tmp.match[-which(tmp.match == 0)]
+      if(nrow(tmp.false.doubles) == 0){
+        next
+      }
+    }
+
+    tmp.summary.table[tmp.match,"false double variant count"] <- tmp.false.doubles[,"counts"]
+    tmp.summary.table[tmp.match,"false double variant coverage"] <- tmp.false.doubles[,"cov"]
+    tmp.summary.table <- as.data.frame(tmp.summary.table)
+
+    ## 4.) Maximum likelihood estimation (MLE) over all events, iterating over codon positions
+
+    ### summarise data by position
+    tmp.summary.table.pos <- matrix(NA, nrow = length(unique(str_split_fixed(rownames(tmp.summary.table), ">", 2)[,1])), ncol = 5)
+    colnames(tmp.summary.table.pos) <- colnames(tmp.summary.table)
+    rownames(tmp.summary.table.pos) <- unique(str_split_fixed(rownames(tmp.summary.table), ">", 2)[,1])
+    tmp.summary.table.pos[,"codon distance"] <- abs(as.numeric(str_split_fixed(rownames(tmp.summary.table.pos), ":", 2)[,1]) - as.numeric(str_split_fixed(input.counts.processed[i,"codon_mut"], ":", 2)[,1]))
+    for(j in 1:nrow(tmp.summary.table.pos)){
+      tmp.name <- rownames(tmp.summary.table.pos)[j]
+      tmp.summary.table.pos[j,"true high-conf. variant count"] <- sum(tmp.summary.table[grep(paste0("^",tmp.name), rownames(tmp.summary.table)),"true high-conf. variant count"])
+      tmp.summary.table.pos[j,"true high-conf. variant coverage"] <- max(tmp.summary.table[grep(paste0("^",tmp.name), rownames(tmp.summary.table)),"true high-conf. variant coverage"])
+      tmp.summary.table.pos[j,"false double variant count"] <- sum(tmp.summary.table[grep(paste0("^",tmp.name), rownames(tmp.summary.table)),"false double variant count"])
+      tmp.summary.table.pos[j,"false double variant coverage"] <- max(tmp.summary.table[grep(paste0("^",tmp.name), rownames(tmp.summary.table)),"false double variant coverage"], na.rm = T)
+
+      ## infer a good proxy for the false double variant coverage with 0 counts (based estimated distance-dependent % coverage decay observed across all observed double mutants)
+      if(tmp.summary.table.pos[j,"false double variant coverage"] == "-Inf"){
+        tmp.summary.table.pos[j,"false double variant coverage"] <- c(dual.codon.coverage.estimate[abs(tmp.summary.table.pos[j,"codon distance"]),"Estimate % max. possible coverage"] / 100) * tmp.summary.table.pos[j,"true high-conf. variant coverage"]
+        tmp.summary.table.pos[j,"false double variant coverage"] <- round(tmp.summary.table.pos[j,"false double variant coverage"])
+      }
+    }
+    tmp.summary.table.pos <- as.data.frame(tmp.summary.table.pos)
+    e_MLE <- sum(tmp.summary.table.pos\$`false double variant count`) /
+      sum(tmp.summary.table.pos\$`true high-conf. variant count` * c(tmp.summary.table.pos\$`false double variant coverage` / tmp.summary.table.pos\$`true high-conf. variant coverage`))
+    seq.error.rate[tmp.single,"1nt false counts/coverage"] <- e_MLE
+    ## clamped here too - the guard below only tests the rounded count, so a subtraction landing in
+    ## (-0.5, 0] rounds to 0, skips the guard, and leaves a negative frequency behind
+    input.counts.processed[i,"counts_per_cov_corrected"] <- max(0, input.counts.processed[i,"counts_per_cov"] - e_MLE)
+
+    ## 5.) Apply correction factor
+    input.counts.processed[i,"counts_corrected"] <- input.counts.processed[i,"counts"] - c(e_MLE * input.counts.processed[i,"cov"])
+
+    ## round to nearest integer, do not allow for negative counts
+    input.counts.processed[i,"counts_corrected"] <- round(input.counts.processed[i,"counts_corrected"])
+    if(input.counts.processed[i,"counts_corrected"] < 0){
+      input.counts.processed[i,"counts_corrected"] <- 0
+      input.counts.processed[i,"counts_per_cov_corrected"] <- 0
+    }
+
+  }
+
+  # Write the processed data
+  write.csv(seq.error.rate, file = seq_error_rate_path, row.names = T)
+  write.csv(input.counts.processed, file = output_file_path, row.names = F)
+
+}
+
+## -------------------------------------------------------------------------------------------------
+## EB estimator (verbatim)
+## -------------------------------------------------------------------------------------------------
+seq_error_correct_by_false_doubles_EB <- function(wt_path, input_count_path_raw, input_count_path_processed, output_file_path, seq_error_rate_path, codon_window){
+
+  ## load data (nucleotide-level counts)
+
+  # WT sequence
+  wt.seq <- readDNAStringSet(wt_path)
+
+  # for the seq error rates, build a vector to be filled
+  seq.error.names <- c()
+  for(i in 1:nchar(wt.seq)){
+    tmp.wt <- strsplit(as.character(wt.seq), "")[[1]][i]
+    seq.error.names <- c(seq.error.names, paste0(i, ":", tmp.wt, ">", c("A", "T", "G", "C"))[-match(tmp.wt, c("A", "T", "G", "C"))])
+  }
+  seq.error.rate <- matrix(0, nrow = length(seq.error.names), ncol = 2)
+  rownames(seq.error.rate) <- seq.error.names
+  colnames(seq.error.rate) <- c("1nt counts/coverage", "1nt false counts/coverage")
+
+  # Set the column names
+  colnames <- c("counts", "cov", "mean_length_variant_reads", "varying_bases",
+                "base_mut", "varying_codons", "codon_mut", "aa_mut", "pos_mut")
+  input.counts.raw <- read.table(input_count_path_raw, sep = "\\t", header = FALSE, fill = TRUE, col.names = colnames)
+  input.counts.processed <- read.csv(input_count_path_processed)
+
+  ## append key columns
+  input.counts.processed\$counts_corrected <- input.counts.processed\$counts
+  input.counts.processed\$counts_per_cov_corrected <- input.counts.processed\$counts_per_cov
+
+  ## calculate some things upfront
+
+  ### highest accessible codon
+  max.codon <- max(as.numeric(str_split_fixed(input.counts.raw\$codon_mut, ":", 2)[,1]), na.rm = T)
+
+  ### total distribution of false double mutant coverage decay (%) vs. codon-codon distance
+  cat("Estimating the distance-dependent coverage decay of false double mutants...\\n")
+  all.false.doubles <- input.counts.raw
+  all.false.doubles <- all.false.doubles[which(all.false.doubles[,"varying_bases"] >= 3 & all.false.doubles[,"varying_bases"] < 5 & all.false.doubles[,"varying_codons"] == 2),,drop = F]
+  all.false.doubles\$codon_dist <- vapply(regmatches(all.false.doubles[,"codon_mut"], gregexpr("\\\\d+(?=:)", all.false.doubles[,"codon_mut"], perl = TRUE)), function(z) abs(diff(as.integer(z))), integer(1))
+
+  ### additional filters:
+
+  #### remove outlier double codon mutants with very high counts
+  all.false.doubles <- all.false.doubles[which(all.false.doubles\$counts <= quantile(all.false.doubles\$counts, c(0.995))),]
+
+  #### only keep 2+1 nt and 3+1 nt double codon mutants
+  all.false.doubles.nt <- all.false.doubles\$codon_mut
+  all.false.doubles.nt <- str_split_fixed(all.false.doubles.nt, ", ", 2)
+  all.false.doubles.nt[,1] <- str_split_fixed(all.false.doubles.nt[,1], ":", 2)[,2]
+  all.false.doubles.nt[,2] <- str_split_fixed(all.false.doubles.nt[,2], ":", 2)[,2]
+  classify_double_codon_variant <- function(x) {
+    parts <- strsplit(x, ">", fixed = TRUE)
+    n_changes <- vapply(parts, function(p) {
+      if (length(p) != 2L || nchar(p[1]) != nchar(p[2])) {
+        return(NA_integer_)
+      }
+      sum(strsplit(p[1], "")[[1]] != strsplit(p[2], "")[[1]])
+    }, integer(1))
+    labels <- c(1, 2, 3)
+    ifelse(n_changes %in% 1:3,
+           labels[n_changes],
+           ifelse(n_changes == 0L, "no change", NA_character_))
+  }
+  all.false.doubles.nt[,1] <- classify_double_codon_variant(all.false.doubles.nt[,1])
+  all.false.doubles.nt[,2] <- classify_double_codon_variant(all.false.doubles.nt[,2])
+  if(length(which(all.false.doubles.nt[,1] == 2 & all.false.doubles.nt[,2] == 2) != 0)){
+    all.false.doubles <- all.false.doubles[-which(all.false.doubles.nt[,1] == 2 & all.false.doubles.nt[,2] == 2),]
+    all.false.doubles.nt <- all.false.doubles.nt[-which(all.false.doubles.nt[,1] == 2 & all.false.doubles.nt[,2] == 2),]
+  }
+  if(length(which(all.false.doubles.nt[,1] == 1 & all.false.doubles.nt[,2] == 1) != 0)){
+    all.false.doubles <- all.false.doubles[-which(all.false.doubles.nt[,1] == 1 & all.false.doubles.nt[,2] == 1),]
+    all.false.doubles.nt <- all.false.doubles.nt[-which(all.false.doubles.nt[,1] == 1 & all.false.doubles.nt[,2] == 1),]
+  }
+  if(length(which(c(all.false.doubles.nt[,1] == 3 & all.false.doubles.nt[,2] == 2) | c(all.false.doubles.nt[,1] == 2 & all.false.doubles.nt[,2] == 3)) != 0)){
+    all.false.doubles <- all.false.doubles[-which(c(all.false.doubles.nt[,1] == 3 & all.false.doubles.nt[,2] == 2) | c(all.false.doubles.nt[,1] == 2 & all.false.doubles.nt[,2] == 3)),]
+    all.false.doubles.nt <- all.false.doubles.nt[-which(c(all.false.doubles.nt[,1] == 3 & all.false.doubles.nt[,2] == 2) | c(all.false.doubles.nt[,1] == 2 & all.false.doubles.nt[,2] == 3)),]
+  }
+  if(length(which(is.na(all.false.doubles.nt[,1]) == T | is.na(all.false.doubles.nt[,2]) == T) != 0)){
+    all.false.doubles <- all.false.doubles[-which(is.na(all.false.doubles.nt[,1]) == T | is.na(all.false.doubles.nt[,2]) == T),]
+    all.false.doubles.nt <- all.false.doubles.nt[-which(is.na(all.false.doubles.nt[,1]) == T | is.na(all.false.doubles.nt[,2]) == T),]
+  }
+
+  all.false.doubles.codons <- str_split_fixed(all.false.doubles\$codon_mut, ", ", 2)
+  all.false.doubles.codons[,1] <- as.integer(sub(":.*", "", all.false.doubles.codons[,1]))
+  all.false.doubles.codons[,2] <- as.integer(sub(":.*", "", all.false.doubles.codons[,2]))
+  median.high.conf.coverage.per.pos <- rep(NA, max(as.numeric(str_split_fixed(input.counts.processed\$codon_mut, ":", 2)[,1])))
+  names(median.high.conf.coverage.per.pos) <- 1:max(as.numeric(str_split_fixed(input.counts.processed\$codon_mut, ":", 2)[,1]))
+  for(i in 1:length(median.high.conf.coverage.per.pos)){
+    median.high.conf.coverage.per.pos[i] <- median(input.counts.processed[which(names(median.high.conf.coverage.per.pos)[i] == str_split_fixed(input.counts.processed\$codon_mut, ":", 2)[,1]),"cov"])
+  }
+  all.false.doubles.codons[,1] <- median.high.conf.coverage.per.pos[match(all.false.doubles.codons[,1], names(median.high.conf.coverage.per.pos))]
+  all.false.doubles.codons[,2] <- median.high.conf.coverage.per.pos[match(all.false.doubles.codons[,2], names(median.high.conf.coverage.per.pos))]
+  all.false.doubles\$max_cov_possible <- as.numeric(apply(all.false.doubles.codons, 1, min))
+  all.false.doubles\$perc_cov_to_max <- 100 * all.false.doubles\$cov / all.false.doubles\$max_cov_possible
+
+  ## deepmutscan guard (not in the verbatim script) - see the identical guard in the MLE estimator.
+  ## With no estimable coverage decay there is no evidence of codon-level sequencing error, so the
+  ## correct answer is a no-op: corrected == raw, rather than lm() dying on an empty frame.
+  .usable <- is.finite(log(all.false.doubles\$perc_cov_to_max))
+  if (nrow(all.false.doubles) == 0 || length(unique(all.false.doubles\$codon_dist[.usable])) < 3) {
+    cat("WARNING: only", sum(.usable), "usable false double mutant(s) found - the distance-dependent\\n",
+        "coverage decay is not estimable, so NO sequencing-error correction is applied and the\\n",
+        "corrected counts are identical to the raw counts.\\n")
+    write.csv(seq.error.rate, file = seq_error_rate_path, row.names = T)
+    write.csv(input.counts.processed, file = output_file_path, row.names = F)
+    return(invisible(NULL))
+  }
+
+  ## keep the full distance range before dropping rows, so the lookup below stays long enough
+  .max_dist <- max(c(all.false.doubles\$codon_dist, codon_window), na.rm = TRUE)
+  ## fit on the finite rows only - lm() errors on the -Inf that log() gives a zero-coverage double
+  all.false.doubles <- all.false.doubles[.usable, , drop = FALSE]
+
+  fit <- lm(log(all.false.doubles\$perc_cov_to_max) ~ all.false.doubles\$codon_dist + I(all.false.doubles\$codon_dist^2))
+  all.false.doubles\$pred_cov_perc <- exp(predict(fit))
+
+  ### convert this into a look-up table: "% max. possible coverage" depending on codon-codon distance
+  dual.codon.coverage.estimate <- matrix(NA, nrow = .max_dist, ncol = 2)
+  colnames(dual.codon.coverage.estimate) <- c("Codon distance", "Estimate % max. possible coverage")
+  dual.codon.coverage.estimate[,"Codon distance"] <- 1:nrow(dual.codon.coverage.estimate)
+  ## deepmutscan fix (deliberate deviation from the original script): fill the coverage-decay
+  ## lookup from the fitted quadratic evaluated at EVERY integer codon distance, instead of match()ing
+  ## only the distances that happen to have an observed double. `pred_cov_perc = exp(predict(fit))`
+  ## depends solely on codon_dist, so this is bit-identical to the original at every observed distance
+  ## and merely fills the previously-NA gaps -- those NAs otherwise crash MLE and leave NA corrected
+  ## counts in EB (a distance with no observed double, e.g. around codon 288 on the GID1A min_counts=10
+  ## data).
+  .dccd <- dual.codon.coverage.estimate[,"Codon distance"]
+  .cf <- coef(fit)
+  dual.codon.coverage.estimate[,"Estimate % max. possible coverage"] <- exp(.cf[1] + .cf[2] * .dccd + .cf[3] * .dccd^2)
+
+  ## Process the count file, only look at single nucleotide variants
+  cat("Sequencing error correction of raw counts...\\n")
+  out.summary <- matrix(NA, ncol = 7, nrow = length(grep("[,]", input.counts.processed[,"base_mut"], invert = T)))
+  colnames(out.summary) <- c("SNV", "Category",
+                             "False_double_variant_count", "False_double_variant_coverage",
+                             "True_high_conf_variant_count", "True_high_conf_variant_coverage",
+                             "Exposure_m")
+  out.summary[,"SNV"] <- input.counts.processed[grep("[,]", input.counts.processed[,"base_mut"], invert = T),"base_mut"]
+  out.summary[,"Category"] <- str_split_fixed(out.summary[,"SNV"], ":", 2)[,2]
+  category.translate <- cbind(c("C>A", "C>G", "C>T", "T>A", "T>C", "T>G"),
+                              c("G>T", "G>C", "G>A", "A>T", "A>G", "A>C"))
+  for(i in 1:nrow(out.summary)){
+    if(out.summary[i,"Category"] %in% category.translate[,2]){
+      out.summary[i,"Category"] <- category.translate[match(out.summary[i,"Category"],category.translate[,2]),1]
+    }
+  }
+
+  for (i in grep("[,]", input.counts.processed[,"base_mut"], invert = T)){
+
+    ### algorithm:
+    ### 1.) isolate 1nt variant of interest
+    ### 2.) look for 2/3nt hits in a pre-specified window of codons upstream or downstream
+    ### -> if there are zero false double mutants: skip / set correction factor to zero
+    ### -> if there are any false double mutants: continue
+    ### 3.) generate a table for ALL possible 2/3nt variants in the selected window:
+    ### true high-conf. variant count | true high-conf. variant coverage | false double variant count (often 0 or 1) | false double variant coverage
+    ### 4.) Iterate of all positions
+    ### 5.) Calculate the empirical Bayes
+    ### 6.) Apply correction factor
+
+    ## 1.) isolate 1nt variant of interest
+    tmp.single <- input.counts.processed[i,"base_mut"]
+    seq.error.rate[tmp.single, "1nt counts/coverage"] <- input.counts.processed[i,"counts_per_cov"]
+
+    ## 2.) look for 2/3nt hits in up to 40 codons (120 bp) upstream or downstream
+    tmp.false.doubles <- all.false.doubles[grep(paste0("(?<!\\\\d)",input.counts.processed[i,"codon_mut"]), all.false.doubles[,"codon_mut"], perl = T),,drop = F]
+    tmp.ref.codon <- as.numeric(strsplit(input.counts.processed[i,"codon_mut"] , ":")[[1]][1])
+    tmp.min.from.ref <- max(c(1, tmp.ref.codon - c(codon_window - 1)))
+    tmp.max.from.ref <- min(c(max.codon, tmp.ref.codon + c(codon_window - 1)))
+    tmp.false.doubles <- tmp.false.doubles[which(tmp.false.doubles\$codon_dist < codon_window),,drop = F]
+
+    ## 3.) generate a table for ALL possible 2/3nt variants in the selected window
+    tmp.summary.table.entries <- input.counts.processed
+    tmp.summary.table.entries <- tmp.summary.table.entries[which(as.numeric(str_split_fixed(tmp.summary.table.entries\$codon_mut, ":", 2)[,1]) != tmp.ref.codon),]
+    tmp.summary.table.entries <- tmp.summary.table.entries[which(as.numeric(str_split_fixed(tmp.summary.table.entries\$codon_mut, ":", 2)[,1]) >= tmp.min.from.ref &
+                                                                   as.numeric(str_split_fixed(tmp.summary.table.entries\$codon_mut, ":", 2)[,1]) <= tmp.max.from.ref),]
+    tmp.summary.table.entries <- tmp.summary.table.entries[which(tmp.summary.table.entries\$varying_bases > 1),]
+
+    ### fill the table
+    tmp.summary.table <- matrix(NA, ncol = 5, nrow = nrow(tmp.summary.table.entries))
+    colnames(tmp.summary.table) <- c("codon distance",
+                                     "true high-conf. variant count",
+                                     "true high-conf. variant coverage",
+                                     "false double variant count",
+                                     "false double variant coverage")
+    rownames(tmp.summary.table) <- paste0(c(tmp.summary.table.entries\$codon_mut),", ", c(tmp.summary.table.entries\$base_mut))
+
+    tmp.summary.table[,"codon distance"] <- abs(as.numeric(str_split_fixed(tmp.summary.table.entries\$codon_mut, ":", 2)[,1]) - tmp.ref.codon)
+    tmp.summary.table[,"true high-conf. variant count"] <- tmp.summary.table.entries\$counts
+    tmp.summary.table[,"true high-conf. variant coverage"] <- tmp.summary.table.entries\$cov
+    tmp.summary.table[,"false double variant count"] <- 0
+
+    ## input the actual hits
+    tmp.double.codon <- str_split_fixed(tmp.false.doubles\$codon_mut, ", ", 2)
+    tmp.match1 <- match(tmp.double.codon[,1], str_split_fixed(rownames(tmp.summary.table), ", ", 2)[,1])
+    tmp.match1[is.na(tmp.match1)] <- 0
+    tmp.match2 <- match(tmp.double.codon[,2], str_split_fixed(rownames(tmp.summary.table), ", ", 2)[,1])
+    tmp.match2[is.na(tmp.match2)] <- 0
+    tmp.match <- c(tmp.match1 + tmp.match2)
+
+    ### remove those false doubles for which there are no matched true high confidence calls
+    if(any(tmp.match == 0)){
+      tmp.false.doubles <- tmp.false.doubles[-which(tmp.match == 0),,drop = F]
+      tmp.match <- tmp.match[-which(tmp.match == 0)]
+      if(nrow(tmp.false.doubles) == 0){
+        next
+      }
+    }
+
+    tmp.summary.table[tmp.match,"false double variant count"] <- tmp.false.doubles[,"counts"]
+    tmp.summary.table[tmp.match,"false double variant coverage"] <- tmp.false.doubles[,"cov"]
+    tmp.summary.table <- as.data.frame(tmp.summary.table)
+
+    ## 4.) Iterate over codon positions
+
+    ### summarise data by position
+    tmp.summary.table.pos <- matrix(NA, nrow = length(unique(str_split_fixed(rownames(tmp.summary.table), ">", 2)[,1])), ncol = 5)
+    colnames(tmp.summary.table.pos) <- colnames(tmp.summary.table)
+    rownames(tmp.summary.table.pos) <- unique(str_split_fixed(rownames(tmp.summary.table), ">", 2)[,1])
+    tmp.summary.table.pos[,"codon distance"] <- abs(as.numeric(str_split_fixed(rownames(tmp.summary.table.pos), ":", 2)[,1]) - as.numeric(str_split_fixed(input.counts.processed[i,"codon_mut"], ":", 2)[,1]))
+    for(j in 1:nrow(tmp.summary.table.pos)){
+      tmp.name <- rownames(tmp.summary.table.pos)[j]
+      tmp.summary.table.pos[j,"true high-conf. variant count"] <- sum(tmp.summary.table[grep(paste0("^",tmp.name), rownames(tmp.summary.table)),"true high-conf. variant count"])
+      tmp.summary.table.pos[j,"true high-conf. variant coverage"] <- max(tmp.summary.table[grep(paste0("^",tmp.name), rownames(tmp.summary.table)),"true high-conf. variant coverage"])
+      tmp.summary.table.pos[j,"false double variant count"] <- sum(tmp.summary.table[grep(paste0("^",tmp.name), rownames(tmp.summary.table)),"false double variant count"])
+      tmp.summary.table.pos[j,"false double variant coverage"] <- max(tmp.summary.table[grep(paste0("^",tmp.name), rownames(tmp.summary.table)),"false double variant coverage"], na.rm = T)
+
+      ## infer a good proxy for the false double variant coverage with 0 counts (based estimated distance-dependent % coverage decay observed across all observed double mutants)
+      if(tmp.summary.table.pos[j,"false double variant coverage"] == "-Inf"){
+        tmp.summary.table.pos[j,"false double variant coverage"] <- c(dual.codon.coverage.estimate[abs(tmp.summary.table.pos[j,"codon distance"]),"Estimate % max. possible coverage"] / 100) * tmp.summary.table.pos[j,"true high-conf. variant coverage"]
+        tmp.summary.table.pos[j,"false double variant coverage"] <- round(tmp.summary.table.pos[j,"false double variant coverage"])
+      }
+    }
+    tmp.summary.table.pos <- as.data.frame(tmp.summary.table.pos)
+
+    ### fill the master table
+    out.summary[match(tmp.single,out.summary[,"SNV"]),"False_double_variant_count"] <- sum(tmp.summary.table.pos\$`false double variant count`)
+    out.summary[match(tmp.single,out.summary[,"SNV"]),"False_double_variant_coverage"] <- sum(tmp.summary.table.pos\$`false double variant coverage`)
+    out.summary[match(tmp.single,out.summary[,"SNV"]),"True_high_conf_variant_count"] <- sum(tmp.summary.table.pos\$`true high-conf. variant count`)
+    out.summary[match(tmp.single,out.summary[,"SNV"]),"True_high_conf_variant_coverage"] <- sum(tmp.summary.table.pos\$`true high-conf. variant coverage`)
+    out.summary[match(tmp.single,out.summary[,"SNV"]),"Exposure_m"] <- sum(tmp.summary.table.pos\$`true high-conf. variant count` * c(tmp.summary.table.pos\$`false double variant coverage` / tmp.summary.table.pos\$`true high-conf. variant coverage`))
+
+  }
+
+  ## 5.) Calculate the empirical-Bayes sequencing error estimates
+  if(length(which(is.na(out.summary[,"False_double_variant_count"]) == T)) != 0){
+    out.summary <- out.summary[-which(is.na(out.summary[,"False_double_variant_count"]) == T),]
+  }
+  out.summary <- as.data.frame(out.summary)
+  class(out.summary\$False_double_variant_count) <- "numeric"
+  class(out.summary\$False_double_variant_coverage) <- "numeric"
+  class(out.summary\$True_high_conf_variant_count) <- "numeric"
+  class(out.summary\$True_high_conf_variant_coverage) <- "numeric"
+  class(out.summary\$Exposure_m) <- "numeric"
+
+  ### calculate e_MLE across all
+  out.summary\$e_MLE <- out.summary\$False_double_variant_count / out.summary\$Exposure_m
+
+  ### calculate all the necessary rates, priors, etc.
+
+  #### global background rate
+  mu_global <- sum(out.summary\$False_double_variant_count) / sum(out.summary\$Exposure_m)
+  mu_global <- max(mu_global, 1e-12) ##
+
+  ##### marginal log-likelihood under Gamma-Poisson with fixed mean mu
+  ## prior: e ~ Gamma(shape = nu * mu, rate = nu)
+  ## mean = mu, variance = mu / nu
+  gp_loglik_nu <- function(log_nu, y, m, mu) {
+    nu <- exp(log_nu)
+    a <- max(nu * mu, 1e-12)
+
+    # Negative-binomial marginal:
+    # y ~ NB(size = a, prob = nu / (nu + m))
+    ll <- lgamma(y + a) - lgamma(a) - lgamma(y + 1) +
+      a * log(nu / (nu + m)) +
+      y * log(m / (nu + m))
+
+    -sum(ll)
+  }
+  opt_global <- optimize(f = gp_loglik_nu,
+                         interval = c(log(1e-6), log(1e6)),
+                         y = out.summary\$False_double_variant_count,
+                         m = out.summary\$Exposure_m,
+                         mu = mu_global)
+  nu_global <- exp(opt_global\$minimum)
+
+  #### category-level rates, shrunk to global rate
+  mu_cat <- vector(mode = "list", length = 6)
+  names(mu_cat) <- category.translate[,1]
+  mu_cat_raw <- mu_cat
+  for(i in 1:length(mu_cat)){
+    mu_cat[[i]] <- out.summary[which(out.summary\$Category == names(mu_cat)[i]),]
+    mu_cat_raw[[i]] <- sum(mu_cat[[i]]\$False_double_variant_count) / sum(mu_cat[[i]]\$Exposure_m)
+    mu_cat_raw[[i]] <- pmax(mu_cat_raw[[i]], 1e-12)
+    mu_cat[[i]] <- c(sum(mu_cat[[i]]\$False_double_variant_count) + nu_global * mu_global) / c(sum(mu_cat[[i]]\$Exposure_m) + nu_global)
+    mu_cat[[i]] <- pmax(mu_cat[[i]], 1e-12)
+  }
+  mu_cat_raw <- do.call(c, mu_cat_raw)
+  mu_cat <- do.call(c, mu_cat)
+
+  ##### category-specific prior strength nu_cat, by maximum marginal likelihood
+  ## with fallback to global if category is too small
+  nu_cat <- rep(NA, 6)
+  names(nu_cat) <- names(mu_cat)
+  for (i in 1:length(mu_cat)) {
+    cat_name <- names(mu_cat)[i]
+    idx <- out.summary\$Category == cat_name
+
+    yk <- out.summary\$False_double_variant_count[idx]
+    mk <- out.summary\$Exposure_m[idx]
+    mu_k <- mu_cat[i]
+
+    opt_k <- try(optimize(f = gp_loglik_nu,
+                          interval = c(log(1e-6), log(1e6)),
+                          y = yk,
+                          m = mk,
+                          mu = mu_k),
+                 silent = TRUE)
+
+    if (inherits(opt_k, "try-error")) {
+      nu_cat[i] <- nu_global
+    } else {
+      nu_cat[i] <- exp(opt_k\$minimum)
+    }
+  }
+
+  #### posterior mean for each specific SNV
+  out.summary\$mu_category <- rep(NA, nrow(out.summary))
+  out.summary\$nu_category <- rep(NA, nrow(out.summary))
+  for(i in 1:6){
+    out.summary\$mu_category[which(out.summary\$Category == names(mu_cat)[i])] <- mu_cat[i]
+    out.summary\$nu_category[which(out.summary\$Category == names(nu_cat)[i])] <- nu_cat[i]
+  }
+
+  ##### prior: e_j ~ Gamma(shape = nu_cat * mu_cat, rate = nu_cat)
+  out.summary\$prior_shape <- out.summary\$mu_category * out.summary\$nu_category
+  out.summary\$prior_rate <- out.summary\$nu_category
+
+  ##### posterior: e_j | data ~ Gamma(shape = prior_shape + F, rate = prior_rate [= nu_cat] + M)
+  out.summary\$post_shape <- out.summary\$prior_shape + out.summary\$False_double_variant_count
+  out.summary\$post_rate  <- out.summary\$nu_category + out.summary\$Exposure_m
+
+  ##### posterior mean = shrunk EB estimate
+  out.summary\$e_EB  <- out.summary\$post_shape / out.summary\$post_rate
+
+  ## 6.) Apply correction factors systematically (use upper 95% percentile to correct, a.k.a. "conservative")
+  seq.error.rate[match(out.summary\$SNV, rownames(seq.error.rate)),"1nt false counts/coverage"] <- out.summary\$e_EB
+  ## clamped here too - the guard below only tests the rounded count (see the MLE branch)
+  input.counts.processed[match(out.summary\$SNV, input.counts.processed\$base_mut),"counts_per_cov_corrected"] <- pmax(0, input.counts.processed[match(out.summary\$SNV, input.counts.processed\$base_mut),"counts_per_cov"] - out.summary\$e_EB)
+  input.counts.processed[match(out.summary\$SNV, input.counts.processed\$base_mut),"counts_corrected"] <- input.counts.processed[match(out.summary\$SNV, input.counts.processed\$base_mut),"counts"] - c(out.summary\$e_EB * input.counts.processed[match(out.summary\$SNV, input.counts.processed\$base_mut),"cov"])
+
+  ## round to nearest integer, do not allow for negative counts
+  input.counts.processed[match(out.summary\$SNV, input.counts.processed\$base_mut),"counts_corrected"] <- round(input.counts.processed[match(out.summary\$SNV, input.counts.processed\$base_mut),"counts_corrected"])
+  if(any(which(input.counts.processed[,"counts_corrected"] < 0))){
+    input.counts.processed[which(input.counts.processed[,"counts_corrected"] < 0),"counts_per_cov_corrected"] <- 0
+    input.counts.processed[which(input.counts.processed[,"counts_corrected"] < 0),"counts_corrected"] <- 0
+  }
+
+  # Write the processed data
+  write.csv(seq.error.rate, file = seq_error_rate_path, row.names = T)
+  write.csv(input.counts.processed, file = output_file_path, row.names = F)
+
+}
+
+## -------------------------------------------------------------------------------------------------
+## I/O wrappers preserved from the pipeline module (not part of the estimator logic)
+## -------------------------------------------------------------------------------------------------
+
+# propagate corrected counts onto the completed library table (used by e.g. logdiff)
+correct_library_completed <- function(completed_path, corrected_filtered_path, output_file_path){
+  completed <- read.csv(completed_path)
+  corrected <- read.csv(corrected_filtered_path)
+  idx <- match(completed\$codon_mut, corrected\$codon_mut)
+  has <- !is.na(idx)
+  completed\$counts[has] <- corrected\$counts[idx[has]]
+  completed\$counts_per_cov[has] <- corrected\$counts_per_cov[idx[has]]
+  write.csv(completed, file = output_file_path, row.names = FALSE)
+}
+
+# re-make the AA-level heatmap table from corrected counts (canonical column names)
+seq_error_correct_counts_for_heatmaps <- function(input_csv_path, aa_seq_file_path, output_csv_path, threshold = 3) {
+
+  raw_counts <- read.table(input_csv_path, sep = ",", header = TRUE, stringsAsFactors = FALSE)
+
+  wt_seq <- readLines(aa_seq_file_path)
+  wt_seq <- unlist(strsplit(wt_seq, ""))
+  wt_seq[wt_seq == "X"] <- "*"
+
+  # summarise corrected counts for each unique pos_mut
+  aggregated_counts_per_cov <- aggregate(
+    counts_per_cov_corrected ~ pos_mut,
+    data = raw_counts,
+    FUN = function(x) sum(x, na.rm = TRUE)
+  )
+  aggregated_counts <- aggregate(
+    counts_corrected ~ pos_mut,
+    data = raw_counts,
+    FUN = function(x) sum(x, na.rm = TRUE)
+  )
+  aggregated_data <- merge(aggregated_counts_per_cov, aggregated_counts, by = "pos_mut", all = TRUE)
+
+  # canonical output names so the count heatmaps consume this drop-in
+  names(aggregated_data)[names(aggregated_data) == "counts_per_cov_corrected"] <- "total_counts_per_cov"
+  names(aggregated_data)[names(aggregated_data) == "counts_corrected"] <- "total_counts"
+
+  aggregated_data\$wt_aa <- sub("(\\\\D)(\\\\d+)(\\\\D)", "\\\\1", aggregated_data\$pos_mut)
+  aggregated_data\$position <- as.numeric(sub("(\\\\D)(\\\\d+)(\\\\D)", "\\\\2", aggregated_data\$pos_mut))
+  aggregated_data\$mut_aa <- sub("(\\\\D)(\\\\d+)(\\\\D)", "\\\\3", aggregated_data\$pos_mut)
+  aggregated_data\$mut_aa[aggregated_data\$mut_aa == "X"] <- "*"
+
+  all_amino_acids <- c("A", "C", "D", "E", "F", "G", "H", "I", "K", "L",
+                       "M", "N", "P", "Q", "R", "S", "T", "V", "W", "Y", "*")
+  all_positions <- seq_along(wt_seq)
+  complete_data <- expand.grid(mut_aa = all_amino_acids, position = all_positions, stringsAsFactors = FALSE)
+
+  heatmap_data <- merge(complete_data, aggregated_data, by = c("mut_aa", "position"), all.x = TRUE, sort = FALSE)
+
+  heatmap_data\$total_counts_per_cov[is.na(heatmap_data\$total_counts_per_cov)] <- 0
+  heatmap_data\$wt_aa <- wt_seq[heatmap_data\$position]
+
+  low_count <- is.na(heatmap_data\$total_counts) | heatmap_data\$total_counts < threshold
+  heatmap_data\$total_counts_per_cov[low_count] <- NA
+  heatmap_data\$total_counts[low_count] <- NA
+
+  missing_pos_mut <- is.na(heatmap_data\$pos_mut)
+  heatmap_data\$pos_mut[missing_pos_mut] <- paste0(
+    heatmap_data\$wt_aa[missing_pos_mut],
+    heatmap_data\$position[missing_pos_mut],
+    heatmap_data\$mut_aa[missing_pos_mut]
+  )
+
+  out.order <- paste0(rep(1:max(heatmap_data\$position), each = 21),
+                      rep(c("A", "C", "D", "E", "F", "G", "H",
+                            "I", "K", "L","M", "N", "P", "Q",
+                            "R", "S", "T", "V", "W", "Y", "*"),
+                          max(heatmap_data\$position)))
+  heatmap_data <- heatmap_data[match(out.order, paste0(heatmap_data\$position, heatmap_data\$mut_aa)),]
+  rownames(heatmap_data) <- 1:nrow(heatmap_data)
+
+  write.csv(heatmap_data, file = output_csv_path, row.names = FALSE)
+
+}
+
+# Defensive coordinate-contract check (general, not GID1A-specific): base_mut positions must be
+# 1-based within the supplied reference, and the stated WT base must match the reference base there.
+# A mismatch means the reference and the counts are in different frames -> stop rather than
+# silently mis-index seq.error.rate.
+check_reference_frame <- function(wt_path, processed_path){
+  ref <- strsplit(as.character(readDNAStringSet(wt_path)[[1]]), "")[[1]]
+  proc <- read.csv(processed_path)
+  snv <- proc[grep("[,]", proc[,"base_mut"], invert = TRUE), "base_mut"]
+  if(length(snv) == 0) return(invisible(NULL))
+  pos <- suppressWarnings(as.integer(str_split_fixed(snv, ":", 2)[,1]))
+  wtbase <- substr(str_split_fixed(snv, ":", 2)[,2], 1, 1)
+  ok <- !is.na(pos) & pos >= 1 & pos <= length(ref)
+  if(any(!ok)){
+    stop("Reference/counts coordinate mismatch: ", sum(!ok), " single-nucleotide base_mut positions ",
+         "fall outside the reference (length ", length(ref), "). The reference fasta must be the same ",
+         "one the reads were aligned to, in the same coordinate frame as base_mut.")
+  }
+  mism <- ref[pos] != wtbase
+  if(any(mism, na.rm = TRUE)){
+    stop("Reference/counts coordinate mismatch: ", sum(mism, na.rm = TRUE), " single-nucleotide ",
+         "base_mut entries state a wild-type base that disagrees with the reference at that position ",
+         "(e.g. ", snv[which(mism)[1]], "). Check that --fasta matches the counts.")
+  }
+  invisible(NULL)
+}
+
+## -------------------------------------------------------------------------------------------------
+## Run
+## -------------------------------------------------------------------------------------------------
+method <- "$method"
+codon_window <- $codon_window
+
+# fail loudly if the supplied reference and the counts are in different coordinate frames
+check_reference_frame("$wt_fasta", "$filtered")
+
+intermediate <- "variantCounts_intermediate_corrected.csv"
+if (method == "eb") {
+  seq_error_correct_by_false_doubles_EB(
+    wt_path = "$wt_fasta",
+    input_count_path_raw = "$raw_counts",
+    input_count_path_processed = "$filtered",
+    output_file_path = intermediate,
+    seq_error_rate_path = "seq_error_rate.csv",
+    codon_window = codon_window
+  )
+} else {
+  seq_error_correct_by_false_doubles_MLE(
+    wt_path = "$wt_fasta",
+    input_count_path_raw = "$raw_counts",
+    input_count_path_processed = "$filtered",
+    output_file_path = intermediate,
+    seq_error_rate_path = "seq_error_rate.csv",
+    codon_window = codon_window
+  )
+}
+
+## corrected counts become the canonical columns so every count-dependent step uses them;
+## the originals are preserved as *_raw for the error-correction report
+ic <- read.csv(intermediate)
+ic\$counts_raw <- ic\$counts
+ic\$counts_per_cov_raw <- ic\$counts_per_cov
+ic\$counts <- ic\$counts_corrected
+ic\$counts_per_cov <- ic\$counts_per_cov_corrected
+write.csv(ic, file = "variantCounts_filtered_by_library_error_corrected.csv", row.names = FALSE)
+
+seq_error_correct_counts_for_heatmaps(
+  input_csv_path = "variantCounts_filtered_by_library_error_corrected.csv",
+  aa_seq_file_path = "$aa_seq",
+  output_csv_path = "variantCounts_for_heatmaps_error_corrected.csv",
+  threshold = 1  # every observed variant; fitness applies --min_counts to the input libraries
+)
+
+correct_library_completed(
+  completed_path = "$completed",
+  corrected_filtered_path = "variantCounts_filtered_by_library_error_corrected.csv",
+  output_file_path = "library_completed_variantCounts_error_corrected.csv"
+)
+
+## --- versions ---
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+if (is.null(r_version)) r_version <- "unknown"
+stringr_version <- as.character(packageVersion("stringr"))
+biostrings_version <- as.character(packageVersion("Biostrings"))
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-stringr:', stringr_version),
+    paste('    r-Biostrings:', biostrings_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/dmsanalysis/error_correction_wildtype/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt b/modules/local/dmsanalysis/error_correction_wildtype/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
new file mode 100644
index 0000000..be8d7e8
--- /dev/null
+++ b/modules/local/dmsanalysis/error_correction_wildtype/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
@@ -0,0 +1,2942 @@
+version: 6
+environments:
+  default:
+    channels:
+    - url: https://conda.anaconda.org/conda-forge/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
+      linux-64:
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.46.1-default_hfdba357_103.conda
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-biocgenerics-0.56.0-r45hdfd78af_2.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-biostrings-2.78.0-r45h01b2380_0.conda
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diff --git a/modules/local/dmsanalysis/error_correction_wildtype/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt b/modules/local/dmsanalysis/error_correction_wildtype/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
new file mode 100644
index 0000000..10e5993
--- /dev/null
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+  size: 615477
+  timestamp: 1786599613561
diff --git a/modules/local/dmsanalysis/error_correction_wildtype/environment.yml b/modules/local/dmsanalysis/error_correction_wildtype/environment.yml
new file mode 100644
index 0000000..5f5785d
--- /dev/null
+++ b/modules/local/dmsanalysis/error_correction_wildtype/environment.yml
@@ -0,0 +1,19 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+# Shared by 17 R modules (all except RUN_MUTSCAN, RUN_DIMSUM and the false-doubles error correction).
+# Keep the copies identical:
+# the one container image these modules use is built from exactly this list.
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/dmsanalysis/error_correction_wildtype/main.nf b/modules/local/dmsanalysis/error_correction_wildtype/main.nf
new file mode 100644
index 0000000..138286e
--- /dev/null
+++ b/modules/local/dmsanalysis/error_correction_wildtype/main.nf
@@ -0,0 +1,37 @@
+process DMSANALYSIS_ERROR_CORRECTION_WILDTYPE {
+    tag "$meta.id"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7'}"
+
+    input:
+    tuple val(meta), path(filtered, stageAs: 'input_counts.csv'), path(wt_counts, stageAs: 'wildtype_counts.csv'), path(completed)
+    path aa_seq
+
+    output:
+    tuple val(meta),
+        path("variantCounts_filtered_by_library_error_corrected.csv"),
+        path("variantCounts_for_heatmaps_error_corrected.csv"),
+        path("library_completed_variantCounts_error_corrected.csv"),
+        emit: corrected
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'error_correction_wildtype.R'
+
+    stub:
+    """
+    touch variantCounts_filtered_by_library_error_corrected.csv
+    touch variantCounts_for_heatmaps_error_corrected.csv
+    touch library_completed_variantCounts_error_corrected.csv
+    echo "DMSANALYSIS_ERROR_CORRECTION_WILDTYPE:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+    """
+}
diff --git a/modules/local/dmsanalysis/error_correction_wildtype/meta.yml b/modules/local/dmsanalysis/error_correction_wildtype/meta.yml
new file mode 100644
index 0000000..08c023d
--- /dev/null
+++ b/modules/local/dmsanalysis/error_correction_wildtype/meta.yml
@@ -0,0 +1,87 @@
+name: "dmsanalysis_error_correction_wildtype"
+description: "Sequencing-error correction of single-codon variant counts using an additional deep wildtype-only sequencing sample to measure the position-specific error profile, which is then subtracted from each library sample."
+keywords:
+  - deep mutational scanning
+  - error correction
+  - wildtype sequencing
+  - variant counting
+tools:
+  - "r-base":
+      description: "The R programming language (base only)"
+      homepage: "https://www.r-project.org/"
+      licence: ["GPL-2.0-or-later"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', sample:'ORF1', type:'input', replicate:1 ]`
+    - filtered:
+        type: file
+        description: Library-filtered single-codon variant counts (annotated CSV) for this sample.
+        pattern: "*.csv"
+    - wt_counts:
+        type: file
+        description: Library-filtered variant counts of the matching wildtype-only sample.
+        pattern: "*.csv"
+    - completed:
+        type: file
+        description: Completed library table (all possible variants) to also correct.
+        pattern: "*.csv"
+  - - aa_seq:
+        type: file
+        description: Reference amino-acid sequence (one line).
+
+output:
+  - corrected:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - variantCounts_filtered_by_library_error_corrected.csv:
+          type: file
+          description: Library-filtered counts with added `counts_corrected` / `counts_per_cov_corrected` columns.
+          pattern: "variantCounts_filtered_by_library_error_corrected.csv"
+      - variantCounts_for_heatmaps_error_corrected.csv:
+          type: file
+          description: Amino-acid-level heatmap table built from corrected counts (canonical `total_counts` columns).
+          pattern: "variantCounts_for_heatmaps_error_corrected.csv"
+      - library_completed_variantCounts_error_corrected.csv:
+          type: file
+          description: Completed library table with corrected counts propagated (for logdiff etc.).
+          pattern: "library_completed_variantCounts_error_corrected.csv"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+maintainers:
+  - "@BenjaminWehnert1008"
+containers:
+  docker:
+    linux/arm64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb
+      build_id: bd-880bbdecb19543bb_1
+      scan_id: sc-09c9d098a09068b9_1
+    linux/amd64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7
+      build_id: bd-32f1af0a83a018e7_1
+      scan_id: sc-337510c2c910aa1c_1
+  singularity:
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb
+      build_id: bd-7202e5d19667bddb_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7
+      build_id: bd-db46247d9810b3e7_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/dmsanalysis/error_correction_wildtype/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
+    linux/arm64:
+      lock_file: modules/local/dmsanalysis/error_correction_wildtype/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
diff --git a/modules/local/dmsanalysis/error_correction_wildtype/templates/error_correction_wildtype.R b/modules/local/dmsanalysis/error_correction_wildtype/templates/error_correction_wildtype.R
new file mode 100644
index 0000000..c3d9425
--- /dev/null
+++ b/modules/local/dmsanalysis/error_correction_wildtype/templates/error_correction_wildtype.R
@@ -0,0 +1,159 @@
+#!/usr/bin/env Rscript
+
+## Wildtype-based sequencing error correction in nf-core/deepmutscan
+## An additional deep wildtype-only sequencing sample measures the position-specific error
+## profile, which is subtracted from each library sample's per-base coverage.
+
+## --- Helper functions ---
+
+# WT-based sequencing error correction (nucleotide level)
+seq_error_correct_by_WT_nt <- function(wt_seq_count_path, input_count_path, output_file_path){
+
+  WT.counts <- read.csv(wt_seq_count_path)
+  input.counts <- read.csv(input_count_path)
+
+  ## append key columns
+  input.counts\$counts_corrected <- input.counts\$counts
+  input.counts\$counts_per_cov_corrected <- input.counts\$counts_per_cov
+
+  for (i in 1:nrow(WT.counts)){
+
+    ## only look at variants observed in both the input and WT sequencing
+    tmp.id <- match(WT.counts[i,"base_mut"], input.counts[,"base_mut"])
+    if(is.na(tmp.id) == T){
+      next
+    }
+
+    ## subtract the observed per-base coverage in the WT sequencing
+    input.counts[tmp.id,"counts_per_cov_corrected"] <- input.counts[tmp.id,"counts_per_cov"] - WT.counts[i,"counts_per_cov"]
+
+    ## adjust the total counts by cross-multiplication
+    input.counts[tmp.id,"counts_corrected"] <- input.counts[tmp.id,"counts"] * c(input.counts[tmp.id,"counts_per_cov_corrected"] / input.counts[tmp.id,"counts_per_cov"])
+
+    ## round to nearest integer, do not allow for negative counts
+    input.counts[tmp.id,"counts_corrected"] <- round(input.counts[tmp.id,"counts_corrected"])
+    if(input.counts[tmp.id,"counts_corrected"] < 0){
+      input.counts[tmp.id,"counts_corrected"] <- 0
+      input.counts[tmp.id,"counts_per_cov_corrected"] <- 0
+    }
+
+  }
+
+  ## corrected counts become the canonical columns so every count-dependent step uses them;
+  ## the originals are preserved as *_raw for the error-correction report
+  input.counts\$counts_raw <- input.counts\$counts
+  input.counts\$counts_per_cov_raw <- input.counts\$counts_per_cov
+  input.counts\$counts <- input.counts\$counts_corrected
+  input.counts\$counts_per_cov <- input.counts\$counts_per_cov_corrected
+
+  write.csv(input.counts, file = output_file_path, row.names = F)
+
+}
+
+# propagate corrected counts onto the completed library table (used by e.g. logdiff)
+correct_library_completed <- function(completed_path, corrected_filtered_path, output_file_path){
+  completed <- read.csv(completed_path)
+  corrected <- read.csv(corrected_filtered_path)
+  idx <- match(completed\$codon_mut, corrected\$codon_mut)
+  has <- !is.na(idx)
+  completed\$counts[has] <- corrected\$counts[idx[has]]
+  completed\$counts_per_cov[has] <- corrected\$counts_per_cov[idx[has]]
+  write.csv(completed, file = output_file_path, row.names = FALSE)
+}
+
+# re-make the AA-level heatmap table from corrected counts (canonical column names)
+seq_error_correct_counts_for_heatmaps <- function(input_csv_path, aa_seq_file_path, output_csv_path, threshold = 3) {
+
+  raw_counts <- read.table(input_csv_path, sep = ",", header = TRUE, stringsAsFactors = FALSE)
+
+  wt_seq <- readLines(aa_seq_file_path)
+  wt_seq <- unlist(strsplit(wt_seq, ""))
+  wt_seq[wt_seq == "X"] <- "*"
+
+  # summarise corrected counts for each unique pos_mut
+  aggregated_counts_per_cov <- aggregate(
+    counts_per_cov_corrected ~ pos_mut,
+    data = raw_counts,
+    FUN = function(x) sum(x, na.rm = TRUE)
+  )
+  aggregated_counts <- aggregate(
+    counts_corrected ~ pos_mut,
+    data = raw_counts,
+    FUN = function(x) sum(x, na.rm = TRUE)
+  )
+  aggregated_data <- merge(aggregated_counts_per_cov, aggregated_counts, by = "pos_mut", all = TRUE)
+
+  # canonical output names so the count heatmaps consume this drop-in
+  names(aggregated_data)[names(aggregated_data) == "counts_per_cov_corrected"] <- "total_counts_per_cov"
+  names(aggregated_data)[names(aggregated_data) == "counts_corrected"] <- "total_counts"
+
+  aggregated_data\$wt_aa <- sub("(\\\\D)(\\\\d+)(\\\\D)", "\\\\1", aggregated_data\$pos_mut)
+  aggregated_data\$position <- as.numeric(sub("(\\\\D)(\\\\d+)(\\\\D)", "\\\\2", aggregated_data\$pos_mut))
+  aggregated_data\$mut_aa <- sub("(\\\\D)(\\\\d+)(\\\\D)", "\\\\3", aggregated_data\$pos_mut)
+  aggregated_data\$mut_aa[aggregated_data\$mut_aa == "X"] <- "*"
+
+  all_amino_acids <- c("A", "C", "D", "E", "F", "G", "H", "I", "K", "L",
+                       "M", "N", "P", "Q", "R", "S", "T", "V", "W", "Y", "*")
+  all_positions <- seq_along(wt_seq)
+  complete_data <- expand.grid(mut_aa = all_amino_acids, position = all_positions, stringsAsFactors = FALSE)
+
+  heatmap_data <- merge(complete_data, aggregated_data, by = c("mut_aa", "position"), all.x = TRUE, sort = FALSE)
+
+  heatmap_data\$total_counts_per_cov[is.na(heatmap_data\$total_counts_per_cov)] <- 0
+  heatmap_data\$wt_aa <- wt_seq[heatmap_data\$position]
+
+  low_count <- is.na(heatmap_data\$total_counts) | heatmap_data\$total_counts < threshold
+  heatmap_data\$total_counts_per_cov[low_count] <- NA
+  heatmap_data\$total_counts[low_count] <- NA
+
+  missing_pos_mut <- is.na(heatmap_data\$pos_mut)
+  heatmap_data\$pos_mut[missing_pos_mut] <- paste0(
+    heatmap_data\$wt_aa[missing_pos_mut],
+    heatmap_data\$position[missing_pos_mut],
+    heatmap_data\$mut_aa[missing_pos_mut]
+  )
+
+  out.order <- paste0(rep(1:max(heatmap_data\$position), each = 21),
+                      rep(c("A", "C", "D", "E", "F", "G", "H",
+                            "I", "K", "L","M", "N", "P", "Q",
+                            "R", "S", "T", "V", "W", "Y", "*"),
+                          max(heatmap_data\$position)))
+  heatmap_data <- heatmap_data[match(out.order, paste0(heatmap_data\$position, heatmap_data\$mut_aa)),]
+  rownames(heatmap_data) <- 1:nrow(heatmap_data)
+
+  write.csv(heatmap_data, file = output_csv_path, row.names = FALSE)
+
+}
+
+## --- Run ---
+seq_error_correct_by_WT_nt(
+  wt_seq_count_path = "$wt_counts",
+  input_count_path = "$filtered",
+  output_file_path = "variantCounts_filtered_by_library_error_corrected.csv"
+)
+
+seq_error_correct_counts_for_heatmaps(
+  input_csv_path = "variantCounts_filtered_by_library_error_corrected.csv",
+  aa_seq_file_path = "$aa_seq",
+  output_csv_path = "variantCounts_for_heatmaps_error_corrected.csv",
+  threshold = 1  # every observed variant; fitness applies --min_counts to the input libraries
+)
+
+correct_library_completed(
+  completed_path = "$completed",
+  corrected_filtered_path = "variantCounts_filtered_by_library_error_corrected.csv",
+  output_file_path = "library_completed_variantCounts_error_corrected.csv"
+)
+
+## --- versions ---
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+if (is.null(r_version)) r_version <- "unknown"
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/dmsanalysis/possible_mutations/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt b/modules/local/dmsanalysis/possible_mutations/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
new file mode 100644
index 0000000..be8d7e8
--- /dev/null
+++ b/modules/local/dmsanalysis/possible_mutations/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
@@ -0,0 +1,2942 @@
+version: 6
+environments:
+  default:
+    channels:
+    - url: https://conda.anaconda.org/conda-forge/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
+      linux-64:
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.46.1-default_hfdba357_103.conda
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-biocgenerics-0.56.0-r45hdfd78af_2.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-biostrings-2.78.0-r45h01b2380_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-iranges-2.44.0-r45h01b2380_1.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-s4vectors-0.48.1-r45h262fe30_0.conda
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-seqinfo-1.0.0-r45hdfd78af_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-xvector-0.50.0-r45h01b2380_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/bwidget-1.10.1-hfc2019e_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_10.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.9-hebe6cf0_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.10.23-hbd8a1cb_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-h3394656_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/curl-8.21.0-hcf29cc6_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.18.3-h4db4eae_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.14.3-ha770c72_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fribidi-1.0.17-h7cc23a3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-16.2.0-h9891192_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gfortran_impl_linux-64-16.2.0-hf930d89_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.15-h54a6638_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gsl-2.7-he838d99_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-16.2.0-h0d273dc_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-12.2.0-h15599e2_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-4.18.0-he073ed8_9.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-h7cc23a3_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.22.2-hbc21106_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.46.1-default_hbd61a6d_103.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.2.0-hdb68285_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-11_h4a7cf45_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-11_h0358290_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.21.0-hcf29cc6_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.25-hd45a770_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20260512-pl5321h373387f_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-h280c20c_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.9.0-hd2095e1_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.14.3-ha770c72_2.conda
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diff --git a/modules/local/dmsanalysis/possible_mutations/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt b/modules/local/dmsanalysis/possible_mutations/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
new file mode 100644
index 0000000..10e5993
--- /dev/null
+++ b/modules/local/dmsanalysis/possible_mutations/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
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diff --git a/modules/local/dmsanalysis/possible_mutations/environment.yml b/modules/local/dmsanalysis/possible_mutations/environment.yml
new file mode 100644
index 0000000..5f5785d
--- /dev/null
+++ b/modules/local/dmsanalysis/possible_mutations/environment.yml
@@ -0,0 +1,19 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+# Shared by 17 R modules (all except RUN_MUTSCAN, RUN_DIMSUM and the false-doubles error correction).
+# Keep the copies identical:
+# the one container image these modules use is built from exactly this list.
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/dmsanalysis/possible_mutations/main.nf b/modules/local/dmsanalysis/possible_mutations/main.nf
new file mode 100644
index 0000000..d049621
--- /dev/null
+++ b/modules/local/dmsanalysis/possible_mutations/main.nf
@@ -0,0 +1,33 @@
+process DMSANALYSIS_POSSIBLE_MUTATIONS {
+    tag "table /w all possible variants"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7'}"
+
+    input:
+    tuple val(meta), path(wt_seq)
+    val pos_range
+    val mutagenesis_type
+    path custom_codon_library
+
+    output:
+    tuple val(meta), path("possible_mutations.csv"), emit: possible_mutations
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'possible_mutations.R'
+
+    stub:
+    """
+    touch possible_mutations.csv
+    echo "DMSANALYSIS_POSSIBLE_MUTATIONS:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+    """
+}
diff --git a/modules/local/dmsanalysis/possible_mutations/meta.yml b/modules/local/dmsanalysis/possible_mutations/meta.yml
new file mode 100644
index 0000000..03424e7
--- /dev/null
+++ b/modules/local/dmsanalysis/possible_mutations/meta.yml
@@ -0,0 +1,79 @@
+name: "dmsanalysis_possible_mutations"
+description: |
+  Generates a comprehensive table of all theoretically possible variants based on the
+  mutagenesis strategy (e.g., NNK, NNS) and the reference sequence. This table acts
+  as the ground truth for downstream filtering and analysis.
+keywords:
+  - deep mutational scanning
+  - dms
+  - mutagenesis
+  - library design
+  - variants
+tools: null
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'ref' ]`
+    - wt_seq:
+        type: file
+        description: FASTA file containing the wild-type reference DNA sequence
+        pattern: "*.{fasta,fa}"
+  - - pos_range:
+        type: string
+        description: Start and stop codon positions (ORF) in the format 'start-stop', e.g., '352-1383'
+  - - mutagenesis_type:
+        type: string
+        description: |
+          Type of mutagenic primers used.
+          Supported types: nnk, nns, nnh, nnn, nnk_nns, nnk_nns_nnh, or custom.
+  - - custom_codon_library:
+        type: file
+        description: |
+          Optional CSV file defining a custom codon library.
+          Only required if mutagenesis_type is set to 'custom'.
+
+output:
+  - possible_mutations:
+      - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+      - possible_mutations.csv:
+          type: file
+          description: CSV file containing all theoretically possible mutations for the experiment
+          pattern: "possible_mutations.csv"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+containers:
+  docker:
+    linux/amd64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7
+      build_id: bd-32f1af0a83a018e7_1
+      scan_id: sc-337510c2c910aa1c_1
+    linux/arm64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb
+      build_id: bd-880bbdecb19543bb_1
+      scan_id: sc-09c9d098a09068b9_1
+  singularity:
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb
+      build_id: bd-7202e5d19667bddb_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7
+      build_id: bd-db46247d9810b3e7_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/dmsanalysis/possible_mutations/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
+    linux/arm64:
+      lock_file: modules/local/dmsanalysis/possible_mutations/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
diff --git a/modules/local/dmsanalysis/possible_mutations/templates/possible_mutations.R b/modules/local/dmsanalysis/possible_mutations/templates/possible_mutations.R
new file mode 100644
index 0000000..d811069
--- /dev/null
+++ b/modules/local/dmsanalysis/possible_mutations/templates/possible_mutations.R
@@ -0,0 +1,211 @@
+#!/usr/bin/env Rscript
+
+# ------------------------------------------------------------------------------
+# Script: Generate Programmed Codon Variants
+# Description: Generates all possible programmed codon mutations for a given
+#              wild-type sequence based on a specified mutagenesis strategy.
+# Input:
+#   - wt_seq_input: Wild-type sequence (string or path to FASTA file).
+#   - start_stop_pos: Target sequence range format "start-stop".
+#   - mutagenesis_type: Strategy ('nnk', 'nns', 'nnh', 'nnn', 'nnk_nns', 'nnk_nns_nnh', 'custom').
+#   - custom_codon_library_path: Path to custom library. Automatically detects
+#     if the file is a global list ("AAA, AAC...") or a position-wise CSV
+#     (requires a "Position" header).
+#   - output_file: Desired name/path for the output CSV.
+# Output: A CSV file containing all possible mutated codons per position.
+# ------------------------------------------------------------------------------
+
+suppressMessages(library(Biostrings))
+suppressMessages(library(methods))
+
+generate_possible_variants <- function(wt_seq_input, start_stop_pos, mutagenesis_type,
+                                       custom_codon_library_path, output_file) {
+
+  # Parse the start and stop positions from the input format "start-stop"
+  positions <- unlist(strsplit(start_stop_pos, "-"))
+  start_pos <- as.numeric(positions[1])
+  stop_pos <- as.numeric(positions[2])
+
+  # Load sequence from file or process as a direct string
+  if (file.exists(wt_seq_input)) {
+    seq_data <- Biostrings::readDNAStringSet(filepath = wt_seq_input)
+    wt_seq <- seq_data[[1]]
+  } else {
+    wt_seq <- Biostrings::DNAString(wt_seq_input)
+  }
+
+  # Extract the target coding sequence
+  coding_seq <- Biostrings::subseq(wt_seq, start = start_pos, end = stop_pos)
+  coding_seq <- as.character(coding_seq)
+
+  # Predefined codon dictionaries
+  nnk_codons <- c('AAG', 'AAT', 'ATG', 'ATT', 'AGG', 'AGT', 'ACG', 'ACT', 'TAG', 'TAT', 'TTG', 'TTT', 'TGG', 'TGT', 'TCG', 'TCT', 'GAG', 'GAT', 'GTG', 'GTT', 'GGG', 'GGT', 'GCG', 'GCT', 'CAG', 'CAT', 'CTG', 'CTT', 'CGG', 'CGT', 'CCG', 'CCT')
+  nns_codons <- c('AAG', 'AAC', 'ATG', 'ATC', 'AGG', 'AGC', 'ACG', 'ACC', 'TAG', 'TAC', 'TTG', 'TTC', 'TGG', 'TGC', 'TCG', 'TCC', 'GAG', 'GAC', 'GTG', 'GTC', 'GGG', 'GGC', 'GCG', 'GCC', 'CAG', 'CAC', 'CTG', 'CTC', 'CGG', 'CGC', 'CCG', 'CCC')
+  nnh_codons <- c('AAA', 'AAC', 'AAT', 'ATA', 'ATC', 'ATT', 'AGA', 'AGC', 'AGT', 'ACA', 'ACC', 'ACT', 'TAA', 'TAC', 'TAT', 'TTA', 'TTC', 'TTT', 'TGA', 'TGC', 'TGT', 'TCA', 'TCC', 'TCT', 'GAA', 'GAC', 'GAT', 'GTA', 'GTC', 'GTT', 'GGA', 'GGC', 'GGT', 'GCA', 'GCC', 'GCT', 'CAA', 'CAC', 'CAT', 'CTA', 'CTC', 'CTT', 'CGA', 'CGC', 'CGT', 'CCA', 'CCC', 'CCT')
+  nnn_codons <- c('AAA', 'AAC', 'AAG', 'AAT', 'ATA', 'ATC', 'ATG', 'ATT', 'AGA', 'AGC', 'AGG', 'AGT', 'ACA', 'ACC', 'ACG', 'ACT', 'TAA', 'TAC', 'TAG', 'TAT', 'TTA', 'TTC', 'TTG', 'TTT', 'TGA', 'TGC', 'TGG', 'TGT', 'TCA', 'TCC', 'TCG', 'TCT', 'GAA', 'GAC', 'GAG', 'GAT', 'GTA', 'GTC', 'GTG', 'GTT', 'GGA', 'GGC', 'GGG', 'GGT', 'GCA', 'GCC', 'GCG', 'GCT', 'CAA', 'CAC', 'CAG', 'CAT', 'CTA', 'CTC', 'CTG', 'CTT', 'CGA', 'CGC', 'CGG', 'CGT', 'CCA', 'CCC', 'CCG', 'CCT')
+
+  # --------------------------------------------------------------------------
+  # Custom Library Parsing with Auto-Detection
+  # --------------------------------------------------------------------------
+  is_position_wise <- FALSE
+  position_lookup <- list()
+  custom_codons <- NULL
+
+  if (mutagenesis_type == "custom") {
+    if (is.null(custom_codon_library_path) || !nzchar(custom_codon_library_path) ||
+        !file.exists(custom_codon_library_path)) {
+      stop("--mutagenesis_type custom requires a valid --custom_codon_library file.")
+    }
+
+    # Auto-detect format by inspecting the first line
+    first_line <- readLines(custom_codon_library_path, n = 1)
+
+    if (grepl("Position", first_line, ignore.case = TRUE)) {
+      # Format 1: Position-wise CSV file
+      is_position_wise <- TRUE
+
+      # Read line-by-line instead of read.csv to avoid strict column matching errors
+      lines <- readLines(custom_codon_library_path)
+
+      # Loop through lines, skipping the header (index 1)
+      for (line in lines[-1]) {
+        # Skip empty lines
+        if (trimws(line) == "") next
+
+        # Split the line by commas
+        parts <- trimws(unlist(strsplit(line, ",")))
+
+        # The first part is the position, everything else are the codons
+        pos_idx <- parts[1]
+        codon_vec <- parts[-1]
+
+        # Remove any accidental empty strings (e.g., from trailing commas)
+        codon_vec <- codon_vec[codon_vec != ""]
+
+        position_lookup[[pos_idx]] <- codon_vec
+      }
+    } else {
+      # Format 2: Global comma-separated list (Legacy compatibility)
+      custom_codons <- unlist(strsplit(readLines(custom_codon_library_path), ","))
+      custom_codons <- trimws(custom_codons)
+    }
+  }
+
+  # Helper function to split a DNA sequence into nucleotide triplets
+  split_into_codons <- function(seq) {
+    # Note: Double escaping is required for Perl regular expressions here
+    return(strsplit(seq, "(?<=.{3})", perl = TRUE)[[1]])
+  }
+
+  wt_codons <- split_into_codons(coding_seq)
+
+  # Initialize dataframe to store final variant results
+  # Note: \$ escaping is maintained for Nextflow compatibility
+  result <- data.frame(Codon_Number = integer(), wt_codon = character(), Variant = character(), stringsAsFactors = FALSE)
+
+  # Helper function to determine the target codon list per position
+  get_codon_list <- function(wt_codon, codon_index) {
+    if (mutagenesis_type == "nnk") {
+      return(nnk_codons)
+    } else if (mutagenesis_type == "nns") {
+      return(nns_codons)
+    } else if (mutagenesis_type == "nnh") {
+            return(nnh_codons)
+    } else if (mutagenesis_type == "nnn") {
+            return(nnn_codons)
+    } else if (mutagenesis_type == "nnk_nns") {
+      if (substr(wt_codon, 3, 3) == "T") return(nns_codons) else return(nnk_codons)
+    } else if (mutagenesis_type == "nnk_nns_nnh") {
+            if (substr(wt_codon, 3, 3) == "T") {
+                return(nns_codons)
+            } else if (substr(wt_codon, 3, 3) == "G"){
+                return(nnh_codons)
+            } else {
+                return(nnk_codons)
+            }
+    } else if (mutagenesis_type == "custom") {
+      if (is_position_wise) {
+        idx_str <- as.character(codon_index)
+        if (!is.null(position_lookup[[idx_str]])) {
+          return(position_lookup[[idx_str]])
+        } else {
+          return(NULL) # Skip positions not explicitly defined in the CSV
+        }
+      } else {
+        return(custom_codons)
+      }
+    } else {
+      stop("Invalid mutagenesis_type. Choose from 'nnk', 'nns', 'nnh', 'nnn', 'nnk_nns', 'nnk_nns_nnh', or 'custom'.")
+    }
+  }
+
+  # Iterate over each wild-type codon to assign programmed variants. Collect one data.frame per
+  # position in a list and bind once at the end: growing `result` with rbind() inside the loop
+  # reallocates the whole table on every variant, which is O(n^2) and becomes the dominant cost
+  # for a long ORF with a permissive library (nnn gives 63 variants per position).
+  per_position <- vector("list", length(wt_codons))
+
+  for (i in seq_along(wt_codons)) {
+    wt_codon <- wt_codons[i]
+    codon_list <- get_codon_list(wt_codon, i)
+
+    # Skip iteration if no custom codons were assigned to this specific position
+    if (is.null(codon_list)) next
+
+    # Filter out the wild-type codon from the mutation list
+    possible_variants <- codon_list[codon_list != wt_codon]
+    if (length(possible_variants) == 0) next
+
+    per_position[[i]] <- data.frame(
+      Codon_Number = i,
+      wt_codon = wt_codon,
+      Variant = possible_variants,
+      stringsAsFactors = FALSE
+    )
+  }
+
+  result <- do.call(rbind, per_position)
+  if (is.null(result)) {
+    result <- data.frame(Codon_Number = integer(0), wt_codon = character(0),
+                         Variant = character(0), stringsAsFactors = FALSE)
+  }
+
+  write.csv(result, output_file, row.names = FALSE)
+}
+
+# ------------------------------------------------------------------------------
+# Main Execution Block (Nextflow variable substitution)
+# ------------------------------------------------------------------------------
+
+# Without '--custom_codon_library', Nextflow stages no file and the path is empty.
+custom_lib_arg <- if ("$custom_codon_library" == "") {
+  NULL
+} else {
+  "$custom_codon_library"
+}
+
+generate_possible_variants(
+  wt_seq_input              = "$wt_seq",
+  start_stop_pos            = "$pos_range",
+  mutagenesis_type          = "$mutagenesis_type",
+  custom_codon_library_path = custom_lib_arg,
+  output_file               = "possible_mutations.csv"
+)
+
+# ------------------------------------------------------------------------------
+# Versioning Generation
+# ------------------------------------------------------------------------------
+
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+biostrings_version <- as.character(packageVersion("Biostrings"))
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    biostrings:', biostrings_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/dmsanalysis/process_variant_counts/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt b/modules/local/dmsanalysis/process_variant_counts/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
new file mode 100644
index 0000000..be8d7e8
--- /dev/null
+++ b/modules/local/dmsanalysis/process_variant_counts/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
@@ -0,0 +1,2942 @@
+version: 6
+environments:
+  default:
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+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
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diff --git a/modules/local/dmsanalysis/process_variant_counts/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt b/modules/local/dmsanalysis/process_variant_counts/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
new file mode 100644
index 0000000..10e5993
--- /dev/null
+++ b/modules/local/dmsanalysis/process_variant_counts/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
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diff --git a/modules/local/dmsanalysis/process_variant_counts/environment.yml b/modules/local/dmsanalysis/process_variant_counts/environment.yml
new file mode 100644
index 0000000..5f5785d
--- /dev/null
+++ b/modules/local/dmsanalysis/process_variant_counts/environment.yml
@@ -0,0 +1,19 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+# Shared by 17 R modules (all except RUN_MUTSCAN, RUN_DIMSUM and the false-doubles error correction).
+# Keep the copies identical:
+# the one container image these modules use is built from exactly this list.
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/dmsanalysis/process_variant_counts/main.nf b/modules/local/dmsanalysis/process_variant_counts/main.nf
new file mode 100644
index 0000000..6d49264
--- /dev/null
+++ b/modules/local/dmsanalysis/process_variant_counts/main.nf
@@ -0,0 +1,38 @@
+process DMSANALYSIS_PROCESS_VARIANT_COUNTS {
+    tag "$meta.id"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7'}"
+
+    input:
+    tuple val(meta), path(variantCounts)
+    path possible_mutations
+    path aa_seq
+
+    output:
+    tuple val(meta),
+        path("annotated_variantCounts.csv"),
+        path("variantCounts_filtered_by_library.csv"),
+        path("library_completed_variantCounts.csv"),
+        path("variantCounts_for_heatmaps.csv"),
+        emit: processed_variantCounts
+
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'process_variant_counts.R'
+
+    stub:
+    """
+    touch annotated_variantCounts.csv variantCounts_filtered_by_library.csv library_completed_variantCounts.csv variantCounts_for_heatmaps.csv
+    echo "DMSANALYSIS_PROCESS_VARIANT_COUNTS:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+    """
+}
diff --git a/modules/local/dmsanalysis/process_variant_counts/meta.yml b/modules/local/dmsanalysis/process_variant_counts/meta.yml
new file mode 100644
index 0000000..cae424c
--- /dev/null
+++ b/modules/local/dmsanalysis/process_variant_counts/meta.yml
@@ -0,0 +1,82 @@
+name: "dmsanalysis_process_variant_counts"
+description: "Processes the raw variant count table, filters it against expected libraries, and prepares data for downstream visualization and fitness calculation."
+keywords:
+  - deep mutational scanning
+  - variant counting
+  - variant filtering
+  - dms
+tools: null
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', single_end:false ]`
+    - variantCounts:
+        type: file
+        description: TSV file containing the raw variant counts
+        pattern: "*.{tsv,csv}"
+  - - possible_mutations:
+        type: file
+        description: CSV file containing all theoretically possible mutations based on library design
+        pattern: "*.{csv}"
+  - - aa_seq:
+        type: file
+        description: FASTA or text file containing the reference amino acid sequence
+
+output:
+  - processed_variantCounts:
+      - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. `[ id:'test', single_end:false ]`
+      - annotated_variantCounts.csv:
+          type: file
+          description: Raw variant counts annotated with mutation details
+          pattern: "annotated_variantCounts.csv"
+      - variantCounts_filtered_by_library.csv:
+          type: file
+          description: Variant counts filtered to include only those present in the provided library
+          pattern: "variantCounts_filtered_by_library.csv"
+      - library_completed_variantCounts.csv:
+          type: file
+          description: Full library table including zero-count variants
+          pattern: "library_completed_variantCounts.csv"
+      - variantCounts_for_heatmaps.csv:
+          type: file
+          description: Processed counts formatted specifically for heatmap visualization
+          pattern: "variantCounts_for_heatmaps.csv"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+containers:
+  docker:
+    linux/arm64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb
+      build_id: bd-880bbdecb19543bb_1
+      scan_id: sc-09c9d098a09068b9_1
+    linux/amd64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7
+      build_id: bd-32f1af0a83a018e7_1
+      scan_id: sc-337510c2c910aa1c_1
+  singularity:
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb
+      build_id: bd-7202e5d19667bddb_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7
+      build_id: bd-db46247d9810b3e7_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/dmsanalysis/process_variant_counts/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
+    linux/arm64:
+      lock_file: modules/local/dmsanalysis/process_variant_counts/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
diff --git a/modules/local/dmsanalysis/process_variant_counts/templates/process_variant_counts.R b/modules/local/dmsanalysis/process_variant_counts/templates/process_variant_counts.R
new file mode 100644
index 0000000..897ddf9
--- /dev/null
+++ b/modules/local/dmsanalysis/process_variant_counts/templates/process_variant_counts.R
@@ -0,0 +1,328 @@
+#!/usr/bin/env Rscript
+
+# four parts condensed into one R script
+
+#########
+# process raw counts
+#########
+
+# input: counts variantcounts tsv file, output_path
+# output: csv with column names. Creates additional counts_per_cov column. Fills pos_mut column for synonymous mutations. Sorted out variants that have mutations, but do not show up in the specifying columns -> was affecting roughly 30 low-count variants out of over 15000 in Taylor's data
+
+library("dplyr")
+process_raw_counts <- function(counts_file_path, output_csv_path) {
+
+  # Set the column names
+  colnames <- c("counts", "cov", "mean_length_variant_reads", "varying_bases",
+                          "base_mut", "varying_codons", "codon_mut", "aa_mut", "pos_mut")
+
+  # Read the variant-count file into a data frame
+  counts_raw <- read.table(counts_file_path, sep = "\\t", header = FALSE, fill = TRUE, col.names = colnames)
+
+  # Filter out rows where 'aa_mut' is empty or NA
+  counts_raw <- counts_raw[!(counts_raw\$aa_mut == "" | is.na(counts_raw\$aa_mut)), ]
+
+  # Handle synonymous mutations: where aa_mut starts with "S" and pos_mut is either NA or ""
+  # (ifelse, paste0 and sub work on whole columns, so no row-by-row evaluation is needed)
+  counts_raw <- counts_raw %>%
+    mutate(
+      pos_mut = ifelse(
+        (is.na(pos_mut) | pos_mut == "") & grepl("^S:", aa_mut),
+        # Construct the new 'pos_mut' entry for synonymous mutations
+        paste0(
+          sub("S:([A-Z])>[A-Z]", "\\\\1", aa_mut),  # Get the original amino acid from 'aa_mut'
+          sub("^(\\\\d+):.*", "\\\\1", codon_mut),    # Get the position from 'codon_mut'
+          sub("S:[A-Z]>([A-Z])", "\\\\1", aa_mut)   # Get the mutated amino acid from 'aa_mut'
+        ),
+        pos_mut  # Keep the existing 'pos_mut' if it's not NA or ""
+      )
+    ) %>%
+    ungroup() %>%
+    mutate(counts_per_cov = counts / cov)
+
+  # Write the cleaned data frame to a CSV file
+  write.csv(counts_raw, file = output_csv_path, row.names = FALSE)
+}
+
+#####
+# run function
+#####
+process_raw_counts(
+  counts_file_path = "$variantCounts",
+  output_csv_path = "annotated_variantCounts.csv"
+)
+
+
+
+
+
+
+
+
+#########
+# filter counts by codon library
+#########
+
+# Input: pre_processed_raw_counts_path, mutation library .csv path (former "possible_NNK_mutations.csv"), output_path
+# Output: counts table filtered for only single-codon mutations that are part of the library
+
+library("dplyr")
+library("stringr")
+
+filter_counts_by_codon_library <- function(counts_file_path, codon_library_path, output_file_path) {
+  # Load the variant-count table from the provided file path
+  counts_table <- read.csv(counts_file_path)
+
+  # Load the codon library from the provided .csv file
+  codon_library <- read.csv(codon_library_path)
+
+  # Ensure the codon library has the expected columns
+  if (!all(c("Codon_Number", "wt_codon", "Variant") %in% colnames(codon_library))) {
+    stop("Codon library must contain columns 'Codon_Number', 'wt_codon', and 'Variant'.")
+  }
+
+  # Filter the variant-count table: single-codon variants whose position and mutated codon are in
+  # the library (as a programmed variant or as the wildtype codon), and whose changed bases lie
+  # within one codon (first to last changed position at most 2 apart). Both checks are evaluated
+  # for whole columns at once; row-by-row evaluation (rowwise()) was the slow part for long ORFs.
+  counts_table\$codon_mut <- toupper(counts_table\$codon_mut)
+  library_keys <- unique(c(
+    paste(codon_library\$Codon_Number, codon_library\$Variant),
+    paste(codon_library\$Codon_Number, codon_library\$wt_codon)
+  ))
+  changed_base_span <- function(base_mut) {
+    vapply(strsplit(base_mut, ",\\\\s*"), function(mutations) {
+      positions <- suppressWarnings(as.numeric(str_extract(mutations, "^[0-9]+")))
+      suppressWarnings(max(positions, na.rm = TRUE) - min(positions, na.rm = TRUE))
+    }, numeric(1))
+  }
+  filtered_counts <- counts_table %>%
+    filter(varying_codons == 1) %>%
+    filter(paste(as.numeric(sub(":.*", "", codon_mut)), sub(".*>", "", codon_mut)) %in% library_keys) %>%
+    filter(changed_base_span(base_mut) <= 2)
+
+  # Write the filtered variant-count table to the output file path
+  write.csv(filtered_counts, file = output_file_path, row.names = FALSE)
+}
+
+#####
+# run function
+#####
+filter_counts_by_codon_library(
+  counts_file_path = "annotated_variantCounts.csv",
+  codon_library_path = "$possible_mutations",
+  output_file_path = "variantCounts_filtered_by_library.csv"
+)
+
+
+
+
+#########
+# complete filtered counts
+#########
+
+# input: NNK_codon_library_filtered_counts.csv-path, prefiltered_counts.csv-path (containing only NNK mutations), output_folder-path
+# output: completed counts_file with all possible variants, including those never measured in sequencing (counts and counts_per_cov 0, observed FALSE)
+
+library(dplyr)
+library(Biostrings)  # Required for codon-to-amino-acid translation
+
+# Hamming distance between two codons (varying_bases), for whole columns at once
+hamming_distance <- function(wt_codon, variant_codon) {
+  Reduce(`+`, lapply(1:3, function(i) as.integer(substr(wt_codon, i, i) != substr(variant_codon, i, i))))
+}
+
+# Amino acid of each codon (NA for invalid codons), for whole columns at once
+get_amino_acid <- function(codon) {
+  unname(GENETIC_CODE[toupper(codon)])
+}
+
+complete_prefiltered_counts <- function(possible_nnk_path, prefiltered_counts_path, output_file_path) {
+
+  # Load the possible NNK mutations CSV
+  possible_nnk <- read.csv(possible_nnk_path)
+
+  # Load the prefiltered variant-count CSV
+  prefiltered_counts <- read.csv(prefiltered_counts_path)
+
+  # Create codon_mut column in possible_NNK_mutations in the format 'Codon_Number:wt_codon>Variant'
+  possible_nnk <- possible_nnk %>%
+    mutate(codon_mut = paste0(Codon_Number, ":", wt_codon, ">", Variant))
+
+  # Merge both dataframes based on the codon_mut column (full join to include all)
+  prefiltered_counts\$codon_mut <- toupper(prefiltered_counts\$codon_mut)
+  merged_data <- full_join(prefiltered_counts, possible_nnk, by = "codon_mut")
+
+  # Programmed variants that were never observed get their true count of 0, flagged with
+  # observed = FALSE. Plots on a log scale (the LogDiff plot) place zeros themselves, at a floor
+  # derived from the data; a fixed placeholder (previously 1e-6) can rank above real variants in
+  # deeply sequenced libraries, where one read is fewer than 1e-6 counts per coverage.
+  merged_data <- merged_data %>%
+    mutate(observed = !is.na(counts),
+           counts_per_cov = ifelse(is.na(counts_per_cov), 0, counts_per_cov),
+           counts = ifelse(is.na(counts), 0, counts))
+
+  # Hamming distance (varying_bases) and mutation details: aa_mut (M = missense, S = synonymous,
+  # e.g. M:D>S) and pos_mut (wildtype AA, codon position, mutated AA, e.g. D2Q)
+  wt_aa      <- get_amino_acid(merged_data\$wt_codon)
+  variant_aa <- get_amino_acid(merged_data\$Variant)
+  merged_data <- merged_data %>%
+    mutate(varying_bases = hamming_distance(wt_codon, Variant),
+           aa_mut = paste0(ifelse(wt_aa != variant_aa, "M", "S"), ":", wt_aa, ">", variant_aa),
+           pos_mut = paste0(wt_aa, Codon_Number, variant_aa))
+
+  # Save the merged data to a new CSV file
+  write.csv(merged_data, file = output_file_path, row.names = FALSE)
+}
+
+#####
+# run function
+#####
+complete_prefiltered_counts(
+  possible_nnk_path = "$possible_mutations",
+  prefiltered_counts_path = "variantCounts_filtered_by_library.csv",
+  output_file_path = "library_completed_variantCounts.csv"
+)
+
+
+
+
+
+
+
+
+
+#########
+# prepare counts data for counts heatmap
+#########
+
+# input: prefiltered variant-count path (filtered for codon library), aa-seq file path, output path, threshold (for minimum counts to recognize variant)
+# output: csv file serving as basis for counts_per_cov_heatmap function
+
+suppressMessages(library(dplyr))
+suppressMessages(library(ggplot2))
+suppressMessages(library(tidyr))
+suppressMessages(library(reshape2))
+suppressMessages(library(scales))
+
+prepare_counts_data_for_counts_heatmaps <- function(counts_file_path, aa_seq_file_path, output_csv_path, threshold = 3) {
+  # Load the raw variant-count data
+  raw_counts <- read.table(counts_file_path, sep = ",", header = TRUE)
+
+  # Read the wild-type amino acid sequence from the text file
+  wt_seq <- readLines(aa_seq_file_path)
+  wt_seq <- unlist(strsplit(wt_seq, ""))  # Split the sequence into individual amino acids
+
+  # Summarize counts-per-cov for each unique aa mutation in pos_mut
+  aggregated_data <- raw_counts %>%
+    group_by(pos_mut) %>%
+    summarize(total_counts_per_cov = sum(counts_per_cov, na.rm = TRUE),
+              total_counts = sum(counts, na.rm = TRUE))  # Also sum the counts
+
+  # Extract the wild-type position and mutations from 'pos_mut'
+  aggregated_data <- aggregated_data %>%
+    mutate(
+      wt_aa = sub("(\\\\D)(\\\\d+)(\\\\D)", "\\\\1", pos_mut),  # Wild-type amino acid (e.g., S)
+      position = as.numeric(sub("(\\\\D)(\\\\d+)(\\\\D)", "\\\\2", pos_mut)),  # Position (e.g., 3)
+      mut_aa = sub("(\\\\D)(\\\\d+)(\\\\D)", "\\\\3", pos_mut)   # Mutant amino acid (e.g., R)
+    )
+
+  # Replace 'X' with '*', indicating the stop codon
+  aggregated_data <- aggregated_data %>%
+    mutate(mut_aa = ifelse(mut_aa == "X", "*", mut_aa))
+
+  # Replace 'X' with '*' in the wild-type amino acid sequence as well
+  wt_seq <- ifelse(wt_seq == "X", "*", wt_seq)
+
+  # Define all 20 standard amino acids and the stop codon "*"
+  all_amino_acids <- c("A", "C", "D", "E", "F", "G", "H", "I", "K", "L",
+                       "M", "N", "P", "Q", "R", "S", "T", "V", "W", "Y", "*")
+
+  # Create a list of all positions in the wild-type sequence
+  all_positions <- 1:length(wt_seq)
+
+  # Create a complete grid of all possible combinations of positions and amino acids
+  complete_data <- expand.grid(mut_aa = all_amino_acids, position = all_positions)
+
+  # Merge the summarized data with the complete grid (filling missing entries with 0)
+  heatmap_data <- complete_data %>%
+    left_join(aggregated_data, by = c("mut_aa", "position")) %>%
+    mutate(total_counts_per_cov = ifelse(is.na(total_counts_per_cov), 0, total_counts_per_cov),
+           wt_aa = wt_seq[position])  # Assign the wild-type amino acid
+
+  # Set variants with counts < threshold to NA
+  heatmap_data <- heatmap_data %>%
+    mutate(
+      total_counts_per_cov = ifelse(total_counts < threshold, NA, total_counts_per_cov),
+      total_counts = ifelse(total_counts < threshold, NA, total_counts)
+    )
+
+  # Fill pos_mut column
+  heatmap_data <- heatmap_data %>%
+    mutate(
+      pos_mut = ifelse(is.na(pos_mut),
+                       paste0(wt_aa, position, mut_aa),
+                       pos_mut)
+    )
+
+  # Save the aggregated data to a CSV file
+  write.csv(heatmap_data, file = output_csv_path, row.names = FALSE)
+  print(paste("Aggregated data saved to:", output_csv_path))
+}
+
+
+#####
+# run function
+#####
+prepare_counts_data_for_counts_heatmaps(
+  counts_file_path = "variantCounts_filtered_by_library.csv",
+  aa_seq_file_path = "$aa_seq",
+  output_csv_path = "variantCounts_for_heatmaps.csv",
+  threshold = 1  # every observed variant; fitness applies --min_counts to the input libraries
+)
+
+
+
+
+
+
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+dplyr_version <- as.character(packageVersion("dplyr"))
+Biostrings_version <- as.character(packageVersion("Biostrings"))
+stringr_version <- as.character(packageVersion("stringr"))
+ggplot2_version <- as.character(packageVersion("ggplot2"))
+tidyr_version <- as.character(packageVersion("tidyr"))
+reshape2_version <- as.character(packageVersion("reshape2"))
+scales_version <- as.character(packageVersion("scales"))
+
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(dplyr_version) == 0) dplyr_version <- "unknown"
+if (length(Biostrings_version) == 0) Biostrings_version <- "unknown"
+if (length(stringr_version) == 0) stringr_version <- "unknown"
+if (length(ggplot2_version) == 0) ggplot2_version <- "unknown"
+if (length(tidyr_version) == 0) tidyr_version <- "unknown"
+if (length(reshape2_version) == 0) reshape2_version <- "unknown"
+if (length(scales_version) == 0) scales_version <- "unknown"
+
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-dplyr:', dplyr_version),
+    paste('    r-Biostrings:', Biostrings_version),
+    paste('    r-stringr:', stringr_version),
+    paste('    r-ggplot2:', ggplot2_version),
+    paste('    r-tidyr:', tidyr_version),
+    paste('    r-reshape2:', reshape2_version),
+    paste('    r-scales:', scales_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/fastq_check/.conda-lock/linux_amd64-bd-b3082ff93d0ccff0_1.txt b/modules/local/fastq_check/.conda-lock/linux_amd64-bd-b3082ff93d0ccff0_1.txt
new file mode 100644
index 0000000..1443ea2
--- /dev/null
+++ b/modules/local/fastq_check/.conda-lock/linux_amd64-bd-b3082ff93d0ccff0_1.txt
@@ -0,0 +1,109 @@
+version: 6
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+  default:
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+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    - url: https://conda.anaconda.org/bioconda/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
+      linux-64:
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/fq-0.12.0-h54198d6_1.conda
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+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-16.2.0-he0feb66_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-16.2.0-h934c35e_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.6-hdb14827_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/procps-ng-4.0.6-h18c060e_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/which-2.25-h171cf75_0.conda
+packages:
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+  build_number: 20
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+  md5: a9f577daf3de00bca7c3c76c0ecbd1de
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+  md5: 6935724b55726a0e761235c951ed9a69
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+  license_family: GPL
+  size: 1058444
+  timestamp: 1790303109576
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+  license: GPL-3.0-only WITH GCC-exception-3.1
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+  - libgcc >=14
+  license: X11 AND BSD-3-Clause
+  size: 911196
+  timestamp: 1786355078102
+- conda: https://conda.anaconda.org/conda-forge/linux-64/procps-ng-4.0.6-h18c060e_0.conda
+  sha256: 4ce2e1ee31a6217998f78c31ce7dc0a3e0557d9238b51d49dd20c52d467a126d
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+  license: GPL-2.0-or-later AND LGPL-2.0-or-later
+  license_family: GPL
+  size: 593603
+  timestamp: 1769710381284
+- conda: https://conda.anaconda.org/conda-forge/linux-64/which-2.25-h171cf75_0.conda
+  sha256: 2bf9a40d86d29fd5c30d0febec99d06398143ddeece9ebb175cc5a6d20279943
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+  - libstdcxx >=14
+  - libgcc >=14
+  license: GPL-3.0-only
+  license_family: GPL
+  size: 38560
+  timestamp: 1779439547766
diff --git a/modules/local/fastq_check/.conda-lock/linux_arm64-bd-41c3b62452310f82_1.txt b/modules/local/fastq_check/.conda-lock/linux_arm64-bd-41c3b62452310f82_1.txt
new file mode 100644
index 0000000..b7ef410
--- /dev/null
+++ b/modules/local/fastq_check/.conda-lock/linux_arm64-bd-41c3b62452310f82_1.txt
@@ -0,0 +1,101 @@
+version: 6
+environments:
+  default:
+    channels:
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    - url: https://conda.anaconda.org/bioconda/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
+      linux-aarch64:
+      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/_openmp_mutex-4.5-20_gnu.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-aarch64/fq-0.12.0-h389c417_1.conda
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+      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/which-2.25-hdc560ac_0.conda
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+  timestamp: 1790302364054
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/ncurses-6.6-h2b6f883_1.conda
+  sha256: d69a04914139627f0a6bfd19412d6c7f1e37edc896af84a6011e07e8bc1e69fa
+  md5: c3b4349171ca987ff33a1de61f7b3f96
+  depends:
+  - libgcc >=14
+  license: X11 AND BSD-3-Clause
+  size: 955422
+  timestamp: 1786355019431
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/procps-ng-4.0.6-h1779866_0.conda
+  sha256: e9cbcbc94e151ada3d6dc365380aaaf591f65012c16d9a2abaea4b9b90adc402
+  md5: ab7288cc39545556d1bc5e71ab2df9a9
+  depends:
+  - libgcc >=14
+  - ncurses >=6.5,<7.0a0
+  license: GPL-2.0-or-later AND LGPL-2.0-or-later
+  license_family: GPL
+  size: 636733
+  timestamp: 1769712412683
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/which-2.25-hdc560ac_0.conda
+  sha256: 720691eb59b86f11ceb21b236b9402d59c47d156183375d2b24c53a201dd874b
+  md5: 6dc4b55fc391a3d51ff12a6fed979562
+  depends:
+  - libstdcxx >=14
+  - libgcc >=14
+  license: GPL-3.0-only
+  license_family: GPL
+  size: 40249
+  timestamp: 1779439555623
diff --git a/modules/local/fastq_check/environment.yml b/modules/local/fastq_check/environment.yml
new file mode 100644
index 0000000..27d6d2b
--- /dev/null
+++ b/modules/local/fastq_check/environment.yml
@@ -0,0 +1,7 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+channels:
+  - bioconda
+  - conda-forge
+dependencies:
+  - bioconda::fq=0.12.0
diff --git a/modules/local/fastq_check/main.nf b/modules/local/fastq_check/main.nf
new file mode 100644
index 0000000..b5050eb
--- /dev/null
+++ b/modules/local/fastq_check/main.nf
@@ -0,0 +1,69 @@
+process FASTQ_CHECK {
+    tag "$meta.id"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+    container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/1e/1edbe701f591caa7f7a930781d0364392706d291016f1028b5b9fe501ec6ef8c/data'
+        : 'community.wave.seqera.io/library/fq:0.12.0--b3082ff93d0ccff0'}"
+
+    input:
+    tuple val(meta), path(reads, arity: '2')
+
+    output:
+    tuple val(meta), path("*.fastq_check.tsv"), emit: report
+    tuple val("${task.process}"), val('fq'), eval("fq lint --version | sed 's/fq-lint //; s/ .*//'"), emit: versions_fq, topic: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    def args   = task.ext.args ?: ''
+    def prefix = task.ext.prefix ?: "${meta.id}"
+    def r1     = reads[0]
+    def r2     = reads[1]
+    """
+    # fq lint reads both files completely and checks gzip integrity, the format of every FASTQ
+    # record, equal read numbers, and matching read names in the same order. Empty files, and the
+    # same file given twice, are caught before this, at start-up (validateInputFiles). The task never
+    # fails: it writes PASS or FAIL with a plain-language reason to ${prefix}.fastq_check.tsv, and the
+    # workflow stops with one message that lists every library that failed.
+    status=0
+    fq lint ${args} ${r1} ${r2} > /dev/null 2> fq_lint.log || status=\$?
+    sed 's/\\x1b\\[[0-9;]*m//g' fq_lint.log > fq_lint.clean.log
+    cat fq_lint.clean.log >&2
+
+    # fq's own message: the first lines that are not log lines or stack trace
+    detail=\$(grep -v -E ' INFO |^ *[0-9]+: |^ +at |^Stack backtrace|^ *\$' fq_lint.clean.log | head -n 3 | tr '\\n' ' ' | sed 's/  */ /g; s/ *\$//' || true)
+
+    if [ "\$status" -ne 0 ]; then
+        if grep -q -E 'deflate|gzip|I/O error|unexpected end' fq_lint.clean.log; then
+            # Find out which of the two files is broken
+            broken=""
+            for fastq in ${r1} ${r2}; do
+                fq lint "\$fastq" > /dev/null 2>&1 || broken="\$broken \$fastq"
+            done
+            result="FAIL\tcorrupted, truncated or not gzip-compressed file:\${broken:- file1 or file2} (\$detail)"
+        elif grep -q 'r1-src unexpectedly ended' fq_lint.clean.log; then
+            result="FAIL\tfile1 (${r1}) has fewer reads than file2 (${r2}): one of them is incomplete, or they are not mates"
+        elif grep -q 'r2-src unexpectedly ended' fq_lint.clean.log; then
+            result="FAIL\tfile2 (${r2}) has fewer reads than file1 (${r1}): one of them is incomplete, or they are not mates"
+        elif grep -q '\\[P001\\]' fq_lint.clean.log; then
+            result="FAIL\tread names of file1 and file2 do not match (\$detail). vsearch pairs reads by position, so the mates must be in the same order. If both files contain the same reads in a different order (e.g. lanes concatenated in a different order), re-pair them with 'seqkit pair' (https://github.com/shenwei356/seqkit) or BBTools 'repair.sh' (https://github.com/bbushnell/BBTools); otherwise check that the two files belong to the same library"
+        elif grep -q -E '\\[S[0-9]+\\]' fq_lint.clean.log; then
+            result="FAIL\tmalformed FASTQ record (\$detail)"
+        else
+            result="FAIL\tfq lint failed (\$detail)"
+        fi
+    else
+        result="PASS\tcomplete and consistent"
+    fi
+    printf '%b\\n' "\$result" > ${prefix}.fastq_check.tsv
+    """
+
+    stub:
+    def prefix = task.ext.prefix ?: "${meta.id}"
+    """
+    printf 'PASS\\tstub\\n' > ${prefix}.fastq_check.tsv
+    """
+}
diff --git a/modules/local/fastq_check/meta.yml b/modules/local/fastq_check/meta.yml
new file mode 100644
index 0000000..d96f240
--- /dev/null
+++ b/modules/local/fastq_check/meta.yml
@@ -0,0 +1,74 @@
+name: "fastq_check"
+description: Checks that the two FASTQ files of a paired-end library are complete and consistent with fq lint (gzip integrity, FASTQ record format, equal read numbers, matching read names in the same order), and reports PASS or FAIL with a plain-language reason instead of failing the task.
+keywords:
+  - fastq
+  - validation
+  - lint
+  - paired-end
+tools:
+  - "fq":
+      description: "FASTQ manipulation and validation tool"
+      homepage: "https://github.com/stjude-rust-labs/fq"
+      documentation: "https://github.com/stjude-rust-labs/fq"
+      tool_dev_url: "https://github.com/stjude-rust-labs/fq"
+      licence: ["MIT"]
+      identifier: ""
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'GID1A_input_1_pe', sample:'GID1A', type:'input', replicate:1 ]`
+    - reads:
+        type: file
+        description: Forward and reverse FASTQ files of one paired-end library
+        pattern: "*.{fastq,fq}{,.gz}"
+
+output:
+  - report:
+      - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+      - "*.fastq_check.tsv":
+          type: file
+          description: One line, tab-separated - PASS or FAIL, and the reason
+          pattern: "*.fastq_check.tsv"
+  - versions_fq:
+      - ${task.process}:
+          type: string
+          description: The name of the process
+      - fq:
+          type: string
+          description: The name of the tool
+      - "fq lint --version | sed 's/fq-lint //; s/ .*//'":
+          type: eval
+          description: The expression to obtain the version of the tool
+
+authors:
+  - "@MaximilianStammnitz"
+containers:
+  docker:
+    linux/amd64:
+      name: community.wave.seqera.io/library/fq:0.12.0--b3082ff93d0ccff0
+      build_id: bd-b3082ff93d0ccff0_1
+      scan_id: sc-f21a88680f9cdf16_1
+    linux/arm64:
+      name: community.wave.seqera.io/library/fq:0.12.0--41c3b62452310f82
+      build_id: bd-41c3b62452310f82_1
+      scan_id: sc-55cb6598f426fae1_1
+  singularity:
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/fq:0.12.0--8bfef4392e820de8
+      build_id: bd-8bfef4392e820de8_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/80/80ba09e7d98be1120310df58960280440253953eaba0da00d71cd6b3f13211f9/data
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/fq:0.12.0--2eebf4e58a46525e
+      build_id: bd-2eebf4e58a46525e_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/1e/1edbe701f591caa7f7a930781d0364392706d291016f1028b5b9fe501ec6ef8c/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/fastq_check/.conda-lock/linux_amd64-bd-b3082ff93d0ccff0_1.txt
+    linux/arm64:
+      lock_file: modules/local/fastq_check/.conda-lock/linux_arm64-bd-41c3b62452310f82_1.txt
diff --git a/modules/local/fitness/counts_to_fitness/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt b/modules/local/fitness/counts_to_fitness/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
new file mode 100644
index 0000000..be8d7e8
--- /dev/null
+++ b/modules/local/fitness/counts_to_fitness/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
@@ -0,0 +1,2942 @@
+version: 6
+environments:
+  default:
+    channels:
+    - url: https://conda.anaconda.org/conda-forge/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
+      linux-64:
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.46.1-default_hfdba357_103.conda
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-biocgenerics-0.56.0-r45hdfd78af_2.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-biostrings-2.78.0-r45h01b2380_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-iranges-2.44.0-r45h01b2380_1.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-s4vectors-0.48.1-r45h262fe30_0.conda
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-seqinfo-1.0.0-r45hdfd78af_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-xvector-0.50.0-r45h01b2380_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/bwidget-1.10.1-hfc2019e_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_10.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.9-hebe6cf0_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.10.23-hbd8a1cb_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-h3394656_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/curl-8.21.0-hcf29cc6_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda
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diff --git a/modules/local/fitness/counts_to_fitness/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt b/modules/local/fitness/counts_to_fitness/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
new file mode 100644
index 0000000..10e5993
--- /dev/null
+++ b/modules/local/fitness/counts_to_fitness/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
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diff --git a/modules/local/fitness/counts_to_fitness/environment.yml b/modules/local/fitness/counts_to_fitness/environment.yml
new file mode 100644
index 0000000..5f5785d
--- /dev/null
+++ b/modules/local/fitness/counts_to_fitness/environment.yml
@@ -0,0 +1,19 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+# Shared by 17 R modules (all except RUN_MUTSCAN, RUN_DIMSUM and the false-doubles error correction).
+# Keep the copies identical:
+# the one container image these modules use is built from exactly this list.
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/fitness/counts_to_fitness/main.nf b/modules/local/fitness/counts_to_fitness/main.nf
new file mode 100644
index 0000000..4755d76
--- /dev/null
+++ b/modules/local/fitness/counts_to_fitness/main.nf
@@ -0,0 +1,32 @@
+process COUNTS_TO_FITNESS {
+    tag "$meta.id"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7'}"
+
+    input:
+    tuple val(meta), path(variantCounts_filtered_by_library)
+    path wt_seq
+    val pos_range
+
+    output:
+    tuple val(meta), path("${meta.id}_fitness_input.tsv"), emit: fitness_input
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'counts_to_fitness.R'
+
+    stub:
+    """
+    touch ${meta.id}_fitness_input.tsv
+    echo "COUNTS_TO_FITNESS:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+    """
+}
diff --git a/modules/local/fitness/counts_to_fitness/meta.yml b/modules/local/fitness/counts_to_fitness/meta.yml
new file mode 100644
index 0000000..9c100f4
--- /dev/null
+++ b/modules/local/fitness/counts_to_fitness/meta.yml
@@ -0,0 +1,74 @@
+name: "counts_to_fitness"
+description: Reformats the library-filtered variant count table into a standardized TSV compatible with downstream fitness calculation tools, ensuring mutation nomenclature is consistent with the reference sequence.
+keywords:
+  - deep mutational scanning
+  - dms
+  - variant counting
+  - format conversion
+  - fitness
+tools:
+  - "bioconductor-biostrings":
+      description: "Efficient manipulation of biological strings"
+      homepage: "https://bioconductor.org/packages/Biostrings"
+      licence: ["Artistic-2.0"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', single_end:false ]`
+    - variantCounts_filtered_by_library:
+        type: file
+        description: CSV file containing variant counts already filtered against the design library
+        pattern: "*.{csv}"
+  - - wt_seq:
+        type: file
+        description: FASTA file containing the wild-type DNA reference sequence
+        pattern: "*.{fasta,fa}"
+  - - pos_range:
+        type: string
+        description: Start and stop codon positions (ORF) in the format 'start-stop'
+
+output:
+  - fitness_input:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - "${meta.id}_fitness_input.tsv":
+          type: file
+          description: A standardized TSV file ready for input into fitness calculation modules
+          pattern: "*_fitness_input.tsv"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+containers:
+  docker:
+    linux/amd64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7
+      build_id: bd-32f1af0a83a018e7_1
+      scan_id: sc-337510c2c910aa1c_1
+    linux/arm64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb
+      build_id: bd-880bbdecb19543bb_1
+      scan_id: sc-09c9d098a09068b9_1
+  singularity:
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb
+      build_id: bd-7202e5d19667bddb_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7
+      build_id: bd-db46247d9810b3e7_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/fitness/counts_to_fitness/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
+    linux/arm64:
+      lock_file: modules/local/fitness/counts_to_fitness/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
diff --git a/modules/local/fitness/counts_to_fitness/templates/counts_to_fitness.R b/modules/local/fitness/counts_to_fitness/templates/counts_to_fitness.R
new file mode 100644
index 0000000..2fcd787
--- /dev/null
+++ b/modules/local/fitness/counts_to_fitness/templates/counts_to_fitness.R
@@ -0,0 +1,104 @@
+#!/usr/bin/env Rscript
+
+suppressMessages(library(Biostrings))
+
+generate_fitness_input <- function(wt_seq_path, counts_file, pos_range, output_file_path) {
+  # Parse the position range
+  positions <- unlist(strsplit(pos_range, "-"))
+  start_pos <- as.numeric(positions[1])
+  stop_pos <- as.numeric(positions[2])
+
+  # Load the wild-type sequence
+  seq_data <- Biostrings::readDNAStringSet(filepath = wt_seq_path)
+  wt_seq <- seq_data[[1]]  # Extract the sequence
+  wt_seq <- subseq(wt_seq, start = start_pos, end = stop_pos)
+
+  # Convert wt_seq to a character string
+  wt_seq <- as.character(wt_seq)
+
+  # Split the wild-type sequence into codons (groups of 3 bases)
+  wt_codons <- substring(wt_seq, seq(1, nchar(wt_seq), 3), seq(3, nchar(wt_seq), 3))
+
+  # Helper function to process variant-count CSVs into count data
+  process_counts_file <- function(counts_csv) {
+    # Load the input variant-count CSV file
+    counts_data <- read.csv(counts_csv, stringsAsFactors = FALSE)
+
+    # Pre-allocate one slot per row. Growing `results` with rbind() inside the loop reallocated
+    # the whole frame on every variant (O(n^2)); for a long ORF that dominated the runtime.
+    n_rows <- nrow(counts_data)
+    seq_strings <- character(n_rows)
+    seq_counts  <- numeric(n_rows)
+
+    if (n_rows == 0) {
+      return(data.frame(nt_seq = character(0), count = numeric(0), stringsAsFactors = FALSE))
+    }
+
+    # Iterate over each row in the input data
+    for (i in 1:n_rows) {
+      # Extract the mutation info
+      codon_mut <- counts_data\$codon_mut[i]
+
+      if("counts_corrected" %in% colnames(counts_data)){
+        counts <- counts_data\$counts_corrected[i]
+      }else{
+        counts <- counts_data\$counts[i]
+      }
+
+      # Create a mutable copy of the wild-type codons
+      mutated_codons <- wt_codons
+
+      # Apply the mutation
+      mutations <- strsplit(codon_mut, ", ")[[1]]
+      for (mutation in mutations) {
+        codon_position <- as.numeric(sub(":.*", "", mutation))
+        new_codon <- sub(".*>", "", mutation)
+        # Replace the codon at the specified position
+        mutated_codons[codon_position] <- new_codon
+      }
+
+      # Convert the mutated codons back to a sequence string
+      seq_strings[i] <- paste(mutated_codons, collapse = "")
+      seq_counts[i]  <- counts
+    }
+
+    return(data.frame(nt_seq = seq_strings, count = seq_counts, stringsAsFactors = FALSE))
+  }
+
+  # Process the variant-count file
+  cat("Processing variant-count file...\\n")
+  processed_data <- process_counts_file(counts_file)
+
+  # Write the processed data to a file without column names
+  write.table(processed_data, file = output_file_path, sep = "\\t", row.names = FALSE, col.names = FALSE, quote = FALSE)
+}
+
+#####
+# run function
+#####
+generate_fitness_input(
+  wt_seq_path = "$wt_seq",
+  counts_file = "$variantCounts_filtered_by_library",
+  pos_range = "$pos_range",
+  output_file_path = "${meta.id}_fitness_input.tsv"
+)
+
+#####
+# create versions.yml
+#####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+Biostrings_version <- as.character(packageVersion("Biostrings"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(Biostrings_version) == 0) Biostrings_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-Biostrings:', Biostrings_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/fitness/find_synonymous_mutation/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt b/modules/local/fitness/find_synonymous_mutation/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
new file mode 100644
index 0000000..be8d7e8
--- /dev/null
+++ b/modules/local/fitness/find_synonymous_mutation/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
@@ -0,0 +1,2942 @@
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diff --git a/modules/local/fitness/find_synonymous_mutation/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt b/modules/local/fitness/find_synonymous_mutation/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
new file mode 100644
index 0000000..10e5993
--- /dev/null
+++ b/modules/local/fitness/find_synonymous_mutation/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
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+  timestamp: 1787103097310
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxrender-0.9.13-h5bc82ec_0.conda
+  sha256: fc8a9ff04d1f2a83cc230cacccd3253a09e76e10a351bc5d0d1c880dec6fe639
+  md5: e3d2ab9bb788649f839f2fab676b8d14
+  depends:
+  - libgcc >=15
+  - xorg-libx11 >=1.8.13,<2.0a0
+  license: MIT
+  license_family: MIT
+  size: 36304
+  timestamp: 1792530356575
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxt-1.3.1-h5bc82ec_1.conda
+  sha256: 90cde7ffd12c2f02ec4a6b893dfa0c1fcb8f7b23b468ff575d3222df2cd187c6
+  md5: 06a15405538666fbdc272718ff4e0fe4
+  depends:
+  - libgcc >=15
+  - xorg-libice >=1.1.2,<2.0a0
+  - xorg-libsm >=1.2.6,<2.0a0
+  - xorg-libx11 >=1.8.13,<2.0a0
+  license: MIT
+  license_family: MIT
+  size: 394356
+  timestamp: 1787103094046
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/zstd-1.5.7-h9d15635_7.conda
+  sha256: 427fd14bcb3b8659796fecc682716617409350fb5a98e5b7b47558a10d1a2fc7
+  md5: d942e34ac3920ba83f8b2d0169570187
+  depends:
+  - libzlib >=1.3.2,<2.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 615477
+  timestamp: 1786599613561
diff --git a/modules/local/fitness/find_synonymous_mutation/environment.yml b/modules/local/fitness/find_synonymous_mutation/environment.yml
new file mode 100644
index 0000000..5f5785d
--- /dev/null
+++ b/modules/local/fitness/find_synonymous_mutation/environment.yml
@@ -0,0 +1,19 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+# Shared by 17 R modules (all except RUN_MUTSCAN, RUN_DIMSUM and the false-doubles error correction).
+# Keep the copies identical:
+# the one container image these modules use is built from exactly this list.
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/fitness/find_synonymous_mutation/main.nf b/modules/local/fitness/find_synonymous_mutation/main.nf
new file mode 100644
index 0000000..7529031
--- /dev/null
+++ b/modules/local/fitness/find_synonymous_mutation/main.nf
@@ -0,0 +1,31 @@
+process FIND_SYNONYMOUS_MUTATION {
+  tag { sample.sample }
+  label 'process_single'
+
+  conda "${moduleDir}/environment.yml"
+
+  container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+      ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data'
+      : 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7'}"
+
+  input:
+    tuple val(sample), path(counts_merged)   // from MERGE_COUNTS.out.merged_counts
+    path wt_fasta                            // broadcast singleton
+    val pos_range                            // "start-end", broadcast singleton
+
+  output:
+    tuple val(sample), path("synonymous_wt.txt"), emit: synonymous_wt
+    path "versions.yml", emit: versions
+
+  script:
+  template 'find_syn_mutation.R'
+
+  stub:
+  """
+  touch synonymous_wt.txt
+  cat <<-END_VERSIONS > versions.yml
+  "${task.process}":
+      r-base: "0.0.0"
+  END_VERSIONS
+  """
+}
diff --git a/modules/local/fitness/find_synonymous_mutation/meta.yml b/modules/local/fitness/find_synonymous_mutation/meta.yml
new file mode 100644
index 0000000..4e6de93
--- /dev/null
+++ b/modules/local/fitness/find_synonymous_mutation/meta.yml
@@ -0,0 +1,75 @@
+name: "find_synonymous_mutation"
+description: Identifies synonymous mutations within the merged count table, prioritizing those with high counts (typically 2nt changes) to serve as a reference for normalizing fitness scores.
+keywords:
+  - deep mutational scanning
+  - dms
+  - synonymous mutation
+  - fitness calculation
+  - normalization
+tools:
+  - "bioconductor-biostrings":
+      description: "Memory efficient string containers, string matching algorithms, and other utilities, for fast manipulation of large biological sequences or sets of sequences"
+      homepage: "https://bioconductor.org/packages/Biostrings"
+      licence: ["Artistic-2.0"]
+
+input:
+  - - sample:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test' ]`
+    - counts_merged:
+        type: file
+        description: TSV/CSV file containing variant counts merged across samples or replicates
+        pattern: "*.{tsv,csv}"
+  - - wt_fasta:
+        type: file
+        description: FASTA file containing the wild-type reference DNA sequence
+        pattern: "*.{fasta,fa}"
+  - - pos_range:
+        type: string
+        description: Start and stop codon positions (ORF) in the format 'start-stop', e.g., '352-1383'
+
+output:
+  - synonymous_wt:
+      - sample:
+          type: map
+          description: |
+            Groovy Map containing sample information
+      - synonymous_wt.txt:
+          type: file
+          description: Text file identifying the chosen synonymous mutation to be used as a wild-type reference
+          pattern: "synonymous_wt.txt"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+containers:
+  docker:
+    linux/arm64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb
+      build_id: bd-880bbdecb19543bb_1
+      scan_id: sc-09c9d098a09068b9_1
+    linux/amd64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7
+      build_id: bd-32f1af0a83a018e7_1
+      scan_id: sc-337510c2c910aa1c_1
+  singularity:
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb
+      build_id: bd-7202e5d19667bddb_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7
+      build_id: bd-db46247d9810b3e7_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/fitness/find_synonymous_mutation/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
+    linux/arm64:
+      lock_file: modules/local/fitness/find_synonymous_mutation/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
diff --git a/modules/local/fitness/find_synonymous_mutation/templates/find_syn_mutation.R b/modules/local/fitness/find_synonymous_mutation/templates/find_syn_mutation.R
new file mode 100644
index 0000000..a0ee07a
--- /dev/null
+++ b/modules/local/fitness/find_synonymous_mutation/templates/find_syn_mutation.R
@@ -0,0 +1,119 @@
+#!/usr/bin/env Rscript
+
+suppressMessages(library(Biostrings))
+
+# Pick a synonymous "WT substitute" for DiMSum normalization using a fixed coding window.
+# Inputs:
+#   wt_fasta          : path to FASTA (single WT sequence)
+#   counts_merged_tsv : path to merged counts (columns: nt_seq, input1..N, output1..M)
+#   pos_range         : "start-end" (1-based, inclusive), e.g. "352-1383"
+# Returns:
+#   character scalar: chosen nt sequence
+#
+# Preference:
+#   1) AA-identical (fully synonymous) AND exactly 2 nt mismatches vs WT, both within ONE codon.
+#   2) If none, AA-identical AND exactly 1 nt mismatch vs WT.
+#   3) If more than one, pick highest mean of input counts.
+#   4) If still none, stop with an error.
+
+pick_synonymous_wt_from_range <- function(wt_fasta, counts_merged_tsv, pos_range) {
+  ## ---- parse range ----
+  pr <- strsplit(as.character(pos_range), "-", fixed = TRUE)[[1]]
+  if (length(pr) != 2L) stop("pos_range must be 'start-end', got: ", pos_range)
+  start_pos <- as.integer(pr[1]); end_pos <- as.integer(pr[2])
+  if (is.na(start_pos) || is.na(end_pos) || start_pos < 1L || end_pos < start_pos)
+    stop("Invalid pos_range: ", pos_range)
+
+  ## ---- WT window ----
+  wt_set <- Biostrings::readDNAStringSet(wt_fasta)
+  if (length(wt_set) != 1L) stop("WT FASTA must contain exactly one sequence.")
+  wt_subseq <- Biostrings::subseq(wt_set[[1]], start = start_pos, end = end_pos)
+  wt_seq_chr <- as.character(wt_subseq)
+  wt_len <- nchar(wt_seq_chr)
+  if ((wt_len %% 3) != 0) stop("Provided window length is not divisible by 3: ", wt_len)
+  wt_aa  <- Biostrings::translate(wt_subseq, if.fuzzy.codon = "X")
+  wt_chars <- strsplit(wt_seq_chr, "", fixed = TRUE)[[1]]
+
+  ## ---- counts ----
+  df <- utils::read.delim(counts_merged_tsv, sep = "\\t", header = TRUE,
+                          stringsAsFactors = FALSE, check.names = FALSE)
+  if (!"nt_seq" %in% names(df)) stop("counts_merged_tsv must have a 'nt_seq' column.")
+
+  df\$nt_seq <- toupper(df\$nt_seq)
+  keep_len <- nchar(df\$nt_seq) == wt_len
+  if (!any(keep_len)) stop("No sequences match WT window length (", wt_len, ").")
+  if (!all(keep_len)) df <- df[keep_len, , drop = FALSE]
+
+  # input columns & mean (works with 1+ replicates)
+  input_cols <- grep("^input", names(df), value = TRUE)
+  if (length(input_cols) == 0L) stop("No input columns found (expect names starting with 'input').")
+  input_mat <- as.data.frame(lapply(df[, input_cols, drop = FALSE], function(x) as.numeric(as.character(x))))
+  input_mean <- if (length(input_cols) == 1L) input_mat[[1]] else rowMeans(as.matrix(input_mat), na.rm = TRUE)
+
+  ## ---- synonymous filter ----
+  var_set <- Biostrings::DNAStringSet(df\$nt_seq)
+  var_aa  <- Biostrings::translate(var_set, if.fuzzy.codon = "X")
+  syn_idx <- which(as.character(var_aa) == as.character(wt_aa))
+  if (length(syn_idx) == 0L) stop("No fully-synonymous variants found relative to WT translation.")
+
+  # helpers
+  mismatch_positions <- function(seq_nt_chars) which(seq_nt_chars != wt_chars) # 1-based positions
+  codon_index <- function(pos_vec) floor((pos_vec - 1L) / 3L)                  # 0-based codon bin
+
+  # preference 1: exactly 2 mismatches, both within the same codon
+  cand_two_one <- Filter(function(i) {
+    vchars <- strsplit(df\$nt_seq[i], "", fixed = TRUE)[[1]]
+    pos <- mismatch_positions(vchars)
+    length(pos) == 2L && length(unique(codon_index(pos))) == 1L
+  }, syn_idx)
+
+  choose_best <- function(idx_vec) idx_vec[ which.max(input_mean[idx_vec]) ]
+
+  if (length(cand_two_one) > 0L) {
+    best_i <- choose_best(cand_two_one)
+    return(as.character(df\$nt_seq[best_i]))
+  }
+
+  # preference 2 (fallback): exactly 1 mismatch (still synonymous)
+  cand_one <- Filter(function(i) {
+    vchars <- strsplit(df\$nt_seq[i], "", fixed = TRUE)[[1]]
+    length(mismatch_positions(vchars)) == 1L
+  }, syn_idx)
+
+  if (length(cand_one) > 0L) {
+    best_i <- choose_best(cand_one)
+    return(as.character(df\$nt_seq[best_i]))
+  }
+
+  stop("No suitable synonymous variant found: neither 2-in-1-codon nor 1-nt synonymous candidates present.")
+}
+
+#####
+# run function
+#####
+seq <- pick_synonymous_wt_from_range(
+	wt_fasta = "$wt_fasta",
+	counts_merged_tsv = "$counts_merged",
+	pos_range = "$pos_range"
+	)
+write(seq, file='synonymous_wt.txt')
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+Biostrings_version <- as.character(packageVersion("Biostrings"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(Biostrings_version) == 0) Biostrings_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-Biostrings:', Biostrings_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/fitness/fitness_QC/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt b/modules/local/fitness/fitness_QC/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
new file mode 100644
index 0000000..be8d7e8
--- /dev/null
+++ b/modules/local/fitness/fitness_QC/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
@@ -0,0 +1,2942 @@
+version: 6
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+  default:
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+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
+      linux-64:
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.46.1-default_hfdba357_103.conda
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-biocgenerics-0.56.0-r45hdfd78af_2.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-biostrings-2.78.0-r45h01b2380_0.conda
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diff --git a/modules/local/fitness/fitness_QC/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt b/modules/local/fitness/fitness_QC/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
new file mode 100644
index 0000000..10e5993
--- /dev/null
+++ b/modules/local/fitness/fitness_QC/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
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diff --git a/modules/local/fitness/fitness_QC/environment.yml b/modules/local/fitness/fitness_QC/environment.yml
new file mode 100644
index 0000000..5f5785d
--- /dev/null
+++ b/modules/local/fitness/fitness_QC/environment.yml
@@ -0,0 +1,19 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+# Shared by 17 R modules (all except RUN_MUTSCAN, RUN_DIMSUM and the false-doubles error correction).
+# Keep the copies identical:
+# the one container image these modules use is built from exactly this list.
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/fitness/fitness_QC/main.nf b/modules/local/fitness/fitness_QC/main.nf
new file mode 100644
index 0000000..51a3c18
--- /dev/null
+++ b/modules/local/fitness/fitness_QC/main.nf
@@ -0,0 +1,31 @@
+process FITNESS_QC {
+  tag { sample.sample }
+  label 'process_single'
+
+  conda "${moduleDir}/environment.yml"
+
+  container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+      ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data'
+      : 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7'}"
+
+  input:
+    tuple val(sample), path(fitness_estimation_tsv)   // from FITNESS_CALCULATION
+
+  output:
+    tuple val(sample), path("fitness_estimation_count_correlation.pdf"), emit: counts_corr_pdf
+    tuple val(sample), path("fitness_estimation_fitness_correlation.pdf"), emit: fitness_corr_pdf
+    path "versions.yml", emit: versions
+
+  script:
+  template 'fitness_QC.R'
+
+  stub:
+  """
+    touch fitness_estimation_count_correlation.pdf
+    touch fitness_estimation_fitness_correlation.pdf
+    cat > versions.yml <<'EOF'
+    "${task.process}":
+      stub-version: "0.0.0"
+    EOF
+  """
+}
diff --git a/modules/local/fitness/fitness_QC/meta.yml b/modules/local/fitness/fitness_QC/meta.yml
new file mode 100644
index 0000000..5cfb2ff
--- /dev/null
+++ b/modules/local/fitness/fitness_QC/meta.yml
@@ -0,0 +1,70 @@
+name: "fitness_qc"
+description: Generates quality control visualizations for fitness estimation results, including correlation plots of raw counts and calculated fitness scores to assess experimental reproducibility.
+keywords:
+  - deep mutational scanning
+  - dms
+  - quality control
+  - correlation
+  - fitness
+tools: null
+input:
+  - - sample:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test' ]`
+    - fitness_estimation_tsv:
+        type: file
+        description: TSV file containing calculated fitness estimates and associated statistics
+        pattern: "*.{tsv}"
+
+output:
+  - counts_corr_pdf:
+      - sample:
+          type: map
+          description: Groovy Map containing sample information
+      - fitness_estimation_count_correlation.pdf:
+          type: file
+          description: PDF plot showing the correlation of variant counts between samples/replicates
+          pattern: "fitness_estimation_count_correlation.pdf"
+  - fitness_corr_pdf:
+      - sample:
+          type: map
+          description: Groovy Map containing sample information
+      - fitness_estimation_fitness_correlation.pdf:
+          type: file
+          description: PDF plot showing the correlation of fitness scores between samples/replicates
+          pattern: "fitness_estimation_fitness_correlation.pdf"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+containers:
+  docker:
+    linux/amd64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7
+      build_id: bd-32f1af0a83a018e7_1
+      scan_id: sc-337510c2c910aa1c_1
+    linux/arm64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb
+      build_id: bd-880bbdecb19543bb_1
+      scan_id: sc-09c9d098a09068b9_1
+  singularity:
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb
+      build_id: bd-7202e5d19667bddb_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7
+      build_id: bd-db46247d9810b3e7_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/fitness/fitness_QC/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
+    linux/arm64:
+      lock_file: modules/local/fitness/fitness_QC/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
diff --git a/modules/local/fitness/fitness_QC/templates/fitness_QC.R b/modules/local/fitness/fitness_QC/templates/fitness_QC.R
new file mode 100644
index 0000000..3a1a3f8
--- /dev/null
+++ b/modules/local/fitness/fitness_QC/templates/fitness_QC.R
@@ -0,0 +1,139 @@
+#!/usr/bin/env Rscript
+
+## fitness QC plots for nf-core/deepmutscan
+## 28.10.2025
+## maximilian.stammnitz@crg.eu
+
+## lower panels: scatter + x=y (log-log version for counts)
+panel_xy_abline_counts <- function(x, y, ...) {
+  op <- par("xpd"); on.exit(par(xpd = op), add = TRUE)
+  par(xpd = FALSE)
+  points(x, y, pch = 16, cex = 0.1, ...)
+  abline(a = 0, b = 1, lty = 2, col = "grey50")
+}
+
+## upper panels: Pearson r (log-log-transformed)
+panel_cor_counts <- function(x, y, digits = 2, prefix = "r = ", cex.text = 1.4, ...) {
+  r <- suppressWarnings(cor(log(x), log(y), use = "pairwise.complete.obs", method = "pearson"))
+  lab <- if (is.finite(r)) bquote(italic(r) == .(round(r, digits))) else bquote(italic(r) == NA)
+
+  ## Save/restore full graphics state we touch
+  op <- par(c("usr", "xpd", "xlog", "ylog"))
+  on.exit(par(op), add = TRUE)
+
+  ## Draw in normalized 0..1 panel coords with logs OFF so text is visible
+  par(xlog = FALSE, ylog = FALSE, xpd = FALSE, usr = c(0, 1, 0, 1))
+  text(0.5, 0.5, labels = lab, cex = cex.text, font = 1, col = "black")
+}
+
+## lower panels: scatter + x=y (linear version for fitness)
+panel_xy_abline_fitness <- function(x, y, ...) {
+  op <- par("xpd"); on.exit(par(xpd = op), add = TRUE)
+  par(xpd = FALSE)
+  points(x, y, pch = 16, cex = 0.5, ...)
+  abline(a = 0, b = 1, lty = 2, col = "grey50")
+}
+
+## upper panels: Pearson r (linear)
+panel_cor_fitness <- function(x, y, digits = 2, prefix = "r = ", cex.text = 1.4, ...) {
+  r <- suppressWarnings(cor(x, y, use = "pairwise.complete.obs", method = "pearson"))
+  lab <- if (is.finite(r)) bquote(italic(r) == .(round(r, digits))) else bquote(italic(r) == NA)
+
+  ## Save/restore full graphics state we touch
+  op <- par(c("usr", "xpd", "xlog", "ylog"))
+  on.exit(par(op), add = TRUE)
+
+  ## Draw in normalized 0..1 panel coords with logs OFF so text is visible
+  par(xlog = FALSE, ylog = FALSE, xpd = FALSE, usr = c(0, 1, 0, 1))
+  text(0.5, 0.5, labels = lab, cex = cex.text, font = 1, col = "black")
+}
+
+#' Plot input/output count correlations and fitness replicate correlations
+#'
+#' @param fitness_table_path Path to the input table (fitness_estimation.tsv)
+#' @param out_counts_corr_pdf Path to write the counts correlation PDF
+#' @param out_fitness_corr_pdf Path to write the fitness correlation PDF
+#'
+#' @return Invisibly returns TRUE; writes the two PDFs.
+run_fitness_plots <- function(fitness_table_path,
+                              out_counts_corr_pdf,
+                              out_fitness_corr_pdf) {
+
+  merged.counts.fitness <- read.table(fitness_table_path, sep = "\\t", header = TRUE, check.names = FALSE)
+
+  ## identify the right samples
+  inputs  <- grep("input",  colnames(merged.counts.fitness))
+  outputs <- grep("output", colnames(merged.counts.fitness))
+
+  ## 5. Plot input vs. output counts ##
+  #####################################
+  pdf(out_counts_corr_pdf, height = 9, width = 14)
+  pairs(merged.counts.fitness[, c(inputs, outputs)] + 1,  ## use a pseudo-count
+        lower.panel = panel_xy_abline_counts,
+        upper.panel = panel_cor_counts,
+        cex.text = 2,
+        log = "xy")
+  dev.off()
+
+  ## 6. Plot fitness correlations ##
+  ##################################
+  fitness.repl <- grep("rescaled_fitness", colnames(merged.counts.fitness))
+
+  if (length(fitness.repl) > 1) {
+    pdf(out_fitness_corr_pdf, height = 9, width = 14)
+    ## Axis range from the data instead of a hard-coded c(-3, 1), which silently cropped
+    ## strongly deleterious variants out of the replicate-correlation panels. Fitness is
+    ## rescaled so synonymous = 0 and stop = -1, so the usual range is roughly [-1.5, 0.5],
+    ## but nothing bounds it: padding the observed range keeps every point visible.
+    fitness.range <- range(unlist(merged.counts.fitness[, fitness.repl]), finite = TRUE)
+    if (!all(is.finite(fitness.range))) {
+      fitness.range <- c(-3, 1)
+    }
+    fitness.pad <- max(0.1, diff(fitness.range) * 0.05)
+    fitness.lim <- c(fitness.range[1] - fitness.pad, fitness.range[2] + fitness.pad)
+
+    pairs(merged.counts.fitness[, fitness.repl],
+          lower.panel = panel_xy_abline_fitness,
+          upper.panel = panel_cor_fitness,
+          cex.text = 2,
+          xlim = fitness.lim,
+          ylim = fitness.lim)
+    dev.off()
+  } else {
+    ## If only one (or zero) rescaled_fitness columns exist, still create an empty placeholder
+    ## so Nextflow finds the declared output.
+    pdf(out_fitness_corr_pdf, height = 9, width = 14)
+    plot.new()
+    title("No replicate fitness columns found (need ≥2 'rescaled_fitness...')\\nCreated placeholder PDF.")
+    dev.off()
+  }
+
+  invisible(TRUE)
+}
+
+
+#####
+# run function
+#####
+run_fitness_plots(
+  fitness_table_path = "$fitness_estimation_tsv",
+  out_counts_corr_pdf = "fitness_estimation_count_correlation.pdf",
+  out_fitness_corr_pdf = "fitness_estimation_fitness_correlation.pdf"
+)
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+
+if (is.null(r_version)) r_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/fitness/fitness_calculation/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt b/modules/local/fitness/fitness_calculation/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
new file mode 100644
index 0000000..be8d7e8
--- /dev/null
+++ b/modules/local/fitness/fitness_calculation/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
@@ -0,0 +1,2942 @@
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diff --git a/modules/local/fitness/fitness_calculation/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt b/modules/local/fitness/fitness_calculation/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
new file mode 100644
index 0000000..10e5993
--- /dev/null
+++ b/modules/local/fitness/fitness_calculation/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
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diff --git a/modules/local/fitness/fitness_calculation/environment.yml b/modules/local/fitness/fitness_calculation/environment.yml
new file mode 100644
index 0000000..5f5785d
--- /dev/null
+++ b/modules/local/fitness/fitness_calculation/environment.yml
@@ -0,0 +1,19 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+# Shared by 17 R modules (all except RUN_MUTSCAN, RUN_DIMSUM and the false-doubles error correction).
+# Keep the copies identical:
+# the one container image these modules use is built from exactly this list.
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/fitness/fitness_calculation/main.nf b/modules/local/fitness/fitness_calculation/main.nf
new file mode 100644
index 0000000..01d9eb8
--- /dev/null
+++ b/modules/local/fitness/fitness_calculation/main.nf
@@ -0,0 +1,31 @@
+process FITNESS_CALCULATION {
+  tag { sample.sample }
+  label 'process_single'
+
+  conda "${moduleDir}/environment.yml"
+
+  container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+      ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data'
+      : 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7'}"
+
+  input:
+    tuple val(sample), path(counts_merged)
+    path(exp_design)
+    path(syn_wt_txt)
+    val(min_counts)
+    val(output_pseudocount)
+
+  output:
+    tuple val(sample), path("fitness_estimation.tsv"), emit: fitness_estimation
+    path "versions.yml", emit: versions
+
+  script:
+  template 'fitness_calculation.R'
+
+  stub:
+  """
+    touch fitness_estimation.tsv
+    echo "FITNESS_CALCULATION:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+  """
+}
diff --git a/modules/local/fitness/fitness_calculation/meta.yml b/modules/local/fitness/fitness_calculation/meta.yml
new file mode 100644
index 0000000..19bcf0b
--- /dev/null
+++ b/modules/local/fitness/fitness_calculation/meta.yml
@@ -0,0 +1,76 @@
+name: "fitness_calculation"
+description: Calculates fitness scores for Deep Mutational Scanning (DMS) variants. It typically uses a log-enrichment ratio approach, comparing variant frequencies in selection (output) versus baseline (input) samples, normalized to a synonymous wild-type reference.
+keywords:
+  - deep mutational scanning
+  - dms
+  - fitness calculation
+  - log-ratio
+  - enrichment
+tools: null
+input:
+  - - sample:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test' ]`
+    - counts_merged:
+        type: file
+        description: TSV or CSV file containing the consolidated variant counts across all replicates
+        pattern: "*.{tsv,csv}"
+  - - exp_design:
+        type: file
+        description: CSV or text file defining the experimental design, including input/output mappings and replicates
+        pattern: "*.{csv,txt}"
+  - - syn_wt_txt:
+        type: file
+        description: Text file identifying the synonymous wild-type variant used as the normalization baseline
+        pattern: "*.txt"
+  - - min_counts:
+        type: integer
+        description: Minimum number of reads a variant needs in every input replicate to receive a fitness estimate (`--min_counts`)
+  - - output_pseudocount:
+        type: integer
+        description: Pseudocount for output replicates in which a variant observed in the input was not detected; 0 disables (`--output_pseudocount`)
+
+output:
+  - fitness_estimation:
+      - sample:
+          type: map
+          description: Groovy Map containing sample information
+      - fitness_estimation.tsv:
+          type: file
+          description: TSV file containing the final calculated fitness scores for all variants
+          pattern: "fitness_estimation.tsv"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+containers:
+  docker:
+    linux/amd64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7
+      build_id: bd-32f1af0a83a018e7_1
+      scan_id: sc-337510c2c910aa1c_1
+    linux/arm64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb
+      build_id: bd-880bbdecb19543bb_1
+      scan_id: sc-09c9d098a09068b9_1
+  singularity:
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7
+      build_id: bd-db46247d9810b3e7_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb
+      build_id: bd-7202e5d19667bddb_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/fitness/fitness_calculation/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
+    linux/arm64:
+      lock_file: modules/local/fitness/fitness_calculation/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
diff --git a/modules/local/fitness/fitness_calculation/templates/fitness_calculation.R b/modules/local/fitness/fitness_calculation/templates/fitness_calculation.R
new file mode 100644
index 0000000..f1c146a
--- /dev/null
+++ b/modules/local/fitness/fitness_calculation/templates/fitness_calculation.R
@@ -0,0 +1,355 @@
+#!/usr/bin/env Rscript
+
+## default fitness estimation for nf-core/deepmutscan
+## 18.02.2026
+## maximilian.stammnitz@crg.eu
+
+## 0. Libraries ##
+##################
+
+suppressPackageStartupMessages({
+  library(Biostrings)
+})
+
+## --- Helper functions ---
+
+# calculate nt hamming distances from the specified WT
+compute_nt_hamming <- function(merged.counts, wt.seq) {
+  merged.counts <- cbind("nt_ham" = rep(NA, nrow(merged.counts)), merged.counts)
+  for (i in 1:nrow(merged.counts)){
+    tmp.wt <- strsplit(as.character(wt.seq), "")[[1]]
+    tmp.mut <- strsplit(as.character(merged.counts\$nt_seq[i]), "")[[1]]
+    if(length(which(tmp.mut != tmp.wt)) == 0){
+      merged.counts\$nt_ham[i] <- 0
+      rm(tmp.mut, tmp.wt)
+      next
+    }else{
+      merged.counts\$nt_ham[i] <- length(which(tmp.mut != tmp.wt))
+      rm(tmp.mut, tmp.wt)
+      next
+    }
+  }
+  merged.counts
+}
+
+# translate sequences and add aa_seq
+add_aa_seq <- function(merged.counts) {
+  merged.counts <- cbind("aa_seq" = as.character(translate(DNAStringSet(merged.counts\$nt_seq))), merged.counts)
+  merged.counts
+}
+
+# calculate AA hamming distances from the WT
+compute_aa_hamming <- function(merged.counts, wt.seq.aa) {
+  merged.counts <- cbind("aa_ham" = rep(NA, nrow(merged.counts)), merged.counts)
+  for (i in 1:nrow(merged.counts)){
+    tmp.wt <- strsplit(as.character(wt.seq.aa), "")[[1]]
+    tmp.mut <- strsplit(as.character(merged.counts\$aa_seq[i]), "")[[1]]
+    if(length(which(tmp.mut != tmp.wt)) == 0){
+      merged.counts\$aa_ham[i] <- 0
+      rm(tmp.mut, tmp.wt)
+      next
+    }else{
+      merged.counts\$aa_ham[i] <- length(which(tmp.mut != tmp.wt))
+      rm(tmp.mut, tmp.wt)
+      next
+    }
+  }
+  merged.counts
+}
+
+# name the mutations
+name_mutations <- function(merged.counts, wt.seq.aa) {
+  merged.counts <- cbind("wt_aa" = rep(NA, nrow(merged.counts)),
+                         "pos" = rep(NA, nrow(merged.counts)),
+                         "mut_aa" = rep(NA, nrow(merged.counts)), merged.counts)
+  for (i in 1:nrow(merged.counts)){
+    if(merged.counts\$aa_ham[i] == 0){
+      next
+    }else{
+      tmp.wt <- strsplit(as.character(wt.seq.aa), "")[[1]]
+      tmp.mut <- strsplit(as.character(merged.counts\$aa_seq[i]), "")[[1]]
+      merged.counts\$pos[i] <- which(tmp.mut != tmp.wt)
+      merged.counts\$'wt_aa'[i] <- tmp.wt[merged.counts\$pos[i]]
+      merged.counts\$'mut_aa'[i] <- tmp.mut[merged.counts\$pos[i]]
+      rm(tmp.mut, tmp.wt)
+    }
+  }
+  merged.counts
+}
+
+# find stops, WT and WT; aggregate counts of variants which are identical on the aa (but not nt) level
+aggregate_by_aa <- function(merged.counts) {
+  ## find stops, WT and WT
+  merged.counts <- cbind(merged.counts,
+                         "wt" = rep(NA, nrow(merged.counts)),
+                         "stop" = rep(NA, nrow(merged.counts)))
+  merged.counts\$wt[which(merged.counts\$nt_ham == 0)] <- TRUE
+  merged.counts\$stop[which(merged.counts\$'mut_aa' == "*")] <- TRUE
+
+  ## aggregate counts of variants which are identical on the aa (but not nt) level
+  ## exception: wildtype ones
+  ## thereby shrinking the matrix
+  uniq.aa.vars <- unique(merged.counts\$aa_seq)
+  uniq.aa.vars <- uniq.aa.vars[-which(uniq.aa.vars == merged.counts\$aa_seq[which(merged.counts\$wt == TRUE)])]
+  for(i in 1:length(uniq.aa.vars)){
+    tmp.aa_seq <- uniq.aa.vars[i]
+    hits <- which(as.character(merged.counts\$aa_seq) == tmp.aa_seq)
+    if(length(hits) == 1){
+      rm(tmp.aa_seq, hits)
+      next
+    }else{
+      for(j in grep("input|output", colnames(merged.counts))){
+        merged.counts[hits[1],j] <- sum(merged.counts[hits,j], na.rm = TRUE)
+      }
+      merged.counts[hits[1], "nt_seq"] <- paste(merged.counts[hits, "nt_seq"], collapse = ", ")
+      merged.counts <- merged.counts[-hits[-1],]
+      rm(tmp.aa_seq, hits)
+      next
+    }
+  }
+  merged.counts
+}
+
+# keep nucleotide variants with at least `min_counts` reads in every input replicate, before
+# synonymous codons are merged - the same filter as DiMSum's '--fitnessMinInputCountAll'
+# (the wildtype is always kept, as it anchors every fitness estimate)
+filter_low_input_counts <- function(merged.counts, wt.seq, min_counts) {
+  input.cols <- grep("^input[0-9]+\$", colnames(merged.counts), value = TRUE)
+  keep <- apply(merged.counts[, input.cols, drop = FALSE] >= min_counts, 1, all)
+  keep[is.na(keep)] <- FALSE
+  keep[merged.counts\$nt_seq == as.character(wt.seq)] <- TRUE
+  merged.counts[keep, , drop = FALSE]
+}
+
+# calculate bimodal fitness distribution
+density_peaks <- function(x, adjust = 1, ...) {
+
+  ## obtain density distribution
+  d <- density(x, adjust = adjust, n = 5000, na.rm = T, ...)
+  y <- d\$y
+  idx <- which(diff(sign(diff(y))) == -2) + 1
+  if (length(idx) == 0) return(numeric(0))
+
+  ## order density peaks by height (y value); take top 2
+  idx <- idx[order(y[idx], decreasing = TRUE)]
+  peaks_x <- d\$x[idx]
+  peaks_x[seq_len(min(2, length(peaks_x)))]
+
+}
+
+# 3. Raw fitness calculations ##
+calc_raw_fitness <- function(merged.counts, exp.design, output_pseudocount) {
+  ## how many fitness replicates are there
+  reps <- length(unique(exp.design\$experiment_replicate))
+  for (i in 1:reps){
+    merged.counts <- cbind(merged.counts, rep(NA, nrow(merged.counts)))
+    colnames(merged.counts)[ncol(merged.counts)] <- paste0("raw_fitness_rep", i)
+  }
+
+  ## calculate raw fitness of all variants vs. WT variant
+  for (i in 1:reps){
+
+    ### collect counts
+    tmp.input.counts <- merged.counts[,paste0("input", i)]
+    tmp.output.counts <- merged.counts[,paste0("output", i)]
+
+    ### dropouts (zero output count, non-zero input count): add the output pseudocount,
+    ### or leave them without a fitness estimate in this replicate if it is 0
+    tmp.dropout <- which(tmp.output.counts == 0 & tmp.input.counts != 0)
+    if (output_pseudocount > 0) {
+      tmp.output.counts[tmp.dropout] <- output_pseudocount
+    } else {
+      tmp.output.counts[tmp.dropout] <- NA
+    }
+
+    ### take logs
+    tmp.wt.log.ratio <- log(tmp.output.counts[which(merged.counts\$wt == TRUE)] /
+                              tmp.input.counts[which(merged.counts\$wt == TRUE)])
+    tmp.fitness <- log(tmp.output.counts /
+                         tmp.input.counts) - tmp.wt.log.ratio
+
+    ### uncertain values to NA
+    tmp.fitness[which(is.na(tmp.fitness) == TRUE)] <- NA
+    tmp.fitness[which(tmp.fitness == "Inf")] <- NA
+
+    ### add to table
+    merged.counts[,c(ncol(merged.counts) - reps + i)] <- tmp.fitness
+
+    ### clean up
+    rm(tmp.fitness, tmp.wt.log.ratio, tmp.output.counts, tmp.input.counts, tmp.dropout)
+  }
+
+  list(merged.counts = merged.counts, reps = reps)
+}
+
+# 4. Fitness and error refinements ##
+rescale_and_summarize <- function(merged.counts, reps) {
+  ## center the raw fitness distributions on 0 (median of wildtype synonymous) and -1 (median of stops)
+  for (i in 1:reps){
+
+    merged.counts <- cbind(merged.counts, rep(NA, nrow(merged.counts)))
+    colnames(merged.counts)[ncol(merged.counts)] <- paste0("rescaled_fitness_rep", i)
+
+    ### anchors: median fitness of synonymous (aa_ham == 0) -> 0, of stops -> -1
+    raw_col <- ncol(merged.counts) - reps
+    tmp.raw <- merged.counts[,raw_col]
+    tmp.wt.fitness.med   <- median(tmp.raw[which(merged.counts\$aa_ham == 0)], na.rm = TRUE)
+    tmp.stop.fitness.med <- median(tmp.raw[which(merged.counts\$stop == TRUE)], na.rm = TRUE)
+
+    ### if an anchor is missing, or the two anchors coincide (e.g. on sparse data,
+    ### where lm() would return an NA slope), fall back to the modes of the
+    ### (bimodal) fitness distribution to define the missing/degenerate anchor
+    if(is.na(tmp.wt.fitness.med) | is.na(tmp.stop.fitness.med) |
+       (!is.na(tmp.wt.fitness.med) & !is.na(tmp.stop.fitness.med) & tmp.wt.fitness.med == tmp.stop.fitness.med)){
+      tmp.peaks <- sort(density_peaks(x = tmp.raw))
+      tmp.hi <- if(!is.na(tmp.wt.fitness.med)) tmp.wt.fitness.med else if(length(tmp.peaks) >= 1) tmp.peaks[length(tmp.peaks)] else NA
+      if(!is.na(tmp.stop.fitness.med) & (is.na(tmp.hi) || tmp.stop.fitness.med != tmp.hi)){
+        tmp.lo <- tmp.stop.fitness.med
+      }else{
+        tmp.cand <- tmp.peaks[!is.na(tmp.peaks) & tmp.peaks != tmp.hi]
+        tmp.lo <- if(length(tmp.cand) >= 1) tmp.cand[1] else NA
+      }
+      tmp.wt.fitness.med <- tmp.hi
+      tmp.stop.fitness.med <- tmp.lo
+      rm(tmp.peaks, tmp.hi)
+    }
+
+    ### two distinct anchors -> linear rescale; a single usable anchor -> WT-centre only
+    if(!is.na(tmp.wt.fitness.med) & !is.na(tmp.stop.fitness.med) & tmp.wt.fitness.med != tmp.stop.fitness.med){
+      lm.rescale <- lm(c(0, -1) ~ c(tmp.wt.fitness.med, tmp.stop.fitness.med))
+      merged.counts[,ncol(merged.counts)] <- tmp.raw * lm.rescale\$coefficients[[2]] + lm.rescale\$coefficients[[1]]
+      rm(lm.rescale)
+    }else if(!is.na(tmp.wt.fitness.med)){
+      merged.counts[,ncol(merged.counts)] <- tmp.raw - tmp.wt.fitness.med
+    }
+
+    rm(raw_col, tmp.raw, tmp.wt.fitness.med, tmp.stop.fitness.med)
+  }
+
+  ## calculate fitness mean and standard deviation across replicates
+  merged.counts <- cbind(merged.counts,
+                         "mean fitness" = rep(NA, nrow(merged.counts)),
+                         "fitness sd" = rep(NA, nrow(merged.counts)))
+
+  if(reps == 1){
+
+    merged.counts\$'mean fitness' <- merged.counts[,ncol(merged.counts) - 2]
+
+  }else if(reps > 1){
+
+    merged.counts\$'mean fitness' <- apply(merged.counts[,c(ncol(merged.counts) - 1 - reps):c(ncol(merged.counts) - 2)],
+                                          1,
+                                          mean,
+                                          na.rm = TRUE)
+    merged.counts\$'fitness sd' <- apply(merged.counts[,c(ncol(merged.counts) - 1 - reps):c(ncol(merged.counts) - 2)],
+                                        1,
+                                        sd,
+                                        na.rm = TRUE)
+
+  }
+
+  merged.counts
+}
+
+## --- Main function ---
+
+#' Run default fitness estimation with configurable I/O paths
+#'
+#' @param counts_path Path to counts_merged.tsv
+#' @param design_path Path to experimentalDesign.tsv
+#' @param wt_seq_path Path to synonymous_wt.txt (single line DNA sequence)
+#' @param output_path Path to write fitness_estimation.tsv
+#' @param min_counts Minimum number of reads in every input replicate for a fitness estimate
+#' @param output_pseudocount Pseudocount for output dropouts (0 = no estimate for dropouts)
+#'
+#' @return Invisibly returns the final data.frame; writes the output to output_path.
+run_fitness_estimation <- function(counts_path,
+                                   design_path,
+                                   wt_seq_path,
+                                   output_path,
+                                   min_counts = 10,
+                                   output_pseudocount = 1) {
+  ## 1. Import key files ##
+  #########################
+
+  merged.counts <- read.table(counts_path, sep = "\t", header = TRUE, check.names = FALSE)
+  exp.design   <- read.table(design_path, sep = "\t", header = TRUE, check.names = FALSE)
+  wt.seq       <- DNAString(as.character(read.table(wt_seq_path)))
+  wt.seq.aa    <- translate(wt.seq)
+
+  ## 2. Pre-processing the count table ##
+  #######################################
+
+  ## keep variants with at least `min_counts` reads in every input replicate
+  merged.counts <- filter_low_input_counts(merged.counts, wt.seq, min_counts)
+
+  ## calculate nt hamming distances from the specified WT
+  merged.counts <- compute_nt_hamming(merged.counts, wt.seq)
+
+  ## translate sequences
+  merged.counts <- add_aa_seq(merged.counts)
+
+  ## calculate AA hamming distances from the WT
+  merged.counts <- compute_aa_hamming(merged.counts, wt.seq.aa)
+
+  ## name the mutations
+  merged.counts <- name_mutations(merged.counts, wt.seq.aa)
+
+  ## find stops, WT and WT; aggregate AA-identical variants (except WT)
+  merged.counts <- aggregate_by_aa(merged.counts)
+
+  ## 3. Raw fitness calculations ##
+  #################################
+  fitness_res <- calc_raw_fitness(merged.counts, exp.design, output_pseudocount)
+  merged.counts <- fitness_res\$merged.counts
+  reps <- fitness_res\$reps
+
+  ## 4. Fitness and error refinements ##
+  ######################################
+  merged.counts <- rescale_and_summarize(merged.counts, reps)
+
+  ## clean up
+  rm(reps)
+
+  ## export
+  write.table(merged.counts, output_path,
+              col.names = TRUE, row.names = FALSE, quote = FALSE, sep = "\t", na = "")
+
+  invisible(merged.counts)
+}
+
+
+
+
+
+#####
+# run function
+#####
+run_fitness_estimation(
+  counts_path = "$counts_merged",
+  design_path = "$exp_design",
+  wt_seq_path = "$syn_wt_txt",
+  output_path = "fitness_estimation.tsv",
+  min_counts = as.numeric("$min_counts"),
+  output_pseudocount = as.numeric("$output_pseudocount")
+)
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+Biostrings_version <- as.character(packageVersion("Biostrings"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(Biostrings_version) == 0) Biostrings_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-Biostrings:', Biostrings_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/fitness/fitness_experimental_design/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt b/modules/local/fitness/fitness_experimental_design/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
new file mode 100644
index 0000000..be8d7e8
--- /dev/null
+++ b/modules/local/fitness/fitness_experimental_design/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
@@ -0,0 +1,2942 @@
+version: 6
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+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
+      linux-64:
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diff --git a/modules/local/fitness/fitness_experimental_design/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt b/modules/local/fitness/fitness_experimental_design/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
new file mode 100644
index 0000000..10e5993
--- /dev/null
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diff --git a/modules/local/fitness/fitness_experimental_design/environment.yml b/modules/local/fitness/fitness_experimental_design/environment.yml
new file mode 100644
index 0000000..5f5785d
--- /dev/null
+++ b/modules/local/fitness/fitness_experimental_design/environment.yml
@@ -0,0 +1,19 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+# Shared by 17 R modules (all except RUN_MUTSCAN, RUN_DIMSUM and the false-doubles error correction).
+# Keep the copies identical:
+# the one container image these modules use is built from exactly this list.
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/fitness/fitness_experimental_design/main.nf b/modules/local/fitness/fitness_experimental_design/main.nf
new file mode 100644
index 0000000..97a21fd
--- /dev/null
+++ b/modules/local/fitness/fitness_experimental_design/main.nf
@@ -0,0 +1,29 @@
+process EXPDESIGN_FITNESS {
+  tag "experimentalDesign"
+  label 'process_single'
+
+  conda "${moduleDir}/environment.yml"
+
+  container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+      ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data'
+      : 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7'}"
+
+  input:
+    path samplesheet_csv
+
+  output:
+    path "experimentalDesign.tsv", emit: experimental_design
+    path "versions.yml",          emit: versions
+
+  script:
+  template 'dimsum_experimentalDesign.R'
+
+  stub:
+  """
+  touch experimentalDesign.tsv
+  cat <<-END_VERSIONS > versions.yml
+  "${task.process}":
+      r-base: "0.0.0"
+  END_VERSIONS
+  """
+}
diff --git a/modules/local/fitness/fitness_experimental_design/meta.yml b/modules/local/fitness/fitness_experimental_design/meta.yml
new file mode 100644
index 0000000..a8a606d
--- /dev/null
+++ b/modules/local/fitness/fitness_experimental_design/meta.yml
@@ -0,0 +1,60 @@
+name: "expdesign_fitness"
+description: Transforms a standard nf-core samplesheet into a tab-separated experimental design file that defines replicates, selection conditions, and input/output relationships for fitness calculation.
+keywords:
+  - deep mutational scanning
+  - dms
+  - experimental design
+  - samplesheet
+  - dimsum
+tools:
+  - "r-base":
+      description: "R is a free software environment for statistical computing and graphics"
+      homepage: "https://www.r-project.org/"
+      documentation: "https://cran.r-project.org/manuals.html"
+      licence: ["GPL-2.0-or-later"]
+
+input:
+  - - samplesheet_csv:
+        type: file
+        description: The primary nf-core pipeline samplesheet containing metadata for all sequencing libraries
+        pattern: "*.{csv}"
+
+output:
+  - experimental_design:
+      - experimentalDesign.tsv:
+          type: file
+          description: A TSV file formatted specifically for DiMSum or mutscan experimental design requirements
+          pattern: "experimentalDesign.tsv"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+containers:
+  docker:
+    linux/amd64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7
+      build_id: bd-32f1af0a83a018e7_1
+      scan_id: sc-337510c2c910aa1c_1
+    linux/arm64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb
+      build_id: bd-880bbdecb19543bb_1
+      scan_id: sc-09c9d098a09068b9_1
+  singularity:
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb
+      build_id: bd-7202e5d19667bddb_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7
+      build_id: bd-db46247d9810b3e7_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/fitness/fitness_experimental_design/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
+    linux/arm64:
+      lock_file: modules/local/fitness/fitness_experimental_design/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
diff --git a/modules/local/fitness/fitness_experimental_design/templates/dimsum_experimentalDesign.R b/modules/local/fitness/fitness_experimental_design/templates/dimsum_experimentalDesign.R
new file mode 100644
index 0000000..475353d
--- /dev/null
+++ b/modules/local/fitness/fitness_experimental_design/templates/dimsum_experimentalDesign.R
@@ -0,0 +1,111 @@
+#!/usr/bin/env Rscript
+
+# Make a DiMSum experimental design from a deepmutscan samplesheet.
+# - samplesheet_csv: path to CSV with columns sample,type,replicate,file1,file2
+# - out_path: where to write the TSV (default "experimentalDesign.tsv")
+# Returns: the experimental design as a data.frame
+make_dimsum_experimental_design <- function(samplesheet_csv, out_path = "experimentalDesign.tsv") {
+  # ---- read & normalize ----
+  ss <- read.csv(samplesheet_csv, stringsAsFactors = FALSE, check.names = FALSE)
+  names(ss) <- tolower(names(ss))
+
+  # tolerate missing file2 column (single-end)
+  if (!"file2" %in% names(ss)) ss\$file2 <- ""
+
+  required <- c("sample", "type", "replicate", "file1", "file2")
+  missing  <- setdiff(required, names(ss))
+  if (length(missing) > 0) stop("Samplesheet missing columns: ", paste(missing, collapse = ", "))
+
+  # coerce types
+  ss\$replicate <- as.integer(ss\$replicate)
+
+  # ---- keep only the samples DiMSum scores ----
+  # A samplesheet legitimately carries other row types: `wildtype` (required by
+  # --error_correction wildtype) and `quality`. They are real samples, but they are not part of the
+  # selection design - they feed error correction and QC - and MERGE_COUNTS drops them too. Leaving
+  # them in gave them an empty `selection_id`, which DiMSum reads back as NA and then dies in
+  # dimsum__check_experiment_design on `sum(exp_design[,"selection_id"] < 0)` with the opaque
+  # "missing value where TRUE/FALSE needed".
+  ss <- ss[ss\$type %in% c("input", "output"), , drop = FALSE]
+  if (nrow(ss) == 0) {
+    stop("Samplesheet has no 'input'/'output' rows - DiMSum needs both to compute fitness.")
+  }
+
+  # ---- order rows: by sample (if multiple), input before output, then replicate ----
+  # Sorting happens before naming, because the names are derived from the row order below.
+  multi_base <- length(unique(ss\$sample)) > 1
+  type_rank  <- match(ss\$type, c("input", "output"))
+  ord <- if (multi_base) {
+    order(ss\$sample, type_rank, ss\$replicate)
+  } else {
+    order(type_rank, ss\$replicate)
+  }
+  ss <- ss[ord, , drop = FALSE]
+
+  # ---- sample_name: must equal the count column MERGE_COUNTS writes for this row ----
+  # merge_counts.R names its columns positionally - paste0("input", seq_len(n_in)) over the files
+  # sorted by replicate - so the name has to come from each row's POSITION within its type, not from
+  # the samplesheet's `replicate` value. The two only coincide when the replicates happen to be a
+  # contiguous 1..N; replicates 1,2,4 would otherwise produce "input4" for a column named "input3"
+  # and DiMSum could not match them. DiMSum also requires sample_name to be alphanumeric and to
+  # start with a letter, so the parts are joined without a separator.
+  idx <- stats::ave(seq_len(nrow(ss)), ss\$sample, ss\$type, FUN = seq_along)
+  # NOTE: for multiple biological samples these names still will not match the counts file, because
+  # one design is built from the whole samplesheet and broadcast to every per-sample DiMSum run
+  # (see subworkflows/local/calculate_fitness/main.nf). Multi-sample + DiMSum is a separate,
+  # known-broken case; single-sample runs are correct.
+  sample_name <- if (multi_base) paste0(ss\$sample, ss\$type, idx) else paste0(ss\$type, idx)
+
+  # ---- build DiMSum columns ----
+  experiment_replicate <- ss\$replicate           # pairs an input with an output; need not be 1..N
+  selection_id <- ifelse(ss\$type == "input", 0L, 1L)
+  # assume one selection batch; DiMSum requires this to be blank for input samples
+  selection_replicate <- ifelse(ss\$type == "output", 1L, NA_integer_)
+  # assume one technical batch
+  technical_replicate <- rep(1L, nrow(ss))
+
+  pair1 <- basename(ss\$file1)
+  # keep empty string for single-end / missing file2
+  pair2 <- ifelse(is.na(ss\$file2) | ss\$file2 == "", "", basename(ss\$file2))
+
+  ed <- data.frame(
+    sample_name          = sample_name,
+    experiment_replicate = experiment_replicate,
+    selection_id         = selection_id,
+    selection_replicate  = selection_replicate,
+    technical_replicate  = technical_replicate,
+    pair1                = pair1,
+    pair2                = pair2,
+    stringsAsFactors     = FALSE
+  )
+  rownames(ed) <- NULL
+
+  # ---- write & return ----
+  write.table(ed, file = out_path, sep = "\\t", row.names = FALSE, col.names = TRUE, quote = FALSE, na = "")
+  return(ed)
+}
+
+#####
+# run function
+#####
+make_dimsum_experimental_design(
+  samplesheet_csv = "$samplesheet_csv",
+  out_path = "experimentalDesign.tsv"
+)
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+
+if (is.null(r_version)) r_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/fitness/fitness_heatmap/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt b/modules/local/fitness/fitness_heatmap/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
new file mode 100644
index 0000000..be8d7e8
--- /dev/null
+++ b/modules/local/fitness/fitness_heatmap/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
@@ -0,0 +1,2942 @@
+version: 6
+environments:
+  default:
+    channels:
+    - url: https://conda.anaconda.org/conda-forge/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
+      linux-64:
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.46.1-default_hfdba357_103.conda
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-biocgenerics-0.56.0-r45hdfd78af_2.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-biostrings-2.78.0-r45h01b2380_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-iranges-2.44.0-r45h01b2380_1.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-s4vectors-0.48.1-r45h262fe30_0.conda
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-seqinfo-1.0.0-r45hdfd78af_0.conda
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diff --git a/modules/local/fitness/fitness_heatmap/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt b/modules/local/fitness/fitness_heatmap/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
new file mode 100644
index 0000000..10e5993
--- /dev/null
+++ b/modules/local/fitness/fitness_heatmap/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
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+  size: 615477
+  timestamp: 1786599613561
diff --git a/modules/local/fitness/fitness_heatmap/environment.yml b/modules/local/fitness/fitness_heatmap/environment.yml
new file mode 100644
index 0000000..5f5785d
--- /dev/null
+++ b/modules/local/fitness/fitness_heatmap/environment.yml
@@ -0,0 +1,19 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+# Shared by 17 R modules (all except RUN_MUTSCAN, RUN_DIMSUM and the false-doubles error correction).
+# Keep the copies identical:
+# the one container image these modules use is built from exactly this list.
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/fitness/fitness_heatmap/main.nf b/modules/local/fitness/fitness_heatmap/main.nf
new file mode 100644
index 0000000..f2ab33a
--- /dev/null
+++ b/modules/local/fitness/fitness_heatmap/main.nf
@@ -0,0 +1,30 @@
+process FITNESS_HEATMAP {
+  tag { sample.sample }
+  label 'process_single'
+
+  conda "${moduleDir}/environment.yml"
+
+  container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+      ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data'
+      : 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7'}"
+
+  input:
+    tuple val(sample), path(fitness_estimation_tsv)   // from FITNESS_CALCULATION
+    tuple val(sample2), path(wt_seq)   		      // WT sequence
+
+  output:
+    tuple val(sample), path("fitness_heatmap.pdf"), emit: fitness_heatmap
+    path "versions.yml", emit: versions
+
+  script:
+    template 'fitness_heatmap.R'
+
+  stub:
+  """
+    touch fitness_heatmap.pdf
+    cat > versions.yml <<'EOF'
+    FITNESS_HEATMAP:
+      stub-version: "0.0.0"
+    EOF
+  """
+}
diff --git a/modules/local/fitness/fitness_heatmap/meta.yml b/modules/local/fitness/fitness_heatmap/meta.yml
new file mode 100644
index 0000000..1de711c
--- /dev/null
+++ b/modules/local/fitness/fitness_heatmap/meta.yml
@@ -0,0 +1,76 @@
+name: "fitness_heatmap"
+description: Generates a fitness landscape heatmap from calculated fitness scores, displaying the functional impact of amino acid substitutions at each position of the reference sequence.
+keywords:
+  - deep mutational scanning
+  - dms
+  - fitness
+  - visualization
+  - heatmap
+  - mutation landscape
+tools:
+  - "bioconductor-biostrings":
+      description: "Efficient manipulation of biological strings in R"
+      homepage: "https://bioconductor.org/packages/Biostrings"
+      licence: ["Artistic-2.0"]
+
+input:
+  - - sample:
+        type: map
+        description: |
+          Groovy Map containing sample information for the fitness data
+          e.g. `[ id:'test' ]`
+    - fitness_estimation_tsv:
+        type: file
+        description: TSV file containing calculated fitness scores and statistics
+        pattern: "*.{tsv}"
+  - - sample2:
+        type: map
+        description: |
+          Groovy Map containing metadata for the reference sequence
+    - wt_seq:
+        type: file
+        description: FASTA file or text file containing the wild-type reference sequence
+        pattern: "*.{fasta,fa,txt}"
+
+output:
+  - fitness_heatmap:
+      - sample:
+          type: map
+          description: Groovy Map containing sample information
+      - fitness_heatmap.pdf:
+          type: file
+          description: PDF heatmap visualizing the fitness scores across the protein sequence
+          pattern: "fitness_heatmap.pdf"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+containers:
+  docker:
+    linux/amd64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7
+      build_id: bd-32f1af0a83a018e7_1
+      scan_id: sc-337510c2c910aa1c_1
+    linux/arm64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb
+      build_id: bd-880bbdecb19543bb_1
+      scan_id: sc-09c9d098a09068b9_1
+  singularity:
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb
+      build_id: bd-7202e5d19667bddb_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7
+      build_id: bd-db46247d9810b3e7_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/fitness/fitness_heatmap/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
+    linux/arm64:
+      lock_file: modules/local/fitness/fitness_heatmap/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
diff --git a/modules/local/fitness/fitness_heatmap/templates/fitness_heatmap.R b/modules/local/fitness/fitness_heatmap/templates/fitness_heatmap.R
new file mode 100644
index 0000000..e860382
--- /dev/null
+++ b/modules/local/fitness/fitness_heatmap/templates/fitness_heatmap.R
@@ -0,0 +1,312 @@
+#!/usr/bin/env Rscript
+
+suppressPackageStartupMessages({
+  library(methods)
+  library(dplyr)
+  library(ggplot2)
+  library(grid)        # for unit()
+})
+
+# ---------- helper functions ----------
+find_col <- function(df, candidates) {
+  norm <- function(x) gsub("[^a-z0-9]+", "_", tolower(x))
+  nms <- colnames(df); nn <- norm(nms)
+  for (cand in candidates) {
+    hit <- which(nn == norm(cand))
+    if (length(hit) == 1) return(nms[hit])
+  }
+  stop(sprintf("Could not find any of columns: %s", paste(candidates, collapse = ", ")))
+}
+
+get_rescaled_cols <- function(df) {
+  nms  <- colnames(df)
+  hits <- grep("^rescaled[_ ]?fitness", nms, ignore.case = TRUE, value = TRUE)
+  if (!length(hits)) stop("No 'rescaled_fitness' columns found.")
+  idx <- suppressWarnings(as.integer(gsub(".*?([0-9]+)\$", "\\\\1", hits))) 	# additional backslashs to make it groovy readable
+  hits[order(is.na(idx), idx, hits)]
+}
+
+# Find "mean fitness" column (if present)
+find_mean_col <- function(df) {
+  nms <- colnames(df)
+  key <- tolower(gsub("[^a-z0-9]+", "_", nms))
+  hit <- which(key == "mean_fitness")
+  if (length(hit) == 1) nms[hit] else NULL
+}
+
+# NEW: read WT amino acid sequence from .txt file (single line)
+read_wt_seq_aa_txt <- function(path) {
+  if (is.null(path)) stop("wt_seq_aa_txt_path must be provided.")
+  x <- readLines(path, warn = FALSE)
+  x <- x[nzchar(x)]
+  if (!length(x)) stop("WT AA TXT is empty.")
+  aa <- toupper(gsub("\\\\s+", "", x[which.max(nchar(x))]))
+  # Keep only valid amino acid letters (including stop '*')
+  aa <- gsub("[^ACDEFGHIKLMNPQRSTVWY*]", "", aa)
+  if (!nchar(aa)) stop("WT AA TXT contains no valid AA letters.")
+  aa
+}
+
+# Build full AA×position grid for positions 1..wt_len (from WT sequence),
+# join fitness values; any missing combos stay NA (-> grey).
+# Positions > wt_len are considered padded and will be white.
+build_heatmap_long <- function(df,
+                               wt_aa_col,
+                               pos_col,
+                               mut_aa_col,
+                               fitness_col,
+                               positions_per_row = 75,
+                               wt_seq_aa,
+                               fill_missing_as_zero = FALSE) {
+
+  # authoritative WT length from provided sequence
+  letters <- strsplit(wt_seq_aa, "", fixed = TRUE)[[1]]
+  wt_len  <- length(letters)
+
+  # normalize data
+  df0 <- df %>%
+    transmute(
+      position = suppressWarnings(as.numeric(.data[[pos_col]])),
+      wt_aa_in = .data[[wt_aa_col]],
+      mut_aa   = .data[[mut_aa_col]],
+      fitness  = suppressWarnings(as.numeric(.data[[fitness_col]]))
+    ) %>%
+    filter(is.finite(position))
+
+  # drop any rows that claim positions beyond WT length
+  if (nrow(df0) && any(df0\$position > wt_len, na.rm = TRUE)) {
+    dropped <- sum(df0\$position > wt_len, na.rm = TRUE)
+    warning(sprintf("Dropping %d row(s) with position > WT length (%d).", dropped, wt_len))
+    df0 <- df0 %>% filter(position <= wt_len)
+  }
+
+  # pad to next multiple of 75 (by rows)
+  rem       <- wt_len %% positions_per_row
+  pad_need  <- if (rem == 0) 0 else positions_per_row - rem
+  max_paded <- wt_len + pad_need
+
+  # full grid: positions 1..max_paded (so the tail exists), AA set of 21
+  all_positions   <- seq_len(max_paded)
+  all_amino_acids <- c("A","C","D","E","F","G","H","I","K","L",
+                       "M","N","P","Q","R","S","T","V","W","Y","*")
+
+  grid_df <- expand.grid(position = all_positions,
+                         mut_aa   = all_amino_acids,
+                         KEEP.OUT.ATTRS = FALSE, stringsAsFactors = FALSE) %>%
+    mutate(is_padded = position > wt_len)
+
+  # join fitness only for real positions (<= wt_len)
+  fit_df <- df0 %>% select(position, mut_aa, fitness)
+  d <- grid_df %>%
+    left_join(fit_df, by = c("position","mut_aa"))
+
+  if (fill_missing_as_zero) {
+    d\$fitness[is.na(d\$fitness) & d\$position <= wt_len] <- 0
+  }
+
+  # authoritative WT AA per real position; tail gets placeholder 'Y'
+  wt_map <- tibble(position = seq_len(wt_len), wt_aa = letters)
+  d <- d %>%
+    left_join(wt_map, by = "position") %>%
+    mutate(wt_aa = ifelse(is.na(wt_aa) & position > wt_len, "Y", wt_aa))
+
+  # layout fields
+  d <- d %>%
+    mutate(
+      row_group = ((position - 1) %/% positions_per_row) + 1,
+      wt_aa_pos = paste0(wt_aa, position),
+      wt_aa_pos = factor(wt_aa_pos, levels = unique(wt_aa_pos)),
+      synonymous = mut_aa == wt_aa
+    )
+
+  # IMPORTANT: use WT length as the true end of the protein
+  d\$max_pos <- wt_len
+  d
+}
+
+syn_segments <- function(d, positions_per_row = 75) {
+  amino_order <- rev(c("G", "A", "V", "L", "M", "I", "F",
+                         "Y", "W", "K", "R", "H", "D", "E",
+                         "S", "T", "C", "N", "Q", "P", "*"))
+  d %>%
+    mutate(
+      mut_aa = factor(mut_aa, levels = amino_order),
+      x = as.numeric(factor(wt_aa_pos, levels = levels(wt_aa_pos))) -
+          ((row_group - 1) * positions_per_row),
+      y = as.numeric(factor(mut_aa, levels = amino_order))
+    ) %>%
+    filter(synonymous, position <= max_pos)
+}
+
+# Draw one solid white rectangle per row group covering the padded tail region
+white_tail_rects <- function(d, positions_per_row = 75) {
+  wt_len <- unique(d\$max_pos)[1]
+  if (!is.finite(wt_len)) return(dplyr::tibble()[0,])
+
+  # if perfectly divisible by 75, there is no tail to cover
+  if (wt_len %% positions_per_row == 0) return(dplyr::tibble()[0,])
+
+  # which facet (row group) contains the last real position?
+  last_group     <- ((wt_len - 1) %/% positions_per_row) + 1
+  last_local_idx <- ((wt_len - 1) %% positions_per_row) + 1  # 1..75 within the facet
+
+  tibble::tibble(
+    row_group = last_group,
+    xmin = last_local_idx + 0.5 - 0.025,     # tiny epsilon to avoid hairlines
+    xmax = positions_per_row + 0.5 + 0.025,
+    ymin = 0.5  - 0.025,
+    ymax = 21.5 + 0.025
+  )
+}
+
+plot_heatmap <- function(d, title_text, positions_per_row = 75) {
+  amino_order <- rev(c("G", "A", "V", "L", "M", "I", "F",
+                         "Y", "W", "K", "R", "H", "D", "E",
+                         "S", "T", "C", "N", "Q", "P", "*"))
+  d <- d %>% mutate(mut_aa = factor(mut_aa, levels = amino_order))
+
+  min_f <- suppressWarnings(min(d\$fitness, na.rm = TRUE)); if (!is.finite(min_f)) min_f <- 0
+  max_f <- suppressWarnings(max(d\$fitness, na.rm = TRUE)); if (!is.finite(max_f)) max_f <- 0
+  max_orig_pos <- unique(d\$max_pos)[1]
+
+  syn  <- syn_segments(d, positions_per_row)
+  rect <- white_tail_rects(d, positions_per_row)
+
+  ggplot(d, aes(x = wt_aa_pos, y = mut_aa, fill = fitness)) +
+    scale_fill_gradientn(
+      colours = c("#D73027", "#F0F0F0", "#4575B4"),
+      values  = if ((abs(min_f) + max_f) > 0) c(0, abs(min_f)/(abs(min_f)+max_f), 1) else c(0, 0.5, 1),
+      na.value = "grey35",
+      limits   = c(min_f, max_f)
+    ) +
+    scale_x_discrete(
+      labels = function(x) {
+        num <- suppressWarnings(as.numeric(gsub("[^0-9]", "", x)))
+        ifelse(num > max_orig_pos, " ", x)
+      },
+      expand = expansion(mult = c(0, 0))  # no extra margin area
+    ) +
+    geom_tile() +
+    # Solid white block covering the tail (no pattern / no seams)
+    { if (nrow(rect)) geom_rect(data = rect, inherit.aes = FALSE,
+                                aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
+                                fill = "white", color = NA) } +
+    geom_segment(
+      data = syn,
+      aes(x = x - 0.485, xend = x + 0.485, y = y - 0.485, yend = y + 0.485),
+      linewidth = 0.2, inherit.aes = FALSE, color = "grey10"
+    ) +
+    theme_minimal() +
+    labs(title = title_text, x = "Wild-type amino acid", y = "Mutant amino acid", fill = "Fitness") +
+    theme(
+      plot.title   = element_text(size = 16, face = "bold"),
+      axis.text.x  = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 10),
+      axis.text.y  = element_text(size = 10),
+      axis.title.x = element_text(size = 14),
+      axis.title.y = element_text(size = 14),
+      legend.title = element_text(size = 12),
+      legend.text  = element_text(size = 10),
+      panel.grid.major = element_blank(),
+      panel.grid.minor = element_blank(),
+      strip.text = element_blank(),
+      strip.background = element_blank(),
+      panel.spacing = grid::unit(0.2, "lines")
+    ) +
+    facet_wrap(~ row_group, scales = "free_x", ncol = 1)
+}
+
+# ---------- main callable ----------
+# fitness_table_path : path to fitness_estimation.tsv
+# wt_seq_aa_txt_path : path to TXT file containing WT AA sequence (one line)
+# output_pdf_path    : output PDF (default "fitness_heatmap.pdf")
+# positions_per_row  : default 75
+run_fitness_rescaled_heatmaps <- function(fitness_table_path,
+                                          wt_seq_aa_txt_path,
+                                          output_pdf_path = "fitness_heatmap.pdf",
+                                          positions_per_row = 75) {
+
+  df <- read.table(
+    fitness_table_path, sep = "\t", header = TRUE,
+    check.names = FALSE, quote = "", comment.char = ""
+  )
+
+  wt_aa_col     <- find_col(df, c("wt aa", "wt_aa", "wt"))
+  pos_col       <- find_col(df, c("pos", "position"))
+  mut_aa_col    <- find_col(df, c("mut aa", "mut_aa", "aa"))
+  rescaled_cols <- get_rescaled_cols(df)
+
+  wt_seq_aa <- read_wt_seq_aa_txt(wt_seq_aa_txt_path)
+
+  plots <- list()
+
+  ## 1) Mean first – use existing "mean fitness" column if available
+  mean_col <- find_mean_col(df)
+  if (is.null(mean_col)) {
+    df\$`rescaled_fitness_mean` <- if (length(rescaled_cols) == 1) df[[rescaled_cols[1]]] else rowMeans(df[, rescaled_cols], na.rm = TRUE)
+    mean_col <- "rescaled_fitness_mean"
+  }
+  long_df_mean <- build_heatmap_long(df, wt_aa_col, pos_col, mut_aa_col, mean_col,
+                                     positions_per_row, wt_seq_aa)
+  plots[[length(plots) + 1]] <- list(
+    title = sprintf("Fitness — mean of %d replicate(s)", length(rescaled_cols)),
+    data  = long_df_mean
+  )
+
+  ## 2) Then individual replicates
+  for (i in seq_along(rescaled_cols)) {
+    col <- rescaled_cols[i]
+    long_df <- build_heatmap_long(df, wt_aa_col, pos_col, mut_aa_col, col,
+                                  positions_per_row, wt_seq_aa)
+    plots[[length(plots) + 1]] <- list(
+      title = sprintf("Fitness — rep%d", i),
+      data  = long_df
+    )
+  }
+
+  # Device height: (#row groups × 4)
+  page_heights <- vapply(plots, function(p) max(p\$data\$row_group, na.rm = TRUE), numeric(1))
+  device_height <- max(4, as.numeric(page_heights) * 4, na.rm = TRUE)
+
+  grDevices::pdf(output_pdf_path, width = 16, height = device_height)
+  on.exit(try(grDevices::dev.off(), silent = TRUE), add = TRUE)
+  for (p in plots) print(plot_heatmap(p\$data, p\$title, positions_per_row))
+  invisible(TRUE)
+}
+
+####
+# run function
+####
+run_fitness_rescaled_heatmaps(
+  fitness_table_path = "$fitness_estimation_tsv",
+  wt_seq_aa_txt_path = "$wt_seq",
+  output_pdf_path    = "fitness_heatmap.pdf"
+)
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+dplyr_version <- as.character(packageVersion("dplyr"))
+ggplot2_version <- as.character(packageVersion("ggplot2"))
+methods_version <- as.character(packageVersion("methods"))
+grid_version <- as.character(packageVersion("grid"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(dplyr_version) == 0) dplyr_version <- "unknown"
+if (length(ggplot2_version) == 0) ggplot2_version <- "unknown"
+if (length(methods_version) == 0) methods_version <- "unknown"
+if (length(grid_version) == 0) grid_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-dplyr:', dplyr_version),
+    paste('    r-ggplot2:', ggplot2_version),
+    paste('    r-methods:', methods_version),
+    paste('    r-grid:', grid_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/fitness/merge_counts/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt b/modules/local/fitness/merge_counts/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
new file mode 100644
index 0000000..be8d7e8
--- /dev/null
+++ b/modules/local/fitness/merge_counts/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
@@ -0,0 +1,2942 @@
+version: 6
+environments:
+  default:
+    channels:
+    - url: https://conda.anaconda.org/conda-forge/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
+      linux-64:
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.46.1-default_hfdba357_103.conda
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-biocgenerics-0.56.0-r45hdfd78af_2.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-biostrings-2.78.0-r45h01b2380_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-iranges-2.44.0-r45h01b2380_1.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-s4vectors-0.48.1-r45h262fe30_0.conda
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-seqinfo-1.0.0-r45hdfd78af_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-xvector-0.50.0-r45h01b2380_0.conda
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diff --git a/modules/local/fitness/merge_counts/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt b/modules/local/fitness/merge_counts/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
new file mode 100644
index 0000000..10e5993
--- /dev/null
+++ b/modules/local/fitness/merge_counts/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
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+  size: 615477
+  timestamp: 1786599613561
diff --git a/modules/local/fitness/merge_counts/environment.yml b/modules/local/fitness/merge_counts/environment.yml
new file mode 100644
index 0000000..5f5785d
--- /dev/null
+++ b/modules/local/fitness/merge_counts/environment.yml
@@ -0,0 +1,19 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+# Shared by 17 R modules (all except RUN_MUTSCAN, RUN_DIMSUM and the false-doubles error correction).
+# Keep the copies identical:
+# the one container image these modules use is built from exactly this list.
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/fitness/merge_counts/main.nf b/modules/local/fitness/merge_counts/main.nf
new file mode 100644
index 0000000..1150fcb
--- /dev/null
+++ b/modules/local/fitness/merge_counts/main.nf
@@ -0,0 +1,29 @@
+process MERGE_COUNTS {
+  tag "${sample.sample}"
+  label 'process_single'
+
+  conda "${moduleDir}/environment.yml"
+
+  container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+      ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data'
+      : 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7'}"
+
+  input:
+    tuple val(sample), path(input_counts), path(output_counts)
+
+  output:
+    tuple val(sample), path("counts_merged.tsv"), emit: merged_counts
+    path "versions.yml", emit: versions
+
+  script:
+  template 'merge_counts.R'
+
+  stub:
+  """
+  touch counts_merged.tsv
+  cat <<-END_VERSIONS > versions.yml
+  "${task.process}":
+      r-base: "0.0.0"
+  END_VERSIONS
+  """
+}
diff --git a/modules/local/fitness/merge_counts/meta.yml b/modules/local/fitness/merge_counts/meta.yml
new file mode 100644
index 0000000..2a84b75
--- /dev/null
+++ b/modules/local/fitness/merge_counts/meta.yml
@@ -0,0 +1,64 @@
+name: "merge_counts"
+description: Consolidates variant counts from multiple input (baseline) and output (selection) files into a single merged TSV table, organized by sample or replicate.
+keywords:
+  - deep mutational scanning
+  - dms
+  - count merging
+  - variant table
+  - preprocessing
+tools: null
+input:
+  - - sample:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test' ]`
+    - input_counts:
+        type: list
+        description: A list of paths to count files representing the baseline/input population
+    - output_counts:
+        type: list
+        description: A list of paths to count files representing the selection/output population
+
+output:
+  - merged_counts:
+      - sample:
+          type: map
+          description: Groovy Map containing sample information
+      - counts_merged.tsv:
+          type: file
+          description: A consolidated TSV file containing merged counts for all provided inputs and outputs
+          pattern: "counts_merged.tsv"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+containers:
+  docker:
+    linux/amd64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7
+      build_id: bd-32f1af0a83a018e7_1
+      scan_id: sc-337510c2c910aa1c_1
+    linux/arm64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb
+      build_id: bd-880bbdecb19543bb_1
+      scan_id: sc-09c9d098a09068b9_1
+  singularity:
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb
+      build_id: bd-7202e5d19667bddb_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7
+      build_id: bd-db46247d9810b3e7_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/fitness/merge_counts/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
+    linux/arm64:
+      lock_file: modules/local/fitness/merge_counts/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
diff --git a/modules/local/fitness/merge_counts/templates/merge_counts.R b/modules/local/fitness/merge_counts/templates/merge_counts.R
new file mode 100644
index 0000000..8e39be8
--- /dev/null
+++ b/modules/local/fitness/merge_counts/templates/merge_counts.R
@@ -0,0 +1,112 @@
+#!/usr/bin/env Rscript
+
+# --------------------------------------------------------------
+# 1. SETUP & INPUTS
+# --------------------------------------------------------------
+
+raw_inputs_str  <- "$input_counts"
+raw_outputs_str <- "$output_counts"
+
+parse_paths <- function(x) {
+  if (x == "" || x == "[]") return(character(0))
+  # Split at spaces
+  parts <- strsplit(x, "\\\\s+")[[1]]
+  # Remove empty strings
+  parts[parts != ""]
+}
+
+input_paths  <- parse_paths(raw_inputs_str)
+output_paths <- parse_paths(raw_outputs_str)
+
+message("Found ", length(input_paths),  " input files.")
+message("Found ", length(output_paths), " output files.")
+
+# --------------------------------------------------------------
+# 2. MERGE LOGIC
+# --------------------------------------------------------------
+
+# Helper to read a 2-col TSV without header
+read_counts <- function(fp) {
+  utils::read.table(
+    fp, header = FALSE, sep = "\\t", quote = "",
+    col.names = c("nt_seq", "count"),
+    colClasses = c("character", "numeric"),
+    comment.char = "", check.names = FALSE
+  )
+}
+
+# Read all inputs / outputs
+input_list  <- lapply(input_paths,  read_counts)
+output_list <- lapply(output_paths, read_counts)
+
+# Collect universe of sequences
+all_seqs <- unique(c(
+  unlist(lapply(input_list,  function(x) x\$nt_seq)),
+  unlist(lapply(output_list, function(x) x\$nt_seq))
+))
+
+# Pre-allocate output frame
+n_in  <- length(input_list)
+n_out <- length(output_list)
+
+col_names <- c(
+  "nt_seq",
+  if (n_in  > 0) paste0("input",  seq_len(n_in))  else character(0),
+  if (n_out > 0) paste0("output", seq_len(n_out)) else character(0)
+)
+
+# Initialize dataframe with 0 counts
+out <- data.frame(
+  nt_seq = all_seqs,
+  matrix(0, nrow = length(all_seqs), ncol = n_in + n_out),
+  stringsAsFactors = FALSE, check.names = FALSE
+)
+names(out) <- col_names
+
+# Fill inputs
+if (n_in > 0) {
+  for (i in seq_len(n_in)) {
+    df <- input_list[[i]]
+    # Match sequences: WICHTIG \$ escaping
+    idx <- match(df\$nt_seq, out\$nt_seq)
+    # Assign counts: WICHTIG \$ escaping
+    out[idx, paste0("input", i)] <- df\$count
+  }
+}
+
+# Fill outputs
+if (n_out > 0) {
+  for (j in seq_len(n_out)) {
+    df <- output_list[[j]]
+    # WICHTIG \$ escaping
+    idx <- match(df\$nt_seq, out\$nt_seq)
+    out[idx, paste0("output", j)] <- df\$count
+  }
+}
+
+# Write Output
+utils::write.table(
+  out,
+  file = "counts_merged.tsv",
+  sep = "\\t",
+  row.names = FALSE,
+  col.names = TRUE,
+  quote = FALSE
+)
+
+# --------------------------------------------------------------
+# 3. VERSIONING
+# --------------------------------------------------------------
+
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+if (is.null(r_version)) r_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/fitness/run_dimsum/.conda-lock/linux_amd64-bd-833765ab7b2cf9aa_1.txt b/modules/local/fitness/run_dimsum/.conda-lock/linux_amd64-bd-833765ab7b2cf9aa_1.txt
new file mode 100644
index 0000000..51a9bf7
--- /dev/null
+++ b/modules/local/fitness/run_dimsum/.conda-lock/linux_amd64-bd-833765ab7b2cf9aa_1.txt
@@ -0,0 +1,3783 @@
+version: 6
+environments:
+  default:
+    channels:
+    - url: https://conda.anaconda.org/conda-forge/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
+      linux-64:
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.16.1-h7cc23a3_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/argcomplete-3.7.2-pyhcf101f3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/backports.zstd-1.8.0-py313h524ba91_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.46.1-default_hfdba357_103.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-biobase-2.66.0-r44h3df3fcb_0.tar.bz2
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-biocgenerics-0.52.0-r44hdfd78af_3.tar.bz2
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-biocparallel-1.40.0-r44he5774e6_1.tar.bz2
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-biostrings-2.74.0-r44h3df3fcb_1.tar.bz2
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-data-packages-20260207-hdfd78af_1.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-delayedarray-0.32.0-r44h3df3fcb_1.tar.bz2
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-genomeinfodb-1.42.0-r44hdfd78af_2.tar.bz2
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-genomeinfodbdata-1.2.13-r44hdfd78af_0.tar.bz2
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-genomicalignments-1.42.0-r44h3df3fcb_1.tar.bz2
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-genomicranges-1.58.0-r44h3df3fcb_2.tar.bz2
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-iranges-2.40.0-r44h3df3fcb_2.tar.bz2
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-matrixgenerics-1.18.0-r44hdfd78af_0.tar.bz2
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diff --git a/modules/local/fitness/run_dimsum/.conda-lock/linux_arm64-bd-8a66e8e1354d6370_1.txt b/modules/local/fitness/run_dimsum/.conda-lock/linux_arm64-bd-8a66e8e1354d6370_1.txt
new file mode 100644
index 0000000..ebe7ac7
--- /dev/null
+++ b/modules/local/fitness/run_dimsum/.conda-lock/linux_arm64-bd-8a66e8e1354d6370_1.txt
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+  - xorg-libx11 >=1.8.13,<2.0a0
+  license: MIT
+  license_family: MIT
+  size: 22039
+  timestamp: 1787250187374
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxi-1.8.4-h5bc82ec_0.conda
+  sha256: 73ea27a634ab205a224500cd41a825584a6cf0df44bb91be74b5374463fa796e
+  md5: a671fa449100496bb422ecc11ea4f917
+  depends:
+  - libgcc >=15
+  - xorg-libx11 >=1.8.13,<2.0a0
+  - xorg-libxext >=1.3.7,<2.0a0
+  - xorg-libxfixes >=6.0.2,<7.0a0
+  license: MIT
+  license_family: MIT
+  size: 51817
+  timestamp: 1792544800425
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxrandr-1.5.5-h5bc82ec_1.conda
+  sha256: f2461efa138a03d25c4cff337e9db52c762ae93ab426f578234a2ea1cac50eb5
+  md5: 2d0c7631c23e56dbb66f4fe711c6ab63
+  depends:
+  - libgcc >=15
+  - xorg-libx11 >=1.8.13,<2.0a0
+  - xorg-libxext >=1.3.7,<2.0a0
+  - xorg-libxrender >=0.9.12,<0.10.0a0
+  license: MIT
+  license_family: MIT
+  size: 31842
+  timestamp: 1787246653258
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxrender-0.9.13-h5bc82ec_0.conda
+  sha256: fc8a9ff04d1f2a83cc230cacccd3253a09e76e10a351bc5d0d1c880dec6fe639
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+  - libgcc >=15
+  - xorg-libx11 >=1.8.13,<2.0a0
+  license: MIT
+  license_family: MIT
+  size: 36304
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+  md5: 06a15405538666fbdc272718ff4e0fe4
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+  - libgcc >=15
+  - xorg-libice >=1.1.2,<2.0a0
+  - xorg-libsm >=1.2.6,<2.0a0
+  - xorg-libx11 >=1.8.13,<2.0a0
+  license: MIT
+  license_family: MIT
+  size: 394356
+  timestamp: 1787103094046
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxtst-1.2.6-h5bc82ec_0.conda
+  sha256: fb783690e69a901f57cd564ad5de9b8f490c6dcf36620205ef9435a0da162b33
+  md5: c3a96b724f4452281ea027cfee29a548
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+  - xorg-libx11 >=1.8.13,<2.0a0
+  - xorg-libxext >=1.3.7,<2.0a0
+  - xorg-libxi >=1.8.3,<2.0a0
+  license: MIT
+  license_family: MIT
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+  - tomlkit >=0.11.6
+  - argcomplete >=1.8.1
+  - python >=3.11
+  - pyyaml >=5.3.1
+  - xmltodict >=0.11.0
+  - python
+  license: Apache-2.0
+  license_family: APACHE
+  size: 39176
+  timestamp: 1791691656430
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+  sha256: a37f07056ac10c7e901cb54241e67af93205c25b84566ad1bce3b19a8088fd69
+  md5: 09f81cb51dfb56946193f30225976b70
+  depends:
+  - libgcc >=15
+  - libstdcxx >=15
+  license: Zlib
+  license_family: Other
+  size: 125444
+  timestamp: 1786736924421
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/zstd-1.5.7-h9d15635_7.conda
+  sha256: 427fd14bcb3b8659796fecc682716617409350fb5a98e5b7b47558a10d1a2fc7
+  md5: d942e34ac3920ba83f8b2d0169570187
+  depends:
+  - libzlib >=1.3.2,<2.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 615477
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diff --git a/modules/local/fitness/run_dimsum/environment.yml b/modules/local/fitness/run_dimsum/environment.yml
new file mode 100644
index 0000000..1c93270
--- /dev/null
+++ b/modules/local/fitness/run_dimsum/environment.yml
@@ -0,0 +1,15 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  # Every pin here mirrors the paired container exactly. r-base must be pinned: with r-dimsum alone the
+  # solver picks an ancient R (4.0.5), not the container's 4.4.2. ggplot2/GGally must ALSO be pinned:
+  # DiMSum 1.4 builds its merge/diagnostics reports through GGally, which breaks under ggplot2 4.0 (the
+  # solver floats to 4.0.3 on R 4.4.2) with "object 'dimsum_meta_new_report' not found" and aborts the
+  # run; the container holds ggplot2 at 3.5.1 / GGally at 2.2.1, so we do the same.
+  - bioconda::r-dimsum=1.4
+  - conda-forge::r-base=4.4.2
+  - conda-forge::r-data.table=1.16.4
+  - bioconda::bioconductor-biostrings=2.74.0
+  - conda-forge::r-ggplot2=3.5.1
+  - conda-forge::r-ggally=2.2.1
diff --git a/modules/local/fitness/run_dimsum/main.nf b/modules/local/fitness/run_dimsum/main.nf
new file mode 100644
index 0000000..cdbe551
--- /dev/null
+++ b/modules/local/fitness/run_dimsum/main.nf
@@ -0,0 +1,62 @@
+process RUN_DIMSUM {
+  tag { sample.sample }
+  label 'process_single'
+
+  conda "${moduleDir}/environment.yml"
+
+  container "${ workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+      ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/92/9298c391f285d7f2c1155f6a519a96c4f7591971780ba4f10569558282f40b6f/data'
+      : 'community.wave.seqera.io/library/bioconductor-biostrings_r-dimsum_r-base_r-data.table_pruned:cca13eed371a9d84' }"
+
+  input:
+    tuple val(sample), path(counts_merged)
+    path(wt_txt)
+    path(exp_design)
+    val(min_counts)
+    val(output_pseudocount)
+
+  output:
+    path "dimsum_results**", emit: results_dir
+    path "versions.yml", emit: versions
+
+  script:
+  def args = task.ext.args ?: ''
+  """
+  set -euo pipefail
+
+  # DiMSum expects the sequence string, not a file path
+  WT=\$(tr -d ' \r\\n\\t' < "$wt_txt")
+
+  DiMSum \
+      --experimentDesignPath "$exp_design" \
+      --wildtypeSequence "\$WT" \
+      --countPath "$counts_merged" \
+      --startStage 4 \
+      --stopStage 5 \
+      --fitnessMinInputCountAll ${min_counts} \
+      --fitnessDropoutPseudocount ${output_pseudocount} \
+      --numCores $task.cpus \
+      --projectName "dimsum_results" \
+      --fastqFileDir . \
+      $args
+
+  R_VERSION=\$(R --version | head -n 1 | sed -E 's/^R version ([0-9.]+).*/\\1/')
+  DIMSUM_VERSION=\$(Rscript -e 'cat(as.character(packageVersion("DiMSum")))')
+  cat <<-END_VERSIONS > versions.yml
+  "${task.process}":
+      r-base: \$R_VERSION
+      DiMSum: \$DIMSUM_VERSION
+END_VERSIONS
+  """
+
+  stub:
+  """
+  mkdir -p dimsum_results
+  touch dimsum_results/report.html
+  cat <<-END_VERSIONS > versions.yml
+  "${task.process}":
+      r-base: "0.0.0"
+      DiMSum: "0.0.0"
+  END_VERSIONS
+  """
+}
diff --git a/modules/local/fitness/run_dimsum/meta.yml b/modules/local/fitness/run_dimsum/meta.yml
new file mode 100644
index 0000000..821ae00
--- /dev/null
+++ b/modules/local/fitness/run_dimsum/meta.yml
@@ -0,0 +1,71 @@
+name: "run_dimsum"
+description: Runs DiMSum to estimate fitness and functionality scores from Deep Mutational Scanning (DMS) count data.
+keywords:
+  - deep mutational scanning
+  - dms
+  - fitness
+  - dimsum
+tools:
+  - "dimsum":
+      description: "An error-model-enriched pipeline for analyzing deep mutational scanning data"
+      homepage: "https://github.com/lehner-lab/DiMSum"
+      documentation: "https://github.com/lehner-lab/DiMSum/blob/master/README.md"
+      doi: "10.1186/s12859-020-03709-3"
+      licence: ["GPL-3.0-or-later"]
+
+input:
+  - - sample:
+        type: map
+        description: |
+          Groovy Map containing sample information.
+          Matches the structure used in the pipeline (e.g., [ id:'test' ])
+    - counts_merged:
+        type: file
+        description: A TSV file containing variant counts merged across samples
+        pattern: "*.{tsv,csv}"
+  - - wt_txt:
+        type: file
+        description: A text file containing the wild-type DNA/amino acid sequence string
+        pattern: "*.{txt}"
+  - - exp_design:
+        type: file
+        description: DiMSum-specific experimental design file describing replicates and conditions
+        pattern: "*.{txt,csv}"
+  - - min_counts:
+        type: integer
+        description: Minimum number of reads a variant needs in every input replicate to receive a fitness estimate (`--min_counts`)
+  - - output_pseudocount:
+        type: integer
+        description: Pseudocount for output replicates in which a variant observed in the input was not detected; 0 disables (`--output_pseudocount`)
+
+output:
+  - results_dir:
+      - dimsum_results**:
+          type: directory
+          description: Directory containing the full suite of DiMSum output files, including fitness estimates and plots
+          pattern: "dimsum_results*"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+containers:
+  # Kept on the pre-existing image (built in #84) instead of the one from `nf-core modules containers create`:
+  # the new Wave/pixi builds lack GenomeInfoDbData, whose data is installed by a conda post-link
+  # script that pixi does not run, so DiMSum fails with "there is no package called 'GenomeInfoDbData'".
+  # No arm64 image for the same reason. The conda lock files are kept from the new build.
+  docker:
+    linux/amd64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-dimsum_r-base_r-data.table_pruned:cca13eed371a9d84
+  singularity:
+    linux/amd64:
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/92/9298c391f285d7f2c1155f6a519a96c4f7591971780ba4f10569558282f40b6f/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/fitness/run_dimsum/.conda-lock/linux_amd64-bd-833765ab7b2cf9aa_1.txt
+    linux/arm64:
+      lock_file: modules/local/fitness/run_dimsum/.conda-lock/linux_arm64-bd-8a66e8e1354d6370_1.txt
diff --git a/modules/local/fitness/run_mutscan/.conda-lock/linux_amd64-bd-c5cefc95c9f1002e_1.txt b/modules/local/fitness/run_mutscan/.conda-lock/linux_amd64-bd-c5cefc95c9f1002e_1.txt
new file mode 100644
index 0000000..4276d2e
--- /dev/null
+++ b/modules/local/fitness/run_mutscan/.conda-lock/linux_amd64-bd-c5cefc95c9f1002e_1.txt
@@ -0,0 +1,3043 @@
+version: 6
+environments:
+  default:
+    channels:
+    - url: https://conda.anaconda.org/conda-forge/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
+      linux-64:
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.46.1-default_hfdba357_103.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-biobase-2.70.0-r45h01b2380_0.conda
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-biocgenerics-0.56.0-r45hdfd78af_2.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-biocparallel-1.44.0-r45ha27e39d_1.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-biostrings-2.78.0-r45h01b2380_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-csaw-1.44.0-r45ha27e39d_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-delayedarray-0.36.1-r45h262fe30_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-edger-4.8.2-r45h262fe30_1.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-genomicranges-1.62.1-r45h01b2380_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-iranges-2.44.0-r45h01b2380_1.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-limma-3.66.0-r45h01b2380_0.conda
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-matrixgenerics-1.22.0-r45hdfd78af_1.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-metapod-1.18.0-r45ha27e39d_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-mutscan-1.0.0-r45ha27e39d_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-rhtslib-3.6.0-r45h01b2380_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-rsamtools-2.26.0-r45ha27e39d_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-s4arrays-1.10.1-r45h01b2380_0.conda
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+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-seqinfo-1.0.0-r45hdfd78af_0.conda
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diff --git a/modules/local/fitness/run_mutscan/environment.yml b/modules/local/fitness/run_mutscan/environment.yml
new file mode 100644
index 0000000..a8b1c61
--- /dev/null
+++ b/modules/local/fitness/run_mutscan/environment.yml
@@ -0,0 +1,7 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - bioconda::bioconductor-mutscan=1.0.0
+  - conda-forge::r-base=4.5.1
diff --git a/modules/local/fitness/run_mutscan/main.nf b/modules/local/fitness/run_mutscan/main.nf
new file mode 100644
index 0000000..2f83d39
--- /dev/null
+++ b/modules/local/fitness/run_mutscan/main.nf
@@ -0,0 +1,34 @@
+process RUN_MUTSCAN {
+  tag "${sample.sample}"
+  label 'process_single'
+
+  conda "${moduleDir}/environment.yml"
+
+  container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+      ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/0a/0a32a5f637634c064a3caecc77d9e99f4367efd459bc28d1d4971ed5d05b73a7/data'
+      : 'community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base:c5cefc95c9f1002e'}"
+
+  input:
+    tuple val(sample), path(counts_merged)
+    path(syn_wt_txt)
+    path(exp_design)
+    val(min_counts)
+
+  output:
+    tuple val(sample), path("fitness_estimation_mutscan_edgeR.tsv"), emit: fitness_mutscan_edgeR
+    tuple val(sample), path("fitness_estimation_mutscan_limma.tsv"), emit: fitness_mutscan_limma
+    tuple val(sample), path("*.pdf"), emit: qc_plots
+    path "versions.yml", emit: versions
+
+  script:
+  template 'fitness_calculation_mutscan.R'
+
+  stub:
+  """
+  touch fitness_estimation_mutscan_edgeR.tsv fitness_estimation_mutscan_limma.tsv mutscan_stub.pdf
+  cat <<-END_VERSIONS > versions.yml
+  "${task.process}":
+      r-base: "0.0.0"
+  END_VERSIONS
+  """
+}
diff --git a/modules/local/fitness/run_mutscan/meta.yml b/modules/local/fitness/run_mutscan/meta.yml
new file mode 100644
index 0000000..e5319aa
--- /dev/null
+++ b/modules/local/fitness/run_mutscan/meta.yml
@@ -0,0 +1,98 @@
+name: "run_mutscan"
+description: Calculates fitness and functionality scores using the mutscan R package, employing edgeR and limma-voom statistical frameworks for robust estimation from input and output variant counts.
+keywords:
+  - deep mutational scanning
+  - dms
+  - fitness estimation
+  - mutscan
+  - edger
+  - limma
+tools:
+  - "mutscan":
+      description: "R package for analysis of deep mutational scanning data"
+      homepage: "https://bioconductor.org/packages/release/bioc/html/mutscan.html"
+      documentation: "https://bioconductor.org/packages/release/bioc/manuals/mutscan/man/mutscan.pdf"
+      tool_dev_url: "https://github.com/fmicompbio/mutscan"
+      doi: "10.1186/s13059-023-02967-0"
+      licence: ["MIT"]
+  - "edger":
+      description: "Empirical Analysis of Digital Gene Expression Data in R"
+      homepage: "https://bioconductor.org/packages/release/bioc/html/edgeR.html"
+      doi: "10.1093/bioinformatics/btp616"
+      licence: ["GPL-2.0-or-later"]
+  - "limma":
+      description: "Linear Models for Microarray Data"
+      homepage: "https://bioconductor.org/packages/release/bioc/html/limma.html"
+      doi: "10.1093/nar/gkv007"
+      licence: ["GPL-2.0-or-later"]
+
+input:
+  - - sample:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test' ]`
+    - counts_merged:
+        type: file
+        description: TSV/CSV file containing variant counts merged across samples or replicates
+        pattern: "*.{tsv,csv}"
+  - - syn_wt_txt:
+        type: file
+        description: Text file identifying the synonymous wild-type mutation used as the reference baseline
+        pattern: "*.txt"
+  - - exp_design:
+        type: file
+        description: Experimental design file defining replicates, conditions, and groupings for statistical analysis
+        pattern: "*.{csv,txt}"
+  - - min_counts:
+        type: integer
+        description: Minimum number of reads a variant needs in every input replicate to receive a fitness estimate (`--min_counts`)
+
+output:
+  - fitness_mutscan_edgeR:
+      - sample:
+          type: map
+          description: Groovy Map containing sample information
+      - fitness_estimation_mutscan_edgeR.tsv:
+          type: file
+          description: Fitness estimation results generated using the edgeR framework
+          pattern: "fitness_estimation_mutscan_edgeR.tsv"
+  - fitness_mutscan_limma:
+      - sample:
+          type: map
+          description: Groovy Map containing sample information
+      - fitness_estimation_mutscan_limma.tsv:
+          type: file
+          description: Fitness estimation results generated using the limma framework
+          pattern: "fitness_estimation_mutscan_limma.tsv"
+  - qc_plots:
+      - sample:
+          type: map
+          description: Groovy Map containing sample information
+      - "*.pdf":
+          type: file
+          description: Quality control plots generated by mutscan during fitness estimation
+          pattern: "*.pdf"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+containers:
+  docker:
+    linux/amd64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base:c5cefc95c9f1002e
+      build_id: bd-c5cefc95c9f1002e_1
+      scan_id: sc-8d031d70a3f2734f_1
+  singularity:
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base:764a8471b126fb35
+      build_id: bd-764a8471b126fb35_2
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/0a/0a32a5f637634c064a3caecc77d9e99f4367efd459bc28d1d4971ed5d05b73a7/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/fitness/run_mutscan/.conda-lock/linux_amd64-bd-c5cefc95c9f1002e_1.txt
diff --git a/modules/local/fitness/run_mutscan/templates/fitness_calculation_mutscan.R b/modules/local/fitness/run_mutscan/templates/fitness_calculation_mutscan.R
new file mode 100644
index 0000000..7598f22
--- /dev/null
+++ b/modules/local/fitness/run_mutscan/templates/fitness_calculation_mutscan.R
@@ -0,0 +1,382 @@
+#!/usr/bin/env Rscript
+
+## mutscan (Soneson et al., Genome Biology 2023) fitness estimation for nf-core/deepmutscan
+## 19.03.2026
+
+## 0. Libraries ##
+##################
+
+suppressPackageStartupMessages({
+  library(mutscan)
+  library(Biostrings)
+})
+
+## --- Helper functions ---
+
+# calculate nt hamming distances from the specified WT
+nbrMutBases <- function(x, wt.seq) {
+  x <- cbind(x, "nbrMutBases" = rep(NA, nrow(x)))
+  for (i in 1:nrow(x)){
+    tmp.wt <- strsplit(as.character(wt.seq), "")[[1]]
+    tmp.mut <- strsplit(as.character(x\$sequence[i]), "")[[1]]
+    if(length(which(tmp.mut != tmp.wt)) == 0){
+      x\$nbrMutBases[i] <- 0
+      rm(tmp.mut, tmp.wt)
+      next
+    }else{
+      x\$nbrMutBases[i] <- length(which(tmp.mut != tmp.wt))
+      rm(tmp.mut, tmp.wt)
+      next
+    }
+  }
+  x\$nbrMutBases <- as.character(x\$nbrMutBases)
+  return(x)
+}
+
+# calculate codon hamming distances from the specified WT
+nbrMutCodons <- function(x, wt.seq) {
+  x <- cbind(x, "nbrMutCodons" = rep(NA, nrow(x)))
+  for (i in 1:nrow(x)){
+    tmp.wt <- strsplit(as.character(wt.seq), "")[[1]]
+    tmp.mut <- strsplit(as.character(x\$sequence[i]), "")[[1]]
+    if(length(which(tmp.mut != tmp.wt)) == 0){
+      x\$nbrMutCodons[i] <- 0
+      rm(tmp.mut, tmp.wt)
+      next
+    }else if(length(which(tmp.mut != tmp.wt)) == 1){
+      x\$nbrMutCodons[i] <- 1
+      rm(tmp.mut, tmp.wt)
+    }else if(length(which(tmp.mut != tmp.wt)) > 1){
+      mut.pos <- which(tmp.mut != tmp.wt)
+      x\$nbrMutCodons[i] <- length(unique(ceiling(mut.pos / 3)))
+      rm(tmp.mut, tmp.wt)
+      next
+    }
+  }
+  x\$nbrMutCodons <- as.character(x\$nbrMutCodons)
+  return(x)
+}
+
+# translate sequences and add aa_seq
+sequenceAA <- function(x) {
+  x <- cbind(x, "sequenceAA" = as.character(translate(DNAStringSet(x\$sequence))))
+  return(x)
+}
+
+# calculate AA hamming distances from the WT
+nbrMutAAs <- function(x, wt.seq) {
+  x <- cbind(x, "nbrMutAAs" = rep(NA, nrow(x)))
+  for (i in 1:nrow(x)){
+    tmp.wt <- strsplit(as.character(translate(wt.seq)), "")[[1]]
+    tmp.mut <- strsplit(as.character(x\$sequenceAA[i]), "")[[1]]
+    if(length(which(tmp.mut != tmp.wt)) == 0){
+      x\$nbrMutAAs[i] <- 0
+      rm(tmp.mut, tmp.wt)
+      next
+    }else{
+      x\$nbrMutAAs[i] <- length(which(tmp.mut != tmp.wt))
+      rm(tmp.mut, tmp.wt)
+      next
+    }
+  }
+  x\$nbrMutAAs <- as.character(x\$nbrMutAAs)
+  return(x)
+}
+
+# mutant base labels
+mutantNameBase <- function(x, wt.seq){
+  x <- cbind(x, "mutantNameBase" = rep(NA, nrow(x)))
+  for(i in 1:nrow(x)){
+    tmp.wt <- strsplit(as.character(wt.seq), "")[[1]]
+    tmp.mut <- strsplit(as.character(x\$sequence[i]), "")[[1]]
+    if(length(which(tmp.mut != tmp.wt)) == 0){
+      x\$mutantNameBase[i] <- "f.0.WT"
+      rm(tmp.mut, tmp.wt)
+      next
+    }else if(length(which(tmp.mut != tmp.wt)) > 0){
+      mut.pos <- which(tmp.mut != tmp.wt)
+      x\$mutantNameBase[i] <- paste0("f.", mut.pos, ".", tmp.mut[mut.pos], collapse = "_")
+      rm(tmp.mut, tmp.wt, mut.pos)
+      next
+    }
+  }
+  return(x)
+}
+
+# mutant codon labels
+mutantNameCodon <- function(x, wt.seq){
+  x <- cbind(x, "mutantNameCodon" = rep(NA, nrow(x)))
+  for(i in 1:nrow(x)){
+    tmp.wt <- strsplit(as.character(wt.seq), "(?<=.{3})", perl = TRUE)[[1]]
+    tmp.mut <- strsplit(as.character(x\$sequence[i]), "(?<=.{3})", perl = TRUE)[[1]]
+    if(length(which(tmp.mut != tmp.wt)) == 0){
+      x\$mutantNameCodon[i] <- "f.0.WT"
+      rm(tmp.mut, tmp.wt)
+      next
+    }else if(length(which(tmp.mut != tmp.wt)) > 0){
+      mut.pos <- which(tmp.mut != tmp.wt)
+      x\$mutantNameCodon[i] <- paste0("f.", mut.pos, ".", tmp.mut[mut.pos], collapse = "_")
+      rm(tmp.mut, tmp.wt, mut.pos)
+      next
+    }
+  }
+  return(x)
+}
+
+# name the mutations (AA level)
+mutantNameAA <- function(x, wt.seq) {
+  x <- cbind(x, "mutantNameAA" = rep(NA, nrow(x)))
+  for (i in 1:nrow(x)){
+    if(x\$nbrMutAAs[i] == 0){
+      x\$mutantNameAA[i] <- "f.0.WT"
+    }else{
+      tmp.wt <- strsplit(as.character(translate(wt.seq)), "")[[1]]
+      tmp.mut <- strsplit(as.character(x\$sequenceAA[i]), "")[[1]]
+      tmp.pos <- which(tmp.mut != tmp.wt)
+      x\$mutantNameAA[i] <- paste0("f.", tmp.pos, ".", tmp.mut[tmp.pos])
+      rm(tmp.mut, tmp.wt,tmp.pos)
+    }
+  }
+  return(x)
+}
+
+# categorise the mutation types
+mutationTypes <- function(x){
+  x <- cbind(x, "mutationTypes" = rep(NA, nrow(x)))
+  x[grep("WT", x\$mutantNameAA), "mutationTypes"] <- "silent" ## silent/synonymous
+  x[grep("[*]", x\$mutantNameAA), "mutationTypes"] <- "stop" ## stop
+  x[-grep("[*]|WT", x\$mutantNameAA), "mutationTypes"] <- "nonsynonymous" ## nonsynonymous
+  x
+}
+
+# mutant name
+mutantName <- function(x, wt.seq){
+  x <- cbind("mutantName" = rep(NA, nrow(x)), x)
+  for (i in 1:nrow(x)){
+    if(x\$nbrMutAAs[i] == 0){
+      x\$mutantName[i] <- "WT"
+    }else{
+      tmp.wt <- strsplit(as.character(translate(wt.seq)), "")[[1]]
+      tmp.mut <- strsplit(as.character(x\$sequenceAA[i]), "")[[1]]
+      tmp.pos <- which(tmp.mut != tmp.wt)
+      x\$mutantName[i] <- paste0(tmp.wt[tmp.pos], tmp.pos,tmp.mut[tmp.pos])
+      rm(tmp.mut, tmp.wt,tmp.pos)
+    }
+  }
+  return(x)
+}
+
+# mutscan 'summaryTable' building (from merged counts TSV file)
+mutscan.summaryTable.from.counts <- function(x, sample, wt.seq){
+
+  ### pre-process
+  x <- x[,c(1, which(colnames(x) == sample))]
+  colnames(x) <- c("sequence", "nbrReads")
+  x <- cbind(x, "maxNbrReads" = x[,"nbrReads"], "nbrUmis" = x[,"nbrReads"])
+
+  ### nbrMutBases
+  x <- nbrMutBases(x, wt.seq)
+
+  ### nbrMutCodons
+  x <- nbrMutCodons(x, wt.seq)
+
+  ### sequenceAA
+  x <- sequenceAA(x)
+
+  ### nbrMutAAs
+  x <- nbrMutAAs(x, wt.seq)
+
+  ### varLengths
+  x <- cbind(x, "varLengths" = as.character(nchar(x\$sequence)))
+
+  ### mutantNameBase
+  x <- mutantNameBase(x, wt.seq)
+
+  ### mutantNameCodon
+  x <- mutantNameCodon(x, wt.seq)
+
+  ### mutantNameBaseHGVS (would ideally look like this: "f:c", "f:c.32_33delinsAC", etc.)
+  x <- cbind(x, "mutantNameBaseHGVS" = x\$mutantNameCodon)
+
+  ### mutantNameAA
+  x <- mutantNameAA(x, wt.seq)
+
+  ### mutantNameAAHGVS (would ideally look like this: "f:p" or "f:p.(Leu11His)")
+  x <- cbind(x, "mutantNameAAHGVS" = x\$mutantNameAA)
+
+  ### mutationTypes: silent, stop, nonsynonymous
+  x <- mutationTypes(x)
+
+  ### mutantName
+  x <- mutantName(x, wt.seq)
+
+  ### re-order columns
+  x <- x[,c(1:7,9:16,8)]
+
+  ### output
+  return(x)
+}
+
+## --- Main function ---
+
+#' Run mutscan fitness estimation with configurable I/O paths
+#'
+#' @param counts_path Path to counts_merged.tsv
+#' @param design_path Path to experimentalDesign.tsv
+#' @param wt_seq_path Path to synonymous_wt.txt (single line DNA sequence)
+#' @param output_path Path to write fitness_estimation.tsv
+#'
+#' @return Invisibly returns the final data.frame; writes the output to output_path.
+run_mutscan_fitness_estimation <- function(counts_path,
+                                           design_path,
+                                           wt_seq_path,
+                                           output_path_edgeR,
+                                           output_path_limma,
+                                           min_counts = 10){
+
+  ## 1. Import key files ##
+  #########################
+
+  merged.counts <- read.table(counts_path, sep = "\t", header = T, check.names = F)
+  exp.design <- read.table(design_path, sep = "\t", header = T, check.names = F)
+  wt.seq <- DNAString(as.character(read.table(wt_seq_path)))
+
+  ## keep variants with at least `min_counts` reads in every input replicate (and the wildtype),
+  ## so that mutscan analyses the same variants as the default estimator and DiMSum
+  input.cols <- grep("^input[0-9]+\$", colnames(merged.counts), value = TRUE)
+  keep <- apply(merged.counts[, input.cols, drop = FALSE] >= min_counts, 1, all)
+  keep[is.na(keep)] <- FALSE
+  keep[merged.counts[, 1] == as.character(wt.seq)] <- TRUE
+  merged.counts <- merged.counts[keep, , drop = FALSE]
+  rm(input.cols, keep)
+
+  ## 2. Variant count matrix reformatting ##
+  ##########################################
+
+  var.tables <- vector(mode = "list", length = nrow(exp.design))
+  names(var.tables) <- exp.design[,"sample_name"]
+  var.tables <- lapply(var.tables, function(x){x <- vector(mode = "list", length = 4);
+  names(x) <- c("summaryTable", "filterSummary", "errorStatistics", "parameters");
+  return(x)})
+
+  # mutscan 'summaryTable'
+  for(i in 1:length(var.tables)){
+    print(i)
+    var.tables[[i]]\$summaryTable <- mutscan.summaryTable.from.counts(merged.counts, names(var.tables)[i], wt.seq)
+  }
+
+  # mutscan 'filterSummary' (fill with minimal decoy)
+  var.tables <- lapply(var.tables, function(x){x\$filterSummary <- data.frame(NA); return(x)})
+
+  # mutscan 'errorStatistics' (fill with minimal decoy)
+  var.tables <- lapply(var.tables, function(x){x\$errorStatistics <- NA; return(x)})
+
+  # mutscan 'parameters' (fill with minimal decoy)
+  var.tables <- lapply(var.tables, function(x){x\$parameters\$mutNameDelimiter <- '.'; return(x)})
+
+  ## 3. summarizeExperiment object ##
+  ###################################
+
+  # mutscan 'coldata' object (from experimental design TSV file)
+  condition <- exp.design\$selection_id
+  condition[which(condition == '0')] <- "input"
+  condition[which(condition == '1')] <- "output"
+  coldata <- as.data.frame(cbind("Name" = exp.design\$sample_name,
+                                 "Condition" = condition,
+                                 "Replicate" = exp.design\$experiment_replicate))
+  coldata\$Replicate <- factor(coldata\$Replicate)
+
+  # mutscan 'summarizeExperiment' object
+  se <- summarizeExperiment(x = var.tables,
+                            coldata = coldata,
+                            countType = "reads")
+
+  ## 4. logFC calculations ##
+  ###########################
+
+  # mutscan 'model.matrix' object to calculate logFC values
+  model.design <- model.matrix(~ Replicate + Condition, data = se@colData)
+
+  # edgeR
+  logFC.edgeR <- calculateRelativeFC(se = se,
+                                     design = model.design,
+                                     coef = "Conditionoutput",
+                                     WTrows = "WT",
+                                     selAssay = "counts",
+                                     pseudocount = 1,
+                                     method = "edgeR")
+
+  # limma
+  logFC.limma <- calculateRelativeFC(se = se,
+                                     design = model.design,
+                                     coef = "Conditionoutput",
+                                     WTrows = "WT",
+                                     selAssay = "counts",
+                                     pseudocount = 1,
+                                     method = "limma")
+
+  ## 5. QC plots ##
+  #################
+
+  # raw counts comparison
+  pdf('mutscan_counts_corr.pdf', height = 9, width = 14)
+  print(plotPairs(se, selAssay = "counts", addIdentityLine = TRUE))
+  dev.off()
+
+  # edgeR volcano plot
+  pdf('mutscan_edgeR_volcano.pdf', height = 9, width = 14)
+  print(plotVolcano(logFC.edgeR, pointSize = "large"))
+  dev.off()
+
+  # limma volcano plot
+  pdf('mutscan_limma_volcano.pdf', height = 9, width = 14)
+  print(plotVolcano(logFC.limma, pointSize = "large"))
+  dev.off()
+
+  ## 6. Data export ##
+  ####################
+
+  write.table(logFC.edgeR, output_path_edgeR,
+              col.names = TRUE, row.names = FALSE, quote = FALSE, sep = "\t", na = "")
+  write.table(logFC.limma, output_path_limma,
+              col.names = TRUE, row.names = FALSE, quote = FALSE, sep = "\t", na = "")
+
+  invisible(logFC.edgeR)
+  invisible(logFC.limma)
+
+}
+
+#####
+# run function
+#####
+run_mutscan_fitness_estimation(
+  counts_path = "$counts_merged",
+  design_path = "$exp_design",
+  wt_seq_path = "$syn_wt_txt",
+  output_path_edgeR = "fitness_estimation_mutscan_edgeR.tsv",
+  output_path_limma = "fitness_estimation_mutscan_limma.tsv",
+  min_counts = as.numeric("$min_counts")
+)
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+mutscan_version <- as.character(packageVersion("mutscan"))
+Biostrings_version <- as.character(packageVersion("Biostrings"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(mutscan_version) == 0) mutscan_version <- "unknown"
+if (length(Biostrings_version) == 0) Biostrings_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-mutscan:', mutscan_version),
+    paste('    r-Biostrings:', Biostrings_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/structure/variant_effect_inspection_tool/.conda-lock/linux_amd64-bd-cfb38dbf0574e16d_1.txt b/modules/local/structure/variant_effect_inspection_tool/.conda-lock/linux_amd64-bd-cfb38dbf0574e16d_1.txt
new file mode 100644
index 0000000..e3629c8
--- /dev/null
+++ b/modules/local/structure/variant_effect_inspection_tool/.conda-lock/linux_amd64-bd-cfb38dbf0574e16d_1.txt
@@ -0,0 +1,556 @@
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+    - url: https://conda.anaconda.org/bioconda/
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diff --git a/modules/local/structure/variant_effect_inspection_tool/.conda-lock/linux_arm64-bd-a15c6f547e2083b8_1.txt b/modules/local/structure/variant_effect_inspection_tool/.conda-lock/linux_arm64-bd-a15c6f547e2083b8_1.txt
new file mode 100644
index 0000000..9ebfc49
--- /dev/null
+++ b/modules/local/structure/variant_effect_inspection_tool/.conda-lock/linux_arm64-bd-a15c6f547e2083b8_1.txt
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diff --git a/modules/local/structure/variant_effect_inspection_tool/environment.yml b/modules/local/structure/variant_effect_inspection_tool/environment.yml
new file mode 100644
index 0000000..e16d4bb
--- /dev/null
+++ b/modules/local/structure/variant_effect_inspection_tool/environment.yml
@@ -0,0 +1,11 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+# Shared by the three Python report modules. Keep the copies identical:
+# the one container image these modules use is built from exactly this list.
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - conda-forge::numpy=1.26.4
+  - conda-forge::pandas=2.2.1
+  - conda-forge::python=3.12.3
diff --git a/modules/local/structure/variant_effect_inspection_tool/main.nf b/modules/local/structure/variant_effect_inspection_tool/main.nf
new file mode 100644
index 0000000..74da972
--- /dev/null
+++ b/modules/local/structure/variant_effect_inspection_tool/main.nf
@@ -0,0 +1,42 @@
+// Build the single, self-contained interactive variant effect inspection tool (HTML).
+// Aggregates per-residue metrics (fitness, pLDDT accuracy, coverage, counts, counts/cov, error bias),
+// aligns the wildtype sequence onto the structure, and inlines 3Dmol.js + PDB + data into
+// one portable HTML file (plus a structure_data.json sidecar).
+process VARIANT_EFFECT_INSPECTION_TOOL {
+    tag "${meta.sample ?: meta.id}"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/f7/f727259d394cff047a26f3e3fa3a43e1a7a1c9dc51a35e6def7aa90a2085bb7b/data'
+        : 'community.wave.seqera.io/library/numpy_pandas_python:cfb38dbf0574e16d'}"
+
+    input:
+    tuple val(meta), path(pdb), path(fitness_tsv)
+    path(variant_counts, stageAs: 'vc*/*')   // per-sample files share a basename -> stage in numbered dirs
+    path aa_seq
+    path threedmol_js
+    path template_html
+
+    output:
+    tuple val(meta), path("variant_effect_inspection_tool.html"), emit: viewer
+    tuple val(meta), path("structure_data.json")                , emit: data
+    path "versions.yml"                                         , emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'build_variant_effect_inspection_tool.py'
+
+    stub:
+    """
+    touch variant_effect_inspection_tool.html structure_data.json
+    cat <<-END_VERSIONS > versions.yml
+    "${task.process}":
+        python: "0.0.0"
+        pandas: "0.0.0"
+    END_VERSIONS
+    """
+}
diff --git a/modules/local/structure/variant_effect_inspection_tool/meta.yml b/modules/local/structure/variant_effect_inspection_tool/meta.yml
new file mode 100644
index 0000000..be5d948
--- /dev/null
+++ b/modules/local/structure/variant_effect_inspection_tool/meta.yml
@@ -0,0 +1,99 @@
+name: "variant_effect_inspection_tool"
+description: Build a single, self-contained interactive variant effect inspection tool (HTML) that projects per-residue fitness, counts, coverage and error-correction bias onto the wildtype 3D structure using 3Dmol.js.
+keywords:
+  - deep mutational scanning
+  - variant effect
+  - 3D structure
+  - visualisation
+tools:
+  - "3Dmol.js":
+      description: "Object-oriented, WebGL-based JavaScript library for molecular visualisation."
+      homepage: "https://3dmol.csb.pitt.edu/"
+      documentation: "https://3dmol.csb.pitt.edu/doc/"
+      licence: ["BSD-3-Clause"]
+  - "pandas":
+      description: "Fast, powerful data structures for data analysis in Python."
+      homepage: "https://pandas.pydata.org/"
+      licence: ["BSD-3-Clause"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', sample:'ORF1' ]`
+    - pdb:
+        type: file
+        description: Wildtype 3D structure (PDB); per-residue confidence read from the B-factor column.
+        pattern: "*.pdb"
+    - fitness_tsv:
+        type: file
+        description: Per-sample default fitness estimation table.
+        pattern: "*.tsv"
+  - - variant_counts:
+        type: file
+        description: Per-sample (error-corrected) library-filtered variant count tables.
+        pattern: "*.csv"
+  - - aa_seq:
+        type: file
+        description: Reference amino-acid sequence (one line).
+  - - threedmol_js:
+        type: file
+        description: Bundled 3Dmol.js library (inlined into the HTML).
+        pattern: "*.js"
+  - - template_html:
+        type: file
+        description: HTML template into which the structure, data and 3Dmol.js are injected.
+        pattern: "*.html"
+
+output:
+  - viewer:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - variant_effect_inspection_tool.html:
+          type: file
+          description: Self-contained interactive variant effect inspection tool.
+          pattern: "variant_effect_inspection_tool.html"
+  - data:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - structure_data.json:
+          type: file
+          description: Sidecar with the per-residue/per-variant data projected onto the structure.
+          pattern: "structure_data.json"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+maintainers:
+  - "@BenjaminWehnert1008"
+containers:
+  docker:
+    linux/amd64:
+      name: community.wave.seqera.io/library/numpy_pandas_python:cfb38dbf0574e16d
+      build_id: bd-cfb38dbf0574e16d_1
+      scan_id: sc-fae0cdb4b6c51968_1
+    linux/arm64:
+      name: community.wave.seqera.io/library/numpy_pandas_python:a15c6f547e2083b8
+      build_id: bd-a15c6f547e2083b8_1
+      scan_id: sc-931239170a8e2353_1
+  singularity:
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/numpy_pandas_python:648dc1860698d21b
+      build_id: bd-648dc1860698d21b_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/22/229745e9be125462f1850ac75f13afb0738d30241558908657bfc802c2c6681f/data
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/numpy_pandas_python:7f16bcac97973062
+      build_id: bd-7f16bcac97973062_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/f7/f727259d394cff047a26f3e3fa3a43e1a7a1c9dc51a35e6def7aa90a2085bb7b/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/structure/variant_effect_inspection_tool/.conda-lock/linux_amd64-bd-cfb38dbf0574e16d_1.txt
+    linux/arm64:
+      lock_file: modules/local/structure/variant_effect_inspection_tool/.conda-lock/linux_arm64-bd-a15c6f547e2083b8_1.txt
diff --git a/modules/local/structure/variant_effect_inspection_tool/templates/build_variant_effect_inspection_tool.py b/modules/local/structure/variant_effect_inspection_tool/templates/build_variant_effect_inspection_tool.py
new file mode 100755
index 0000000..2900d32
--- /dev/null
+++ b/modules/local/structure/variant_effect_inspection_tool/templates/build_variant_effect_inspection_tool.py
@@ -0,0 +1,335 @@
+#!/usr/bin/env python3
+"""
+Build a single self-contained, interactive variant effect inspection tool (HTML) for nf-core/deepmutscan.
+
+Nextflow ``template`` script for the VARIANT_EFFECT_INSPECTION_TOOL module (staged from this module's
+``templates/`` folder). Reads the wildtype structure (PDB), the fitness estimation table and the
+per-sample variant-count tables, aggregates per-residue/per-variant metrics, and writes one portable
+HTML file with 3Dmol.js, the PDB coordinates and all data inlined (no external/CDN/sidecar
+dependency), plus a structure_data.json sidecar and versions.yml.
+
+Note: this is a Nextflow template, so input paths / the sample name are filled in as Nextflow
+variables and every backslash meant for Python is written doubled (as in the pipeline's R templates).
+"""
+import json
+import platform
+import re
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+THREE_TO_ONE = {
+    "ALA": "A", "ARG": "R", "ASN": "N", "ASP": "D", "CYS": "C", "GLN": "Q", "GLU": "E",
+    "GLY": "G", "HIS": "H", "ILE": "I", "LEU": "L", "LYS": "K", "MET": "M", "PHE": "F",
+    "PRO": "P", "SER": "S", "THR": "T", "TRP": "W", "TYR": "Y", "VAL": "V",
+}
+# Grouped by side-chain chemistry, matching the heatmaps in the summary report.
+AA_ORDER = list("GAVLMIFYWKRHDESTCNQP")
+MUT_RE = re.compile(r"^([A-Z])(\\d+)([A-Z*])\$")
+
+
+def parse_pdb(pdb_path):
+    """Return (pdb_text, residues) where residues = [{resnum, aa, plddt}] from CA atoms."""
+    text = Path(pdb_path).read_text()
+    residues = []
+    seen = set()
+    for line in text.splitlines():
+        if not line.startswith("ATOM"):
+            continue
+        atom_name = line[12:16].strip()
+        if atom_name != "CA":
+            continue
+        try:
+            resnum = int(line[22:26])
+            bfac = float(line[60:66])
+        except ValueError:
+            continue
+        if resnum in seen:
+            continue
+        seen.add(resnum)
+        resname = line[17:20].strip().upper()
+        residues.append({"resnum": resnum, "aa": THREE_TO_ONE.get(resname, "X"), "plddt": bfac})
+    residues.sort(key=lambda r: r["resnum"])
+    return text, residues
+
+
+def best_alignment(wt_seq, struct_residues):
+    """Find offset so that wt position i (1-based) maps to struct_residues[off + i - 1].
+
+    Scans all offsets, maximising identity. Returns (offset0, identity_fraction) where the
+    structure index for wt index j (0-based) is off0 + j.
+    """
+    struct_seq = "".join(r["aa"] for r in struct_residues)
+    n, m = len(wt_seq), len(struct_seq)
+    best_off, best_matches = 0, -1
+    for off in range(-n + 1, m):
+        matches = 0
+        for j in range(n):
+            k = off + j
+            if 0 <= k < m and struct_seq[k] == wt_seq[j]:
+                matches += 1
+        if matches > best_matches:
+            best_matches, best_off = matches, off
+    identity = best_matches / max(1, n)
+    return best_off, identity
+
+
+def r6(x):
+    """Round to 6 dp so output is byte-stable regardless of floating-point summation order."""
+    return round(x, 6) if isinstance(x, (int, float)) else x
+
+
+def to_float(x):
+    try:
+        f = float(x)
+        return f if f == f else None  # drop NaN
+    except (TypeError, ValueError):
+        return None
+
+
+REP_RE = re.compile(r"^rescaled_fitness_rep(\\d+)\$", re.I)
+OUT_RE = re.compile(r"^output(\\d+)\$", re.I)
+
+
+def load_fitness(path):
+    """Per-position variant records from the default fitness_estimation.tsv.
+
+    Captures the mean fitness, its SD and every per-replicate (rescaled) fitness, so the viewer can
+    recompute the mean/SD over a user-chosen subset of replicates. That subsetting is exact rather
+    than approximate: the fitness step derives each `rescaled_fitness_rep<N>` from that replicate's
+    own input/output counts and its own synonymous/stop anchors, so a replicate's value never
+    depends on which other replicates were in the run.
+
+    Returns (per_pos, rep_ids). Any number of replicates is handled; missing values are tolerated.
+    """
+    df = pd.read_csv(path, sep="\\t", dtype=str, keep_default_na=False)
+
+    def col(name):
+        return next((c for c in df.columns if c.strip().lower() == name), None)
+
+    def numbered(rx):
+        return sorted((c for c in df.columns if rx.match(c.strip())),
+                      key=lambda c: int(rx.match(c.strip()).group(1)))
+
+    fit_col, sd_col = col("mean fitness"), col("fitness sd")
+    rep_cols = numbered(REP_RE)
+    out_cols = numbered(OUT_RE)
+    rep_ids = [REP_RE.match(c.strip()).group(1) for c in rep_cols]
+
+    per_pos = {}
+    for _, row in df.iterrows():
+        pos = to_float(row.get("pos", ""))
+        wt = (row.get("wt_aa", "") or "").strip()
+        mut = (row.get("mut_aa", "") or "").strip()
+        if pos is None or not wt or not mut:
+            continue
+        pos = int(pos)
+        is_stop = mut == "*" or (row.get("stop", "").strip() not in ("", "0"))
+        per_pos.setdefault(pos, {"wt": wt, "variants": []})["variants"].append({
+            "mut": mut,
+            "fitness": to_float(row.get(fit_col, "")) if fit_col else None,
+            "sd": to_float(row.get(sd_col, "")) if sd_col else None,
+            "reps": [to_float(row.get(c, "")) for c in rep_cols],
+            "n_out": sum((to_float(row.get(c, "")) or 0) for c in out_cols),
+            "syn": mut == wt, "stop": bool(is_stop),
+        })
+    return per_pos, rep_ids
+
+
+def load_counts(paths):
+    """Aggregate coverage / counts / counts_per_cov across all sample CSVs.
+
+    Returns two dicts: per position ``{pos: {...}}`` (used to colour the structure) and per variant
+    ``{(pos, mut): {...}}`` (used for the substitution-specific bee-swarm distributions).
+    """
+    frames = []
+    for p in paths:
+        try:
+            frames.append(pd.read_csv(p))
+        except Exception as exc:  # pragma: no cover - defensive
+            print(f"WARN: could not read {p}: {exc}", file=sys.stderr)
+    if not frames:
+        return {}, {}
+    df = pd.concat(frames, ignore_index=True)
+    # The "A1M"-style WT/pos/mut label lives in the `pos_mut` column
+    # (`aa_mut` holds e.g. "M:A>M"). Fall back to aa_mut only if pos_mut is absent.
+    label_col = "pos_mut" if "pos_mut" in df.columns else ("aa_mut" if "aa_mut" in df.columns else None)
+    if label_col is None:
+        return {}, {}
+    cols = (("coverage", "cov"), ("counts", "counts"), ("counts_per_cov", "counts_per_cov"),
+            ("counts_raw", "counts_raw"))
+    pos_acc, var_acc = {}, {}
+    for _, row in df.iterrows():
+        m = MUT_RE.match(str(row.get(label_col, "")).strip())
+        if not m:
+            continue
+        pos, mut = int(m.group(2)), m.group(3)
+        pa = pos_acc.setdefault(pos, {k: [] for k, _ in cols})
+        va = var_acc.setdefault((pos, mut), {k: [] for k, _ in cols})
+        for key, src in cols:
+            v = to_float(row.get(src, ""))
+            if v is not None:
+                pa[key].append(v)
+                va[key].append(v)
+
+    def meaned(acc):
+        return {k: {kk: (sum(vv) / len(vv) if vv else None) for kk, vv in a.items()}
+                for k, a in acc.items()}
+
+    return meaned(pos_acc), meaned(var_acc)
+
+
+class args:
+    # Filled in by Nextflow when this template is staged and run by the VARIANT_EFFECT_INSPECTION_TOOL process.
+    pdb = "$pdb"
+    fitness = "$fitness_tsv"
+    aa_seq = "$aa_seq"
+    counts = "$variant_counts".split()          # collected per-sample CSVs (staged as vc*/*)
+    threedmol = "$threedmol_js"
+    template = "$template_html"
+    sample = "${meta.sample ?: meta.id}"
+    out_html = "variant_effect_inspection_tool.html"
+    out_json = "structure_data.json"
+
+
+def main():
+
+    wt_seq = Path(args.aa_seq).read_text().strip().replace("\\n", "").replace("\\r", "")
+    pdb_text, struct_residues = parse_pdb(args.pdb)
+    off0, identity = best_alignment(wt_seq, struct_residues)
+    struct_by_index = struct_residues  # 0-based list
+
+    fit_per_pos, rep_ids = load_fitness(args.fitness)
+    # per-position aggregate (structure colour) and per-variant values (substitution-specific swarm)
+    counts_per_pos, counts_per_var = load_counts(sorted(args.counts))  # sorted -> deterministic
+
+    # ---- candidate metrics (dropped automatically when a dataset has no data for them) ----
+    # scope="variant": has a per-substitution value (used in the bee-swarm); the position mean is
+    # still what colours the structure. reverse=True flips the colour ramp (low -> red, high -> blue).
+    METRIC_SPECS = [
+        ("fitness", dict(label="Fitness (nf-core/deepmutscan)", type="diverging", scope="variant",
+                         reverse=True,
+                         desc="Fitness per substitution; the position mean is shown on the structure")),
+        ("fitness_sd", dict(label="Fitness uncertainty (SD)", type="sequential", scope="variant",
+                            desc="Standard deviation of the fitness estimate (lower = more confident)")),
+        ("accuracy", dict(label="Prediction accuracy (pLDDT)", type="plddt", scope="residue", fixed=[0, 100],
+                          desc="Per-residue model confidence from the structure B-factor")),
+        ("coverage", dict(label="Coverage", type="sequential", scope="variant",
+                          desc="Sequencing coverage of this residue (the same for all substitutions at one residue)")),
+        ("counts", dict(label="Counts", type="sequential", scope="variant",
+                        desc="Variant read counts per substitution (position mean shown on the structure)")),
+        ("counts_per_cov", dict(label="Counts / coverage", type="sequential", scope="variant",
+                                desc="Counts normalised by coverage, per substitution (position mean shown on the structure)")),
+        ("n_substitutions", dict(label="Measured substitutions", type="sequential", scope="residue",
+                                 desc="Number of substitutions with a measured fitness at the position")),
+        ("error_bias", dict(label="Positional error bias", type="sequential", scope="residue",
+                            desc="Fraction of counts removed by sequencing-error correction at this position")),
+    ]
+    variant_metrics = {k for k, s in METRIC_SPECS if s["scope"] == "variant"}
+
+    def mean(xs):
+        return (sum(xs) / len(xs)) if xs else None
+
+    residues = []
+    for i, wt_aa in enumerate(wt_seq):  # i is 0-based; position = i + 1
+        pos = i + 1
+        k = off0 + i
+        srec = struct_by_index[k] if 0 <= k < len(struct_by_index) else None
+        variants = fit_per_pos.get(pos, {}).get("variants", [])
+        miss = [v for v in variants if not v["syn"] and not v["stop"]]
+        fit_vals = [v["fitness"] for v in miss if v["fitness"] is not None]
+        sd_vals = [v["sd"] for v in miss if v["sd"] is not None]
+        cp = counts_per_pos.get(pos, {})
+        craw, cnow = cp.get("counts_raw"), cp.get("counts")
+        error_bias = (1.0 - cnow / craw) if (craw not in (None, 0) and cnow is not None) else None
+        res_metrics = {
+            "fitness": r6(mean(fit_vals)),
+            "fitness_sd": r6(mean(sd_vals)),
+            "accuracy": r6(srec["plddt"]) if srec else None,
+            "coverage": r6(cp.get("coverage")),
+            "counts": r6(cp.get("counts")),
+            "counts_per_cov": r6(cp.get("counts_per_cov")),
+            "n_substitutions": (len(fit_vals) or None),
+            "error_bias": r6(error_bias),
+        }
+        vlist = []
+        for v in sorted(variants, key=lambda v: AA_ORDER.index(v["mut"]) if v["mut"] in AA_ORDER else 99):
+            vc = counts_per_var.get((pos, v["mut"]), {})
+            vlist.append({
+                "mut": v["mut"], "syn": v["syn"], "stop": v["stop"], "n_out": r6(v["n_out"]),
+                # per-replicate fitness, so the viewer can re-derive mean/SD for a chosen subset
+                "reps": [r6(x) if x is not None else None for x in v["reps"]],
+                "metrics": {
+                    "fitness": r6(v["fitness"]), "fitness_sd": r6(v["sd"]),
+                    "coverage": r6(vc.get("coverage")), "counts": r6(vc.get("counts")),
+                    "counts_per_cov": r6(vc.get("counts_per_cov")),
+                },
+            })
+        residues.append({
+            "pos": pos, "wt": wt_aa,
+            "resnum": srec["resnum"] if srec else None,
+            "mapped": bool(srec and srec["aa"] == wt_aa),
+            "metrics": res_metrics, "variants": vlist,
+        })
+
+    # keep only metrics that actually have data (generality across diverse datasets)
+    metrics = {}
+    for key, spec in METRIC_SPECS:
+        vals = [r["metrics"].get(key) for r in residues if r["metrics"].get(key) is not None]
+        if not vals:
+            continue
+        if "fixed" in spec:
+            mrange = spec["fixed"]
+        elif spec["type"] == "diverging":
+            a = max(abs(min(vals)), abs(max(vals))) or 1
+            mrange = [r6(-a), r6(a)]
+        else:
+            lo, hi = min(vals), max(vals)
+            mrange = [r6(lo), r6(hi if hi != lo else lo + 1)]
+        metrics[key] = {"label": spec["label"], "type": spec["type"], "scope": spec["scope"],
+                        "range": mrange, "desc": spec["desc"]}
+        if spec.get("reverse"):
+            metrics[key]["reverse"] = True
+
+    present = set(metrics)
+    for r in residues:
+        r["metrics"] = {k: v for k, v in r["metrics"].items() if k in present}
+        for v in r["variants"]:
+            v["metrics"] = {k: val for k, val in v["metrics"].items()
+                            if k in present and k in variant_metrics}
+
+    data = {
+        "sample": args.sample,
+        "sequence": wt_seq,
+        "alignment": {"offset": off0, "identity": round(identity, 4),
+                      "n_residues_structure": len(struct_residues)},
+        "replicates": rep_ids,
+        "metrics": metrics,
+        "residues": residues,
+    }
+
+    Path(args.out_json).write_text(json.dumps(data, indent=2))
+
+    threedmol_js = Path(args.threedmol).read_text()
+    template = Path(args.template).read_text()
+    html = (template
+            .replace("/*__THREEDMOL_JS__*/", threedmol_js)
+            .replace('"__PDB_TEXT__"', json.dumps(pdb_text))
+            .replace("/*__DATA_JSON__*/", json.dumps(data))
+            .replace("__SAMPLE__", args.sample))
+    Path(args.out_html).write_text(html)
+
+    Path("versions.yml").write_text(
+        '"${task.process}":\\n'
+        + "    python: " + platform.python_version() + "\\n"
+        + "    pandas: " + pd.__version__ + "\\n"
+        + "    numpy: " + np.__version__ + "\\n"
+    )
+    print(f"Wrote {args.out_html} (alignment offset={off0}, identity={identity:.2%}, "
+          f"{len(residues)} residues)")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/modules/local/variantcounting/.conda-lock/linux_amd64-bd-f223db6b9aa476d1_1.txt b/modules/local/variantcounting/.conda-lock/linux_amd64-bd-f223db6b9aa476d1_1.txt
new file mode 100644
index 0000000..d770317
--- /dev/null
+++ b/modules/local/variantcounting/.conda-lock/linux_amd64-bd-f223db6b9aa476d1_1.txt
@@ -0,0 +1,1493 @@
+version: 6
+environments:
+  default:
+    channels:
+    - url: https://conda.anaconda.org/conda-forge/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
+      linux-64:
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_3.conda
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+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.9.14-h2aa3ae6_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.14.2-hb03c661_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.3.2-h720e601_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.7.1-h6ffeea8_4.conda
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+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.27.3-h6f4d18d_1.conda
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diff --git a/modules/local/variantcounting/.conda-lock/linux_arm64-bd-4cc0c5d536e48053_1.txt b/modules/local/variantcounting/.conda-lock/linux_arm64-bd-4cc0c5d536e48053_1.txt
new file mode 100644
index 0000000..72104a2
--- /dev/null
+++ b/modules/local/variantcounting/.conda-lock/linux_arm64-bd-4cc0c5d536e48053_1.txt
@@ -0,0 +1,1406 @@
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+  - tzdata
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+  license: Python-2.0
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+  - cpython 3.12.16.*
+  - python_abi * *_cp312
+  license: Python-2.0
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+  license: BSD-3-Clause
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+  - libstdcxx >=15
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+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/zstd-1.5.7-h9d15635_7.conda
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diff --git a/modules/local/variantcounting/environment.yml b/modules/local/variantcounting/environment.yml
new file mode 100644
index 0000000..5e82e39
--- /dev/null
+++ b/modules/local/variantcounting/environment.yml
@@ -0,0 +1,14 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+# polars-lts-cpu (no AVX2 requirement) so the amd64 image also runs under emulation on Apple Silicon.
+# Installed from conda-forge rather than pip, so the container can be built from this file.
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::pysam=0.24.0
+  - conda-forge::biopython=1.87
+  - conda-forge::numpy=2.5.1
+  - conda-forge::polars-lts-cpu=1.33.1
+  - conda-forge::pyarrow=24.0.0
+  - conda-forge::python=3.12.13
diff --git a/modules/local/variantcounting/main.nf b/modules/local/variantcounting/main.nf
new file mode 100644
index 0000000..40315bc
--- /dev/null
+++ b/modules/local/variantcounting/main.nf
@@ -0,0 +1,39 @@
+process VARIANTCOUNTING {
+    tag "$meta.id"
+    label 'process_high'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/62/6247bcc77159c0adea94e0fdc439006f94d499e9d32004470fa7e402ede08538/data'
+        : 'community.wave.seqera.io/library/pysam_biopython_numpy_polars-lts-cpu_pruned:f223db6b9aa476d1'}"
+
+    input:
+    tuple val(meta), path(bam), path(bai)
+    path fasta
+    val reading_frame
+    val base_qual
+    val min_flank
+
+    output:
+    tuple val(meta), path("*.variant_counts.tsv")          , emit: variant_counts
+    path "variant_counts_columns.tsv"                      , emit: columns
+    path "versions.yml"                                    , emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'count_variants.py'
+
+    stub:
+    """
+    touch ${meta.id}.variant_counts.tsv
+    touch variant_counts_columns.tsv
+
+    cat <<-END_VERSIONS > versions.yml
+    "${task.process}":
+        variantcounting: stub
+    END_VERSIONS
+    """
+}
diff --git a/modules/local/variantcounting/meta.yml b/modules/local/variantcounting/meta.yml
new file mode 100644
index 0000000..4688b2b
--- /dev/null
+++ b/modules/local/variantcounting/meta.yml
@@ -0,0 +1,95 @@
+name: "variantcounting"
+description: Count single- and multi-nucleotide variants per read against a reference ORF, producing a GATK-AnalyzeSaturationMutagenesis-compatible variant count table.
+keywords:
+  - deep mutational scanning
+  - variant calling
+  - saturation mutagenesis
+  - counting
+tools:
+  - pysam:
+      description: Python interface for reading/manipulating SAM/BAM alignment files (htslib).
+      homepage: https://pysam.readthedocs.io
+      licence: ["MIT"]
+  - polars:
+      description: Fast DataFrame library.
+      homepage: https://pola.rs
+      licence: ["MIT"]
+  - biopython:
+      description: Tools for biological computation (sequence translation).
+      homepage: https://biopython.org
+      licence: ["BSD-3-Clause"]
+
+input:
+  - - meta:
+        type: map
+        description: Groovy Map containing sample information, e.g. `[ id:'sample1' ]`
+    - bam:
+        type: file
+        description: Coordinate-sorted, indexed BAM of merged reads aligned to the reference.
+        pattern: "*.bam"
+    - bai:
+        type: file
+        description: BAM index (staged alongside the BAM).
+        pattern: "*.{bai,csi}"
+  - - fasta:
+        type: file
+        description: Reference FASTA containing the ORF.
+        pattern: "*.{fa,fasta}"
+  - - reading_frame:
+        type: string
+        description: ORF range as 1-based inclusive `start-stop` (GATK `--orf` convention), e.g. `352-1383`.
+  - - base_qual:
+        type: integer
+        description: Minimum base quality for a mutation to be counted.
+  - - min_flank:
+        type: integer
+        description: Minimum distance (nt) of a mutation/covered base from a read edge; 0 disables edge trimming.
+
+output:
+  - variant_counts:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - "*.variant_counts.tsv":
+          type: file
+          description: Tab-separated variant count table (no header), GATK-compatible 9 columns.
+          pattern: "*.variant_counts.tsv"
+  - columns:
+      - variant_counts_columns.tsv:
+          type: file
+          description: Concise explanation of each output column.
+          pattern: "variant_counts_columns.tsv"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+maintainers:
+  - "@BenjaminWehnert1008"
+containers:
+  docker:
+    linux/amd64:
+      name: community.wave.seqera.io/library/pysam_biopython_numpy_polars-lts-cpu_pruned:f223db6b9aa476d1
+      build_id: bd-f223db6b9aa476d1_1
+      scan_id: sc-7694d04e5969b2e6_1
+    linux/arm64:
+      name: community.wave.seqera.io/library/pysam_biopython_numpy_polars-lts-cpu_pruned:4cc0c5d536e48053
+      build_id: bd-4cc0c5d536e48053_1
+      scan_id: sc-555693b2cbde3457_1
+  singularity:
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/pysam_biopython_numpy_polars-lts-cpu_pruned:52b881e1947dfdb0
+      build_id: bd-52b881e1947dfdb0_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/62/6247bcc77159c0adea94e0fdc439006f94d499e9d32004470fa7e402ede08538/data
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/pysam_biopython_numpy_polars-lts-cpu_pruned:9df342605773fef4
+      build_id: bd-9df342605773fef4_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/00/009b2a9e9a23d373d35a9afb3e353449f962bcc5511cb56aa3a286b942b0d514/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/variantcounting/.conda-lock/linux_amd64-bd-f223db6b9aa476d1_1.txt
+    linux/arm64:
+      lock_file: modules/local/variantcounting/.conda-lock/linux_arm64-bd-4cc0c5d536e48053_1.txt
diff --git a/modules/local/variantcounting/templates/count_variants.py b/modules/local/variantcounting/templates/count_variants.py
new file mode 100644
index 0000000..dfab8ba
--- /dev/null
+++ b/modules/local/variantcounting/templates/count_variants.py
@@ -0,0 +1,542 @@
+#!/usr/bin/env python
+
+# Nextflow template: values below are interpolated by Nextflow. The whole file is compiled
+# as one Groovy GString, so every backslash must be doubled and no triple-quoted strings
+# are allowed anywhere in this file, including comments.
+
+#-- import modules --#
+import os
+import sys
+import re
+import gc
+import platform
+import pysam
+import numpy as np
+import pyarrow as pa
+import polars as pl
+import Bio
+from Bio import SeqIO
+from Bio.Seq import Seq
+from datetime import datetime
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from collections import defaultdict, deque
+
+#-- functions --#
+def init_bam(bam_path):
+    global bam_file_cov
+    bam_file_cov = pysam.AlignmentFile(bam_path, "rb")
+
+def get_base_cov(chrom, start, end, qual, min_flank):
+    if min_flank and min_flank > 0:
+        cov = defaultdict(int)
+        for read in bam_file_cov.fetch(chrom, start, end):
+            if read.is_unmapped:
+                continue
+            quals = read.query_qualities
+            qlen = read.query_length
+            if quals is None or qlen is None:
+                continue
+            lo = min_flank
+            hi = qlen - min_flank
+            for qpos, refpos in read.get_aligned_pairs(matches_only=True):
+                if refpos is None or qpos is None:
+                    continue
+                if refpos < start or refpos >= end:
+                    continue
+                if qpos < lo or qpos >= hi:
+                    continue
+                if quals[qpos] < qual:
+                    continue
+                cov[refpos] += 1
+        return dict(cov)
+
+    A, C, G, T = bam_file_cov.count_coverage(chrom, start, end, quality_threshold = qual)
+    coverage_array = np.array(A, dtype=np.uint32) + \\
+                     np.array(C, dtype=np.uint32) + \\
+                     np.array(G, dtype=np.uint32) + \\
+                     np.array(T, dtype=np.uint32)
+
+    # key on 0-based ref position to match ref_pos lookups in gatk_formating
+    return {start + pos: int(cov) for pos, cov in enumerate(coverage_array)}
+
+def chunk_ranges(start, end, sub_region_size):
+    for s in range(start, end, sub_region_size):
+        yield s, min(s + sub_region_size, end)
+
+def get_base_cov_in_chunk(bam_path, chrom, start, end, qual, sub_region_size, threads, min_flank):
+    chunks = list(chunk_ranges(start, end, sub_region_size))
+    dicts = []
+
+    with ProcessPoolExecutor(max_workers = threads, initializer = init_bam, initargs = (bam_path,)) as executor:
+        futures = [
+            executor.submit(get_base_cov, chrom, s, e, qual, min_flank)
+            for s, e in chunks
+        ]
+
+        for f in as_completed(futures):
+            dicts.append(f.result())
+
+    merged_dict = {}
+    for d in dicts:
+        merged_dict.update(d)
+
+    return merged_dict
+
+# Worker state is passed explicitly through initargs rather than inherited from the parent
+# address space. Relying on inheritance only works under the 'fork' start method: with 'spawn'
+# (macOS) or 'forkserver' (the Python >= 3.14 default on Linux) the child re-imports this file
+# with __name__ != "__main__", so names bound in the main block would be undefined.
+def init_worker(base_cov, codons):
+    global global_dict_base_cov, global_codon_dict
+    global_dict_base_cov = base_cov
+    global_codon_dict = codons
+
+def extract_read_info(read):
+    read_seq = read.query_sequence
+    read_qual = read.query_qualities
+
+    return {
+        'ref':   read.reference_name,
+        'pos':   read.reference_start,
+        'seq':   read_seq,
+        'qual':  read_qual,
+        'md':    read.get_tag('MD'),
+        'cigar': read.cigartuples
+    }
+
+def parse_md(read):
+    read_pos = read.get('pos')
+    read_md = read.get('md')
+    md_pattern = re.finditer(r'(\\d+)|([A-Z]+)|(\\^[A-Z]+)', read_md)
+
+    base_ref_pos = read_pos
+
+    variants = []
+    for match in md_pattern:
+        if match.group(1):
+            num_matches = int(match.group(1))
+            base_ref_pos += num_matches
+        elif match.group(2):
+            for base in match.group(2):
+                variants.append(('X', base_ref_pos, base))
+                base_ref_pos += 1
+        elif match.group(3):
+            deleted_bases = match.group(3)[1:]
+            for base in deleted_bases:
+                variants.append(('D', base_ref_pos, base))
+                base_ref_pos += 1
+
+    return variants
+
+def gatk_formating(variants, orf_start):
+    if not variants:
+        return 0, 0, "", 0, "", "", ""
+
+    varying_bases = 0
+    varying_codons = 0
+    base_mut = ""
+    codon_mut = ""
+    aa_mut = ""
+    pos_mut = ""
+
+    base_pos = [ v['ref_pos'] for v in variants if v['var_type'] == 'X' ]
+    base_cov = [ global_dict_base_cov.get(pos, 0) for pos in base_pos ]
+    base_cov_avg = int(np.mean(base_cov)) if base_cov else 0
+
+    base_variants = [ v for v in variants if v['var_type'] == 'X' ]
+    varying_bases = len(base_variants)
+
+    # base_mut positions are 1-based to match GATK AnalyzeSaturationMutagenesis
+    base_mut = ", ".join(
+        f"{v['ref_pos'] + 1}:{v['ref_base']}>{v['alt_base']}"
+        for v in base_variants
+    )
+
+    codon_changes = defaultdict(list)
+    for v in base_variants:
+        codon_idx = ((v['ref_pos'] - orf_start) // 3) + 1
+        codon_changes[codon_idx].append(v)
+
+    varying_codons = len(codon_changes)
+
+    codon_mut_list = []
+    aa_mut_list = []
+    pos_mut_list = []
+
+    for codon_idx in sorted(codon_changes):
+        vars_in_codon = codon_changes[codon_idx]
+        # parse_read already resolved the reference codon for every variant it kept, keyed on the
+        # read's own reference name - reuse it instead of a second lookup against a global.
+        ref_codon = str(vars_in_codon[0]['ref_codon'])
+        codon_list = list(ref_codon)
+
+        for v in vars_in_codon:
+            pos_in_codon = (v['ref_pos'] - orf_start) % 3
+            codon_list[pos_in_codon] = v['alt_base']
+
+        alt_codon = "".join(codon_list)
+        codon_mut_list.append(f"{codon_idx}:{ref_codon}>{alt_codon}")
+
+        ref_aa = str(Seq(ref_codon).translate())
+        alt_aa = str(Seq(alt_codon).translate())
+
+        if alt_aa == ref_aa:
+            mut_type = "S"
+        elif alt_aa == "*":
+            mut_type = "N"
+        else:
+            mut_type = "M"
+
+        # GATK writes the stop codon as X, not *
+        ref_aa_s = "X" if ref_aa == "*" else ref_aa
+        alt_aa_s = "X" if alt_aa == "*" else alt_aa
+
+        aa_mut_list.append(f"{mut_type}:{ref_aa_s}>{alt_aa_s}")
+        pos_mut_list.append(f"{ref_aa_s}{codon_idx}{alt_aa_s}")
+
+    codon_mut = ", ".join(codon_mut_list)
+    aa_mut = ", ".join(aa_mut_list)
+    pos_mut = ";".join(pos_mut_list)
+
+    return base_cov_avg, varying_bases, base_mut, varying_codons, codon_mut, aa_mut, pos_mut
+
+def parse_read(read, orf_start, orf_end, base_qual, min_flank):
+    read_ref = read.get('ref')
+    read_pos = read.get('pos')
+    read_seq = read.get('seq')
+    read_qual = read.get('qual')
+    read_cigar = read.get('cigar')
+    read_md_variants = parse_md(read)
+
+    if not read_md_variants:
+        return []
+
+    qlen = len(read_seq)
+    base_ref_pos = read_pos
+    base_read_pos = 0
+    md_index = 0
+    variants = []
+    for op, op_len in read_cigar:
+        if op == 0: # M, match
+            for i in range(op_len):
+                if md_index < len(read_md_variants) and read_md_variants[md_index][1] == base_ref_pos:
+                    var_type, _, ref_base = read_md_variants[md_index]
+                    alt_base = read_seq[base_read_pos]
+                    alt_qual = read_qual[base_read_pos]
+                    alt_idx = (base_ref_pos - orf_start) % 3 + 1
+                    codon_idx = ((base_ref_pos - orf_start) // 3) + 1
+                    variants.append({ 'var_type':  var_type,
+                                      'ref_name':  read_ref,
+                                      'ref_pos':   base_ref_pos,
+                                      'ref_base':  ref_base,
+                                      'alt_base':  alt_base,
+                                      'alt_qual':  alt_qual,
+                                      'alt_idx' :  alt_idx,
+                                      'read_idx':  base_read_pos,
+                                      'codon_idx': codon_idx })
+                    md_index += 1
+                base_ref_pos += 1
+                base_read_pos += 1
+        elif op == 1: # I, insertion (reads with insertions or deletions are dropped upstream, in MERGE_ALIGN)
+            base_read_pos += op_len
+        elif op == 2: # D, deletion
+            for i in range(op_len):
+                if md_index < len(read_md_variants) and read_md_variants[md_index][1] == base_ref_pos:
+                    var_type, _, ref_base = read_md_variants[md_index]
+                    alt_idx = (base_ref_pos - orf_start) % 3 + 1
+                    codon_idx = ((base_ref_pos - orf_start) // 3) + 1
+                    variants.append({ 'var_type':  'D',
+                                      'ref_name':  read_ref,
+                                      'ref_pos':   base_ref_pos,
+                                      'ref_base':  ref_base,
+                                      'alt_base':  '-',
+                                      'alt_qual':  99,
+                                      'alt_idx' :  alt_idx,
+                                      'read_idx':  base_read_pos,
+                                      'codon_idx': codon_idx })
+                    md_index += 1
+                base_ref_pos += 1
+        elif op == 3: # N, skip
+            base_ref_pos += op_len
+        elif op == 4: # S, softclip
+            base_read_pos += op_len
+        elif op == 5: # H, hardclip
+            continue
+
+    if not variants:
+        return []
+
+    variants_filtered = []
+    for var in variants:
+        if var['ref_pos'] >= orf_start and var['ref_pos'] <= orf_end:
+            if var['alt_qual'] >= base_qual:
+                # read-edge (min-flanking-length) filter
+                if min_flank > 0:
+                    read_idx = var['read_idx']
+                    if read_idx < min_flank or read_idx >= qlen - min_flank:
+                        continue
+                codons_tmp = global_codon_dict[var['ref_name']]
+                ref_codon = codons_tmp[var['codon_idx']]
+                variants_filtered.append({ 'var_type':  var['var_type'],
+                                           'ref_name':  var['ref_name'],
+                                           'ref_pos':   var['ref_pos'],
+                                           'ref_base':  var['ref_base'],
+                                           'alt_base':  var['alt_base'],
+                                           'alt_qual':  var['alt_qual'],
+                                           'alt_idx' :  var['alt_idx'],
+                                           'codon_idx': var['codon_idx'],
+                                           'ref_codon': ref_codon })
+
+    return gatk_formating(variants_filtered, orf_start)
+
+def batch_parse_reads(batch_reads, orf_start, orf_end, base_qual, min_flank):
+    cols = {
+        "base_cov_avg": [],
+        "varying_bases": [],
+        "base_mut": [],
+        "varying_codons": [],
+        "codon_mut": [],
+        "aa_mut": [],
+        "pos_mut": []
+    }
+
+    for read in batch_reads:
+        result = parse_read(read, orf_start, orf_end, base_qual, min_flank)
+
+        if not result or result[2] == "":
+            continue
+
+        cols["base_cov_avg"].append(result[0])
+        cols["varying_bases"].append(result[1])
+        cols["base_mut"].append(result[2])
+        cols["varying_codons"].append(result[3])
+        cols["codon_mut"].append(result[4])
+        cols["aa_mut"].append(result[5])
+        cols["pos_mut"].append(result[6])
+
+    if not cols["base_mut"]:
+        return None
+
+    return pa.table(cols)
+
+def function_for_processpool(args_tuple):
+    return batch_parse_reads(*args_tuple)
+
+def aggregate_batch(table):
+    # Collapse a finished worker batch to one row per distinct variant straight away. Holding one
+    # row per variant-carrying read until the very end made peak memory scale with read depth;
+    # aggregating here bounds it by the number of distinct variants instead.
+    df = pl.from_arrow(table)
+    df = df.filter(pl.col("base_mut") != "")
+    if df.height == 0:
+        return None
+    return ( df.with_columns(pl.lit(1).alias("counts"))
+               .group_by("base_mut")
+               .agg([pl.col("counts").sum().alias("counts"),
+                     pl.all().exclude(["base_mut", "counts"]).first()]) )
+
+def fold_counts(acc, new):
+    # Every column other than counts is a function of base_mut, so first() is value-stable here.
+    if acc is None:
+        return new
+    return ( pl.concat([acc, new], how = "vertical")
+               .group_by("base_mut")
+               .agg([pl.col("counts").sum().alias("counts"),
+                     pl.all().exclude(["base_mut", "counts"]).first()]) )
+
+def read_bam_in_chunk(bam_path, orf_start, orf_end, base_qual, min_flank, batch_size, threads, base_cov, codons):
+    # Keep roughly two batches per worker in flight. The previous version submitted one batch,
+    # waited for it, then read the next, so only a single worker was ever busy no matter how many
+    # cpus the task was given.
+    max_inflight = max(2, threads * 2)
+
+    with pysam.AlignmentFile(bam_path, "rb", threads = threads) as bam_file, \\
+        ProcessPoolExecutor(max_workers = threads,
+                            initializer = init_worker,
+                            initargs = (base_cov, codons)) as executor:
+
+        inflight = deque()
+        batch = []
+
+        def submit(reads):
+            inflight.append(
+                executor.submit(function_for_processpool,
+                                (reads, orf_start, orf_end, base_qual, min_flank))
+            )
+
+        def drain_one():
+            result = inflight.popleft().result()
+            if result is None:
+                return None
+            return aggregate_batch(result)
+
+        for read in bam_file.fetch(until_eof=True):
+            if read.is_unmapped or read.is_secondary or read.is_supplementary or not read.has_tag('MD'):
+                continue
+
+            # perfectly-aligned (wildtype) reads carry no variants
+            if re.fullmatch(r'\\d+', read.get_tag('MD')):
+                continue
+
+            batch.append(extract_read_info(read))
+
+            if len(batch) >= batch_size:
+                submit(batch)
+                batch = []
+                while len(inflight) >= max_inflight:
+                    agg = drain_one()
+                    if agg is not None:
+                        yield agg
+
+        if batch:
+            submit(batch)
+            batch = []
+
+        while inflight:
+            agg = drain_one()
+            if agg is not None:
+                yield agg
+
+def write_column_sheet(path):
+    rows = [
+        ("column", "description"),
+        ("counts", "Number of reads supporting this variant (observations)"),
+        ("cov", "Mean sequencing coverage across the variant's mutated base positions"),
+        ("mean_length_variant_reads", "Placeholder (0); mean read length of supporting reads is not computed"),
+        ("varying_bases", "Number of nucleotide substitutions in the variant"),
+        ("base_mut", "Nucleotide change(s), 1-based reference position(s), e.g. 352:G>A"),
+        ("varying_codons", "Number of codons altered by the variant"),
+        ("codon_mut", "Codon change(s), 1-based codon index, e.g. 1:GCT>ACT"),
+        ("aa_mut", "Amino-acid change per codon; type S=synonymous, M=missense, N=nonsense; stop=X, e.g. M:A>T"),
+        ("pos_mut", "Compact per-codon amino-acid change, e.g. A1T (stop written as X)"),
+    ]
+    with open(path, "w") as fh:
+        for name, desc in rows:
+            fh.write(name + "\\t" + desc + "\\n")
+
+#-- Nextflow-interpolated arguments --#
+class args:
+    input_bam  = "$bam"
+    reference  = "$fasta"
+    orf_range  = "$reading_frame"          # 1-based inclusive (GATK --orf convention)
+    base_qual  = int("$base_qual")
+    min_flank  = int("$min_flank")
+    threads    = max(1, int("$task.cpus"))
+    batch_size = 5000
+    out_file   = "${meta.id}.variant_counts.tsv"
+
+#-- main execution --#
+if __name__ == "__main__":
+    write_column_sheet("variant_counts_columns.tsv")
+
+    # orf_range is 1-based inclusive (GATK --orf convention); convert to 0-based
+    orf_start_1, orf_end_1 = map(int, args.orf_range.split('-'))
+    orf_start = orf_start_1 - 1
+    orf_end   = orf_end_1 - 1
+
+    ref_dict = SeqIO.to_dict(SeqIO.parse(args.reference, "fasta"))
+
+    orf_dict = {}
+    codon_dict = {}
+
+    if os.path.exists(args.out_file):
+        os.remove(args.out_file)
+
+    print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} Get the reference codons, please wait...", flush = True)
+    for chrom, record in ref_dict.items():
+        orf_seq = record.seq[orf_start : orf_end + 1]
+        orf_dict[chrom] = orf_seq
+        ref_codons = {}
+        for i in range(0, len(orf_seq) - 2, 3):
+            codon_idx = i // 3 + 1 # 1-based codon index
+            ref_codon = orf_seq[i:i + 3]
+            if len(ref_codon) == 3:
+                ref_codons[codon_idx] = ref_codon
+        codon_dict[chrom] = ref_codons
+
+    del ref_dict
+    gc.collect()
+
+    # The counter is built for a single gene-sized reference. Picking one sequence implicitly
+    # (whatever the dict iteration happened to leave behind) would silently count against the
+    # wrong contig, so require exactly one.
+    if len(codon_dict) != 1:
+        sys.exit(
+            "ERROR: --fasta must contain exactly one sequence (found "
+            + str(len(codon_dict)) + "). deepmutscan counts variants against a single ORF."
+        )
+    ref_name = next(iter(codon_dict))
+
+    print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} Get the base coverage, please wait...", flush = True)
+    sub_region_size = max(1, (orf_end - orf_start + 1) // args.threads)
+    dict_base_cov = get_base_cov_in_chunk(args.input_bam, ref_name, orf_start, orf_end + 1, args.base_qual, sub_region_size, args.threads, args.min_flank)
+
+    # Nothing aligned inside the reading frame at all. Left alone this writes an empty table and
+    # exits 0, and the run only dies much later in process_variant_counts.R with "no lines
+    # available". Common causes: a --reading_frame outside the reference, or single-end input.
+    if not dict_base_cov or sum(dict_base_cov.values()) == 0:
+        sys.exit(
+            "ERROR: no reads pass the quality filters inside the reading frame "
+            + args.orf_range + " of " + ref_name + " for sample ${meta.id}.\\n"
+            + "  Check that --reading_frame lies inside the reference, that the reads really "
+            + "come from this amplicon, and that --base_qual (currently "
+            + str(args.base_qual) + ") is not above the instrument quality of the data."
+        )
+
+    print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} Extracting read information, please wait...", flush = True)
+    df_variants_counts = None
+    for i, batch_counts in enumerate(read_bam_in_chunk(args.input_bam, orf_start, orf_end, args.base_qual, args.min_flank, args.batch_size, args.threads, dict_base_cov, codon_dict)):
+        df_variants_counts = fold_counts(df_variants_counts, batch_counts)
+        if (i + 1) % 100 == 0:
+            print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} |--> Processed batch {i+1}", flush = True)
+    print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} |--> Finished.", flush = True)
+    gc.collect()
+
+    print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} Creating the variant matrix, please wait...", flush = True)
+    if df_variants_counts is None:
+        df_variants_counts = pl.DataFrame([], schema={
+            "base_mut": pl.Utf8,
+            "counts": pl.Int64,
+            "base_cov_avg": pl.Int64,
+            "varying_bases": pl.Int64,
+            "varying_codons": pl.Int64,
+            "codon_mut": pl.Utf8,
+            "aa_mut": pl.Utf8,
+            "pos_mut": pl.Utf8
+        }, orient = "row")
+
+    print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} Creating the output file, please wait...", flush = True)
+    df_variants_counts = df_variants_counts.select([pl.col("counts"),
+                                                    pl.col("base_cov_avg").alias("cov"),
+                                                    pl.lit(0).alias("mean_length_variant_reads"),
+                                                    pl.col("varying_bases"),
+                                                    pl.col("base_mut"),
+                                                    pl.col("varying_codons"),
+                                                    pl.col("codon_mut"),
+                                                    pl.col("aa_mut"),
+                                                    pl.col("pos_mut")])
+    # polars group_by does not promise an output order, and with several worker threads the order
+    # genuinely varies between runs. That makes byte-identical reruns impossible (breaking nf-test
+    # snapshots on correct data) and, worse, makes the error correction - which walks this table in
+    # file order - non-reproducible. Sort on the leading reference position, then on the full
+    # variant string, so the file comes out in genomic order every time.
+    df_variants_counts = ( df_variants_counts
+        .with_columns(pl.col("base_mut").str.extract(r"^(\\d+)", 1).cast(pl.Int64).alias("sort_pos"))
+        .sort(["sort_pos", "base_mut"], nulls_last = True)
+        .drop("sort_pos") )
+
+    # --min_counts is applied at the fitness stage, not here, so the error correction
+    # always sees every false double (they are typically observed only once or twice).
+    # GATK variantCounts has no header line
+    df_variants_counts.write_csv(args.out_file, separator = "\\t", include_header = False)
+    print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} Done.", flush = True)
+
+    #-- versions --#
+    with open("versions.yml", "w") as vf:
+        vf.write('"${task.process}":\\n')
+        vf.write("    python: " + platform.python_version() + "\\n")
+        vf.write("    pysam: " + pysam.__version__ + "\\n")
+        vf.write("    polars: " + pl.__version__ + "\\n")
+        vf.write("    pyarrow: " + pa.__version__ + "\\n")
+        vf.write("    numpy: " + np.__version__ + "\\n")
+        vf.write("    biopython: " + Bio.__version__ + "\\n")
diff --git a/modules/local/visualization/counts_heatmap/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt b/modules/local/visualization/counts_heatmap/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
new file mode 100644
index 0000000..be8d7e8
--- /dev/null
+++ b/modules/local/visualization/counts_heatmap/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
@@ -0,0 +1,2942 @@
+version: 6
+environments:
+  default:
+    channels:
+    - url: https://conda.anaconda.org/conda-forge/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
+      linux-64:
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2
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+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-biocgenerics-0.56.0-r45hdfd78af_2.conda
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+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-seqinfo-1.0.0-r45hdfd78af_0.conda
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diff --git a/modules/local/visualization/counts_heatmap/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt b/modules/local/visualization/counts_heatmap/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
new file mode 100644
index 0000000..10e5993
--- /dev/null
+++ b/modules/local/visualization/counts_heatmap/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
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diff --git a/modules/local/visualization/counts_heatmap/environment.yml b/modules/local/visualization/counts_heatmap/environment.yml
new file mode 100644
index 0000000..5f5785d
--- /dev/null
+++ b/modules/local/visualization/counts_heatmap/environment.yml
@@ -0,0 +1,19 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+# Shared by 17 R modules (all except RUN_MUTSCAN, RUN_DIMSUM and the false-doubles error correction).
+# Keep the copies identical:
+# the one container image these modules use is built from exactly this list.
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/visualization/counts_heatmap/main.nf b/modules/local/visualization/counts_heatmap/main.nf
new file mode 100644
index 0000000..09c9eaf
--- /dev/null
+++ b/modules/local/visualization/counts_heatmap/main.nf
@@ -0,0 +1,30 @@
+process VISUALIZATION_COUNTS_HEATMAP {
+    tag "$meta.id"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7'}"
+
+    input:
+    tuple val(meta), path(variantCounts_for_heatmaps)
+
+    output:
+    tuple val(meta), path("counts_heatmap.pdf"), emit: counts_heatmap
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'counts_heatmap.R'
+
+    stub:
+    """
+    touch counts_heatmap.pdf
+    echo "VISUALIZATION_COUNTS_HEATMAP:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+    """
+}
diff --git a/modules/local/visualization/counts_heatmap/meta.yml b/modules/local/visualization/counts_heatmap/meta.yml
new file mode 100644
index 0000000..5a5181e
--- /dev/null
+++ b/modules/local/visualization/counts_heatmap/meta.yml
@@ -0,0 +1,64 @@
+name: "visualization_counts_heatmap"
+description: Generates a comprehensive heatmap of variant counts, visualizing the mutation landscape across the entire reference sequence.
+keywords:
+  - deep mutational scanning
+  - dms
+  - visualization
+  - heatmap
+  - mutation landscape
+tools: null
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', single_end:false ]`
+    - variantCounts_for_heatmaps:
+        type: file
+        description: CSV file containing processed variant counts formatted for heatmap generation
+        pattern: "*.{csv}"
+
+output:
+  - counts_heatmap:
+      - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. `[ id:'test', single_end:false ]`
+      - counts_heatmap.pdf:
+          type: file
+          description: PDF heatmap visualizing mutation frequencies across the sequence positions
+          pattern: "counts_heatmap.pdf"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+containers:
+  docker:
+    linux/amd64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7
+      build_id: bd-32f1af0a83a018e7_1
+      scan_id: sc-337510c2c910aa1c_1
+    linux/arm64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb
+      build_id: bd-880bbdecb19543bb_1
+      scan_id: sc-09c9d098a09068b9_1
+  singularity:
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb
+      build_id: bd-7202e5d19667bddb_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7
+      build_id: bd-db46247d9810b3e7_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/visualization/counts_heatmap/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
+    linux/arm64:
+      lock_file: modules/local/visualization/counts_heatmap/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
diff --git a/modules/local/visualization/counts_heatmap/templates/counts_heatmap.R b/modules/local/visualization/counts_heatmap/templates/counts_heatmap.R
new file mode 100644
index 0000000..6f70550
--- /dev/null
+++ b/modules/local/visualization/counts_heatmap/templates/counts_heatmap.R
@@ -0,0 +1,187 @@
+#!/usr/bin/env Rscript
+
+# Input: prepared GATK data path, output_path, threshold (same as used for prepare_gatk_data_for_counts_per_cov_heatmap function)
+# Output: counts_per_cov_heatmap.pdf
+
+library(dplyr)
+library(ggplot2)
+
+counts_heatmap <- function(input_csv_path, threshold = 3, output_pdf_path, img_format = "pdf") {
+
+  # Inner function to add padding to the last row, adding 21 amino acids per position
+  pad_heatmap_data_long <- function(heatmap_data_long, min_non_na_value, num_positions_per_row = 75) {
+    all_amino_acids <- c("G", "A", "V", "L", "M", "I", "F",
+                         "Y", "W", "K", "R", "H", "D", "E",
+                         "S", "T", "C", "N", "Q", "P", "*")
+
+    max_position <- max(heatmap_data_long\$position)
+    num_missing_positions <- num_positions_per_row - (max_position %% num_positions_per_row)
+
+    if (num_missing_positions < num_positions_per_row) {
+      new_positions <- (max_position + 1):(max_position + num_missing_positions)
+
+      # Add all 21 amino acid variants for each new position
+      padding_data <- expand.grid(
+        mut_aa = all_amino_acids,  # All possible amino acids
+        position = new_positions  # New positions to be padded
+      )
+
+      # Set placeholder values for the added positions to the exact smallest non-NA value
+      padding_data\$total_counts <- min_non_na_value  # Set to the smallest non-NA value
+      padding_data\$wt_aa <- "Y"  # Set wild-type amino acid to 'Y'
+      padding_data\$wt_aa_pos <- paste0("Y", padding_data\$position)  # Create wt_aa_pos with correct positions
+      padding_data\$row_group <- max(heatmap_data_long\$row_group)  # Set row group to the current last group
+
+      # Add the new padding rows to heatmap_data_long
+      heatmap_data_long <- dplyr::bind_rows(heatmap_data_long, padding_data)
+    }
+
+    return(heatmap_data_long)
+  }
+
+  # Load the CSV data
+  heatmap_data <- read.csv(input_csv_path)
+
+  # Check if the necessary column exists in the data
+  if (!"total_counts" %in% colnames(heatmap_data)) {
+    stop("The column 'total_counts' is not found in the data.")
+  }
+
+  # Create heatmap_data_long by selecting necessary columns
+  heatmap_data_long <- heatmap_data %>%
+    select(mut_aa, position, total_counts, wt_aa)  # Use 'total_counts'
+
+  # Find the smallest non-NA value in total_counts
+  min_non_na_value <- min(heatmap_data_long\$total_counts, na.rm = TRUE)
+
+  # Group positions by rows (75 positions per row) and calculate row_group
+  heatmap_data_long <- heatmap_data_long %>%
+    mutate(row_group = ((position - 1) %/% 75) + 1)  # Grouping positions into rows
+
+  # Apply padding to add missing positions at the end of the last row, using the calculated min value
+  heatmap_data_long <- pad_heatmap_data_long(heatmap_data_long, min_non_na_value)
+
+  # Convert positions to numeric, sort them, and create wt_aa_pos for the plot
+  heatmap_data_long <- heatmap_data_long %>%
+    mutate(position = as.numeric(position)) %>%  # Ensure position is numeric
+    arrange(position) %>%  # Sort by position
+    mutate(wt_aa_pos = factor(paste0(wt_aa, position), levels = unique(paste0(wt_aa, position))))  # Create sorted factor levels for wt_aa_pos
+
+  # Add a column to identify synonymous mutations (where mut_aa == wt_aa)
+  heatmap_data_long <- heatmap_data_long %>%
+    mutate(synonymous = mut_aa == wt_aa)
+
+  # Definiere die korrekte Reihenfolge der Aminosäuren
+  amino_acid_order <- rev(c("G", "A", "V", "L", "M", "I", "F",
+                         "Y", "W", "K", "R", "H", "D", "E",
+                         "S", "T", "C", "N", "Q", "P", "*"))
+
+  heatmap_data_long <- heatmap_data_long %>%
+  mutate(mut_aa = factor(mut_aa, levels = amino_acid_order))
+
+  # Bearbeite heatmap_data_long und erstelle syn_positions gleichzeitig
+  syn_positions <- heatmap_data_long %>%
+    mutate(mut_aa = factor(mut_aa, levels = amino_acid_order),
+           # Berechne die x-Koordinate, die pro Gruppe immer von 1 bis 75 verläuft
+           x = as.numeric(factor(wt_aa_pos, levels = unique(wt_aa_pos))) - ((row_group - 1) * 75),
+           y = as.numeric(factor(mut_aa, levels = amino_acid_order))) %>%
+    filter(synonymous == TRUE)
+
+  # Calculate the number of row groups and adjust plot height dynamically
+  num_row_groups <- max(heatmap_data_long\$row_group)
+  plot_height <- num_row_groups * 4
+
+  # Set the limits for the color scale, ignoring NA (negative values are now NA)
+  min_count <- min(heatmap_data_long\$total_counts, na.rm = TRUE)
+  max_count <- max(heatmap_data_long\$total_counts, na.rm = TRUE)
+  max_position <- max(heatmap_data\$position)
+
+  # Create the heatmap plot with explicit handling for positions > max_position
+  heatmap_plot <- ggplot(heatmap_data_long, aes(x = wt_aa_pos, y = mut_aa, fill = total_counts)) +
+    scale_fill_gradientn(colours = c(alpha("blue", 0), "blue"), na.value = "grey35", trans = "log",  # Apply log transformation to the scale
+                         limits = c(min_count, max_count),
+                         breaks = scales::trans_breaks("log10", function(x) 10^x),  # Logarithmic scale breaks
+                         labels = scales::trans_format("log10", scales::math_format(10^.x))) +
+    scale_x_discrete(labels = function(x) {
+      numeric_pos <- as.numeric(gsub("[^0-9]", "", x))
+      ifelse(numeric_pos > max_position, " ", x)
+    }) +
+    geom_tile() +
+
+    # Add diagonal lines for synonymous mutations using geom_segment
+    geom_segment(data = syn_positions[syn_positions\$position <= max_position, ],
+                 aes(x = x - 0.485, xend = x + 0.485,
+                     y = y - 0.485, yend = y + 0.485, color = synonymous),
+                 size = 0.2) +
+
+    # Manuelle Farbskala für die diagonalen Linien
+    scale_color_manual(values = c("TRUE" = "grey10"), labels = c("TRUE" = "")) +
+
+    theme_minimal() +
+    labs(title = "Heatmap of Counts per Variant", x = "Wild-type Amino Acid", y = "Mutant Amino Acid", fill = "Counts", color = "Synonymous Mutation") +
+    theme(plot.title = element_text(size = 16, face = "bold"),
+          axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 12),
+          axis.text.y = element_text(size = 12),  # Larger y-axis labels
+          axis.title.x = element_text(size = 16),
+          axis.title.y = element_text(size = 16),
+          legend.title = element_text(size = 14),  # Larger legend title
+          legend.text = element_text(size = 12),  # Larger legend text
+          panel.spacing = unit(0.1, "lines"),  # Adjust panel spacing
+          strip.text = element_blank(),  # Remove row group labels (facet numbers)
+          strip.background = element_blank(),
+          panel.grid.major = element_blank(),  # Remove major grid lines
+          panel.grid.minor = element_blank()) +  # Remove minor grid lines
+    facet_wrap(~ row_group, scales = "free_x", ncol = 1) +  # Group by 75 positions per row
+    theme(panel.spacing = unit(0.2, "lines"))
+
+  heatmap_plot <- heatmap_plot +
+    geom_point(data = heatmap_data_long, aes(size = ""), colour = "black", alpha = 0)  # Invisible points for legend
+  heatmap_plot <- heatmap_plot +
+    guides(size = guide_legend(paste("Dropout (Counts <", threshold, ")"), override.aes = list(shape = 15, size = 8, colour = "grey35", alpha = 1)))  # Define Legend for Dropouts
+
+  # Save the heatmap plot
+  if (img_format == "pdf") {
+    ggsave(output_pdf_path, plot = heatmap_plot, width = 16, height = plot_height, dpi = 150, device = cairo_pdf)
+  } else {
+    ggsave(output_pdf_path, plot = heatmap_plot, width = 16, height = plot_height, dpi = 150)
+  }
+
+  if (file.exists(output_pdf_path)) {
+    print("Heatmap image successfully created!")
+  } else {
+    print("Error: Heatmap image was not created.")
+  }
+}
+
+#####
+# run function
+#####
+counts_heatmap(
+  input_csv_path = "$variantCounts_for_heatmaps",
+  threshold = 1,
+  output_pdf_path = "counts_heatmap.pdf",
+  img_format = "pdf"
+)
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+dplyr_version <- as.character(packageVersion("dplyr"))
+ggplot2_version <- as.character(packageVersion("ggplot2"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(dplyr_version) == 0) dplyr_version <- "unknown"
+if (length(ggplot2_version) == 0) ggplot2_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-dplyr:', dplyr_version),
+    paste('    r-ggplot2:', ggplot2_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/visualization/counts_per_cov/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt b/modules/local/visualization/counts_per_cov/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
new file mode 100644
index 0000000..be8d7e8
--- /dev/null
+++ b/modules/local/visualization/counts_per_cov/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
@@ -0,0 +1,2942 @@
+version: 6
+environments:
+  default:
+    channels:
+    - url: https://conda.anaconda.org/conda-forge/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
+      linux-64:
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diff --git a/modules/local/visualization/counts_per_cov/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt b/modules/local/visualization/counts_per_cov/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
new file mode 100644
index 0000000..10e5993
--- /dev/null
+++ b/modules/local/visualization/counts_per_cov/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
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diff --git a/modules/local/visualization/counts_per_cov/environment.yml b/modules/local/visualization/counts_per_cov/environment.yml
new file mode 100644
index 0000000..5f5785d
--- /dev/null
+++ b/modules/local/visualization/counts_per_cov/environment.yml
@@ -0,0 +1,19 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+# Shared by 17 R modules (all except RUN_MUTSCAN, RUN_DIMSUM and the false-doubles error correction).
+# Keep the copies identical:
+# the one container image these modules use is built from exactly this list.
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/visualization/counts_per_cov/main.nf b/modules/local/visualization/counts_per_cov/main.nf
new file mode 100644
index 0000000..0a8c23f
--- /dev/null
+++ b/modules/local/visualization/counts_per_cov/main.nf
@@ -0,0 +1,30 @@
+process VISUALIZATION_COUNTS_PER_COV {
+    tag "$meta.id"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7'}"
+
+    input:
+    tuple val(meta), path(variantCounts_for_heatmaps)
+
+    output:
+    tuple val(meta), path("counts_per_cov_heatmap.pdf"), emit: counts_per_cov_heatmap
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'counts_per_cov_heatmap.R'
+
+    stub:
+    """
+    touch counts_per_cov_heatmap.pdf
+    echo "VISUALIZATION_COUNTS_PER_COV:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+    """
+}
diff --git a/modules/local/visualization/counts_per_cov/meta.yml b/modules/local/visualization/counts_per_cov/meta.yml
new file mode 100644
index 0000000..59873e5
--- /dev/null
+++ b/modules/local/visualization/counts_per_cov/meta.yml
@@ -0,0 +1,70 @@
+name: "visualization_counts_per_cov"
+description: "Generates a heatmap visualizing the distribution of variant counts per coverage depth in Deep Mutational Scanning data."
+keywords:
+  - deep mutational scanning
+  - dms
+  - visualization
+  - heatmap
+  - coverage
+tools:
+  - "r-base":
+      description: "R is a free software environment for statistical computing and graphics"
+      homepage: "https://www.r-project.org/"
+      documentation: "https://cran.r-project.org/manuals.html"
+      licence: ["GPL-2.0-or-later"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', single_end:false ]`
+    - variantCounts_for_heatmaps:
+        type: file
+        description: CSV file containing processed variant counts formatted for heatmap generation
+        pattern: "*.{csv}"
+
+output:
+  - counts_per_cov_heatmap:
+      - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. `[ id:'test', single_end:false ]`
+      - counts_per_cov_heatmap.pdf:
+          type: file
+          description: PDF heatmap showing variant counts distributed across coverage depths
+          pattern: "counts_per_cov_heatmap.pdf"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+containers:
+  docker:
+    linux/amd64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7
+      build_id: bd-32f1af0a83a018e7_1
+      scan_id: sc-337510c2c910aa1c_1
+    linux/arm64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb
+      build_id: bd-880bbdecb19543bb_1
+      scan_id: sc-09c9d098a09068b9_1
+  singularity:
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7
+      build_id: bd-db46247d9810b3e7_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb
+      build_id: bd-7202e5d19667bddb_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/visualization/counts_per_cov/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
+    linux/arm64:
+      lock_file: modules/local/visualization/counts_per_cov/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
diff --git a/modules/local/visualization/counts_per_cov/templates/counts_per_cov_heatmap.R b/modules/local/visualization/counts_per_cov/templates/counts_per_cov_heatmap.R
new file mode 100644
index 0000000..f258a17
--- /dev/null
+++ b/modules/local/visualization/counts_per_cov/templates/counts_per_cov_heatmap.R
@@ -0,0 +1,187 @@
+#!/usr/bin/env Rscript
+
+# Input: prefiltered GATK data path, output_path, threshold (same as used for prepare_gatk_data_for_counts_per_cov_heatmap function)
+# Output: counts_per_cov_heatmap.pdf
+
+library(dplyr)
+library(ggplot2)
+
+counts_per_cov_heatmap <- function(input_csv_path, threshold = 3, output_pdf_path, img_format = "pdf") {
+
+  # Inner function to add padding to the last row, adding 21 amino acids per position
+  pad_heatmap_data_long <- function(heatmap_data_long, min_non_na_value, num_positions_per_row = 75) {
+    all_amino_acids <- c("G", "A", "V", "L", "M", "I", "F",
+                         "Y", "W", "K", "R", "H", "D", "E",
+                         "S", "T", "C", "N", "Q", "P", "*")
+
+    max_position <- max(heatmap_data_long\$position)
+    num_missing_positions <- num_positions_per_row - (max_position %% num_positions_per_row)
+
+    if (num_missing_positions < num_positions_per_row) {
+      new_positions <- (max_position + 1):(max_position + num_missing_positions)
+
+      # Add all 21 amino acid variants for each new position
+      padding_data <- expand.grid(
+        mut_aa = all_amino_acids,  # All possible amino acids
+        position = new_positions  # New positions to be padded
+      )
+
+      # Set placeholder values for the added positions to the exact smallest non-NA value
+      padding_data\$total_counts_per_cov <- min_non_na_value  # Set to the smallest non-NA value
+      padding_data\$wt_aa <- "Y"  # Set wild-type amino acid to 'Y'
+      padding_data\$wt_aa_pos <- paste0("Y", padding_data\$position)  # Create wt_aa_pos with correct positions
+      padding_data\$row_group <- max(heatmap_data_long\$row_group)  # Set row group to the current last group
+
+      # Add the new padding rows to heatmap_data_long
+      heatmap_data_long <- dplyr::bind_rows(heatmap_data_long, padding_data)
+    }
+
+    return(heatmap_data_long)
+  }
+
+  # Load the CSV data
+  heatmap_data <- read.csv(input_csv_path)
+
+  # Check if the necessary column exists in the data
+  if (!"total_counts_per_cov" %in% colnames(heatmap_data)) {
+    stop("The column 'total_counts_per_cov' is not found in the data.")
+  }
+
+  # Create heatmap_data_long by selecting necessary columns
+  heatmap_data_long <- heatmap_data %>%
+    select(mut_aa, position, total_counts_per_cov, wt_aa)  # Use 'total_counts_per_cov'
+
+  # Find the smallest non-NA value in total_counts_per_cov
+  min_non_na_value <- min(heatmap_data_long\$total_counts_per_cov, na.rm = TRUE)
+
+  # Group positions by rows (75 positions per row) and calculate row_group
+  heatmap_data_long <- heatmap_data_long %>%
+    mutate(row_group = ((position - 1) %/% 75) + 1)  # Grouping positions into rows
+
+  # Apply padding to add missing positions at the end of the last row, using the calculated min value
+  heatmap_data_long <- pad_heatmap_data_long(heatmap_data_long, min_non_na_value)
+
+  # Convert positions to numeric, sort them, and create wt_aa_pos for the plot
+  heatmap_data_long <- heatmap_data_long %>%
+    mutate(position = as.numeric(position)) %>%  # Ensure position is numeric
+    arrange(position) %>%  # Sort by position
+    mutate(wt_aa_pos = factor(paste0(wt_aa, position), levels = unique(paste0(wt_aa, position))))  # Create sorted factor levels for wt_aa_pos
+
+  # Add a column to identify synonymous mutations (where mut_aa == wt_aa)
+  heatmap_data_long <- heatmap_data_long %>%
+    mutate(synonymous = mut_aa == wt_aa)
+
+  # Definiere die korrekte Reihenfolge der Aminosäuren
+  amino_acid_order <- rev(c("G", "A", "V", "L", "M", "I", "F",
+                         "Y", "W", "K", "R", "H", "D", "E",
+                         "S", "T", "C", "N", "Q", "P", "*"))
+
+  heatmap_data_long <- heatmap_data_long %>%
+  mutate(mut_aa = factor(mut_aa, levels = amino_acid_order))
+
+  # Bearbeite heatmap_data_long und erstelle syn_positions gleichzeitig
+  syn_positions <- heatmap_data_long %>%
+    mutate(mut_aa = factor(mut_aa, levels = amino_acid_order),
+           # Berechne die x-Koordinate, die pro Gruppe immer von 1 bis 75 verläuft
+           x = as.numeric(factor(wt_aa_pos, levels = unique(wt_aa_pos))) - ((row_group - 1) * 75),
+           y = as.numeric(factor(mut_aa, levels = amino_acid_order))) %>%
+    filter(synonymous == TRUE)
+
+  # Calculate the number of row groups and adjust plot height dynamically
+  num_row_groups <- max(heatmap_data_long\$row_group)
+  plot_height <- num_row_groups * 4
+
+  # Set the limits for the color scale, ignoring NA (negative values are now NA)
+  min_count <- min(heatmap_data_long\$total_counts_per_cov, na.rm = TRUE)
+  max_count <- max(heatmap_data_long\$total_counts_per_cov, na.rm = TRUE)
+  max_position <- max(heatmap_data\$position)
+
+  # Create the heatmap plot with explicit handling for positions > max_position
+  heatmap_plot <- ggplot(heatmap_data_long, aes(x = wt_aa_pos, y = mut_aa, fill = total_counts_per_cov)) +
+    scale_fill_gradientn(colours = c(alpha("blue", 0), "blue"), na.value = "grey35", trans = "log",  # Apply log transformation to the scale
+                         limits = c(min_count, max_count),
+                         breaks = scales::trans_breaks("log10", function(x) 10^x),  # Logarithmic scale breaks
+                         labels = scales::trans_format("log10", scales::math_format(10^.x))) +
+    scale_x_discrete(labels = function(x) {
+      numeric_pos <- as.numeric(gsub("[^0-9]", "", x))
+      ifelse(numeric_pos > max_position, " ", x)
+    }) +
+    geom_tile() +
+
+    # Add diagonal lines for synonymous mutations using geom_segment
+    geom_segment(data = syn_positions[syn_positions\$position <= max_position, ],
+                 aes(x = x - 0.485, xend = x + 0.485,
+                     y = y - 0.485, yend = y + 0.485, color = synonymous),
+                 size = 0.2) +
+
+    # Manuelle Farbskala für die diagonalen Linien
+    scale_color_manual(values = c("TRUE" = "grey10"), labels = c("TRUE" = "")) +
+
+    theme_minimal() +
+    labs(title = "Heatmap of Counts per Coverage for Mutations", x = "Wild-type Amino Acid", y = "Mutant Amino Acid", fill = "Counts per \\n Coverage", color = "Synonymous Mutation") +
+    theme(plot.title = element_text(size = 16, face = "bold"),
+          axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 12),
+          axis.text.y = element_text(size = 12),  # Larger y-axis labels
+          axis.title.x = element_text(size = 16),
+          axis.title.y = element_text(size = 16),
+          legend.title = element_text(size = 14),  # Larger legend title
+          legend.text = element_text(size = 12),  # Larger legend text
+          panel.spacing = unit(0.1, "lines"),  # Adjust panel spacing
+          strip.text = element_blank(),  # Remove row group labels (facet numbers)
+          strip.background = element_blank(),
+          panel.grid.major = element_blank(),  # Remove major grid lines
+          panel.grid.minor = element_blank()) +  # Remove minor grid lines
+    facet_wrap(~ row_group, scales = "free_x", ncol = 1) +  # Group by 75 positions per row
+    theme(panel.spacing = unit(0.2, "lines"))
+
+  heatmap_plot <- heatmap_plot +
+    geom_point(data = heatmap_data_long, aes(size = ""), colour = "black", alpha = 0)  # Invisible points for legend
+  heatmap_plot <- heatmap_plot +
+    guides(size = guide_legend(paste("Dropout (Counts <", threshold, ")"), override.aes = list(shape = 15, size = 8, colour = "grey35", alpha = 1)))  # Define Legend for Dropouts
+
+  # Save the heatmap plot
+  if (img_format == "pdf") {
+    ggsave(output_pdf_path, plot = heatmap_plot, width = 16, height = plot_height, dpi = 150, device = cairo_pdf)
+  } else {
+    ggsave(output_pdf_path, plot = heatmap_plot, width = 16, height = plot_height, dpi = 150)
+  }
+
+  if (file.exists(output_pdf_path)) {
+    print("Heatmap image successfully created!")
+  } else {
+    print("Error: Heatmap image was not created.")
+  }
+}
+
+#####
+# run function
+#####
+counts_per_cov_heatmap(
+  input_csv_path = "$variantCounts_for_heatmaps",
+  threshold = 1,
+  output_pdf_path = "counts_per_cov_heatmap.pdf",
+  img_format = "pdf"
+)
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+dplyr_version <- as.character(packageVersion("dplyr"))
+ggplot2_version <- as.character(packageVersion("ggplot2"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(dplyr_version) == 0) dplyr_version <- "unknown"
+if (length(ggplot2_version) == 0) ggplot2_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-dplyr:', dplyr_version),
+    paste('    r-ggplot2:', ggplot2_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/visualization/error_correction_report/.conda-lock/linux_amd64-bd-cfb38dbf0574e16d_1.txt b/modules/local/visualization/error_correction_report/.conda-lock/linux_amd64-bd-cfb38dbf0574e16d_1.txt
new file mode 100644
index 0000000..e3629c8
--- /dev/null
+++ b/modules/local/visualization/error_correction_report/.conda-lock/linux_amd64-bd-cfb38dbf0574e16d_1.txt
@@ -0,0 +1,556 @@
+version: 6
+environments:
+  default:
+    channels:
+    - url: https://conda.anaconda.org/conda-forge/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
+      linux-64:
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_10.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.10.23-hbd8a1cb_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.46.1-default_hbd61a6d_103.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-11_h4a7cf45_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-11_h0358290_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.9.0-hd2095e1_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.7.0-h81df57d_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-16.2.0-ha9f2e26_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-16.2.0-h69a702a_7.conda
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+      - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.11.0-11_h47877c9_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.3-hb03c661_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-devel-5.8.3-hb03c661_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hb9d3cd8_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.34-pthreads_hf13c14d_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.54.1-h0737f62_101.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-16.2.0-h934c35e_7.conda
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+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.38-h280c20c_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.2-h25fd6f3_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.6-hdb14827_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.4-py312heda63a1_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.6.5-h781a0a9_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.1-py312hfb8ada1_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/procps-ng-4.0.6-h18c060e_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.12.3-hab00c5b_0_cpython.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-3.0.0-pyh5ded981_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2026.6-pyh5ded981_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.12-9_cp312.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/pytz-2026.5-pyh5ded981_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/readline-8.3-hd6e31c0_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h1df4ec4_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2026e-h5d301ff_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/which-2.25-h171cf75_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/xz-5.8.3-ha02ee65_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/xz-gpl-tools-5.8.3-ha02ee65_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/xz-tools-5.8.3-hb03c661_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_7.conda
+packages:
+- conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+  build_number: 20
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+  - mkl <2027
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diff --git a/modules/local/visualization/error_correction_report/.conda-lock/linux_arm64-bd-a15c6f547e2083b8_1.txt b/modules/local/visualization/error_correction_report/.conda-lock/linux_arm64-bd-a15c6f547e2083b8_1.txt
new file mode 100644
index 0000000..9ebfc49
--- /dev/null
+++ b/modules/local/visualization/error_correction_report/.conda-lock/linux_arm64-bd-a15c6f547e2083b8_1.txt
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+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xz-gpl-tools-5.8.3-hd704e39_1.conda
+  sha256: 3f0b45a1039c86265f41f3bb5f248cd4d901acff49164f86122fab7cc4efc049
+  md5: 6543af33661ff21061b6db52c84a86fa
+  depends:
+  - libgcc >=14
+  - liblzma 5.8.3 he30d5cf_1
+  constrains:
+  - xz 5.8.3.*
+  license: 0BSD AND LGPL-2.1-or-later AND GPL-2.0-or-later
+  size: 40115
+  timestamp: 1786348586456
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xz-tools-5.8.3-he30d5cf_1.conda
+  sha256: 1619ea83757336aaaac4cde221e4cd0982551c82def3276c91d0d53b0b7159db
+  md5: 7c211f37ed725871ce3043090d5addd8
+  depends:
+  - libgcc >=14
+  - liblzma 5.8.3 he30d5cf_1
+  constrains:
+  - xz 5.8.3.*
+  license: 0BSD AND LGPL-2.1-or-later
+  size: 103133
+  timestamp: 1786348578460
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/zstd-1.5.7-h9d15635_7.conda
+  sha256: 427fd14bcb3b8659796fecc682716617409350fb5a98e5b7b47558a10d1a2fc7
+  md5: d942e34ac3920ba83f8b2d0169570187
+  depends:
+  - libzlib >=1.3.2,<2.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 615477
+  timestamp: 1786599613561
diff --git a/modules/local/visualization/error_correction_report/environment.yml b/modules/local/visualization/error_correction_report/environment.yml
new file mode 100644
index 0000000..e16d4bb
--- /dev/null
+++ b/modules/local/visualization/error_correction_report/environment.yml
@@ -0,0 +1,11 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+# Shared by the three Python report modules. Keep the copies identical:
+# the one container image these modules use is built from exactly this list.
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - conda-forge::numpy=1.26.4
+  - conda-forge::pandas=2.2.1
+  - conda-forge::python=3.12.3
diff --git a/modules/local/visualization/error_correction_report/main.nf b/modules/local/visualization/error_correction_report/main.nf
new file mode 100644
index 0000000..3863f43
--- /dev/null
+++ b/modules/local/visualization/error_correction_report/main.nf
@@ -0,0 +1,43 @@
+// Build the self-contained error-correction HTML report for the whole run.
+//
+// One report, not one per file: every sequencing file's numbers travel in the page and each panel
+// recomputes in the browser from whichever files the reader selects, so a single-file view is still
+// one click away while cross-replicate spread (the error bars) becomes possible at all.
+process VISUALIZATION_ERROR_CORRECTION_REPORT {
+    tag "${sample}"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/f7/f727259d394cff047a26f3e3fa3a43e1a7a1c9dc51a35e6def7aa90a2085bb7b/data'
+        : 'community.wave.seqera.io/library/numpy_pandas_python:cfb38dbf0574e16d'}"
+
+    input:
+    val sample                               // run-level name, for the heading
+    val ids_b64                              // base64(JSON) [file id, ...] aligned with `corrected`
+    path corrected, stageAs: 'c*/*'          // per-file tables share a basename -> numbered dirs
+    val method
+    val false_doubles_method                 // 'mle' | 'eb' - names the estimator in the report heading
+    path template_html
+
+    output:
+    path "error_correction_report.html", emit: report
+    path "versions.yml"                , emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'build_ec_report.py'
+
+    stub:
+    """
+    touch error_correction_report.html
+    cat <<-END_VERSIONS > versions.yml
+    "${task.process}":
+        python: "0.0.0"
+        pandas: "0.0.0"
+    END_VERSIONS
+    """
+}
diff --git a/modules/local/visualization/error_correction_report/meta.yml b/modules/local/visualization/error_correction_report/meta.yml
new file mode 100644
index 0000000..4588cfa
--- /dev/null
+++ b/modules/local/visualization/error_correction_report/meta.yml
@@ -0,0 +1,80 @@
+name: "visualization_error_correction_report"
+description: Build a self-contained, per-sample HTML report visualising the effect of the sequencing-error correction (positional error bias, correction magnitude, and per-variant raw-vs-corrected counts).
+keywords:
+  - deep mutational scanning
+  - error correction
+  - report
+  - visualisation
+tools:
+  - "pandas":
+      description: "Fast, powerful data structures for data analysis in Python."
+      homepage: "https://pandas.pydata.org/"
+      licence: ["BSD-3-Clause"]
+
+input:
+  - - sample:
+        type: string
+        description: Run-level sample name, used for the report heading.
+  - - ids_b64:
+        type: string
+        description: |
+          Base64-encoded JSON array of the library identifiers, in the same order as `corrected`.
+          Base64 keeps the JSON free of quoting problems when it is interpolated into the template.
+  - - corrected:
+        type: file
+        description: |
+          Error-corrected, library-filtered counts for every library of the run (with `counts`
+          corrected and `counts_raw` preserved). Staged into numbered directories because the
+          basenames repeat across libraries.
+        pattern: "*.csv"
+  - - method:
+        type: string
+        description: Error-correction method label (`false_doubles` or `wildtype`).
+  - - false_doubles_method:
+        type: string
+        description: Estimator naming in the report heading, `mle` or `eb`.
+  - - template_html:
+        type: file
+        description: The report's HTML shell; data and plots are injected into it.
+        pattern: "*.html"
+
+output:
+  - report:
+      - error_correction_report.html:
+          type: file
+          description: Self-contained, run-level error-correction report.
+          pattern: "error_correction_report.html"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+maintainers:
+  - "@BenjaminWehnert1008"
+containers:
+  docker:
+    linux/amd64:
+      name: community.wave.seqera.io/library/numpy_pandas_python:cfb38dbf0574e16d
+      build_id: bd-cfb38dbf0574e16d_1
+      scan_id: sc-fae0cdb4b6c51968_1
+    linux/arm64:
+      name: community.wave.seqera.io/library/numpy_pandas_python:a15c6f547e2083b8
+      build_id: bd-a15c6f547e2083b8_1
+      scan_id: sc-931239170a8e2353_1
+  singularity:
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/numpy_pandas_python:7f16bcac97973062
+      build_id: bd-7f16bcac97973062_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/f7/f727259d394cff047a26f3e3fa3a43e1a7a1c9dc51a35e6def7aa90a2085bb7b/data
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/numpy_pandas_python:648dc1860698d21b
+      build_id: bd-648dc1860698d21b_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/22/229745e9be125462f1850ac75f13afb0738d30241558908657bfc802c2c6681f/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/visualization/error_correction_report/.conda-lock/linux_amd64-bd-cfb38dbf0574e16d_1.txt
+    linux/arm64:
+      lock_file: modules/local/visualization/error_correction_report/.conda-lock/linux_arm64-bd-a15c6f547e2083b8_1.txt
diff --git a/modules/local/visualization/error_correction_report/templates/build_ec_report.py b/modules/local/visualization/error_correction_report/templates/build_ec_report.py
new file mode 100644
index 0000000..e6719a9
--- /dev/null
+++ b/modules/local/visualization/error_correction_report/templates/build_ec_report.py
@@ -0,0 +1,158 @@
+#!/usr/bin/env python
+
+# Build the self-contained error-correction HTML report for a whole run.
+#
+# One report covers every sequencing file: the page carries each file's numbers separately and
+# recomputes every panel in the browser from whichever files are selected, so deselecting down to a
+# single file reproduces exactly what a per-file report used to show.
+#
+# Nextflow template: values below are interpolated; backslashes meant for Python are doubled.
+
+import base64
+import json
+import platform
+import re
+
+import numpy as np
+import pandas as pd
+
+sample = "$sample"
+method = "$method"
+# The false-doubles estimator actually used (mle|eb), so the report can name it. Empty/other for the
+# wildtype method, where it does not apply.
+estimator = "$false_doubles_method"
+# Each entry is {id, type, replicate} - type/replicate come straight from the samplesheet (general
+# for any protein, not hard-coded here) and drive the input-vs-output split in the report.
+files = json.loads(base64.b64decode("$ids_b64").decode("utf-8"))
+paths = "$corrected".split()
+
+if len(files) != len(paths):
+    raise SystemExit(f"{len(files)} ids but {len(paths)} files staged")
+
+MUT = re.compile(r"^([A-Za-z*]+)(\\d+)([A-Za-z*]+)")
+
+# `base_mut` holds the nucleotide change(s) of a variant as "<pos>:<ref>><alt>", comma-separated when
+# the codon carries 2-3 changes (the NNK case). Positions are reference coordinates.
+SNV = re.compile(r"^(\\d+):([ACGTacgt])>([ACGTacgt])\$")
+
+COMPLEMENT = {"A": "T", "C": "G", "G": "C", "T": "A"}
+
+# The six strand-symmetric substitution classes, pyrimidine-centric: a purine reference is
+# reverse-complemented, so C>A and G>T are one and the same event. Order is canonical (as used for
+# mutational signatures) and fixed - the colour of a class never depends on the data.
+SNV_CLASSES = ["C>A", "C>G", "C>T", "T>A", "T>C", "T>G"]
+SNV_LABELS = {
+    "C>A": "C>A / G>T", "C>G": "C>G / G>C", "C>T": "C>T / G>A",
+    "T>A": "T>A / A>T", "T>C": "T>C / A>G", "T>G": "T>G / A>C",
+}
+
+
+def parse_pos(s):
+    m = MUT.match(str(s))
+    return (m.group(1), int(m.group(2)), m.group(3)) if m else (None, None, None)
+
+
+def parse_snvs(base_mut):
+    """"1220:T>G, 1221:T>G" -> [(1220,'T','G'), (1221,'T','G')]"""
+    out = []
+    for part in str(base_mut).split(","):
+        m = SNV.match(part.strip())
+        if m:
+            out.append((int(m.group(1)), m.group(2).upper(), m.group(3).upper()))
+    return out
+
+
+def classify_snv(ref, alt):
+    """Collapse a substitution onto its pyrimidine-centric class key."""
+    if ref not in COMPLEMENT or alt not in COMPLEMENT or ref == alt:
+        return None
+    if ref in ("A", "G"):
+        ref, alt = COMPLEMENT[ref], COMPLEMENT[alt]
+    key = ref + ">" + alt
+    return key if key in SNV_LABELS else None
+
+
+def numcol(df, name):
+    return (pd.to_numeric(df[name], errors="coerce") if name in df.columns
+            else pd.Series([np.nan] * len(df)))
+
+
+def f(x, nd):
+    return None if x is None or x != x else round(float(x), nd)
+
+
+# Variants are keyed on `codon_mut`, not `pos_mut`. `pos_mut` is NOT unique - one amino-acid
+# substitution is reachable through up to three codons (6898 unique pos_mut across 10254 rows on the
+# GID1A run), so keying on it would silently merge distinct nucleotide variants. `codon_mut` is
+# unique (10254/10254).
+#
+# The files do not share a variant set either (union 10771 vs intersection 6886 on GID1A), so this is
+# a union: a variant absent from a file gets None there, never 0. Absent means "not observed above
+# threshold in that file", which is missing data, not a measured zero - averaging it in as 0 would
+# drag every frequency down. The browser skips Nones and reports how many files contributed.
+variants = {}
+n_files = len(files)
+
+for fi, (fid, path) in enumerate(zip(files, paths)):
+    df = pd.read_csv(path)
+    raw = numcol(df, "counts_raw")
+    cor = numcol(df, "counts")
+    cpc_raw = numcol(df, "counts_per_cov_raw")
+    cpc_cor = numcol(df, "counts_per_cov")
+    posmut = df["pos_mut"].astype(str)
+    basemut = df["base_mut"].astype(str) if "base_mut" in df.columns else pd.Series([""] * len(df))
+    codonmut = df["codon_mut"].astype(str) if "codon_mut" in df.columns else posmut
+
+    for i in range(len(df)):
+        r = raw.iloc[i]
+        if pd.isna(r):
+            continue
+        key = codonmut.iloc[i].strip()
+        v = variants.get(key)
+        if v is None:
+            wt, pos, _mut = parse_pos(posmut.iloc[i])
+            nt = basemut.iloc[i].strip()
+            snvs = parse_snvs(nt)
+            # A class is only meaningful when a single nucleotide changed; NNK codon variants
+            # (2-3 changes) mix classes and stay unclassified by design.
+            kls = classify_snv(snvs[0][1], snvs[0][2]) if len(snvs) == 1 else None
+            v = variants[key] = {
+                "key": key, "pos_mut": posmut.iloc[i], "pos": pos, "wt_aa": wt,
+                "nt": nt, "cls": kls,
+                "ntpos": (snvs[0][0] if len(snvs) == 1 else None),
+                "raw": [None] * n_files, "cor": [None] * n_files,
+                "f_raw": [None] * n_files, "f_cor": [None] * n_files,
+            }
+        c = cor.iloc[i]
+        v["raw"][fi] = f(r, 3)
+        v["cor"][fi] = 0.0 if pd.isna(c) else f(c, 3)
+        v["f_raw"][fi] = f(cpc_raw.iloc[i], 10)
+        v["f_cor"][fi] = 0.0 if pd.isna(cpc_cor.iloc[i]) else f(cpc_cor.iloc[i], 10)
+
+data = {
+    "sample": sample,
+    "method": method,
+    "estimator": estimator,
+    "files": files,
+    "classes": [{"cls": k, "label": SNV_LABELS[k]} for k in SNV_CLASSES],
+    "variants": sorted(variants.values(), key=lambda v: (v["pos"] if v["pos"] is not None else 0, v["key"])),
+}
+
+data_json = json.dumps(data, separators=(",", ":")).replace("</", "<\\\\/")
+
+html = open("$template_html", encoding="utf-8").read()
+html = html.replace("__EC_DATA__", data_json).replace("__SAMPLE__", sample)
+with open("error_correction_report.html", "w", encoding="utf-8") as fh:
+    fh.write(html)
+
+with open("versions.yml", "w") as vf:
+    vf.write('"${task.process}":\\n')
+    vf.write("    python: " + platform.python_version() + "\\n")
+    vf.write("    pandas: " + pd.__version__ + "\\n")
+    vf.write("    numpy: " + np.__version__ + "\\n")
+
+import os
+mb = os.path.getsize("error_correction_report.html") / 1e6
+n_snv = sum(1 for v in data["variants"] if v["cls"])
+print(f"Wrote error_correction_report.html ({mb:.1f} MB) - {len(files)} files, "
+      f"{len(data['variants'])} variants ({n_snv} single-nucleotide)")
diff --git a/modules/local/visualization/global_pos_biases_counts/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt b/modules/local/visualization/global_pos_biases_counts/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
new file mode 100644
index 0000000..be8d7e8
--- /dev/null
+++ b/modules/local/visualization/global_pos_biases_counts/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
@@ -0,0 +1,2942 @@
+version: 6
+environments:
+  default:
+    channels:
+    - url: https://conda.anaconda.org/conda-forge/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
+      linux-64:
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.46.1-default_hfdba357_103.conda
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-biocgenerics-0.56.0-r45hdfd78af_2.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-biostrings-2.78.0-r45h01b2380_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-iranges-2.44.0-r45h01b2380_1.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-s4vectors-0.48.1-r45h262fe30_0.conda
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-seqinfo-1.0.0-r45hdfd78af_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-xvector-0.50.0-r45h01b2380_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/bwidget-1.10.1-hfc2019e_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_10.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.9-hebe6cf0_0.conda
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+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda
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+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gfortran_impl_linux-64-16.2.0-hf930d89_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.15-h54a6638_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gsl-2.7-he838d99_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-16.2.0-h0d273dc_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-12.2.0-h15599e2_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-4.18.0-he073ed8_9.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-h7cc23a3_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.22.2-hbc21106_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.46.1-default_hbd61a6d_103.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.2.0-hdb68285_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-11_h4a7cf45_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-11_h0358290_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.21.0-hcf29cc6_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.25-hd45a770_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20260512-pl5321h373387f_0.conda
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diff --git a/modules/local/visualization/global_pos_biases_counts/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt b/modules/local/visualization/global_pos_biases_counts/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
new file mode 100644
index 0000000..10e5993
--- /dev/null
+++ b/modules/local/visualization/global_pos_biases_counts/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
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diff --git a/modules/local/visualization/global_pos_biases_counts/environment.yml b/modules/local/visualization/global_pos_biases_counts/environment.yml
new file mode 100644
index 0000000..5f5785d
--- /dev/null
+++ b/modules/local/visualization/global_pos_biases_counts/environment.yml
@@ -0,0 +1,19 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+# Shared by 17 R modules (all except RUN_MUTSCAN, RUN_DIMSUM and the false-doubles error correction).
+# Keep the copies identical:
+# the one container image these modules use is built from exactly this list.
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/visualization/global_pos_biases_counts/main.nf b/modules/local/visualization/global_pos_biases_counts/main.nf
new file mode 100644
index 0000000..662fe3f
--- /dev/null
+++ b/modules/local/visualization/global_pos_biases_counts/main.nf
@@ -0,0 +1,34 @@
+process VISUALIZATION_GLOBAL_POS_BIASES_COUNTS {
+    tag "$meta.id"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7'}"
+
+    input:
+    tuple val(meta), path(variantCounts_filtered_by_library)
+    path aa_seq
+    val sliding_window_size
+
+    output:
+    tuple val(meta), path("rolling_counts.pdf"), emit: rolling_counts
+    tuple val(meta), path("rolling_counts_per_cov.pdf"), emit: rolling_counts_per_cov
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'global_position_biases_counts_and_counts_per_cov.R'
+
+    stub:
+    """
+    touch rolling_counts.pdf
+    touch rolling_counts_per_cov.pdf
+    echo "VISUALIZATION_GLOBAL_POS_BIASES_COUNTS:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+    """
+}
diff --git a/modules/local/visualization/global_pos_biases_counts/meta.yml b/modules/local/visualization/global_pos_biases_counts/meta.yml
new file mode 100644
index 0000000..b7a2a08
--- /dev/null
+++ b/modules/local/visualization/global_pos_biases_counts/meta.yml
@@ -0,0 +1,77 @@
+name: "visualization_global_pos_biases_counts"
+description: Calculates and visualizes the rolling mean of variant counts and counts normalized by coverage across the reference sequence to identify regional biases or mutation hotspots.
+keywords:
+  - deep mutational scanning
+  - dms
+  - visualization
+  - rolling mean
+  - sliding window
+  - bias
+tools: null
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', single_end:false ]`
+    - variantCounts_filtered_by_library:
+        type: file
+        description: CSV file containing variant counts filtered against the target library
+        pattern: "*.{csv}"
+  - - aa_seq:
+        type: file
+        description: Text or FASTA file containing the reference amino acid sequence
+  - - sliding_window_size:
+        type: integer
+        description: The size of the window (number of amino acids) used for calculating the rolling mean
+
+output:
+  - rolling_counts:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - rolling_counts.pdf:
+          type: file
+          description: PDF plot showing the rolling mean of raw variant counts across positions
+          pattern: "rolling_counts.pdf"
+  - rolling_counts_per_cov:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - rolling_counts_per_cov.pdf:
+          type: file
+          description: PDF plot showing the rolling mean of counts normalized by local coverage
+          pattern: "rolling_counts_per_cov.pdf"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+containers:
+  docker:
+    linux/arm64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb
+      build_id: bd-880bbdecb19543bb_1
+      scan_id: sc-09c9d098a09068b9_1
+    linux/amd64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7
+      build_id: bd-32f1af0a83a018e7_1
+      scan_id: sc-337510c2c910aa1c_1
+  singularity:
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb
+      build_id: bd-7202e5d19667bddb_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7
+      build_id: bd-db46247d9810b3e7_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/visualization/global_pos_biases_counts/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
+    linux/arm64:
+      lock_file: modules/local/visualization/global_pos_biases_counts/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
diff --git a/modules/local/visualization/global_pos_biases_counts/templates/global_position_biases_counts_and_counts_per_cov.R b/modules/local/visualization/global_pos_biases_counts/templates/global_position_biases_counts_and_counts_per_cov.R
new file mode 100644
index 0000000..2ec9d91
--- /dev/null
+++ b/modules/local/visualization/global_pos_biases_counts/templates/global_position_biases_counts_and_counts_per_cov.R
@@ -0,0 +1,186 @@
+#!/usr/bin/env Rscript
+
+# input: prefiltered gatk path, aa seq path, window_size (sliding window), output_path_folder
+# output: two lineplots showing counts & counts per coverage divided in types of variants (single-/double-/triple-base exchange)
+
+library(zoo) # sliding window
+library(dplyr)
+library(ggplot2)
+library(scales)
+
+position_biases <- function(prefiltered_gatk_path, aa_seq_path, window_size = 10) {
+
+  # Load and process the data
+  prefiltered_gatk <- read.table(prefiltered_gatk_path, sep = ",", fill = NA, header = TRUE)
+  prefiltered_gatk\$pos <- as.numeric(sub("(\\\\D)(\\\\d+)(\\\\D)", "\\\\2", prefiltered_gatk\$pos_mut))
+  unique_pos <- unique(as.numeric(prefiltered_gatk\$pos))
+  aa_seq <- readLines(aa_seq_path, warn = FALSE)
+  aa_seq_length <- nchar(aa_seq)
+  aa_positions <- seq(nchar(aa_seq))
+
+  means_counts_single <- rep(NA, nchar(aa_seq))
+  means_counts_double <- rep(NA, nchar(aa_seq))
+  means_counts_triple <- rep(NA, nchar(aa_seq))
+  means_counts_per_cov_single <- rep(NA, nchar(aa_seq))
+  means_counts_per_cov_double <- rep(NA, nchar(aa_seq))
+  means_counts_per_cov_triple <- rep(NA, nchar(aa_seq))
+
+
+  # Loop through each position in the amino acid sequence
+  for (i in 1:(nchar(aa_seq))) {
+
+    # Filter the data for the current position in aa_positions
+    window_data <- prefiltered_gatk %>% filter(prefiltered_gatk\$pos %in% aa_positions[i])
+
+    # Calculate mean for Single mutations (where varying_bases == 1)
+    window_data_single <- window_data %>% filter(varying_bases == 1)
+    means_counts_single[i] <- mean(window_data_single\$counts, na.rm = FALSE)
+    means_counts_per_cov_single[i] <- mean(window_data_single\$counts_per_cov, na.rm = FALSE)
+
+    # Calculate mean for Double mutations (where varying_bases == 2)
+    window_data_double <- window_data %>% filter(varying_bases == 2)
+    means_counts_double[i] <- mean(window_data_double\$counts, na.rm = FALSE)
+    means_counts_per_cov_double[i] <- mean(window_data_double\$counts_per_cov, na.rm = FALSE)
+
+    # Calculate mean for Triple mutations (where varying_bases == 3)
+    window_data_triple <- window_data %>% filter(varying_bases == 3)
+    means_counts_triple[i] <- mean(window_data_triple\$counts, na.rm = FALSE)
+    means_counts_per_cov_triple[i] <- mean(window_data_triple\$counts_per_cov, na.rm = FALSE)
+  }
+
+  pos_bias_df <- data.frame(pos = seq(nchar(aa_seq)))
+
+
+  pos_bias_df\$rolling_mean_counts_single <- rollapply(means_counts_single, width = window_size,
+                                               FUN = function(x) (mean(x, na.rm = TRUE) + 0.00001), fill = "extend")
+  pos_bias_df\$rolling_mean_counts_double <- rollapply(means_counts_double, width = window_size,
+                                                      FUN = function(x) (mean(x, na.rm = TRUE) + 0.00001), fill = "extend")
+  pos_bias_df\$rolling_mean_counts_triple <- rollapply(means_counts_triple, width = window_size,
+                                                      FUN = function(x) (mean(x, na.rm = TRUE) + 0.00001), fill = "extend")
+
+  pos_bias_df\$rolling_mean_counts_per_cov_single <- rollapply(means_counts_per_cov_single, width = window_size,
+                                                       FUN = function(x) (mean(x, na.rm = TRUE) + 0.00001), fill = "extend")
+  pos_bias_df\$rolling_mean_counts_per_cov_double <- rollapply(means_counts_per_cov_double, width = window_size,
+                                                              FUN = function(x) (mean(x, na.rm = TRUE) + 0.00001), fill = "extend")
+  pos_bias_df\$rolling_mean_counts_per_cov_triple <- rollapply(means_counts_per_cov_triple, width = window_size,
+                                                              FUN = function(x) (mean(x, na.rm = TRUE) + 0.00001), fill = "extend")
+
+
+  # Replace NAs with 0 for rolling means and SE
+  pos_bias_df\$rolling_mean_counts_single[is.na(pos_bias_df\$rolling_mean_counts_single)] <- 0.00001
+  pos_bias_df\$rolling_mean_counts_double[is.na(pos_bias_df\$rolling_mean_counts_double)] <- 0.00001
+  pos_bias_df\$rolling_mean_counts_triple[is.na(pos_bias_df\$rolling_mean_counts_triple)] <- 0.00001
+  pos_bias_df\$rolling_mean_counts_per_cov_single[is.na(pos_bias_df\$rolling_mean_counts_per_cov_single)] <- 0.000001
+  pos_bias_df\$rolling_mean_counts_per_cov_double[is.na(pos_bias_df\$rolling_mean_counts_per_cov_double)] <- 0.000001
+  pos_bias_df\$rolling_mean_counts_per_cov_triple[is.na(pos_bias_df\$rolling_mean_counts_per_cov_triple)] <- 0.000001
+
+
+
+  plots_theme <- list(
+
+    # Add the minimal theme to the list
+    theme_minimal(),
+
+    # Customize legend title and appearance
+    theme(legend.title = element_text(size = 10, face = "bold"),
+          legend.position = "right"),
+
+    # Customize guides for the legend elements
+    guides(
+      color = guide_legend(title = "Mutation Type", order = 1),  # Title for line color
+      fill = guide_legend(title = "Standard Deviation", order = 2),  # Title for ribbon fill
+      linetype = guide_legend(title = "Required Coverage", order = 3)  # Title for line type
+    )
+  )
+
+
+   # Placeholder name for the linetype
+  linetype_placeholder <- "Required coverage"
+
+  rolling_counts_plot <- ggplot(pos_bias_df, aes(x = pos)) +
+
+    # Add line for rolling mean coverage
+    geom_line(aes(y = rolling_mean_counts_single, color = "One Varying Base")) +
+    geom_line(aes(y = rolling_mean_counts_double, color = "Two Varying Bases")) +
+    geom_line(aes(y = rolling_mean_counts_triple, color = "Three Varying Bases")) +
+
+    # Individual axis labels for this plot
+    xlab("Amino Acid Position") +
+    ylab("Counts") +
+
+    # Manually set color and fill labels
+    scale_color_manual(name = "Mutation Type",
+                       values = c("One Varying Base" = "chocolate", "Two Varying Bases" = "darkolivegreen3", "Three Varying Bases" = "deepskyblue1"),
+                       limits = c("One Varying Base", "Two Varying Bases", "Three Varying Bases")) + # Color for line
+
+    # Apply the saved theme and design elements at the end
+    plots_theme +
+
+    scale_y_continuous(trans = 'log10', labels = scales::comma)
+
+
+
+
+  rolling_counts_per_cov_plot <- ggplot(pos_bias_df, aes(x = pos)) +
+
+    # Add line for rolling mean coverage
+    geom_line(aes(y = rolling_mean_counts_per_cov_single, color = "One Varying Base")) +
+    geom_line(aes(y = rolling_mean_counts_per_cov_double, color = "Two Varying Bases")) +
+    geom_line(aes(y = rolling_mean_counts_per_cov_triple, color = "Three Varying Bases")) +
+
+    # Individual axis labels for this plot
+    xlab("Amino Acid Position") +
+    ylab("Counts") +
+
+    # Manually set color and fill labels
+    scale_color_manual(name = "Mutation Type",
+                       values = c("One Varying Base" = "chocolate", "Two Varying Bases" = "darkolivegreen3", "Three Varying Bases" = "deepskyblue1"),
+                       limits = c("One Varying Base", "Two Varying Bases", "Three Varying Bases")) + # Color and legend order for lines
+
+    # Apply the saved theme and design elements at the end
+    plots_theme +
+
+    scale_y_continuous(trans = 'log10', labels = scales::comma)
+
+
+  ggsave(filename = "rolling_counts.pdf", plot = rolling_counts_plot, device = "pdf", width = 10, height = 6)
+  ggsave(filename = "rolling_counts_per_cov.pdf", plot = rolling_counts_per_cov_plot, device = "pdf", width = 10, height = 6)
+}
+
+#####
+# run pipeline
+#####
+position_biases(
+  prefiltered_gatk_path = "$variantCounts_filtered_by_library",
+  aa_seq_path = "$aa_seq",
+  window_size = $sliding_window_size
+)
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+dplyr_version <- as.character(packageVersion("dplyr"))
+ggplot2_version <- as.character(packageVersion("ggplot2"))
+scales_version <- as.character(packageVersion("scales"))
+zoo_version <- as.character(packageVersion("zoo"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(dplyr_version) == 0) dplyr_version <- "unknown"
+if (length(ggplot2_version) == 0) ggplot2_version <- "unknown"
+if (length(scales_version) == 0) scales_version <- "unknown"
+if (length(zoo_version) == 0) zoo_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-dplyr:', dplyr_version),
+    paste('    r-ggplot2:', ggplot2_version),
+    paste('    r-scales:', scales_version),
+    paste('    r-zoo:', zoo_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/visualization/global_pos_biases_cov/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt b/modules/local/visualization/global_pos_biases_cov/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
new file mode 100644
index 0000000..be8d7e8
--- /dev/null
+++ b/modules/local/visualization/global_pos_biases_cov/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
@@ -0,0 +1,2942 @@
+version: 6
+environments:
+  default:
+    channels:
+    - url: https://conda.anaconda.org/conda-forge/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
+      linux-64:
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.46.1-default_hfdba357_103.conda
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-biocgenerics-0.56.0-r45hdfd78af_2.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-biostrings-2.78.0-r45h01b2380_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-iranges-2.44.0-r45h01b2380_1.conda
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diff --git a/modules/local/visualization/global_pos_biases_cov/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt b/modules/local/visualization/global_pos_biases_cov/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
new file mode 100644
index 0000000..10e5993
--- /dev/null
+++ b/modules/local/visualization/global_pos_biases_cov/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
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diff --git a/modules/local/visualization/global_pos_biases_cov/environment.yml b/modules/local/visualization/global_pos_biases_cov/environment.yml
new file mode 100644
index 0000000..5f5785d
--- /dev/null
+++ b/modules/local/visualization/global_pos_biases_cov/environment.yml
@@ -0,0 +1,19 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+# Shared by 17 R modules (all except RUN_MUTSCAN, RUN_DIMSUM and the false-doubles error correction).
+# Keep the copies identical:
+# the one container image these modules use is built from exactly this list.
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/visualization/global_pos_biases_cov/main.nf b/modules/local/visualization/global_pos_biases_cov/main.nf
new file mode 100644
index 0000000..41fd60c
--- /dev/null
+++ b/modules/local/visualization/global_pos_biases_cov/main.nf
@@ -0,0 +1,33 @@
+process VISUALIZATION_GLOBAL_POS_BIASES_COV {
+    tag "$meta.id"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7'}"
+
+    input:
+    tuple val(meta), path(variantCounts_filtered_by_library)
+    path aa_seq
+    val sliding_window_size
+    val aimed_cov
+
+    output:
+    tuple val(meta), path("rolling_coverage.pdf"), emit: rolling_coverage
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'global_position_biases_cov.R'
+
+    stub:
+    """
+    touch rolling_coverage.pdf
+    echo "VISUALIZATION_GLOBAL_POS_BIASES_COV:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+    """
+}
diff --git a/modules/local/visualization/global_pos_biases_cov/meta.yml b/modules/local/visualization/global_pos_biases_cov/meta.yml
new file mode 100644
index 0000000..782c488
--- /dev/null
+++ b/modules/local/visualization/global_pos_biases_cov/meta.yml
@@ -0,0 +1,72 @@
+name: "visualization_global_pos_biases_cov"
+description: Calculates and plots the rolling mean coverage across the protein sequence to identify positional biases, regions of low coverage, or potential experimental dropouts.
+keywords:
+  - deep mutational scanning
+  - dms
+  - visualization
+  - coverage
+  - rolling window
+  - bias
+tools: null
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', single_end:false ]`
+    - variantCounts_filtered_by_library:
+        type: file
+        description: CSV file containing variant counts filtered against the target library
+        pattern: "*.{csv}"
+  - - aa_seq:
+        type: file
+        description: Text file containing the reference amino acid sequence
+  - - sliding_window_size:
+        type: integer
+        description: The size of the window (in amino acids) used for calculating the rolling mean coverage
+  - - aimed_cov:
+        type: integer
+        description: The target coverage threshold to visualize as a horizontal reference line in the plot
+
+output:
+  - rolling_coverage:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - rolling_coverage.pdf:
+          type: file
+          description: PDF plot showing the rolling mean coverage across sequence positions
+          pattern: "rolling_coverage.pdf"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+containers:
+  docker:
+    linux/amd64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7
+      build_id: bd-32f1af0a83a018e7_1
+      scan_id: sc-337510c2c910aa1c_1
+    linux/arm64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb
+      build_id: bd-880bbdecb19543bb_1
+      scan_id: sc-09c9d098a09068b9_1
+  singularity:
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb
+      build_id: bd-7202e5d19667bddb_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7
+      build_id: bd-db46247d9810b3e7_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/visualization/global_pos_biases_cov/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
+    linux/arm64:
+      lock_file: modules/local/visualization/global_pos_biases_cov/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
diff --git a/modules/local/visualization/global_pos_biases_cov/templates/global_position_biases_cov.R b/modules/local/visualization/global_pos_biases_cov/templates/global_position_biases_cov.R
new file mode 100644
index 0000000..0a2fda1
--- /dev/null
+++ b/modules/local/visualization/global_pos_biases_cov/templates/global_position_biases_cov.R
@@ -0,0 +1,121 @@
+#!/usr/bin/env Rscript
+
+# input: prefiltered gatk path, aa seq path, window_size (sliding window), output_path_folder, aimed counts per aa variant
+# output: lineplot showing coverage over positions and dotted line: Assuming all potential 21 variants are equally distributed, this is the coverage one need to get the aimed counts per variant.
+
+library(zoo) # sliding window
+library(dplyr)
+library(ggplot2)
+library(scales)
+position_biases <- function(prefiltered_gatk_path, aa_seq_path, window_size = 10, output_file_path, targeted_counts_per_aa_variant = 100) {
+
+  # Load and process the data
+  prefiltered_gatk <- read.table(prefiltered_gatk_path, sep = ",", fill = NA, header = TRUE)
+  prefiltered_gatk\$pos <- as.numeric(sub("(\\\\D)(\\\\d+)(\\\\D)", "\\\\2", prefiltered_gatk\$pos_mut))
+  unique_pos <- unique(as.numeric(prefiltered_gatk\$pos))
+  aa_seq <- readLines(aa_seq_path, warn = FALSE)
+  aa_seq_length <- nchar(aa_seq)
+  aa_positions <- seq(nchar(aa_seq))
+
+  means_cov <- rep(NA, nchar(aa_seq))
+
+  # Calculate means for cov (should be the same over all variants in one position)
+  for (i in 1:(nchar(aa_seq))) {
+    window_data <- prefiltered_gatk %>% filter(prefiltered_gatk\$pos %in% aa_positions[i])
+    means_cov[i] <- mean(window_data\$cov, na.rm = FALSE)
+  }
+
+  pos_bias_df <- data.frame(pos = seq(nchar(aa_seq)))
+
+  # Log-transform the rolling means to avoid issues with zeros (log(y + 0.001))
+  pos_bias_df\$rolling_mean_cov <- rollapply(means_cov, width = window_size,
+                                            FUN = function(x) (mean(x, na.rm = TRUE)), fill = "extend")
+
+  # Generate the plot
+  plots_theme <- list(
+
+    # Customize legend appearance (leave titles blank)
+    guides(
+      color = guide_legend(title = NULL, order = 1),  # Title for line color
+      fill = guide_legend(title = NULL, order = 2),  # Title for ribbon fill
+      linetype = guide_legend(title = NULL, order = 3)  # Title for line type
+    ),
+
+    # Apply minimal theme
+    theme_minimal()
+  )
+
+
+  linetype_placeholder <- "Required coverage"
+
+  rolling_cov_plot <- ggplot(pos_bias_df, aes(x = pos, y = rolling_mean_cov)) +
+
+    # Add line for rolling mean coverage
+    geom_line(aes(color = "Coverage")) +
+
+    # Add a horizontal line with a mapped linetype so it appears in the legend
+    geom_hline(aes(yintercept = ((21 * nchar(aa_seq) * targeted_counts_per_aa_variant)),
+                   linetype = linetype_placeholder),
+               color = "black", linewidth = 0.3) +
+
+    # Individual axis labels for this plot
+    xlab("Amino Acid Position") +
+    ylab("Coverage") +
+
+    # Manually set color and fill labels
+    scale_color_manual(values = c("Coverage" = "black")) +  # Color for line
+
+    # Set the linetype with a label that uses the targeted threshold dynamically
+    scale_linetype_manual(values = c("Required coverage" = "dotted"),
+                          labels = paste("Required coverage \\n for", as.character(targeted_counts_per_aa_variant), "counts per variant \\n if equally present")) +
+
+    # Apply the saved theme and design elements at the end
+    plots_theme +
+
+    # Set y-axis to log scale and apply comma formatting
+    scale_y_continuous(trans = 'log10', labels = scales::comma)
+
+
+  # Return the plot
+  ggsave(filename = output_file_path, plot = rolling_cov_plot, device = "pdf", width = 10, height = 6)
+}
+
+#####
+# run function
+#####
+position_biases(
+  prefiltered_gatk_path = "$variantCounts_filtered_by_library",
+  aa_seq_path = "$aa_seq",
+  window_size = $sliding_window_size,
+  output_file_path = "rolling_coverage.pdf",
+  targeted_counts_per_aa_variant = $aimed_cov
+)
+
+#####
+# create versions.yml
+#####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+dplyr_version <- as.character(packageVersion("dplyr"))
+ggplot2_version <- as.character(packageVersion("ggplot2"))
+scales_version <- as.character(packageVersion("scales"))
+zoo_version <- as.character(packageVersion("zoo"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(dplyr_version) == 0) dplyr_version <- "unknown"
+if (length(ggplot2_version) == 0) ggplot2_version <- "unknown"
+if (length(scales_version) == 0) scales_version <- "unknown"
+if (length(zoo_version) == 0) zoo_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-dplyr:', dplyr_version),
+    paste('    r-ggplot2:', ggplot2_version),
+    paste('    r-scales:', scales_version),
+    paste('    r-zoo:', zoo_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/visualization/logdiff/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt b/modules/local/visualization/logdiff/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
new file mode 100644
index 0000000..be8d7e8
--- /dev/null
+++ b/modules/local/visualization/logdiff/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
@@ -0,0 +1,2942 @@
+version: 6
+environments:
+  default:
+    channels:
+    - url: https://conda.anaconda.org/conda-forge/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
+      linux-64:
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.46.1-default_hfdba357_103.conda
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-biocgenerics-0.56.0-r45hdfd78af_2.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-biostrings-2.78.0-r45h01b2380_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-iranges-2.44.0-r45h01b2380_1.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-s4vectors-0.48.1-r45h262fe30_0.conda
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-seqinfo-1.0.0-r45hdfd78af_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-xvector-0.50.0-r45h01b2380_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/bwidget-1.10.1-hfc2019e_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_10.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.9-hebe6cf0_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.10.23-hbd8a1cb_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-h3394656_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/curl-8.21.0-hcf29cc6_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.18.3-h4db4eae_1.conda
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+      - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.14.3-ha770c72_2.conda
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new file mode 100644
index 0000000..10e5993
--- /dev/null
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+  - libgcc >=15
+  - libxcb >=1.17.0,<2.0a0
+  license: MIT
+  license_family: MIT
+  size: 892361
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+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxau-1.0.12-h5bc82ec_2.conda
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+  license_family: MIT
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+  sha256: db2b37227a888d23269855409a9b2179e493c59f2ec9ee2071bf3fd198767ffd
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+  license_family: MIT
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+  license_family: MIT
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+  - xorg-libx11 >=1.8.13,<2.0a0
+  license: MIT
+  license_family: MIT
+  size: 36304
+  timestamp: 1792530356575
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxt-1.3.1-h5bc82ec_1.conda
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+  license_family: MIT
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+  md5: d942e34ac3920ba83f8b2d0169570187
+  depends:
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+  license: BSD-3-Clause
+  license_family: BSD
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diff --git a/modules/local/visualization/logdiff/environment.yml b/modules/local/visualization/logdiff/environment.yml
new file mode 100644
index 0000000..5f5785d
--- /dev/null
+++ b/modules/local/visualization/logdiff/environment.yml
@@ -0,0 +1,19 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+# Shared by 17 R modules (all except RUN_MUTSCAN, RUN_DIMSUM and the false-doubles error correction).
+# Keep the copies identical:
+# the one container image these modules use is built from exactly this list.
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/visualization/logdiff/main.nf b/modules/local/visualization/logdiff/main.nf
new file mode 100644
index 0000000..b41d520
--- /dev/null
+++ b/modules/local/visualization/logdiff/main.nf
@@ -0,0 +1,32 @@
+process VISUALIZATION_LOGDIFF {
+    tag "$meta.id"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7'}"
+
+    input:
+    tuple val(meta), path(library_completed_variantCounts)
+
+    output:
+    tuple val(meta), path("logdiff_plot.pdf"), emit: logdiff_plot
+    tuple val(meta), path("logdiff_varying_bases.pdf"), emit: logdiff_varying_bases
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'logdiff.R'
+
+    stub:
+    """
+    touch logdiff_plot.pdf
+    touch logdiff_varying_bases.pdf
+    echo "VISUALIZATION_LOGDIFF:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+    """
+}
diff --git a/modules/local/visualization/logdiff/meta.yml b/modules/local/visualization/logdiff/meta.yml
new file mode 100644
index 0000000..98ed780
--- /dev/null
+++ b/modules/local/visualization/logdiff/meta.yml
@@ -0,0 +1,70 @@
+name: "visualization_logdiff"
+description: Generates diagnostic plots to visualize the logarithmic differences in variant counts, specifically focusing on the distribution of variants across different nucleotide compositions.
+keywords:
+  - deep mutational scanning
+  - dms
+  - visualization
+  - diagnostics
+  - logdiff
+tools: null
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', single_end:false ]`
+    - library_completed_variantCounts:
+        type: file
+        description: CSV file containing processed variant counts, including zero-count entries from the library
+        pattern: "*.{csv}"
+
+output:
+  - logdiff_plot:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - logdiff_plot.pdf:
+          type: file
+          description: PDF plot showing the overall logarithmic differences in variant counts
+          pattern: "logdiff_plot.pdf"
+  - logdiff_varying_bases:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - logdiff_varying_bases.pdf:
+          type: file
+          description: PDF plot showing logarithmic differences categorized by varying nucleotide bases
+          pattern: "logdiff_varying_bases.pdf"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+containers:
+  docker:
+    linux/arm64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb
+      build_id: bd-880bbdecb19543bb_1
+      scan_id: sc-09c9d098a09068b9_1
+    linux/amd64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7
+      build_id: bd-32f1af0a83a018e7_1
+      scan_id: sc-337510c2c910aa1c_1
+  singularity:
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb
+      build_id: bd-7202e5d19667bddb_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7
+      build_id: bd-db46247d9810b3e7_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/visualization/logdiff/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
+    linux/arm64:
+      lock_file: modules/local/visualization/logdiff/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
diff --git a/modules/local/visualization/logdiff/templates/logdiff.R b/modules/local/visualization/logdiff/templates/logdiff.R
new file mode 100644
index 0000000..34d2c31
--- /dev/null
+++ b/modules/local/visualization/logdiff/templates/logdiff.R
@@ -0,0 +1,128 @@
+#!/usr/bin/env Rscript
+
+library(dplyr)
+library(ggplot2)
+library(scales)
+
+# input: completed library table (all programmed variants; counts_per_cov 0 for variants with no
+#        counts, observed = FALSE for those never observed)
+# output: two LogDiff plots (counts per coverage, sorted, log scale): a line plot, and a dot plot
+#         coloured by the number of changed bases per codon
+#
+# Variants with no counts cannot be drawn on a log scale. They are placed at a floor derived from
+# the data - half the lowest non-zero counts per coverage in the library, i.e. below every observed
+# variant - and shown in grey. LogDiff is log10 of the 90th over the 10th percentile of all
+# programmed variants. When more than 10 % of the library has no counts, the 10th percentile would
+# be the floor, an artefact of the plot, so LogDiff is not reported; the share of variants without
+# counts, printed on every plot, is then the meaningful measure.
+
+logdiff_plot <- function(completed_path) {
+
+  completed <- read.table(completed_path, sep = ",", fill = NA, header = TRUE)
+  cpc <- completed\$counts_per_cov
+  cpc[is.na(cpc)] <- 0
+  if (!"observed" %in% names(completed)) {
+    completed\$observed <- cpc > 0
+  }
+
+  n_total    <- length(cpc)
+  n_never    <- sum(!completed\$observed)
+  n_zeroed   <- sum(completed\$observed & cpc == 0)    # observed, but corrected to 0
+  n_no_count <- n_never + n_zeroed
+
+  nonzero <- cpc[cpc > 0]
+  if (length(nonzero) == 0) {
+    # Nothing to rank: write placeholder plots that say so, instead of failing the run
+    empty <- ggplot() +
+      annotate("text", x = 0, y = 0, label = "No programmed variant has counts in this library.") +
+      theme_void()
+    ggsave(filename = "logdiff_plot.pdf", plot = empty, device = "pdf", width = 10, height = 6)
+    ggsave(filename = "logdiff_varying_bases.pdf", plot = empty, device = "pdf", width = 10, height = 6)
+    return(invisible(NULL))
+  }
+  floor_value <- min(nonzero) / 2
+
+  sorted_counts <- completed %>%
+    mutate(counts_per_cov_plot = ifelse(cpc > 0, cpc, floor_value),
+           category = ifelse(cpc > 0, as.character(varying_bases), "none")) %>%
+    arrange(counts_per_cov_plot)
+  sorted_counts\$ids <- seq_len(nrow(sorted_counts))
+
+  Q3 <- quantile(sorted_counts\$counts_per_cov_plot, 0.90, na.rm = TRUE)
+  if (n_no_count / n_total > 0.10) {
+    Q1 <- NA
+    logdiff_text <- "LogDiff: not defined (more than 10 % of variants without counts)"
+  } else {
+    Q1 <- quantile(sorted_counts\$counts_per_cov_plot, 0.10, na.rm = TRUE)
+    logdiff_text <- paste0("LogDiff = ", round(log10(Q3) - log10(Q1), 2))
+  }
+  percentile_lines <- c(Q1, Q3)[!is.na(c(Q1, Q3))]
+
+  label <- paste0(
+    logdiff_text, "\\n",
+    "No counts: ", n_no_count, " of ", n_total, " variants (", round(100 * n_no_count / n_total, 1), " %)",
+    if (n_zeroed > 0) paste0(", ", n_zeroed, " of them corrected to 0") else ""
+  )
+  caption <- paste0(
+    "Variants without counts are drawn at half the lowest observed counts per coverage (",
+    format(signif(floor_value, 3), scientific = TRUE), ", dashed line)."
+  )
+  y_top <- max(sorted_counts\$counts_per_cov_plot)
+
+  line_plot <- ggplot(sorted_counts, aes(x = ids, y = counts_per_cov_plot)) +
+    geom_line(color = "black") +
+    geom_hline(yintercept = floor_value, linetype = "dashed", color = "grey60") +
+    geom_hline(yintercept = percentile_lines, linetype = "dotted", color = "black") +
+    annotate("text", x = 0, y = y_top, label = label, hjust = 0, vjust = 1, size = 4.5, color = "black") +
+    scale_y_continuous(trans = "log10") +
+    labs(x = "Variants", y = "Counts per Coverage", caption = caption) +
+    theme_minimal()
+  ggsave(filename = "logdiff_plot.pdf", plot = line_plot, device = "pdf", width = 10, height = 6)
+
+  colored_plot <- ggplot(sorted_counts, aes(x = ids, y = counts_per_cov_plot, color = category)) +
+    geom_hline(yintercept = floor_value, linetype = "dashed", color = "grey60") +
+    geom_hline(yintercept = percentile_lines, linetype = "dotted", color = "black") +
+    geom_point(size = 0.9) +
+    annotate("text", x = 1, y = y_top, label = label, hjust = 0, vjust = 1, size = 4.5, color = "black") +
+    scale_y_continuous(trans = "log10") +
+    scale_color_manual(values = c("1" = "chocolate", "2" = "darkolivegreen3", "3" = "deepskyblue1", "none" = "grey70"),
+                       breaks = c("1", "2", "3", "none"),
+                       labels = c("1", "2", "3", "no counts"),
+                       name = "Varying \\n Bases") +
+    labs(x = "Variants", y = "Counts per Coverage", caption = caption) +
+    theme_minimal()
+  ggsave(filename = "logdiff_varying_bases.pdf", plot = colored_plot, device = "pdf", width = 10, height = 6)
+}
+
+#####
+# run function
+#####
+logdiff_plot(
+  completed_path = "$library_completed_variantCounts"
+)
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+dplyr_version <- as.character(packageVersion("dplyr"))
+ggplot2_version <- as.character(packageVersion("ggplot2"))
+scales_version <- as.character(packageVersion("scales"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(dplyr_version) == 0) dplyr_version <- "unknown"
+if (length(ggplot2_version) == 0) ggplot2_version <- "unknown"
+if (length(scales_version) == 0) scales_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-dplyr:', dplyr_version),
+    paste('    r-ggplot2:', ggplot2_version),
+    paste('    r-scales:', scales_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/visualization/seqdepth/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt b/modules/local/visualization/seqdepth/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
new file mode 100644
index 0000000..be8d7e8
--- /dev/null
+++ b/modules/local/visualization/seqdepth/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
@@ -0,0 +1,2942 @@
+version: 6
+environments:
+  default:
+    channels:
+    - url: https://conda.anaconda.org/conda-forge/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
+    packages:
+      linux-64:
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.46.1-default_hfdba357_103.conda
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-biocgenerics-0.56.0-r45hdfd78af_2.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-biostrings-2.78.0-r45h01b2380_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-iranges-2.44.0-r45h01b2380_1.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-s4vectors-0.48.1-r45h262fe30_0.conda
+      - conda: https://conda.anaconda.org/bioconda/noarch/bioconductor-seqinfo-1.0.0-r45hdfd78af_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/bioconductor-xvector-0.50.0-r45h01b2380_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/bwidget-1.10.1-hfc2019e_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_10.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.9-hebe6cf0_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.10.23-hbd8a1cb_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-h3394656_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/curl-8.21.0-hcf29cc6_1.conda
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+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.18.3-h4db4eae_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2
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+      - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.14.3-ha770c72_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fribidi-1.0.17-h7cc23a3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-16.2.0-h9891192_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gfortran_impl_linux-64-16.2.0-hf930d89_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.15-h54a6638_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gsl-2.7-he838d99_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-16.2.0-h0d273dc_7.conda
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+      - conda: https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-4.18.0-he073ed8_9.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-h7cc23a3_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.22.2-hbc21106_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.46.1-default_hbd61a6d_103.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.2.0-hdb68285_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-11_h4a7cf45_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-11_h0358290_openblas.conda
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diff --git a/modules/local/visualization/seqdepth/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt b/modules/local/visualization/seqdepth/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
new file mode 100644
index 0000000..10e5993
--- /dev/null
+++ b/modules/local/visualization/seqdepth/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
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diff --git a/modules/local/visualization/seqdepth/environment.yml b/modules/local/visualization/seqdepth/environment.yml
new file mode 100644
index 0000000..5f5785d
--- /dev/null
+++ b/modules/local/visualization/seqdepth/environment.yml
@@ -0,0 +1,19 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+# Shared by 17 R modules (all except RUN_MUTSCAN, RUN_DIMSUM and the false-doubles error correction).
+# Keep the copies identical:
+# the one container image these modules use is built from exactly this list.
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/visualization/seqdepth/main.nf b/modules/local/visualization/seqdepth/main.nf
new file mode 100644
index 0000000..ce18752
--- /dev/null
+++ b/modules/local/visualization/seqdepth/main.nf
@@ -0,0 +1,37 @@
+process VISUALIZATION_SEQDEPTH {
+    tag "$meta.id"
+    // Closed-form hypergeometric rarefaction - fast, so no longer a heavy process.
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7'}"
+
+    input:
+    tuple val(meta), path(variantCounts_filtered_by_library)
+    path possible_mutations
+
+    output:
+    tuple val(meta), path("SeqDepth.pdf"), emit: SeqDepth
+    tuple val(meta), path("seqdepth_curve.csv"), emit: curve
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'SeqDepth_simulation.R'
+
+    stub:
+    """
+    touch SeqDepth.pdf
+    touch seqdepth_curve.csv
+    cat <<-END_VERSIONS > versions.yml
+    "${task.process}":
+        r-base: "0.0.0"
+        r-ggplot2: "0.0.0"
+    END_VERSIONS
+    """
+}
diff --git a/modules/local/visualization/seqdepth/meta.yml b/modules/local/visualization/seqdepth/meta.yml
new file mode 100644
index 0000000..b331288
--- /dev/null
+++ b/modules/local/visualization/seqdepth/meta.yml
@@ -0,0 +1,76 @@
+name: "visualization_seqdepth"
+description: Performs a sequencing depth simulation to assess library coverage and saturation, helping to determine if the sequencing effort was sufficient to capture all intended variants.
+keywords:
+  - deep mutational scanning
+  - dms
+  - visualization
+  - sequencing depth
+  - saturation
+tools: null
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', single_end:false ]`
+    - variantCounts_filtered_by_library:
+        type: file
+        description: CSV file containing variant counts filtered against the target library
+        pattern: "*.{csv}"
+  - - possible_mutations:
+        type: file
+        description: CSV file defining all theoretically possible mutations for the library design
+        pattern: "*.{csv}"
+
+output:
+  - SeqDepth:
+      - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. `[ id:'test', single_end:false ]`
+      - SeqDepth.pdf:
+          type: file
+          description: PDF plot showing the simulation of variant detection as a function of sequencing depth
+          pattern: "SeqDepth.pdf"
+  - curve:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - seqdepth_curve.csv:
+          type: file
+          description: Rarefaction curve points, re-plotted interactively by the summary report.
+          pattern: "seqdepth_curve.csv"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+containers:
+  docker:
+    linux/amd64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:32f1af0a83a018e7
+      build_id: bd-32f1af0a83a018e7_1
+      scan_id: sc-337510c2c910aa1c_1
+    linux/arm64:
+      name: community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:880bbdecb19543bb
+      build_id: bd-880bbdecb19543bb_1
+      scan_id: sc-09c9d098a09068b9_1
+  singularity:
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:db46247d9810b3e7
+      build_id: bd-db46247d9810b3e7_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/02/02db891e505b0ec37ba6de21f08d576b15a95991026542455b209e20959e50b1/data
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-dplyr_r-ggplot2_pruned:7202e5d19667bddb
+      build_id: bd-7202e5d19667bddb_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/79/7988a2e369e98cec897d48bab3e55daddd8a973d9b7491ad27b6b15d550a4c5b/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/visualization/seqdepth/.conda-lock/linux_amd64-bd-32f1af0a83a018e7_1.txt
+    linux/arm64:
+      lock_file: modules/local/visualization/seqdepth/.conda-lock/linux_arm64-bd-880bbdecb19543bb_1.txt
diff --git a/modules/local/visualization/seqdepth/templates/SeqDepth_simulation.R b/modules/local/visualization/seqdepth/templates/SeqDepth_simulation.R
new file mode 100644
index 0000000..ba349af
--- /dev/null
+++ b/modules/local/visualization/seqdepth/templates/SeqDepth_simulation.R
@@ -0,0 +1,125 @@
+#!/usr/bin/env Rscript
+
+# Sequencing-depth rarefaction: what fraction of the possible library variants stay detectable
+# (>= `threshold` reads) as the sequencing depth is subsampled from full down to zero.
+#
+# This is a closed-form rarefaction, not a stochastic simulation. Subsampling reads without
+# replacement makes each variant's surviving read count hypergeometric, so the expected number of
+# variants still at or above the threshold at depth n is a sum of hypergeometric tail probabilities:
+#
+#   E[detected | n reads] = sum_i  P(Hypergeom(N, c_i, n) >= threshold)
+#                         = sum_i  phyper(threshold - 1, c_i, N - c_i, n, lower.tail = FALSE)
+#
+# where N is the total read count and c_i the reads on variant i. It is exact (no Monte-Carlo noise),
+# needs no random sampling, and is orders of magnitude faster than draw-one-read-at-a-time loops - so
+# the step count can be fine without the runtime blowing up. Being exact at every depth, a finer grid
+# only resolves the curve better; it cannot change it.
+#
+# Depth grid: the step size grows by a constant factor from 0.01% at the bottom to 5% at the top
+# (0.95 -> 1). Each step is then a fixed fraction (~5%) of the depth it starts from, so the steep
+# low-depth rise is resolved as finely as the flat top, whatever depth a given library saturates at.
+# With these defaults that is 123 steps.
+
+library(ggplot2)
+
+# 0, then the cumulative sum of K geometrically growing steps that ends at exactly 1 with a final step
+# of `last_step`. K comes from the closed-form ratio (1 - first_step) / (1 - last_step), rounded up to
+# a whole number of steps; the ratio is then re-solved for that K so both ends stay exact.
+depth_grid <- function(first_step = 1e-4, last_step = 0.05) {
+  K <- ceiling(1 + log(last_step / first_step) / log((1 - first_step) / (1 - last_step)))
+  r <- uniroot(function(r) last_step * (1 - r^-K) / (1 - 1 / r) - 1,
+               c(1 + 1e-9, 2), tol = 1e-12)\$root
+  c(0, cumsum(last_step * r^-((K - 1):0)))
+}
+
+SeqDepth_simulation_plot <- function(prefiltered_gatk_path, possible_mutations_path,
+                                     output_file_path, curve_csv_path,
+                                     first_step = 1e-4, last_step = 0.05,
+                                     threshold = 3) {
+
+  data <- read.csv(prefiltered_gatk_path)
+  counts <- as.numeric(data\$counts)
+  counts <- counts[!is.na(counts) & counts > 0]           # only observed variants carry reads
+  possible_mutations <- read.csv(possible_mutations_path)
+  total_possible <- nrow(possible_mutations)               # denominator: the whole designed library
+
+  N <- sum(counts)
+  if (N <= 0 || total_possible <= 0) {
+    stop("SeqDepth: no counts or no possible mutations to rarefy")
+  }
+
+  # Depth fractions from depth_grid(), as integer read totals. unique() drops the collisions that
+  # rounding produces at the bottom of very small libraries.
+  depths <- unique(round(depth_grid(first_step, last_step) * N))
+
+  # Expected detected variants at each depth. phyper is vectorised over the per-variant counts, so each
+  # depth is one vectorised call; `lower.tail = FALSE` gives P(X > threshold-1) = P(X >= threshold).
+  expected_detected <- vapply(depths, function(n) {
+    if (n <= 0) return(0)
+    sum(phyper(threshold - 1, counts, N - counts, n, lower.tail = FALSE))
+  }, numeric(1))
+
+  curve <- data.frame(
+    depth_fold   = round(depths / N, 6),                       # x: fraction of full sequencing depth
+    variants_pct = round(expected_detected / total_possible * 100, 4)  # y: % of the possible library
+  )
+  write.csv(curve, curve_csv_path, row.names = FALSE)
+
+  plot <- ggplot(curve, aes(x = depth_fold, y = variants_pct)) +
+    geom_line(color = "black", linewidth = 0.4) +
+    geom_hline(yintercept = 100, linetype = "dotted", color = "black") +
+    scale_y_continuous(
+      labels = scales::percent_format(scale = 1),
+      limits = c(0, 100),
+      breaks = seq(0, 100, by = 5)
+    ) +
+    scale_x_continuous(
+      limits = c(0, 1),
+      breaks = seq(0, 1, length.out = 20),
+      labels = scales::number_format(accuracy = 0.01)
+    ) +
+    labs(x = "Fraction of sequencing depth", y = "Variants detected (% of possible library)") +
+    theme_minimal() +
+    theme(
+      panel.border = element_rect(color = "black", fill = NA),
+      panel.grid.major = element_line(linewidth = 0.2, linetype = "solid", color = "grey80"),
+      panel.grid.minor = element_blank(),
+      axis.text.x = element_text(angle = 45, hjust = 1)
+    )
+
+  ggsave(output_file_path, plot = plot, device = "pdf", width = 8, height = 6)
+}
+
+
+#####
+# run function
+#####
+SeqDepth_simulation_plot(
+  prefiltered_gatk_path   = "$variantCounts_filtered_by_library",
+  possible_mutations_path = "$possible_mutations",
+  output_file_path        = "SeqDepth.pdf",
+  curve_csv_path          = "seqdepth_curve.csv",
+  first_step              = 1e-4,
+  last_step               = 0.05,
+  threshold               = 1
+  )
+
+#####
+# create versions.yml
+#####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+ggplot2_version <- as.character(packageVersion("ggplot2"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(ggplot2_version) == 0) ggplot2_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-ggplot2:', ggplot2_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/visualization/summary_report/.conda-lock/linux_amd64-bd-cfb38dbf0574e16d_1.txt b/modules/local/visualization/summary_report/.conda-lock/linux_amd64-bd-cfb38dbf0574e16d_1.txt
new file mode 100644
index 0000000..e3629c8
--- /dev/null
+++ b/modules/local/visualization/summary_report/.conda-lock/linux_amd64-bd-cfb38dbf0574e16d_1.txt
@@ -0,0 +1,556 @@
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+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/conda-forge/
+    options:
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diff --git a/modules/local/visualization/summary_report/.conda-lock/linux_arm64-bd-a15c6f547e2083b8_1.txt b/modules/local/visualization/summary_report/.conda-lock/linux_arm64-bd-a15c6f547e2083b8_1.txt
new file mode 100644
index 0000000..9ebfc49
--- /dev/null
+++ b/modules/local/visualization/summary_report/.conda-lock/linux_arm64-bd-a15c6f547e2083b8_1.txt
@@ -0,0 +1,528 @@
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+  md5: cddcce875e2021c3a5b9dbecffb70d92
+  depends:
+  - python >=3.11
+  - python
+  license: Apache-2.0
+  license_family: APACHE
+  size: 141245
+  timestamp: 1793205938858
+- conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.12-9_cp312.conda
+  build_number: 9
+  sha256: ee9c2922e07afc85fc82d5fa82c9ac2c79da3be3283a5e17bf2f2ae38dbd02fb
+  md5: 4c32076993e6270825441d059ab5c18b
+  constrains:
+  - python 3.12.* *_cpython
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 6751
+  timestamp: 1788302823694
+- conda: https://conda.anaconda.org/conda-forge/noarch/pytz-2026.5-pyh5ded981_0.conda
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+  md5: c5e97ff094d2214da824ccc08ebc5499
+  depends:
+  - python >=3.11
+  - python
+  license: MIT
+  license_family: MIT
+  size: 202211
+  timestamp: 1791103834633
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/readline-8.3-ha7194a6_1.conda
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+  md5: 5b399a7afa10098f2a6b02263181426e
+  depends:
+  - libgcc >=15
+  - ncurses >=6.6,<7.0a0
+  license: GPL-3.0-only
+  license_family: GPL
+  size: 364344
+  timestamp: 1787033761576
+- conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda
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+  md5: 3339e3b65d58accf4ca4fb8748ab16b3
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+  - python >=3.9
+  - python
+  license: MIT
+  license_family: MIT
+  size: 18455
+  timestamp: 1753199211006
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/tk-8.6.13-noxft_hf03c496_4.conda
+  build_number: 104
+  sha256: a1aa0d0f0b3d539644ff992e6dd0facf950c79df58fc37590ce5be84ff5e3f22
+  md5: ebaded4b4b74c2cc43a754ded4eed76f
+  depends:
+  - libgcc >=15
+  - libzlib >=1.3.2,<2.0a0
+  constrains:
+  - xorg-libx11 >=1.8.13,<2.0a0
+  license: TCL
+  size: 3664220
+  timestamp: 1787272859143
+- conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2026e-h5d301ff_0.conda
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+  license: LicenseRef-Public-Domain
+  size: 119208
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+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/which-2.25-hdc560ac_0.conda
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+  - libstdcxx >=14
+  - libgcc >=14
+  license: GPL-3.0-only
+  license_family: GPL
+  size: 40249
+  timestamp: 1779439555623
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xz-5.8.3-hd704e39_1.conda
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+  depends:
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+  - liblzma 5.8.3 he30d5cf_1
+  - liblzma-devel 5.8.3 he30d5cf_1
+  - xz-gpl-tools 5.8.3 hd704e39_1
+  - xz-tools 5.8.3 he30d5cf_1
+  license: 0BSD AND LGPL-2.1-or-later AND GPL-2.0-or-later
+  size: 23459
+  timestamp: 1786348594461
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xz-gpl-tools-5.8.3-hd704e39_1.conda
+  sha256: 3f0b45a1039c86265f41f3bb5f248cd4d901acff49164f86122fab7cc4efc049
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+  depends:
+  - libgcc >=14
+  - liblzma 5.8.3 he30d5cf_1
+  constrains:
+  - xz 5.8.3.*
+  license: 0BSD AND LGPL-2.1-or-later AND GPL-2.0-or-later
+  size: 40115
+  timestamp: 1786348586456
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xz-tools-5.8.3-he30d5cf_1.conda
+  sha256: 1619ea83757336aaaac4cde221e4cd0982551c82def3276c91d0d53b0b7159db
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+  - libgcc >=14
+  - liblzma 5.8.3 he30d5cf_1
+  constrains:
+  - xz 5.8.3.*
+  license: 0BSD AND LGPL-2.1-or-later
+  size: 103133
+  timestamp: 1786348578460
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/zstd-1.5.7-h9d15635_7.conda
+  sha256: 427fd14bcb3b8659796fecc682716617409350fb5a98e5b7b47558a10d1a2fc7
+  md5: d942e34ac3920ba83f8b2d0169570187
+  depends:
+  - libzlib >=1.3.2,<2.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 615477
+  timestamp: 1786599613561
diff --git a/modules/local/visualization/summary_report/environment.yml b/modules/local/visualization/summary_report/environment.yml
new file mode 100644
index 0000000..e16d4bb
--- /dev/null
+++ b/modules/local/visualization/summary_report/environment.yml
@@ -0,0 +1,11 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+# Shared by the three Python report modules. Keep the copies identical:
+# the one container image these modules use is built from exactly this list.
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - conda-forge::numpy=1.26.4
+  - conda-forge::pandas=2.2.1
+  - conda-forge::python=3.12.3
diff --git a/modules/local/visualization/summary_report/main.nf b/modules/local/visualization/summary_report/main.nf
new file mode 100644
index 0000000..630e49c
--- /dev/null
+++ b/modules/local/visualization/summary_report/main.nf
@@ -0,0 +1,43 @@
+// Build the single, self-contained all-in-one HTML report for a run.
+//
+// Every artefact the report shows is passed through one generic pair of inputs: a base64-encoded
+// JSON manifest and the matching staged files, in the same order. That keeps the process signature
+// stable no matter which optional parts of the pipeline ran - a section simply disappears when its
+// entries are absent from the manifest, so no channel juggling is needed for --fitness/--pdb/EC.
+process VISUALIZATION_SUMMARY_REPORT {
+    tag "${meta.sample ?: meta.id}"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/f7/f727259d394cff047a26f3e3fa3a43e1a7a1c9dc51a35e6def7aa90a2085bb7b/data'
+        : 'community.wave.seqera.io/library/numpy_pandas_python:cfb38dbf0574e16d'}"
+
+    input:
+    tuple val(meta), val(run_b64)            // base64(JSON) run metadata (params, samples, versions)
+    val manifest_b64                         // base64(JSON) [{kind,group,name}, ...] aligned with `assets`
+    path assets, stageAs: 'a*/*'             // pdf/csv/tsv/html artefacts; numbered dirs avoid basename clashes
+    path template_html
+    path threedmol_js                        // 3Dmol.js library, inlined only when the run has a structure
+
+    output:
+    tuple val(meta), path("deepmutscan_report.html"), emit: report
+    path "versions.yml"                            , emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'build_summary_report.py'
+
+    stub:
+    """
+    touch deepmutscan_report.html
+    cat <<-END_VERSIONS > versions.yml
+    "${task.process}":
+        python: "0.0.0"
+        pandas: "0.0.0"
+    END_VERSIONS
+    """
+}
diff --git a/modules/local/visualization/summary_report/meta.yml b/modules/local/visualization/summary_report/meta.yml
new file mode 100644
index 0000000..239a936
--- /dev/null
+++ b/modules/local/visualization/summary_report/meta.yml
@@ -0,0 +1,88 @@
+name: "visualization_summary_report"
+description: Build the single self-contained all-in-one HTML report for a run (QC to fitness)
+keywords:
+  - report
+  - html
+  - quality control
+  - deep mutational scanning
+tools:
+  - "pandas":
+      description: "Powerful data structures for data analysis, time series, and statistics"
+      homepage: "https://pandas.pydata.org/"
+      licence: ["BSD-3-Clause"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', sample:'ORF1' ]`
+    - run_b64:
+        type: string
+        description: Base64-encoded JSON with run-level metadata (parameters, samples, versions).
+  - - manifest_b64:
+        type: string
+        description: |
+          Base64-encoded JSON array describing every staged asset as `{kind, group, name}`,
+          in the same order as `assets`. Base64 keeps the JSON free of quoting problems when it is
+          interpolated into the template.
+  - - assets:
+        type: file
+        description: |
+          Every artefact embedded in the report (plot PDFs, count tables, the fitness table and the
+          detail HTML reports). Staged into numbered directories because basenames repeat across
+          samples.
+  - - template_html:
+        type: file
+        description: The report's HTML shell; data, plots and assets are injected into it.
+        pattern: "*.html"
+  - - threedmol_js:
+        type: file
+        description: 3Dmol.js library, inlined only when the run has a structure (`--pdb`).
+        pattern: "*.js"
+
+output:
+  - report:
+      - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. `[ id:'test', sample:'ORF1' ]`
+      - "deepmutscan_report.html":
+          type: file
+          description: Self-contained all-in-one report; no sidecar files are needed to view or share it.
+          pattern: "deepmutscan_report.html"
+  - versions:
+      - "versions.yml":
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+maintainers:
+  - "@BenjaminWehnert1008"
+containers:
+  docker:
+    linux/amd64:
+      name: community.wave.seqera.io/library/numpy_pandas_python:cfb38dbf0574e16d
+      build_id: bd-cfb38dbf0574e16d_1
+      scan_id: sc-fae0cdb4b6c51968_1
+    linux/arm64:
+      name: community.wave.seqera.io/library/numpy_pandas_python:a15c6f547e2083b8
+      build_id: bd-a15c6f547e2083b8_1
+      scan_id: sc-931239170a8e2353_1
+  singularity:
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/numpy_pandas_python:7f16bcac97973062
+      build_id: bd-7f16bcac97973062_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/f7/f727259d394cff047a26f3e3fa3a43e1a7a1c9dc51a35e6def7aa90a2085bb7b/data
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/numpy_pandas_python:648dc1860698d21b
+      build_id: bd-648dc1860698d21b_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/22/229745e9be125462f1850ac75f13afb0738d30241558908657bfc802c2c6681f/data
+  conda:
+    linux/amd64:
+      lock_file: modules/local/visualization/summary_report/.conda-lock/linux_amd64-bd-cfb38dbf0574e16d_1.txt
+    linux/arm64:
+      lock_file: modules/local/visualization/summary_report/.conda-lock/linux_arm64-bd-a15c6f547e2083b8_1.txt
diff --git a/modules/local/visualization/summary_report/templates/build_summary_report.py b/modules/local/visualization/summary_report/templates/build_summary_report.py
new file mode 100644
index 0000000..224a689
--- /dev/null
+++ b/modules/local/visualization/summary_report/templates/build_summary_report.py
@@ -0,0 +1,716 @@
+#!/usr/bin/env python3
+"""Build the self-contained all-in-one HTML report for nf-core/deepmutscan.
+
+Nextflow ``template`` script for the VISUALIZATION_SUMMARY_REPORT module. Everything the report
+shows arrives through one generic mechanism: a base64 JSON manifest describing each staged asset
+(`kind|group|name`) plus the assets themselves, in the same order. Sections that have no entries
+are simply omitted, so the same code serves runs with or without fitness / error correction / a PDB.
+
+Two kinds of payload end up in the HTML:
+  * data  - count tables and the fitness table, aggregated down to what the plots actually need,
+            then rendered natively as interactive SVG in the browser.
+  * blobs - the original R-generated plot PDFs and the standalone detail reports, embedded as
+            base64 data URIs so the single file needs no sidecars to view, download or share.
+
+Note: this is a Nextflow template, so every backslash meant for Python is doubled and literal
+dollar signs are escaped.
+"""
+import base64
+import json
+import platform
+import re
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+MUT = re.compile(r"^([A-Za-z*]+)(\\d+)([A-Za-z*]+)")
+REP_RE = re.compile(r"^rescaled_fitness_rep(\\d+)\$", re.I)
+IN_RE = re.compile(r"^input(\\d+)\$", re.I)
+OUT_RE = re.compile(r"^output(\\d+)\$", re.I)
+
+# Reference data for the citation page. Every entry is transcribed from CITATIONS.md, which is the
+# pipeline's own maintained list - the point is that the report and the repo can never drift apart,
+# and that nothing here is invented. `pkgs` are the names these tools use in the run's versions.yml,
+# which is what decides whether a tool actually ran and therefore appears.
+CITATIONS = [
+    {"id": "deepmutscan", "always": True, "pkgs": [],
+     "authors": "Wehnert B, et al.", "title": "nf-core/deepmutscan",
+     "journal": "", "year": "", "key": "deepmutscan",
+     "note": "bioRxiv preprint, coming soon."},
+    {"id": "nfcore", "always": True, "pkgs": [],
+     "authors": "Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S",
+     "title": "The nf-core framework for community-curated bioinformatics pipelines",
+     "journal": "Nature Biotechnology", "year": "2020", "volume": "38", "number": "3", "pages": "276--278",
+     "doi": "10.1038/s41587-020-0439-x", "key": "Ewels2020"},
+    {"id": "nextflow", "always": True, "pkgs": ["nextflow"],
+     "authors": "Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C",
+     "title": "Nextflow enables reproducible computational workflows",
+     "journal": "Nature Biotechnology", "year": "2017", "volume": "35", "number": "4", "pages": "316--319",
+     "doi": "10.1038/nbt.3820", "key": "DiTommaso2017"},
+    {"id": "fq", "pkgs": ["fq"],
+     "authors": "St. Jude Rust Labs", "title": "fq: FASTQ file manipulation and validation",
+     "year": "", "url": "https://github.com/stjude-rust-labs/fq", "key": "fq"},
+    {"id": "fastqc", "pkgs": ["fastqc"],
+     "authors": "Andrews S", "title": "FastQC: A Quality Control Tool for High Throughput Sequence Data",
+     "year": "2010", "url": "https://www.bioinformatics.babraham.ac.uk/projects/fastqc/", "key": "Andrews2010"},
+    {"id": "multiqc", "pkgs": ["multiqc"],
+     "authors": "Ewels P, Magnusson M, Lundin S, K\\u00e4ller M",
+     "title": "MultiQC: summarize analysis results for multiple tools and samples in a single report",
+     "journal": "Bioinformatics", "year": "2016", "volume": "32", "number": "19", "pages": "3047--3048",
+     "doi": "10.1093/bioinformatics/btw354", "key": "Ewels2016"},
+    {"id": "samtools", "pkgs": ["samtools"],
+     "authors": "Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, Li H",
+     "title": "Twelve years of SAMtools and BCFtools",
+     "journal": "GigaScience", "year": "2021", "volume": "10", "number": "2", "pages": "giab008",
+     "doi": "10.1093/gigascience/giab008", "key": "Danecek2021"},
+    {"id": "vsearch", "pkgs": ["vsearch"],
+     "authors": "Rognes T, Flouri T, Nichols B, Quince C, Mah\\u00e9 F",
+     "title": "VSEARCH: a versatile open source tool for metagenomics",
+     "journal": "PeerJ", "year": "2016", "volume": "4", "pages": "e2584",
+     "doi": "10.7717/peerj.2584", "key": "Rognes2016"},
+    # BWA-MEM, not BWA-backtrack: the pipeline runs `bwa mem`, whose citation is the 2013 arXiv
+    # preprint - the same one nf-core's own bwa/mem module points at. Li & Durbin 2009 describes
+    # `bwa aln`, a different algorithm this pipeline never calls.
+    {"id": "bwa", "pkgs": ["bwa"],
+     "authors": "Li H", "title": "Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM",
+     "journal": "arXiv", "year": "2013", "eprint": "1303.3997", "primaryclass": "q-bio.GN",
+     "url": "https://arxiv.org/abs/1303.3997", "key": "Li2013"},
+    {"id": "pysam", "pkgs": ["pysam"],
+     "authors": "Heger A, et al.", "title": "pysam: a Python module for reading, manipulating and writing genomic data sets",
+     "year": "", "url": "https://github.com/pysam-developers/pysam", "key": "pysam"},
+    {"id": "polars", "pkgs": ["polars"],
+     "authors": "Vink R, et al.", "title": "Polars: Lightning-fast DataFrame library for Rust and Python",
+     "year": "", "url": "https://github.com/pola-rs/polars", "key": "polars"},
+    {"id": "biopython", "pkgs": ["biopython"],
+     "authors": "Cock PJA, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A, Friedberg I, Hamelryck T, Kauff F, Wilczynski B, de Hoon MJL",
+     "title": "Biopython: freely available Python tools for computational molecular biology and bioinformatics",
+     "journal": "Bioinformatics", "year": "2009", "volume": "25", "number": "11", "pages": "1422--1423",
+     "doi": "10.1093/bioinformatics/btp163", "key": "Cock2009"},
+    {"id": "threedmol", "pkgs": ["3dmol"],
+     "authors": "Rego N, Koes D", "title": "3Dmol.js: molecular visualization with WebGL",
+     "journal": "Bioinformatics", "year": "2015", "volume": "31", "number": "8", "pages": "1322--1324",
+     "doi": "10.1093/bioinformatics/btu829", "key": "Rego2015",
+     "note": "Bundled in the interactive variant effect inspection tool (BSD-3-Clause)."},
+    {"id": "dimsum", "pkgs": ["r-dimsum", "dimsum"],
+     "authors": "Faure AJ, Schmiedel JM, Baeza-Centurion P, Lehner B",
+     "title": "DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies",
+     "journal": "Genome Biology", "year": "2020", "volume": "21", "number": "1", "pages": "207",
+     "doi": "10.1186/s13059-020-02091-3", "key": "Faure2020"},
+    {"id": "mutscan", "pkgs": ["r-mutscan", "mutscan"],
+     "authors": "Soneson C, Bendel AM, Diss G, Stadler MB",
+     "title": "mutscan - a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data",
+     "journal": "Genome Biology", "year": "2023", "volume": "24", "number": "1", "pages": "132",
+     "doi": "10.1186/s13059-023-02967-0", "key": "Soneson2023"},
+]
+
+
+class args:
+    run = "$run_b64"
+    manifest = "$manifest_b64"
+    assets = "$assets".split()
+    template = "$template_html"
+    threedmol = "$threedmol_js"          # 3Dmol.js, inlined only when a structure is shown
+    out_html = "deepmutscan_report.html"
+
+
+def b64json(s):
+    return json.loads(base64.b64decode(s).decode("utf-8"))
+
+
+def data_uri(path, mime):
+    return "data:" + mime + ";base64," + base64.b64encode(Path(path).read_bytes()).decode("ascii")
+
+
+# Embedded reports run in a frame whose origin is opaque, and in an opaque origin `localStorage` and
+# `document.cookie` do not return empty - they *throw* SecurityError. MultiQC v1.35 reads both while
+# bootstrapping without a try/catch, so the exception unwinds its ready handler and the report is left
+# frozen on its "Loading report.." placeholder with no plots. Handing it a memory-backed store that
+# never throws is enough; the only thing lost is persistence of the reader's MultiQC preferences.
+# This prepends to the file we embed and does not touch the published multiqc_report.html.
+STORAGE_SHIM = """<script>
+(function () {
+  try { window.localStorage.getItem('__probe__'); } catch (e) {
+    var m = Object.create(null);
+    var s = {
+      getItem: function (k) { return k in m ? m[k] : null; },
+      setItem: function (k, v) { m[k] = String(v); },
+      removeItem: function (k) { delete m[k]; },
+      clear: function () { m = Object.create(null); },
+      key: function (i) { return Object.keys(m)[i] || null; }
+    };
+    Object.defineProperty(s, 'length', { get: function () { return Object.keys(m).length; } });
+    try { Object.defineProperty(window, 'localStorage', { value: s, configurable: true }); } catch (e2) {}
+  }
+  try { void document.cookie; } catch (e) {
+    var jar = '';
+    try {
+      Object.defineProperty(document, 'cookie', {
+        get: function () { return jar; },
+        set: function (v) { jar = String(v).split(';')[0]; },
+        configurable: true
+      });
+    } catch (e2) {}
+  }
+})();
+</script>"""
+
+# A srcdoc frame resolves relative URLs against the PARENT document's URL, so an in-page link such as
+# MultiQC's sidebar `href="#fastqc"` does not scroll the frame: it navigates the frame to
+# deepmutscan_report.html#fastqc, i.e. loads the whole report inside itself, again on every click.
+# Catch same-page links before the browser follows them and scroll to the target instead. Links that
+# only toggle a widget (Bootstrap collapse/tab) keep their own handlers; they just lose the navigation.
+ANCHOR_SHIM = """<script>
+document.addEventListener('click', function (e) {
+  var a = e.target && e.target.closest ? e.target.closest('a[href]') : null;
+  if (!a) return;
+  var href = a.getAttribute('href');
+  if (!href || href.charAt(0) !== '#') return;
+  e.preventDefault();
+  if (a.hasAttribute('data-bs-toggle') || a.hasAttribute('data-toggle')) return;
+  var id = decodeURIComponent(href.slice(1));
+  var t = id && (document.getElementById(id) || document.getElementsByName(id)[0]);
+  if (t) t.scrollIntoView();
+}, true);
+</script>"""
+
+HEAD_RE = re.compile(r"<head[^>]*>", re.I)
+
+
+def html_for_frame(path):
+    """Read an embedded report and make it work inside a srcdoc frame."""
+    text = Path(path).read_text(encoding="utf-8", errors="surrogateescape")
+    m = HEAD_RE.search(text)
+    if m:
+        text = text[: m.end()] + STORAGE_SHIM + ANCHOR_SHIM + text[m.end():]
+    return text
+
+
+def r6(x):
+    return round(float(x), 6) if x is not None and x == x else None
+
+
+def to_float(x):
+    try:
+        v = float(x)
+        return round(v, 6) if v == v else None
+    except (TypeError, ValueError):
+        return None
+
+
+def wt_orf(fasta_path, reading_frame):
+    """Wildtype ORF nucleotides plus the reference offset they start at.
+
+    --reading_frame is the 1-based inclusive range the pipeline uses everywhere else (e.g.
+    "352-1383"), converted to a 0-based half-open slice here. `start` is kept so nucleotide positions
+    can be reported in reference coordinates, which is what base_mut uses.
+
+    Returns None rather than guessing whenever anything fails to line up: a wrong offset would
+    mislabel every nucleotide change while still looking entirely plausible.
+    """
+    if not fasta_path or not reading_frame:
+        return None
+    try:
+        lo_s, hi_s = str(reading_frame).split("-")
+        lo, hi = int(lo_s), int(hi_s)
+    except (TypeError, ValueError):
+        return None
+    seq = "".join(l.strip() for l in Path(fasta_path).read_text().splitlines()
+                  if l.strip() and not l.startswith(">")).upper()
+    if lo < 1 or hi > len(seq) or hi < lo:
+        return None
+    orf = seq[lo - 1:hi]
+    if len(orf) % 3:
+        return None
+    return {"seq": orf, "start": lo}
+
+
+def parse_pos(s):
+    m = MUT.match(str(s))
+    return (m.group(1), int(m.group(2)), m.group(3)) if m else (None, None, None)
+
+
+def num(df, name):
+    return (pd.to_numeric(df[name], errors="coerce") if name in df.columns
+            else pd.Series([np.nan] * len(df)))
+
+
+def per_position(path, orf_len):
+    """Aggregate a filtered-by-library table to per-ORF-position series.
+
+    Deliberately mirrors `global_position_biases_{counts,cov}.R` so the interactive curves and the
+    pipeline's published PDFs describe the same quantity:
+
+      * coverage  - a property of the position, so it is averaged over the substitutions measured
+                    there (the R comment claims it is constant; on real data it varies by ~0.25%
+                    because reads must span every varying base plus --min_flank, which is small
+                    enough that the mean is the right summary).
+      * counts / counts_per_cov
+                  - averaged *within* a varying-bases class and never pooled across classes. The
+                    classes differ several-fold in both counts and frequency, and their mix changes
+                    along the ORF, so a pooled curve would track composition rather than the library.
+      * R uses na.rm=FALSE for these per-position means, hence skipna=False here.
+
+    Series run over every ORF position 1..orf_len, with None where a class has no variants, so that
+    the rolling mean in the browser sees the same gaps rollapply() does.
+
+    `counts_raw` is only present once error correction has run, and drives the per-sample summary.
+    """
+    df = pd.read_csv(path)
+    pos = df["pos_mut"].astype(str).map(lambda s: parse_pos(s)[1])
+    cov, cnt = num(df, "cov"), num(df, "counts")
+    cpc, raw = num(df, "counts_per_cov"), num(df, "counts_raw")
+    vb = num(df, "varying_bases")
+    d = pd.DataFrame({"pos": pos, "cov": cov, "counts": cnt, "cpc": cpc, "raw": raw, "vb": vb})
+    d = d.dropna(subset=["pos"])
+    d["pos"] = d["pos"].astype(int)
+
+    n = int(orf_len) if orf_len else (int(d["pos"].max()) if len(d) else 0)
+    axis = list(range(1, n + 1))
+
+    cov_by = d.groupby("pos")["cov"].mean(numeric_only=False)
+    positions = [{"pos": p, "cov": r6(cov_by.get(p))} for p in axis]
+
+    classes = {}
+    for k in (1, 2, 3):
+        sub = d[d["vb"] == k]
+        cm = sub.groupby("pos")["counts"].apply(lambda s: s.mean(skipna=False))
+        fm = sub.groupby("pos")["cpc"].apply(lambda s: s.mean(skipna=False))
+        classes[str(k)] = [{"pos": p, "counts": r6(cm.get(p)), "cpc": r6(fm.get(p))} for p in axis]
+
+    ec = None
+    if raw.notna().any():
+        tr, tc = float(np.nansum(raw.values)), float(np.nansum(cnt.values))
+        ec = {
+            "n_variants": int(len(d)),
+            "n_corrected": int((raw.fillna(0) != cnt.fillna(0)).sum()),
+            "total_raw": round(tr, 1),
+            "total_corrected": round(tc, 1),
+            "pct_removed": round((tr - tc) / tr * 100.0, 2) if tr > 0 else 0.0,
+        }
+    return positions, classes, ec
+
+
+def heatmap(path):
+    """Counts-per-coverage grid (position x substituted amino acid) for one sample."""
+    df = pd.read_csv(path)
+    if "position" not in df.columns or "mut_aa" not in df.columns:
+        return None
+    pos = pd.to_numeric(df["position"], errors="coerce")
+    val = num(df, "total_counts_per_cov")
+    cells = [{"p": int(p), "a": str(a), "v": r6(v)}
+             for p, a, v in zip(pos, df["mut_aa"].astype(str), val)
+             if p == p and v == v]
+    wt = {}
+    if "wt_aa" in df.columns:
+        for p, w in zip(pos, df["wt_aa"].astype(str)):
+            if p == p and w and w != "nan":
+                wt[int(p)] = w
+    return {"cells": cells, "wt": wt}
+
+
+def codon_changes(nt_seq_field, pos, wt_nt):
+    """Nucleotide changes behind one amino-acid substitution.
+
+    `nt_seq` holds the full ORF for every distinct coding variant that produces this amino-acid
+    change, comma-separated - an NNK library typically reaches one residue through several codons.
+    Each is diffed against the wildtype ORF over this residue's codon only: changes elsewhere belong
+    to other residues and would be double counted. Returns one entry per distinct codon.
+
+    Reported positions are in REFERENCE coordinates, not ORF-relative ones, because that is the
+    convention `base_mut` uses everywhere else in the pipeline - checked against variantCounts, where
+    4000/4000 variants agree with the reference convention and 0/4000 with the ORF-relative one.
+    Emitting ORF-relative numbers here would be off by the reading frame's start and would silently
+    disagree with the error-correction report.
+    """
+    if not wt_nt or not wt_nt.get("seq") or not nt_seq_field:
+        return []
+    seq_wt, rf_lo = wt_nt["seq"], wt_nt["start"]
+    lo = (int(pos) - 1) * 3
+    if lo + 3 > len(seq_wt):
+        return []
+    wt_codon = seq_wt[lo:lo + 3]
+    out, seen = [], set()
+    for seq in (s.strip() for s in str(nt_seq_field).split(",")):
+        if len(seq) < lo + 3:
+            continue
+        mut_codon = seq[lo:lo + 3]
+        if mut_codon == wt_codon or mut_codon in seen:
+            continue
+        seen.add(mut_codon)
+        changes, positions = [], []
+        for k in range(3):
+            if wt_codon[k] != mut_codon[k]:
+                changes.append(f"{wt_codon[k]}>{mut_codon[k]}")
+                positions.append(rf_lo + lo + k)   # reference coords, as base_mut reports them
+        out.append({"wt_codon": wt_codon, "mut_codon": mut_codon,
+                    "changes": changes, "positions": positions})
+    return out
+
+
+def load_fitness(path, wt_nt=None):
+    """Per-substitution fitness plus its per-position mean, from the default fitness table."""
+    df = pd.read_csv(path, sep="\\t", dtype=str, keep_default_na=False)
+
+    def col(n):
+        return next((c for c in df.columns if c.strip().lower() == n), None)
+
+    fc, sc = col("mean fitness"), col("fitness sd")
+    if not fc:
+        return None
+    # Replicate count is a per-run property, so discover the columns instead of assuming rep1/rep2.
+    rep_cols = sorted((c for c in df.columns if REP_RE.match(c.strip())),
+                      key=lambda c: int(REP_RE.match(c.strip()).group(1)))
+    in_cols = [c for c in df.columns if IN_RE.match(c.strip())]
+    out_cols = [c for c in df.columns if OUT_RE.match(c.strip())]
+    ntc = col("nt_seq")
+    rows, per_pos = [], {}
+    for _, r in df.iterrows():
+        try:
+            p = int(float(r.get("pos", "") or "nan"))
+        except (TypeError, ValueError):
+            continue
+        wt, mut = (r.get("wt_aa", "") or "").strip(), (r.get("mut_aa", "") or "").strip()
+        if not wt or not mut:
+            continue
+        try:
+            f = float(r.get(fc, ""))
+        except (TypeError, ValueError):
+            continue
+        if f != f:
+            continue
+        sd = None
+        try:
+            sd = float(r.get(sc, "")) if sc else None
+            if sd != sd:
+                sd = None
+        except (TypeError, ValueError):
+            sd = None
+        stop = mut == "*" or (r.get("stop", "").strip() not in ("", "0"))
+
+        def tot(cols):
+            s = 0.0
+            got = False
+            for c in cols:
+                try:
+                    s += float(r.get(c, "") or 0)
+                    got = True
+                except (TypeError, ValueError):
+                    pass
+            return s if got else None
+
+        cell = {"p": p, "wt": wt, "a": mut, "f": r6(f), "sd": r6(sd),
+                "syn": mut == wt, "stop": bool(stop)}
+        if rep_cols:
+            cell["reps"] = [to_float(r.get(c, "")) for c in rep_cols]
+        cell["n_in"], cell["n_out"] = tot(in_cols), tot(out_cols)
+        if ntc and wt_nt:
+            nt = codon_changes(r.get(ntc, ""), p, wt_nt)
+            if nt:
+                cell["nt"] = nt
+        rows.append(cell)
+        if mut != wt and not stop:
+            per_pos.setdefault(p, []).append(f)
+    return {"cells": rows}
+
+
+THREE_TO_ONE = {
+    "ALA": "A", "ARG": "R", "ASN": "N", "ASP": "D", "CYS": "C", "GLN": "Q", "GLU": "E",
+    "GLY": "G", "HIS": "H", "ILE": "I", "LEU": "L", "LYS": "K", "MET": "M", "PHE": "F",
+    "PRO": "P", "SER": "S", "THR": "T", "TRP": "W", "TYR": "Y", "VAL": "V",
+}
+
+
+def parse_pdb(pdb_path):
+    """(pdb_text, residues) with residues = [{resnum, aa}] from the CA atoms, ordered by residue number."""
+    text = Path(pdb_path).read_text()
+    residues, seen = [], set()
+    for line in text.splitlines():
+        if not line.startswith("ATOM") or line[12:16].strip() != "CA":
+            continue
+        try:
+            resnum = int(line[22:26])
+        except ValueError:
+            continue
+        if resnum in seen:
+            continue
+        seen.add(resnum)
+        residues.append({"resnum": resnum, "aa": THREE_TO_ONE.get(line[17:20].strip().upper(), "X")})
+    residues.sort(key=lambda r: r["resnum"])
+    return text, residues
+
+
+def best_alignment(wt_seq, struct_residues):
+    """Offset mapping wt position j (0-based) to struct_residues[off0 + j], maximising identity.
+
+    The PDB's residue numbering need not start at the ORF's first residue (AFDB models often carry a
+    signal peptide or a different frame), so the mapping is found by sliding the sequences, exactly as
+    the inspection tool does - the two must agree or the structure would be coloured off by a frame.
+    """
+    struct_seq = "".join(r["aa"] for r in struct_residues)
+    n, m = len(wt_seq), len(struct_seq)
+    best_off, best_matches = 0, -1
+    for off in range(-n + 1, m):
+        matches = sum(1 for j in range(n) if 0 <= off + j < m and struct_seq[off + j] == wt_seq[j])
+        if matches > best_matches:
+            best_matches, best_off = matches, off
+    return best_off, best_matches / max(1, n)
+
+
+def structure_data(pdb_path, wt_seq, fitness):
+    """PDB text plus per-residue mean fitness, keyed by PDB residue number, for the spinning viewer.
+
+    The colour at a residue is the mean fitness over the missense substitutions measured there - the
+    same position summary the fitness heatmap collapses to - so the structure and the heatmap tell one
+    story. Returns None (viewer simply hidden) whenever a structure cannot be trusted: no PDB, no
+    fitness, or an alignment that never lines up.
+    """
+    if not pdb_path or not wt_seq or not fitness or not fitness.get("cells"):
+        return None
+    pdb_text, residues = parse_pdb(pdb_path)
+    if not residues:
+        return None
+    off0, identity = best_alignment(wt_seq, residues)
+    by_pos = {}
+    for c in fitness["cells"]:
+        if c.get("syn") or c.get("stop") or c.get("f") is None:
+            continue
+        by_pos.setdefault(c["p"], []).append(c["f"])
+    pos_mean = {p: sum(v) / len(v) for p, v in by_pos.items() if v}
+    resi_fit = {}
+    for j in range(len(wt_seq)):
+        k = off0 + j
+        if 0 <= k < len(residues) and (j + 1) in pos_mean:
+            resi_fit[residues[k]["resnum"]] = round(pos_mean[j + 1], 4)
+    if not resi_fit:
+        return None
+    fmax = max(abs(v) for v in resi_fit.values()) or 1.0
+    return {"resi_fit": resi_fit, "fmax": round(fmax, 4),
+            "identity": round(identity, 4), "n_residues": len(residues), "pdb": pdb_text}
+
+
+def software_versions(path):
+    """Parse pipeline_info's software versions YAML into {process: {package: version}}.
+
+    Hand-parsed rather than via a YAML library: the file is two levels of trivial `key: value` and
+    pandas' container carries no yaml. Duplicate process keys are merged rather than overwritten -
+    they occur in practice when a module fails to interpolate its own name.
+    """
+    out = {}
+    cur = None
+    for line in Path(path).read_text().splitlines():
+        if not line.strip() or line.lstrip().startswith("#"):
+            continue
+        if not line.startswith((" ", "\\t")):
+            cur = line.strip().rstrip(":").strip('"')
+            out.setdefault(cur, {})
+        elif cur:
+            k, _, v = line.strip().partition(":")
+            if k:
+                out[cur][k.strip()] = v.strip()
+    return out
+
+
+def citations(versions, extra=()):
+    """The subset of CITATIONS whose tools actually ran, plus the pipeline and frameworks always.
+
+    Package names in versions.yml are the primary signal, but they are not always sufficient:
+    3Dmol.js, for instance, is vendored as a JS asset and has no package entry at all. `extra`
+    carries the tokens that a run's own flags settle unambiguously (--dimsum, --mutscan, a --pdb
+    structure). A tool outside both signals is never cited - the page is about this run, not the
+    pipeline's full repertoire.
+    """
+    seen = {p.strip().lower() for pkgs in versions.values() for p in pkgs}
+    seen |= {e.lower() for e in extra}
+    out = []
+    for c in CITATIONS:
+        if c.get("always") or any(p.lower() in seen for p in c["pkgs"]):
+            out.append({k: v for k, v in c.items() if k != "pkgs"})
+    return out
+
+
+def biblatex(entries):
+    """A biblatex source block for the given entries."""
+    # The braces are assembled from these two names instead of being written literally: nf-core's
+    # `template_strings` lint reads any doubled-brace pair in the source as a leftover Jinja
+    # placeholder, and biblatex needs exactly such a pair around the title to protect its
+    # capitalisation.
+    ob, cb = "{", "}"
+    lines = []
+    for c in entries:
+        kind = "@article"
+        if c.get("eprint"):
+            kind = "@misc"
+        elif not c.get("journal"):
+            kind = "@misc"
+        lines.append(kind + ob + c["key"] + ",")
+        lines.append("  author       = " + ob + c["authors"].replace(", ", " and ") + cb + ",")
+        lines.append("  title        = " + ob + ob + c["title"] + cb + cb + ",")
+        for fld, key in (("journaltitle", "journal"), ("year", "year"), ("volume", "volume"),
+                         ("number", "number"), ("pages", "pages"), ("doi", "doi"),
+                         ("eprint", "eprint"), ("eprintclass", "primaryclass"), ("url", "url")):
+            if c.get(key):
+                lines.append(f"  {fld:<12} = " + ob + str(c[key]) + cb + ",")
+        if c.get("note"):
+            lines.append("  note         = " + ob + c["note"] + cb + ",")
+        lines.append("}")
+        lines.append("")
+    return "\\n".join(lines)
+
+
+def main():
+    run = b64json(args.run)
+    manifest = b64json(args.manifest)
+    if len(manifest) != len(args.assets):
+        raise SystemExit(f"manifest has {len(manifest)} entries but {len(args.assets)} files were staged")
+
+    samples, plots, reports, fitness, dl, versions = {}, {}, [], None, [], {}
+
+    def sample(sid):
+        # type/replicate drive the input-vs-output colour palette and the seqdepth default selection.
+        # Pipeline ids are "<sample>_<type>_<rep>_<pe|se>", so the type is a delimited token - matching it
+        # that way is robust for any protein name (which is a separate, earlier segment).
+        m = re.search(r"_(input|output|wildtype|quality)_(\\d+)", sid)
+        stype = m.group(1) if m else None
+        srep = int(m.group(2)) if m else None
+        return samples.setdefault(sid, {"id": sid, "type": stype, "replicate": srep,
+                                        "positions": [], "classes": {}, "heatmap": None, "ec": None, "seqdepth": None})
+
+    # The ORF length has to come from the wildtype protein sequence, not from the highest position that
+    # happens to carry a variant, or trailing residues with no library coverage would silently shorten
+    # the axis and shift every rolling window.
+    orf_len, wt_nt, wt_aa_seq, pdb_path = 0, None, None, None
+    for entry, path in zip(manifest, args.assets):
+        if entry["kind"] == "aa_seq":
+            wt_aa_seq = Path(path).read_text().strip().split("\\n")[0].strip()
+            orf_len = len(wt_aa_seq)
+        elif entry["kind"] == "fasta":
+            wt_nt = wt_orf(path, run.get("reading_frame"))
+        elif entry["kind"] == "pdb":
+            pdb_path = path
+    # A frame that disagrees with the protein length means the codon diffs would be off by a whole
+    # residue, so drop the nucleotide detail rather than print something plausible and wrong.
+    if wt_nt and orf_len and len(wt_nt["seq"]) // 3 not in (orf_len, orf_len + 1):
+        print(f"WARNING: ORF is {len(wt_nt['seq'])} nt ({len(wt_nt['seq'])//3} codons) but the protein is "
+              f"{orf_len} aa; skipping nucleotide-level detail")
+        wt_nt = None
+
+    for entry, path in zip(manifest, args.assets):
+        kind, group, name = entry["kind"], entry["group"], entry["name"]
+
+        if kind == "qc":                       # per-sample library-QC plot (original R PDF)
+            # Deliberately NOT embedded. The report re-plots these natively and offers the live chart
+            # as SVG/PNG, so carrying ~25 MB of base64 PDF bought nothing but weight - and the weight
+            # is not free: it is what stops the embedded MultiQC report from finishing its render.
+            # Only the location is kept, for the "open the results folder" links.
+            plots.setdefault(group, {})[Path(name).stem] = None
+
+        elif kind == "filtered":               # per-sample counts -> per-position curves + EC summary
+            positions, classes, ec = per_position(path, orf_len)
+            s = sample(group)
+            s["positions"], s["classes"], s["ec"] = positions, classes, ec
+
+        elif kind == "heatmap":                # per-sample counts-per-cov grid
+            sample(group)["heatmap"] = heatmap(path)
+
+        elif kind == "seqdepth":               # per-sample rarefaction curve (depth fraction -> % library)
+            sd = pd.read_csv(path)
+            sample(group)["seqdepth"] = [
+                {"x": r6(x), "y": r6(y)}
+                for x, y in zip(num(sd, "depth_fold"), num(sd, "variants_pct"))
+                if x == x and y == y
+            ]
+
+        elif kind == "fitness_table":
+            fitness = load_fitness(path, wt_nt)
+
+        elif kind in ("fitness_pdf", "mutscan_pdf"):
+            # Same reasoning as the QC PDFs: recorded by name so the page can link to them in the
+            # results folder, not inlined.
+            dl.append({"section": "fitness", "group": group, "name": name})
+
+        elif kind == "report":                 # standalone detail report, embedded for a lazy iframe
+            # Carried as srcdoc text rather than a data: URI. srcdoc inherits this document's origin
+            # instead of minting an opaque one, and it is ~25% smaller than the base64 equivalent.
+            reports.append({"group": group, "name": name, "html": html_for_frame(path)})
+
+        elif kind == "versions":               # pipeline_info software-versions YAML
+            versions = software_versions(path)
+
+
+    # Coverage needed for every residue outcome to reach --aimed_cov counts, assuming all 21 outcomes
+    # are equally represented. Same expression as the reference line in global_position_biases_cov.R;
+    # it is a per-position number because each read spans the whole ORF in this assay.
+    try:
+        run["required_cov"] = 21 * orf_len * float(run.get("aimed_cov") or 0) or None
+    except (TypeError, ValueError):
+        run["required_cov"] = None
+    run["orf_length"] = orf_len
+
+    # Flags settle the tools that the version strings alone cannot: 3Dmol.js has no package entry
+    # at all, so the structure viewer's presence is the only tell.
+    def truthy(x):
+        return str(x).strip().lower() in ("true", "1", "yes")
+
+    extra = set()
+    if truthy(run.get("dimsum")):
+        extra.add("dimsum")
+    if truthy(run.get("mutscan")):
+        extra.add("mutscan")
+    if any(r["group"] == "viewer" for r in reports):
+        extra.add("3dmol")
+    if pdb_path:
+        extra.add("3dmol")
+    cites = citations(versions, extra)
+    # The spinning structure needs a PDB, fitness, and the wildtype protein sequence to align them; any
+    # missing and the viewer is simply left out (structure = None) so the page still renders.
+    structure = structure_data(pdb_path, wt_aa_seq, fitness)
+    data = {
+        "run": run,
+        "samples": [samples[k] for k in sorted(samples)],
+        "plots": plots,
+        "reports": reports,
+        "fitness": fitness,
+        "structure": structure,
+        "downloads": dl,
+        # Two citation groups for the page: the pipeline itself (always) and the tools that actually
+        # ran. Each carries its own biblatex block so the "get biblatex" button has nothing to compute.
+        # Section 1 is the pipeline and the frameworks it is built on; section 2 is the analysis tools
+        # this particular run actually invoked.
+        "citations": (lambda pipe: {
+            "pipeline": [c for c in cites if c["id"] in pipe],
+            "tools": [c for c in cites if c["id"] not in pipe],
+            "biblatex_pipeline": biblatex([c for c in cites if c["id"] in pipe]),
+            "biblatex_tools": biblatex([c for c in cites if c["id"] not in pipe]),
+        })({"deepmutscan", "nfcore", "nextflow"}),
+        "software": versions,
+    }
+
+    # 3Dmol.js is ~0.5 MB, so inline it only when a structure will actually be shown; otherwise the
+    # placeholder collapses to nothing and a no-PDB run carries no dead weight.
+    threedmol_js = ""
+    if structure and args.threedmol:
+        try:
+            threedmol_js = Path(args.threedmol).read_text(encoding="utf-8", errors="surrogateescape")
+        except OSError:
+            threedmol_js = ""
+
+    html = (Path(args.template).read_text(encoding="utf-8")
+            .replace("__REPORT_DATA__", json.dumps(data, separators=(",", ":")).replace("</", "<\\\\/"))
+            .replace("/*__THREEDMOL_JS__*/", threedmol_js)
+            .replace("__SAMPLE__", str(run.get("sample", "run"))))
+    Path(args.out_html).write_text(html, encoding="utf-8")
+
+    Path("versions.yml").write_text(
+        '"${task.process}":\\n'
+        + "    python: " + platform.python_version() + "\\n"
+        + "    pandas: " + pd.__version__ + "\\n"
+        + "    numpy: " + np.__version__ + "\\n"
+    )
+    mb = Path(args.out_html).stat().st_size / 1e6
+    print(f"Wrote {args.out_html} ({mb:.1f} MB) - {len(samples)} samples, "
+          f"{sum(len(v) for v in plots.values())} plot PDFs, {len(reports)} embedded reports")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/modules/nf-core/bwa/index/environment.yml b/modules/nf-core/bwa/index/environment.yml
new file mode 100644
index 0000000..54e6794
--- /dev/null
+++ b/modules/nf-core/bwa/index/environment.yml
@@ -0,0 +1,13 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+channels:
+  - conda-forge
+  - bioconda
+
+dependencies:
+  # renovate: datasource=conda depName=bioconda/bwa
+  - bioconda::bwa=0.7.19
+  # renovate: datasource=conda depName=bioconda/htslib
+  - bioconda::htslib=1.22.1
+  # renovate: datasource=conda depName=bioconda/samtools
+  - bioconda::samtools=1.22.1
diff --git a/modules/nf-core/bwa/index/main.nf b/modules/nf-core/bwa/index/main.nf
new file mode 100644
index 0000000..a1c98ac
--- /dev/null
+++ b/modules/nf-core/bwa/index/main.nf
@@ -0,0 +1,44 @@
+process BWA_INDEX {
+    tag "$fasta"
+    // NOTE requires 5.37N memory where N is the size of the database
+    // source: https://bio-bwa.sourceforge.net/bwa.shtml#8
+    memory { 7.B * fasta.size() }
+
+    conda "${moduleDir}/environment.yml"
+    container "${ workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container ?
+        'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/d7/d7e24dc1e4d93ca4d3a76a78d4c834a7be3985b0e1e56fddd61662e047863a8a/data' :
+        'community.wave.seqera.io/library/bwa_htslib_samtools:83b50ff84ead50d0' }"
+
+    input:
+    tuple val(meta), path(fasta)
+
+    output:
+    tuple val(meta), path("bwa"), emit: index
+    tuple val("${task.process}"), val('bwa'), eval('bwa 2>&1 | sed -n "s/^Version: //p"'), topic: versions, emit: versions_bwa
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    def prefix = task.ext.prefix ?: "${fasta.baseName}"
+    def args   = task.ext.args ?: ''
+    """
+    mkdir bwa
+    bwa \\
+        index \\
+        $args \\
+        -p bwa/${prefix} \\
+        $fasta
+    """
+
+    stub:
+    def prefix = task.ext.prefix ?: "${fasta.baseName}"
+    """
+    mkdir bwa
+    touch bwa/${prefix}.amb
+    touch bwa/${prefix}.ann
+    touch bwa/${prefix}.bwt
+    touch bwa/${prefix}.pac
+    touch bwa/${prefix}.sa
+    """
+}
diff --git a/modules/nf-core/bwa/index/meta.yml b/modules/nf-core/bwa/index/meta.yml
new file mode 100644
index 0000000..f5bf7f5
--- /dev/null
+++ b/modules/nf-core/bwa/index/meta.yml
@@ -0,0 +1,71 @@
+name: bwa_index
+description: Create BWA index for reference genome
+keywords:
+  - index
+  - fasta
+  - genome
+  - reference
+tools:
+  - bwa:
+      description: |
+        BWA is a software package for mapping DNA sequences against
+        a large reference genome, such as the human genome.
+      homepage: http://bio-bwa.sourceforge.net/
+      documentation: https://bio-bwa.sourceforge.net/bwa.shtml
+      arxiv: arXiv:1303.3997
+      licence: ["GPL-3.0-or-later"]
+      identifier: "biotools:bwa"
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing reference information.
+          e.g. [ id:'test', single_end:false ]
+    - fasta:
+        type: file
+        description: Input genome fasta file
+        ontologies:
+          - edam: "http://edamontology.org/data_2044" # Sequence
+          - edam: "http://edamontology.org/format_1929" # FASTA
+output:
+  index:
+    - - meta:
+          type: map
+          description: |
+            Groovy Map containing reference information.
+            e.g. [ id:'test', single_end:false ]
+      - bwa:
+          type: map
+          description: |
+            Groovy Map containing reference information.
+            e.g. [ id:'test', single_end:false ]
+          pattern: "*.{amb,ann,bwt,pac,sa}"
+          ontologies:
+            - edam: "http://edamontology.org/data_3210" # Genome index
+  versions_bwa:
+    - - ${task.process}:
+          type: string
+          description: The process the versions were collected from
+      - bwa:
+          type: string
+          description: The tool name
+      - 'bwa 2>&1 | sed -n "s/^Version: //p"':
+          type: string
+          description: The command used to generate the version of the tool
+topics:
+  versions:
+    - - ${task.process}:
+          type: string
+          description: The process the versions were collected from
+      - bwa:
+          type: string
+          description: The tool name
+      - 'bwa 2>&1 | sed -n "s/^Version: //p"':
+          type: string
+          description: The command used to generate the version of the tool
+authors:
+  - "@drpatelh"
+  - "@maxulysse"
+maintainers:
+  - "@maxulysse"
+  - "@gallvp"
diff --git a/modules/nf-core/bwa/index/tests/main.nf.test b/modules/nf-core/bwa/index/tests/main.nf.test
new file mode 100644
index 0000000..f0fba82
--- /dev/null
+++ b/modules/nf-core/bwa/index/tests/main.nf.test
@@ -0,0 +1,57 @@
+nextflow_process {
+
+    name "Test Process BWA_INDEX"
+    tag "modules_nfcore"
+    tag "modules"
+    tag "bwa"
+    tag "bwa/index"
+    script "../main.nf"
+    process "BWA_INDEX"
+
+    test("BWA index") {
+
+        when {
+            process {
+                """
+                input[0] = [
+                    [id: 'test'],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)
+                ]
+                """
+            }
+        }
+
+        then {
+            assert process.success
+            assertAll(
+                { assert snapshot(process.out).match() }
+            )
+        }
+
+    }
+
+    test("BWA index - stub") {
+
+        options "-stub"
+
+        when {
+            process {
+                """
+                input[0] = [
+                    [id: 'test'],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)
+                ]
+                """
+            }
+        }
+
+        then {
+            assert process.success
+            assertAll(
+                { assert snapshot(process.out).match() }
+            )
+        }
+
+    }
+
+}
diff --git a/modules/nf-core/bwa/index/tests/main.nf.test.snap b/modules/nf-core/bwa/index/tests/main.nf.test.snap
new file mode 100644
index 0000000..21a6f73
--- /dev/null
+++ b/modules/nf-core/bwa/index/tests/main.nf.test.snap
@@ -0,0 +1,108 @@
+{
+    "BWA index - stub": {
+        "content": [
+            {
+                "0": [
+                    [
+                        {
+                            "id": "test"
+                        },
+                        [
+                            "genome.amb:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "genome.ann:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "genome.bwt:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "genome.pac:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "genome.sa:md5,d41d8cd98f00b204e9800998ecf8427e"
+                        ]
+                    ]
+                ],
+                "1": [
+                    [
+                        "BWA_INDEX",
+                        "bwa",
+                        "0.7.19-r1273"
+                    ]
+                ],
+                "index": [
+                    [
+                        {
+                            "id": "test"
+                        },
+                        [
+                            "genome.amb:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "genome.ann:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "genome.bwt:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "genome.pac:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "genome.sa:md5,d41d8cd98f00b204e9800998ecf8427e"
+                        ]
+                    ]
+                ],
+                "versions_bwa": [
+                    [
+                        "BWA_INDEX",
+                        "bwa",
+                        "0.7.19-r1273"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.2"
+        },
+        "timestamp": "2026-01-23T16:58:59.966558606"
+    },
+    "BWA index": {
+        "content": [
+            {
+                "0": [
+                    [
+                        {
+                            "id": "test"
+                        },
+                        [
+                            "genome.amb:md5,3a68b8b2287e07dd3f5f95f4344ba76e",
+                            "genome.ann:md5,c32e11f6c859f166c7525a9c1d583567",
+                            "genome.bwt:md5,0469c30a1e239dd08f68afe66fde99da",
+                            "genome.pac:md5,983e3d2cd6f36e2546e6d25a0da78d66",
+                            "genome.sa:md5,ab3952cabf026b48cd3eb5bccbb636d1"
+                        ]
+                    ]
+                ],
+                "1": [
+                    [
+                        "BWA_INDEX",
+                        "bwa",
+                        "0.7.19-r1273"
+                    ]
+                ],
+                "index": [
+                    [
+                        {
+                            "id": "test"
+                        },
+                        [
+                            "genome.amb:md5,3a68b8b2287e07dd3f5f95f4344ba76e",
+                            "genome.ann:md5,c32e11f6c859f166c7525a9c1d583567",
+                            "genome.bwt:md5,0469c30a1e239dd08f68afe66fde99da",
+                            "genome.pac:md5,983e3d2cd6f36e2546e6d25a0da78d66",
+                            "genome.sa:md5,ab3952cabf026b48cd3eb5bccbb636d1"
+                        ]
+                    ]
+                ],
+                "versions_bwa": [
+                    [
+                        "BWA_INDEX",
+                        "bwa",
+                        "0.7.19-r1273"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.2"
+        },
+        "timestamp": "2026-01-23T16:58:53.330725134"
+    }
+}
\ No newline at end of file
diff --git a/modules/nf-core/fastqc/.conda-lock/linux_amd64-bd-b342db7b694c9af4_1.txt b/modules/nf-core/fastqc/.conda-lock/linux_amd64-bd-b342db7b694c9af4_1.txt
new file mode 100644
index 0000000..2922abe
--- /dev/null
+++ b/modules/nf-core/fastqc/.conda-lock/linux_amd64-bd-b342db7b694c9af4_1.txt
@@ -0,0 +1,976 @@
+
+version: 6
+environments:
+default:
+channels:
+- url: https://conda.anaconda.org/conda-forge/
+- url: https://conda.anaconda.org/bioconda/
+- url: https://conda.anaconda.org/bioconda/
+options:
+pypi-prerelease-mode: if-necessary-or-explicit
+packages:
+linux-64:
+- conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.16.1-h7cc23a3_1.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_10.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.7.22-hbd8a1cb_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.6-h3c89d7e_0.conda
+- conda: https://conda.anaconda.org/bioconda/noarch/fastqc-0.13.0-hdfd78af_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2
+- conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2
+- conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2
+- conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.18.3-h4db4eae_1.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2
+- conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/giflib-6.1.3-hebe6cf0_2.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.15-h54a6638_1.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.3-py310h44b86e0_2.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-h7cc23a3_1.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.22.2-hbc21106_2.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.19.1-h9073bf1_3.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.46.1-default_hbd61a6d_102.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.2.0-hdb68285_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-h7a8fb5f_6.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.25-hd45a770_1.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h373387f_1.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.8.5-hd2095e1_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.7.0-h81df57d_1.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.14.3-ha770c72_2.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype6-2.14.3-h5e6c136_2.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-16.2.0-ha9f2e26_7.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.90.0-h569388d_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-16.2.0-he0feb66_7.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libharfbuzz-14.5.1-h23af247_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h0cb94f2_3.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.2.0-hb03c661_1.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.3-hb03c661_1.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-devel-5.8.3-hb03c661_1.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb03c661_2.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.59-h922cc85_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libpython-3.14.7-hdc7f604_106_cp314.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.53.4-h0737f62_102.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-16.2.0-h934c35e_7.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.7.2-hcc2c06a_1.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.42.4-hcfc3c73_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.6.0-hd42ef1d_1.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-hb83e432_2.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.2-h25fd6f3_3.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.6-hdb14827_1.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/openjdk-25.0.2-h1602c4f_26.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.6.5-h781a0a9_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.47-h8b3dc9c_1.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/pixman-0.46.4-h54a6638_3.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/procps-ng-4.0.6-h18c060e_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h7cc23a3_1004.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.14.7-hcd007b5_106_cp314.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-9_cp314.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/readline-8.3-hd6e31c0_1.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h1df4ec4_4.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2026c-h151e31d_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/which-2.25-h171cf75_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-h280c20c_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.6-h0d788c3_1.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.8.13-he1eb515_1.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-h7cc23a3_2.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.5-h7cc23a3_2.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxext-1.3.7-h7cc23a3_1.conda
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diff --git a/modules/nf-core/fastqc/.conda-lock/linux_arm64-bd-7c8a53b986d9a000_1.txt b/modules/nf-core/fastqc/.conda-lock/linux_arm64-bd-7c8a53b986d9a000_1.txt
new file mode 100644
index 0000000..911cba1
--- /dev/null
+++ b/modules/nf-core/fastqc/.conda-lock/linux_arm64-bd-7c8a53b986d9a000_1.txt
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+md5: ebaded4b4b74c2cc43a754ded4eed76f
+depends:
+- libgcc >=15
+- libzlib >=1.3.2,<2.0a0
+constrains:
+- xorg-libx11 >=1.8.13,<2.0a0
+license: TCL
+size: 3664220
+timestamp: 1787272859143
+- conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2026c-h151e31d_0.conda
+sha256: b928c30ddcb0e3f544c6eade8352737e6e610e263276b90232db6a578ef899d8
+md5: fcb489df604d100968b737f2cb6076c6
+license: LicenseRef-Public-Domain
+size: 118849
+timestamp: 1784250406640
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/which-2.25-hdc560ac_0.conda
+sha256: 720691eb59b86f11ceb21b236b9402d59c47d156183375d2b24c53a201dd874b
+md5: 6dc4b55fc391a3d51ff12a6fed979562
+depends:
+- libstdcxx >=14
+- libgcc >=14
+license: GPL-3.0-only
+license_family: GPL
+size: 40249
+timestamp: 1779439555623
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libice-1.1.2-h80f16a2_0.conda
+sha256: e56bb636aefe3503a53520a0b7f3737f3a26fe0accf0befffc24d1c0ddd27008
+md5: 0566e37830f85911b141524635939941
+depends:
+- libgcc >=14
+license: MIT
+license_family: MIT
+size: 63847
+timestamp: 1786474771090
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libsm-1.2.6-hf23e593_1.conda
+sha256: 4978f89a764b54dc8be7a115b2d57f570f793243f3227653232742ce0946101b
+md5: f5f4a1233da463827499fb4e5e1f0172
+depends:
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+- xorg-libice >=1.1.2,<2.0a0
+- libuuid >=2.42.2,<3.0a0
+license: MIT
+license_family: MIT
+size: 31476
+timestamp: 1786546266048
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libx11-1.8.13-h63a1b12_1.conda
+sha256: 9ff59d55cbf85561833be3604a8c0a5497b0f13cdf122e8bb490e9cf3138d116
+md5: 8e873e9efc395329b914ee44e305b624
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+- libxcb >=1.17.0,<2.0a0
+license: MIT
+license_family: MIT
+size: 875768
+timestamp: 1787087025456
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxau-1.0.12-h5bc82ec_2.conda
+sha256: 6b22ffc0137003b41d44db995a3536737e2a2c854c8ed55c8a4eb6377d502abf
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+depends:
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+license: MIT
+license_family: MIT
+size: 19649
+timestamp: 1788962171600
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxdmcp-1.1.5-h5bc82ec_2.conda
+sha256: db2b37227a888d23269855409a9b2179e493c59f2ec9ee2071bf3fd198767ffd
+md5: bfd0b70add0a8cff1ee035532b0deaa5
+depends:
+- libgcc >=15
+license: MIT
+license_family: MIT
+size: 23110
+timestamp: 1789678433513
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxext-1.3.7-h5bc82ec_1.conda
+sha256: e2cc4ba2b5291710f9ae3e88d917d778ce7911262450e673ba8a597a5d43490a
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+- libgcc >=15
+- xorg-libx11 >=1.8.13,<2.0a0
+license: MIT
+license_family: MIT
+size: 53801
+timestamp: 1787103097310
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxfixes-6.0.2-h5bc82ec_1.conda
+sha256: fc432dbef14598e2fd283760c2a14536472c2441986264308b4cc1877a263e00
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+- libgcc >=15
+- xorg-libx11 >=1.8.13,<2.0a0
+license: MIT
+license_family: MIT
+size: 22039
+timestamp: 1787250187374
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxi-1.8.3-h5bc82ec_1.conda
+sha256: 3759f62da8f9e093dc94e8fb87a30b99b4a2414e0c197810126e5c2c25a6d63a
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+depends:
+- libgcc >=15
+- xorg-libx11 >=1.8.13,<2.0a0
+- xorg-libxext >=1.3.7,<2.0a0
+- xorg-libxfixes >=6.0.2,<7.0a0
+license: MIT
+license_family: MIT
+size: 50969
+timestamp: 1787259526749
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxrandr-1.5.5-h5bc82ec_1.conda
+sha256: f2461efa138a03d25c4cff337e9db52c762ae93ab426f578234a2ea1cac50eb5
+md5: 2d0c7631c23e56dbb66f4fe711c6ab63
+depends:
+- libgcc >=15
+- xorg-libx11 >=1.8.13,<2.0a0
+- xorg-libxext >=1.3.7,<2.0a0
+- xorg-libxrender >=0.9.12,<0.10.0a0
+license: MIT
+license_family: MIT
+size: 31842
+timestamp: 1787246653258
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxrender-0.9.12-he30d5cf_1.conda
+sha256: 3cbac6f69e4a8634ba6b60cf283cba92606c8c74caa1369b799325f96b2f0cbf
+md5: 1bcdaa1fc263291e6f42ff061666f3ae
+depends:
+- libgcc >=14
+- xorg-libx11 >=1.8.13,<2.0a0
+license: MIT
+license_family: MIT
+size: 35814
+timestamp: 1787100239228
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxt-1.3.1-h5bc82ec_1.conda
+sha256: 90cde7ffd12c2f02ec4a6b893dfa0c1fcb8f7b23b468ff575d3222df2cd187c6
+md5: 06a15405538666fbdc272718ff4e0fe4
+depends:
+- libgcc >=15
+- xorg-libice >=1.1.2,<2.0a0
+- xorg-libsm >=1.2.6,<2.0a0
+- xorg-libx11 >=1.8.13,<2.0a0
+license: MIT
+license_family: MIT
+size: 394356
+timestamp: 1787103094046
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxtst-1.2.5-h5bc82ec_4.conda
+sha256: 075b22e79db93db685281cda76b6250c71ac6b454ca64eafd20710cde25ef949
+md5: ba6d0e75e2fc9d9b81a9f59694970d60
+depends:
+- libgcc >=15
+- xorg-libx11 >=1.8.13,<2.0a0
+- xorg-libxext >=1.3.7,<2.0a0
+- xorg-libxi >=1.8.3,<2.0a0
+license: MIT
+license_family: MIT
+size: 35699
+timestamp: 1787360041772
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xz-5.8.3-hd704e39_1.conda
+sha256: 4e9b4a2eabf215c052d57a54c279a1d79343ae3b961d3f81f834e0bf76e5c7d4
+md5: 96a51f4137a543fd14fe96e1e560a5ac
+depends:
+- libgcc >=14
+- liblzma 5.8.3 he30d5cf_1
+- liblzma-devel 5.8.3 he30d5cf_1
+- xz-gpl-tools 5.8.3 hd704e39_1
+- xz-tools 5.8.3 he30d5cf_1
+license: 0BSD AND LGPL-2.1-or-later AND GPL-2.0-or-later
+size: 23459
+timestamp: 1786348594461
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xz-gpl-tools-5.8.3-hd704e39_1.conda
+sha256: 3f0b45a1039c86265f41f3bb5f248cd4d901acff49164f86122fab7cc4efc049
+md5: 6543af33661ff21061b6db52c84a86fa
+depends:
+- libgcc >=14
+- liblzma 5.8.3 he30d5cf_1
+constrains:
+- xz 5.8.3.*
+license: 0BSD AND LGPL-2.1-or-later AND GPL-2.0-or-later
+size: 40115
+timestamp: 1786348586456
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xz-tools-5.8.3-he30d5cf_1.conda
+sha256: 1619ea83757336aaaac4cde221e4cd0982551c82def3276c91d0d53b0b7159db
+md5: 7c211f37ed725871ce3043090d5addd8
+depends:
+- libgcc >=14
+- liblzma 5.8.3 he30d5cf_1
+constrains:
+- xz 5.8.3.*
+license: 0BSD AND LGPL-2.1-or-later
+size: 103133
+timestamp: 1786348578460
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/zstd-1.5.7-h9d15635_7.conda
+sha256: 427fd14bcb3b8659796fecc682716617409350fb5a98e5b7b47558a10d1a2fc7
+md5: d942e34ac3920ba83f8b2d0169570187
+depends:
+- libzlib >=1.3.2,<2.0a0
+license: BSD-3-Clause
+license_family: BSD
+size: 615477
+timestamp: 1786599613561
diff --git a/modules/nf-core/fastqc/environment.yml b/modules/nf-core/fastqc/environment.yml
index 691d4c7..edb5539 100644
--- a/modules/nf-core/fastqc/environment.yml
+++ b/modules/nf-core/fastqc/environment.yml
@@ -1,5 +1,7 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
 channels:
   - conda-forge
   - bioconda
 dependencies:
-  - bioconda::fastqc=0.12.1
+  - bioconda::fastqc=0.13.0
diff --git a/modules/nf-core/fastqc/main.nf b/modules/nf-core/fastqc/main.nf
index 752c3a1..41cdeca 100644
--- a/modules/nf-core/fastqc/main.nf
+++ b/modules/nf-core/fastqc/main.nf
@@ -1,19 +1,19 @@
 process FASTQC {
-    tag "$meta.id"
-    label 'process_medium'
+    tag "${meta.id}"
+    label 'process_low'
 
     conda "${moduleDir}/environment.yml"
-    container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ?
-        'https://depot.galaxyproject.org/singularity/fastqc:0.12.1--hdfd78af_0' :
-        'biocontainers/fastqc:0.12.1--hdfd78af_0' }"
+    container "${ workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+?         'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/81/816bda6dd014e810753f642611232443ddeabb2b47f9681ad43f9044816f00ff/data'
+:         'community.wave.seqera.io/library/fastqc:0.13.0--b342db7b694c9af4' }"
 
     input:
-    tuple val(meta), path(reads)
+    tuple val(meta), path(reads, stageAs: '?/*')
 
     output:
     tuple val(meta), path("*.html"), emit: html
-    tuple val(meta), path("*.zip") , emit: zip
-    path  "versions.yml"           , emit: versions
+    tuple val(meta), path("*.zip"), emit: zip
+    tuple val("${task.process}"), val('fastqc'), eval('fastqc --version | sed "/FastQC v/!d; s/.*v//"'), emit: versions_fastqc, topic: versions
 
     when:
     task.ext.when == null || task.ext.when
@@ -22,32 +22,30 @@ process FASTQC {
     def args = task.ext.args ?: ''
     def prefix = task.ext.prefix ?: "${meta.id}"
     // Make list of old name and new name pairs to use for renaming in the bash while loop
-    def old_new_pairs = reads instanceof Path || reads.size() == 1 ? [[ reads, "${prefix}.${reads.extension}" ]] : reads.withIndex().collect { entry, index -> [ entry, "${prefix}_${index + 1}.${entry.extension}" ] }
+    def old_new_pairs = reads instanceof Path || reads.size() == 1 ? [[reads, "${prefix}.${reads.extension}"]] : reads.withIndex().collect { entry, index -> [entry, "${prefix}_${index + 1}.${entry.extension}"] }
     def rename_to = old_new_pairs*.join(' ').join(' ')
-    def renamed_files = old_new_pairs.collect{ _old_name, new_name -> new_name }.join(' ')
+    def renamed_files = old_new_pairs.collect { _old_name, new_name -> new_name }.join(' ')
 
     // The total amount of allocated RAM by FastQC is equal to the number of threads defined (--threads) time the amount of RAM defined (--memory)
     // https://github.com/s-andrews/FastQC/blob/1faeea0412093224d7f6a07f777fad60a5650795/fastqc#L211-L222
-    // Dividing the task.memory by task.cpu allows to stick to requested amount of RAM in the label
-    def memory_in_mb = MemoryUnit.of("${task.memory}").toUnit('MB') / task.cpus
+    // Dividing the task.memory by task.cpus allows to stick to requested amount of RAM in the label
+    def memory_in_mb = task.memory
+        ? (task.memory.toUnit('MB') / task.cpus).intValue()
+        : null
     // FastQC memory value allowed range (100 - 10000)
     def fastqc_memory = memory_in_mb > 10000 ? 10000 : (memory_in_mb < 100 ? 100 : memory_in_mb)
+    def fastqc_memory_arg = fastqc_memory ? "--memory ${fastqc_memory}" : ''
 
     """
-    printf "%s %s\\n" $rename_to | while read old_name new_name; do
+    printf "%s %s\\n" ${rename_to} | while read old_name new_name; do
         [ -f "\${new_name}" ] || ln -s \$old_name \$new_name
     done
 
     fastqc \\
-        $args \\
-        --threads $task.cpus \\
-        --memory $fastqc_memory \\
-        $renamed_files
-
-    cat <<-END_VERSIONS > versions.yml
-    "${task.process}":
-        fastqc: \$( fastqc --version | sed '/FastQC v/!d; s/.*v//' )
-    END_VERSIONS
+        ${args} \\
+        --threads ${task.cpus} \\
+        ${fastqc_memory_arg} \\
+        ${renamed_files}
     """
 
     stub:
@@ -55,10 +53,5 @@ process FASTQC {
     """
     touch ${prefix}.html
     touch ${prefix}.zip
-
-    cat <<-END_VERSIONS > versions.yml
-    "${task.process}":
-        fastqc: \$( fastqc --version | sed '/FastQC v/!d; s/.*v//' )
-    END_VERSIONS
     """
 }
diff --git a/modules/nf-core/fastqc/meta.yml b/modules/nf-core/fastqc/meta.yml
index 2b2e62b..4a97029 100644
--- a/modules/nf-core/fastqc/meta.yml
+++ b/modules/nf-core/fastqc/meta.yml
@@ -29,9 +29,10 @@ input:
         description: |
           List of input FastQ files of size 1 and 2 for single-end and paired-end data,
           respectively.
+        ontologies: []
 output:
-  - html:
-      - meta:
+  html:
+    - - meta:
           type: map
           description: |
             Groovy Map containing sample information
@@ -40,8 +41,9 @@ output:
           type: file
           description: FastQC report
           pattern: "*_{fastqc.html}"
-  - zip:
-      - meta:
+          ontologies: []
+  zip:
+    - - meta:
           type: map
           description: |
             Groovy Map containing sample information
@@ -50,11 +52,29 @@ output:
           type: file
           description: FastQC report archive
           pattern: "*_{fastqc.zip}"
-  - versions:
-      - versions.yml:
-          type: file
-          description: File containing software versions
-          pattern: "versions.yml"
+          ontologies: []
+  versions_fastqc:
+    - - ${task.process}:
+          type: string
+          description: The process the versions were collected from
+      - fastqc:
+          type: string
+          description: The tool name
+      - fastqc --version | sed "/FastQC v/!d; s/.*v//":
+          type: eval
+          description: The expression to obtain the version of the tool
+
+topics:
+  versions:
+    - - ${task.process}:
+          type: string
+          description: The process the versions were collected from
+      - fastqc:
+          type: string
+          description: The tool name
+      - fastqc --version | sed "/FastQC v/!d; s/.*v//":
+          type: eval
+          description: The expression to obtain the version of the tool
 authors:
   - "@drpatelh"
   - "@grst"
@@ -65,3 +85,27 @@ maintainers:
   - "@grst"
   - "@ewels"
   - "@FelixKrueger"
+containers:
+  docker:
+    linux/amd64:
+      name: community.wave.seqera.io/library/fastqc:0.13.0--b342db7b694c9af4
+      build_id: bd-b342db7b694c9af4_1
+      scan_id: sc-b9ffd697eb6c276d_1
+    linux/arm64:
+      name: community.wave.seqera.io/library/fastqc:0.13.0--7c8a53b986d9a000
+      build_id: bd-7c8a53b986d9a000_1
+      scan_id: sc-03411c4d2136d823_1
+  singularity:
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/fastqc:0.13.0--aadddbed09923ea6
+      build_id: bd-aadddbed09923ea6_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/81/816bda6dd014e810753f642611232443ddeabb2b47f9681ad43f9044816f00ff/data
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/fastqc:0.13.0--f5a404ab2c13e090
+      build_id: bd-f5a404ab2c13e090_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/ad/add047b79c7b7b0f2c0711161f64256ffe51ebf8a6e14fa7adf31877da6425db/data
+  conda:
+    linux/amd64:
+      lock_file: modules/nf-core/fastqc/.conda-lock/linux_amd64-bd-b342db7b694c9af4_1.txt
+    linux/arm64:
+      lock_file: modules/nf-core/fastqc/.conda-lock/linux_arm64-bd-7c8a53b986d9a000_1.txt
diff --git a/modules/nf-core/fastqc/tests/main.nf.test b/modules/nf-core/fastqc/tests/main.nf.test
index e9d79a0..66c44da 100644
--- a/modules/nf-core/fastqc/tests/main.nf.test
+++ b/modules/nf-core/fastqc/tests/main.nf.test
@@ -30,7 +30,7 @@ nextflow_process {
                 { assert process.out.html[0][1] ==~ ".*/test_fastqc.html" },
                 { assert process.out.zip[0][1] ==~ ".*/test_fastqc.zip" },
                 { assert path(process.out.html[0][1]).text.contains("<tr><td>File type</td><td>Conventional base calls</td></tr>") },
-                { assert snapshot(process.out.versions).match() }
+                { assert snapshot(sanitizeOutput(process.out).findAll { key, val -> key != 'html' && key != 'zip' }).match() }
             )
         }
     }
@@ -58,7 +58,7 @@ nextflow_process {
                 { assert process.out.zip[0][1][1] ==~ ".*/test_2_fastqc.zip" },
                 { assert path(process.out.html[0][1][0]).text.contains("<tr><td>File type</td><td>Conventional base calls</td></tr>") },
                 { assert path(process.out.html[0][1][1]).text.contains("<tr><td>File type</td><td>Conventional base calls</td></tr>") },
-                { assert snapshot(process.out.versions).match() }
+                { assert snapshot(sanitizeOutput(process.out).findAll { key, val -> key != 'html' && key != 'zip' }).match() }
             )
         }
     }
@@ -82,7 +82,7 @@ nextflow_process {
                 { assert process.out.html[0][1] ==~ ".*/test_fastqc.html" },
                 { assert process.out.zip[0][1] ==~ ".*/test_fastqc.zip" },
                 { assert path(process.out.html[0][1]).text.contains("<tr><td>File type</td><td>Conventional base calls</td></tr>") },
-                { assert snapshot(process.out.versions).match() }
+                { assert snapshot(sanitizeOutput(process.out).findAll { key, val -> key != 'html' && key != 'zip' }).match() }
             )
         }
     }
@@ -106,7 +106,7 @@ nextflow_process {
                 { assert process.out.html[0][1] ==~ ".*/test_fastqc.html" },
                 { assert process.out.zip[0][1] ==~ ".*/test_fastqc.zip" },
                 { assert path(process.out.html[0][1]).text.contains("<tr><td>File type</td><td>Conventional base calls</td></tr>") },
-                { assert snapshot(process.out.versions).match() }
+                { assert snapshot(sanitizeOutput(process.out).findAll { key, val -> key != 'html' && key != 'zip' }).match() }
             )
         }
     }
@@ -142,7 +142,7 @@ nextflow_process {
                 { assert path(process.out.html[0][1][1]).text.contains("<tr><td>File type</td><td>Conventional base calls</td></tr>") },
                 { assert path(process.out.html[0][1][2]).text.contains("<tr><td>File type</td><td>Conventional base calls</td></tr>") },
                 { assert path(process.out.html[0][1][3]).text.contains("<tr><td>File type</td><td>Conventional base calls</td></tr>") },
-                { assert snapshot(process.out.versions).match() }
+                { assert snapshot(sanitizeOutput(process.out).findAll { key, val -> key != 'html' && key != 'zip' }).match() }
             )
         }
     }
@@ -166,7 +166,7 @@ nextflow_process {
                 { assert process.out.html[0][1] ==~ ".*/mysample_fastqc.html" },
                 { assert process.out.zip[0][1] ==~ ".*/mysample_fastqc.zip" },
                 { assert path(process.out.html[0][1]).text.contains("<tr><td>File type</td><td>Conventional base calls</td></tr>") },
-                { assert snapshot(process.out.versions).match() }
+                { assert snapshot(sanitizeOutput(process.out).findAll { key, val -> key != 'html' && key != 'zip' }).match() }
             )
         }
     }
diff --git a/modules/nf-core/fastqc/tests/main.nf.test.snap b/modules/nf-core/fastqc/tests/main.nf.test.snap
index d5db309..349e14c 100644
--- a/modules/nf-core/fastqc/tests/main.nf.test.snap
+++ b/modules/nf-core/fastqc/tests/main.nf.test.snap
@@ -1,15 +1,21 @@
 {
     "sarscov2 custom_prefix": {
         "content": [
-            [
-                "versions.yml:md5,e1cc25ca8af856014824abd842e93978"
-            ]
+            {
+                "versions_fastqc": [
+                    [
+                        "FASTQC",
+                        "fastqc",
+                        "0.13.0"
+                    ]
+                ]
+            }
         ],
         "meta": {
-            "nf-test": "0.9.0",
-            "nextflow": "24.04.3"
+            "nf-test": "0.9.2",
+            "nextflow": "25.10.0"
         },
-        "timestamp": "2024-07-22T11:02:16.374038"
+        "timestamp": "2025-10-28T16:39:14.518503"
     },
     "sarscov2 single-end [fastq] - stub": {
         "content": [
@@ -33,7 +39,11 @@
                     ]
                 ],
                 "2": [
-                    "versions.yml:md5,e1cc25ca8af856014824abd842e93978"
+                    [
+                        "FASTQC",
+                        "fastqc",
+                        "0.13.0"
+                    ]
                 ],
                 "html": [
                     [
@@ -44,8 +54,12 @@
                         "test.html:md5,d41d8cd98f00b204e9800998ecf8427e"
                     ]
                 ],
-                "versions": [
-                    "versions.yml:md5,e1cc25ca8af856014824abd842e93978"
+                "versions_fastqc": [
+                    [
+                        "FASTQC",
+                        "fastqc",
+                        "0.13.0"
+                    ]
                 ],
                 "zip": [
                     [
@@ -59,10 +73,10 @@
             }
         ],
         "meta": {
-            "nf-test": "0.9.0",
-            "nextflow": "24.04.3"
+            "nf-test": "0.9.2",
+            "nextflow": "25.10.0"
         },
-        "timestamp": "2024-07-22T11:02:24.993809"
+        "timestamp": "2025-10-28T16:39:19.309008"
     },
     "sarscov2 custom_prefix - stub": {
         "content": [
@@ -86,7 +100,11 @@
                     ]
                 ],
                 "2": [
-                    "versions.yml:md5,e1cc25ca8af856014824abd842e93978"
+                    [
+                        "FASTQC",
+                        "fastqc",
+                        "0.13.0"
+                    ]
                 ],
                 "html": [
                     [
@@ -97,8 +115,12 @@
                         "mysample.html:md5,d41d8cd98f00b204e9800998ecf8427e"
                     ]
                 ],
-                "versions": [
-                    "versions.yml:md5,e1cc25ca8af856014824abd842e93978"
+                "versions_fastqc": [
+                    [
+                        "FASTQC",
+                        "fastqc",
+                        "0.13.0"
+                    ]
                 ],
                 "zip": [
                     [
@@ -112,58 +134,82 @@
             }
         ],
         "meta": {
-            "nf-test": "0.9.0",
-            "nextflow": "24.04.3"
+            "nf-test": "0.9.2",
+            "nextflow": "25.10.0"
         },
-        "timestamp": "2024-07-22T11:03:10.93942"
+        "timestamp": "2025-10-28T16:39:44.94888"
     },
     "sarscov2 interleaved [fastq]": {
         "content": [
-            [
-                "versions.yml:md5,e1cc25ca8af856014824abd842e93978"
-            ]
+            {
+                "versions_fastqc": [
+                    [
+                        "FASTQC",
+                        "fastqc",
+                        "0.13.0"
+                    ]
+                ]
+            }
         ],
         "meta": {
-            "nf-test": "0.9.0",
-            "nextflow": "24.04.3"
+            "nf-test": "0.9.2",
+            "nextflow": "25.10.0"
         },
-        "timestamp": "2024-07-22T11:01:42.355718"
+        "timestamp": "2025-10-28T16:38:45.168496"
     },
     "sarscov2 paired-end [bam]": {
         "content": [
-            [
-                "versions.yml:md5,e1cc25ca8af856014824abd842e93978"
-            ]
+            {
+                "versions_fastqc": [
+                    [
+                        "FASTQC",
+                        "fastqc",
+                        "0.13.0"
+                    ]
+                ]
+            }
         ],
         "meta": {
-            "nf-test": "0.9.0",
-            "nextflow": "24.04.3"
+            "nf-test": "0.9.2",
+            "nextflow": "25.10.0"
         },
-        "timestamp": "2024-07-22T11:01:53.276274"
+        "timestamp": "2025-10-28T16:38:53.268919"
     },
     "sarscov2 multiple [fastq]": {
         "content": [
-            [
-                "versions.yml:md5,e1cc25ca8af856014824abd842e93978"
-            ]
+            {
+                "versions_fastqc": [
+                    [
+                        "FASTQC",
+                        "fastqc",
+                        "0.13.0"
+                    ]
+                ]
+            }
         ],
         "meta": {
-            "nf-test": "0.9.0",
-            "nextflow": "24.04.3"
+            "nf-test": "0.9.2",
+            "nextflow": "25.10.0"
         },
-        "timestamp": "2024-07-22T11:02:05.527626"
+        "timestamp": "2025-10-28T16:39:05.050305"
     },
     "sarscov2 paired-end [fastq]": {
         "content": [
-            [
-                "versions.yml:md5,e1cc25ca8af856014824abd842e93978"
-            ]
+            {
+                "versions_fastqc": [
+                    [
+                        "FASTQC",
+                        "fastqc",
+                        "0.13.0"
+                    ]
+                ]
+            }
         ],
         "meta": {
-            "nf-test": "0.9.0",
-            "nextflow": "24.04.3"
+            "nf-test": "0.9.2",
+            "nextflow": "25.10.0"
         },
-        "timestamp": "2024-07-22T11:01:31.188871"
+        "timestamp": "2025-10-28T16:38:37.2373"
     },
     "sarscov2 paired-end [fastq] - stub": {
         "content": [
@@ -187,7 +233,11 @@
                     ]
                 ],
                 "2": [
-                    "versions.yml:md5,e1cc25ca8af856014824abd842e93978"
+                    [
+                        "FASTQC",
+                        "fastqc",
+                        "0.13.0"
+                    ]
                 ],
                 "html": [
                     [
@@ -198,8 +248,12 @@
                         "test.html:md5,d41d8cd98f00b204e9800998ecf8427e"
                     ]
                 ],
-                "versions": [
-                    "versions.yml:md5,e1cc25ca8af856014824abd842e93978"
+                "versions_fastqc": [
+                    [
+                        "FASTQC",
+                        "fastqc",
+                        "0.13.0"
+                    ]
                 ],
                 "zip": [
                     [
@@ -213,10 +267,10 @@
             }
         ],
         "meta": {
-            "nf-test": "0.9.0",
-            "nextflow": "24.04.3"
+            "nf-test": "0.9.2",
+            "nextflow": "25.10.0"
         },
-        "timestamp": "2024-07-22T11:02:34.273566"
+        "timestamp": "2025-10-28T16:39:24.450398"
     },
     "sarscov2 multiple [fastq] - stub": {
         "content": [
@@ -240,7 +294,11 @@
                     ]
                 ],
                 "2": [
-                    "versions.yml:md5,e1cc25ca8af856014824abd842e93978"
+                    [
+                        "FASTQC",
+                        "fastqc",
+                        "0.13.0"
+                    ]
                 ],
                 "html": [
                     [
@@ -251,8 +309,12 @@
                         "test.html:md5,d41d8cd98f00b204e9800998ecf8427e"
                     ]
                 ],
-                "versions": [
-                    "versions.yml:md5,e1cc25ca8af856014824abd842e93978"
+                "versions_fastqc": [
+                    [
+                        "FASTQC",
+                        "fastqc",
+                        "0.13.0"
+                    ]
                 ],
                 "zip": [
                     [
@@ -266,22 +328,28 @@
             }
         ],
         "meta": {
-            "nf-test": "0.9.0",
-            "nextflow": "24.04.3"
+            "nf-test": "0.9.2",
+            "nextflow": "25.10.0"
         },
-        "timestamp": "2024-07-22T11:03:02.304411"
+        "timestamp": "2025-10-28T16:39:39.758762"
     },
     "sarscov2 single-end [fastq]": {
         "content": [
-            [
-                "versions.yml:md5,e1cc25ca8af856014824abd842e93978"
-            ]
+            {
+                "versions_fastqc": [
+                    [
+                        "FASTQC",
+                        "fastqc",
+                        "0.13.0"
+                    ]
+                ]
+            }
         ],
         "meta": {
-            "nf-test": "0.9.0",
-            "nextflow": "24.04.3"
+            "nf-test": "0.9.2",
+            "nextflow": "25.10.0"
         },
-        "timestamp": "2024-07-22T11:01:19.095607"
+        "timestamp": "2025-10-28T16:38:29.555068"
     },
     "sarscov2 interleaved [fastq] - stub": {
         "content": [
@@ -305,7 +373,11 @@
                     ]
                 ],
                 "2": [
-                    "versions.yml:md5,e1cc25ca8af856014824abd842e93978"
+                    [
+                        "FASTQC",
+                        "fastqc",
+                        "0.13.0"
+                    ]
                 ],
                 "html": [
                     [
@@ -316,8 +388,12 @@
                         "test.html:md5,d41d8cd98f00b204e9800998ecf8427e"
                     ]
                 ],
-                "versions": [
-                    "versions.yml:md5,e1cc25ca8af856014824abd842e93978"
+                "versions_fastqc": [
+                    [
+                        "FASTQC",
+                        "fastqc",
+                        "0.13.0"
+                    ]
                 ],
                 "zip": [
                     [
@@ -331,10 +407,10 @@
             }
         ],
         "meta": {
-            "nf-test": "0.9.0",
-            "nextflow": "24.04.3"
+            "nf-test": "0.9.2",
+            "nextflow": "25.10.0"
         },
-        "timestamp": "2024-07-22T11:02:44.640184"
+        "timestamp": "2025-10-28T16:39:29.193136"
     },
     "sarscov2 paired-end [bam] - stub": {
         "content": [
@@ -358,7 +434,11 @@
                     ]
                 ],
                 "2": [
-                    "versions.yml:md5,e1cc25ca8af856014824abd842e93978"
+                    [
+                        "FASTQC",
+                        "fastqc",
+                        "0.13.0"
+                    ]
                 ],
                 "html": [
                     [
@@ -369,8 +449,12 @@
                         "test.html:md5,d41d8cd98f00b204e9800998ecf8427e"
                     ]
                 ],
-                "versions": [
-                    "versions.yml:md5,e1cc25ca8af856014824abd842e93978"
+                "versions_fastqc": [
+                    [
+                        "FASTQC",
+                        "fastqc",
+                        "0.13.0"
+                    ]
                 ],
                 "zip": [
                     [
@@ -384,9 +468,9 @@
             }
         ],
         "meta": {
-            "nf-test": "0.9.0",
-            "nextflow": "24.04.3"
+            "nf-test": "0.9.2",
+            "nextflow": "25.10.0"
         },
-        "timestamp": "2024-07-22T11:02:53.550742"
+        "timestamp": "2025-10-28T16:39:34.144919"
     }
 }
\ No newline at end of file
diff --git a/modules/nf-core/fastqc/tests/tags.yml b/modules/nf-core/fastqc/tests/tags.yml
deleted file mode 100644
index 7834294..0000000
--- a/modules/nf-core/fastqc/tests/tags.yml
+++ /dev/null
@@ -1,2 +0,0 @@
-fastqc:
-  - modules/nf-core/fastqc/**
diff --git a/modules/nf-core/multiqc/.conda-lock/linux_amd64-bd-c17fb751507e9dfc_1.txt b/modules/nf-core/multiqc/.conda-lock/linux_amd64-bd-c17fb751507e9dfc_1.txt
new file mode 100644
index 0000000..2a91c22
--- /dev/null
+++ b/modules/nf-core/multiqc/.conda-lock/linux_amd64-bd-c17fb751507e9dfc_1.txt
@@ -0,0 +1,1526 @@
+
+version: 6
+environments:
+default:
+channels:
+- url: https://conda.anaconda.org/conda-forge/
+- url: https://conda.anaconda.org/bioconda/
+- url: https://conda.anaconda.org/bioconda/
+options:
+pypi-prerelease-mode: if-necessary-or-explicit
+packages:
+linux-64:
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+- conda: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_1.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/attrs-26.1.0-pyhcf101f3_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/backports.zstd-1.5.0-py314h680f03e_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py314h3de4e8d_1.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.5.20-hbd8a1cb_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2026.5.20-pyhd8ed1ab_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.7-pyhd8ed1ab_0.conda
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+- conda: https://conda.anaconda.org/conda-forge/noarch/coloredlogs-15.0.1-pyhd8ed1ab_4.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/colormath-3.0.0-pyhd8ed1ab_4.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.5-py314hd8ed1ab_100.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/expat-2.8.1-hecca717_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2
+- conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2
+- conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2
+- conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.18.0-h27c8c51_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.3.0-pyhcf101f3_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/humanfriendly-10.0-pyh707e725_8.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/humanize-4.15.0-pyhd8ed1ab_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.1.0-pyhd8ed1ab_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.15-pyhcf101f3_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-9.0.0-pyhcf101f3_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhcf101f3_1.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.26.0-pyhcf101f3_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2025.9.1-pyhcf101f3_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/kaleido-core-0.2.1-h3644ca4_0.tar.bz2
+- conda: https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.19.1-h0c24ade_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_102.conda
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-7_h0358290_openblas.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.25-h17f619e_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.8.1-hecca717_0.conda
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_19.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_19.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.4.1-hb03c661_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.11.0-7_h47877c9_openblas.conda
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb03c661_1.conda
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.53.1-h0c1763c_0.conda
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.7.1-h9d88235_1.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.42.1-h5347b49_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.6.0-hd42ef1d_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-h8a09558_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.2-h25fd6f3_2.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/markdown-3.10.2-pyhcf101f3_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-4.2.0-pyhd8ed1ab_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.3-py314h67df5f8_1.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/mathjax-2.7.7-ha770c72_3.tar.bz2
+- conda: https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_1.conda
+- conda: https://conda.anaconda.org/bioconda/noarch/multiqc-1.35-pyhdfd78af_1.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.21.2-pyhcf101f3_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/natsort-8.4.0-pyhcf101f3_2.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.6-hdb14827_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/networkx-3.6.1-pyhcf101f3_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/nspr-4.38-h29cc59b_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/nss-3.118-h445c969_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.6-py314h2b28147_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.4-h55fea9a_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.6.2-h35e630c_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.2-pyhc364b38_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/pillow-12.2.0-py314h8ec4b1a_0.conda
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+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxau-1.0.12-he30d5cf_1.conda
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diff --git a/modules/nf-core/multiqc/environment.yml b/modules/nf-core/multiqc/environment.yml
index 6f5b867..7a970e2 100644
--- a/modules/nf-core/multiqc/environment.yml
+++ b/modules/nf-core/multiqc/environment.yml
@@ -1,5 +1,7 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
 channels:
   - conda-forge
   - bioconda
 dependencies:
-  - bioconda::multiqc=1.25.1
+  - bioconda::multiqc=1.35
diff --git a/modules/nf-core/multiqc/main.nf b/modules/nf-core/multiqc/main.nf
index cc0643e..c4bc715 100644
--- a/modules/nf-core/multiqc/main.nf
+++ b/modules/nf-core/multiqc/main.nf
@@ -1,24 +1,21 @@
 process MULTIQC {
+    tag "${meta.id}"
     label 'process_single'
 
     conda "${moduleDir}/environment.yml"
-    container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ?
-        'https://depot.galaxyproject.org/singularity/multiqc:1.25.1--pyhdfd78af_0' :
-        'biocontainers/multiqc:1.25.1--pyhdfd78af_0' }"
+    container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/c8/c8e346f4f6080eadf1253505e6ff09ef004454fc18e8d672006fd7b222cc412e/data'
+        : 'community.wave.seqera.io/library/multiqc:1.35--c17fb751507e9dfc'}"
 
     input:
-    path  multiqc_files, stageAs: "?/*"
-    path(multiqc_config)
-    path(extra_multiqc_config)
-    path(multiqc_logo)
-    path(replace_names)
-    path(sample_names)
+    tuple val(meta), path(multiqc_files, stageAs: "?/*"), path(multiqc_config, stageAs: "?/*"), path(multiqc_logo), path(replace_names), path(sample_names)
 
     output:
-    path "*multiqc_report.html", emit: report
-    path "*_data"              , emit: data
-    path "*_plots"             , optional:true, emit: plots
-    path "versions.yml"        , emit: versions
+    tuple val(meta), path("*.html"), emit: report
+    tuple val(meta), path("*_data"), emit: data
+    tuple val(meta), path("*_plots"), emit: plots, optional: true
+    // MultiQC should not push its versions to the `versions` topic. Its input depends on the versions topic to be resolved thus outputting to the topic will let the pipeline hang forever
+    tuple val("${task.process}"), val('multiqc'), eval('multiqc --version | sed "s/.* //g"'), emit: versions
 
     when:
     task.ext.when == null || task.ext.when
@@ -26,38 +23,28 @@ process MULTIQC {
     script:
     def args = task.ext.args ?: ''
     def prefix = task.ext.prefix ? "--filename ${task.ext.prefix}.html" : ''
-    def config = multiqc_config ? "--config $multiqc_config" : ''
-    def extra_config = extra_multiqc_config ? "--config $extra_multiqc_config" : ''
+    def config = multiqc_config ? multiqc_config instanceof List ? "--config ${multiqc_config.join(' --config ')}" : "--config ${multiqc_config}" : ""
     def logo = multiqc_logo ? "--cl-config 'custom_logo: \"${multiqc_logo}\"'" : ''
     def replace = replace_names ? "--replace-names ${replace_names}" : ''
     def samples = sample_names ? "--sample-names ${sample_names}" : ''
     """
     multiqc \\
         --force \\
-        $args \\
-        $config \\
-        $prefix \\
-        $extra_config \\
-        $logo \\
-        $replace \\
-        $samples \\
+        ${args} \\
+        ${config} \\
+        ${prefix} \\
+        ${logo} \\
+        ${replace} \\
+        ${samples} \\
         .
-
-    cat <<-END_VERSIONS > versions.yml
-    "${task.process}":
-        multiqc: \$( multiqc --version | sed -e "s/multiqc, version //g" )
-    END_VERSIONS
     """
 
     stub:
     """
     mkdir multiqc_data
+    touch multiqc_data/.stub
     mkdir multiqc_plots
+    touch multiqc_plots/.stub
     touch multiqc_report.html
-
-    cat <<-END_VERSIONS > versions.yml
-    "${task.process}":
-        multiqc: \$( multiqc --version | sed -e "s/multiqc, version //g" )
-    END_VERSIONS
     """
 }
diff --git a/modules/nf-core/multiqc/meta.yml b/modules/nf-core/multiqc/meta.yml
index b16c187..27ce18d 100644
--- a/modules/nf-core/multiqc/meta.yml
+++ b/modules/nf-core/multiqc/meta.yml
@@ -1,6 +1,6 @@
 name: multiqc
-description: Aggregate results from bioinformatics analyses across many samples into
-  a single report
+description: Aggregate results from bioinformatics analyses across many samples
+  into a single report
 keywords:
   - QC
   - bioinformatics tools
@@ -12,60 +12,91 @@ tools:
         It's a general use tool, perfect for summarising the output from numerous bioinformatics tools.
       homepage: https://multiqc.info/
       documentation: https://multiqc.info/docs/
-      licence: ["GPL-3.0-or-later"]
+      licence:
+        - "GPL-3.0-or-later"
       identifier: biotools:multiqc
 input:
-  - - multiqc_files:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. [ id:'sample1', single_end:false ]
+    - multiqc_files:
         type: file
         description: |
           List of reports / files recognised by MultiQC, for example the html and zip output of FastQC
-  - - multiqc_config:
+        ontologies: []
+    - multiqc_config:
         type: file
         description: Optional config yml for MultiQC
         pattern: "*.{yml,yaml}"
-  - - extra_multiqc_config:
-        type: file
-        description: Second optional config yml for MultiQC. Will override common sections
-          in multiqc_config.
-        pattern: "*.{yml,yaml}"
-  - - multiqc_logo:
+        ontologies:
+          - edam: http://edamontology.org/format_3750
+    - multiqc_logo:
         type: file
         description: Optional logo file for MultiQC
         pattern: "*.{png}"
-  - - replace_names:
+        ontologies: []
+    - replace_names:
         type: file
         description: |
           Optional two-column sample renaming file. First column a set of
           patterns, second column a set of corresponding replacements. Passed via
           MultiQC's `--replace-names` option.
         pattern: "*.{tsv}"
-  - - sample_names:
+        ontologies:
+          - edam: http://edamontology.org/format_3475
+    - sample_names:
         type: file
         description: |
           Optional TSV file with headers, passed to the MultiQC --sample_names
           argument.
         pattern: "*.{tsv}"
+        ontologies:
+          - edam: http://edamontology.org/format_3475
 output:
-  - report:
-      - "*multiqc_report.html":
+  report:
+    - - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. [ id:'sample1', single_end:false ]
+      - "*.html":
           type: file
           description: MultiQC report file
-          pattern: "multiqc_report.html"
-  - data:
+          pattern: ".html"
+          ontologies: []
+  data:
+    - - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. [ id:'sample1', single_end:false ]
       - "*_data":
           type: directory
           description: MultiQC data dir
           pattern: "multiqc_data"
-  - plots:
+  plots:
+    - - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. [ id:'sample1', single_end:false ]
       - "*_plots":
           type: file
           description: Plots created by MultiQC
-          pattern: "*_data"
-  - versions:
-      - versions.yml:
-          type: file
-          description: File containing software versions
-          pattern: "versions.yml"
+          pattern: "*_plots"
+          ontologies: []
+  versions:
+    - - ${task.process}:
+          type: string
+          description: The process the versions were collected from
+      - multiqc:
+          type: string
+          description: The tool name
+      - multiqc --version | sed "s/.* //g":
+          type: eval
+          description: The expression to obtain the version of the tool
 authors:
   - "@abhi18av"
   - "@bunop"
@@ -76,3 +107,27 @@ maintainers:
   - "@bunop"
   - "@drpatelh"
   - "@jfy133"
+containers:
+  conda:
+    linux/amd64:
+      lock_file: modules/nf-core/multiqc/.conda-lock/linux_amd64-bd-c17fb751507e9dfc_1.txt
+    linux/arm64:
+      lock_file: modules/nf-core/multiqc/.conda-lock/linux_arm64-bd-5c84a5000a226ab5_1.txt
+  docker:
+    linux/amd64:
+      name: community.wave.seqera.io/library/multiqc:1.35--c17fb751507e9dfc
+      build_id: bd-c17fb751507e9dfc_1
+      scan_id: sc-3b1b3932f9846892_1
+    linux/arm64:
+      name: community.wave.seqera.io/library/multiqc:1.35--5c84a5000a226ab5
+      build_id: bd-5c84a5000a226ab5_1
+      scan_id: sc-0d39df41e9737bbd_1
+  singularity:
+    linux/amd64:
+      name: oras://community.wave.seqera.io/library/multiqc:1.35--c680f2aea25ccec2
+      build_id: bd-c680f2aea25ccec2_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/c8/c8e346f4f6080eadf1253505e6ff09ef004454fc18e8d672006fd7b222cc412e/data
+    linux/arm64:
+      name: oras://community.wave.seqera.io/library/multiqc:1.35--c0468833d65b2f81
+      build_id: bd-c0468833d65b2f81_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/e4/e48aa28aebc881254a499b24c3e1ce77b8df1b85a5432699ed6f72eb17ac7fb5/data
diff --git a/modules/nf-core/multiqc/tests/custom_prefix.config b/modules/nf-core/multiqc/tests/custom_prefix.config
new file mode 100644
index 0000000..b30b135
--- /dev/null
+++ b/modules/nf-core/multiqc/tests/custom_prefix.config
@@ -0,0 +1,5 @@
+process {
+    withName: 'MULTIQC' {
+        ext.prefix = "custom_prefix"
+    }
+}
diff --git a/modules/nf-core/multiqc/tests/main.nf.test b/modules/nf-core/multiqc/tests/main.nf.test
index 33316a7..4cbdb95 100644
--- a/modules/nf-core/multiqc/tests/main.nf.test
+++ b/modules/nf-core/multiqc/tests/main.nf.test
@@ -15,25 +15,84 @@ nextflow_process {
         when {
             process {
                 """
-                input[0] = Channel.of(file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastqc/test_fastqc.zip', checkIfExists: true))
-                input[1] = []
-                input[2] = []
-                input[3] = []
-                input[4] = []
-                input[5] = []
+                input[0] = channel.of([
+                    [ id: 'FASTQC' ],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastqc/test_fastqc.zip', checkIfExists: true),
+                    [],
+                    [],
+                    [],
+                    []
+                ])
                 """
             }
         }
 
         then {
-            assertAll(
-                { assert process.success },
-                { assert process.out.report[0] ==~ ".*/multiqc_report.html" },
-                { assert process.out.data[0] ==~ ".*/multiqc_data" },
-                { assert snapshot(process.out.versions).match("multiqc_versions_single") }
-            )
+            assert process.success
+            assert snapshot(
+                sanitizeOutput(process.out).collectEntries { key, val ->
+                        if (key == "data") {
+                            return [key, val.collect { [path(it[1]).list().collect { file(it.toString()).name }] }]
+                        }
+                        else if (key == "plots") {
+                            return [key, val.collect { [
+                                "pdf",
+                                path("${it[1]}/pdf").list().collect { file(it.toString()).name },
+                                "png",
+                                path("${it[1]}/png").list().collect { file(it.toString()).name },
+                                "svg",
+                                path("${it[1]}/svg").list().collect { file(it.toString()).name }] }]
+                        }
+                        else if (key == "report") {
+                            return [key, file(val[0][1].toString()).name]
+                        }
+                        return [key, val]
+                    }
+            ).match()
+        }
+    }
+
+    test("sarscov2 single-end [fastqc] - custom prefix") {
+        config "./custom_prefix.config"
+
+        when {
+            process {
+                """
+                input[0] = channel.of([
+                    [ id: 'FASTQC' ],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastqc/test_fastqc.zip', checkIfExists: true),
+                    [],
+                    [],
+                    [],
+                    []
+                ])
+                """
+            }
         }
 
+        then {
+            assert process.success
+            assert snapshot(
+                sanitizeOutput(process.out).collectEntries { key, val ->
+                        if (key == "data") {
+                            return [key, val.collect { [path(it[1]).list().collect { file(it.toString()).name }] }]
+                        }
+                        else if (key == "plots") {
+                            return [key, val.collect { [
+                                "pdf",
+                                path("${it[1]}/pdf").list().collect { file(it.toString()).name },
+                                "png",
+                                path("${it[1]}/png").list().collect { file(it.toString()).name },
+                                "svg",
+                                path("${it[1]}/svg").list().collect { file(it.toString()).name }] }]
+                        }
+                        else if (key == "report") {
+                            return [key, file(val[0][1].toString()).name]
+                        }
+                        return [key, val]
+                    }
+            ).match()
+        }
     }
 
     test("sarscov2 single-end [fastqc] [config]") {
@@ -41,23 +100,85 @@ nextflow_process {
         when {
             process {
                 """
-                input[0] = Channel.of(file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastqc/test_fastqc.zip', checkIfExists: true))
-                input[1] = Channel.of(file("https://github.com/nf-core/tools/raw/dev/nf_core/pipeline-template/assets/multiqc_config.yml", checkIfExists: true))
-                input[2] = []
-                input[3] = []
-                input[4] = []
-                input[5] = []
+                input[0] = channel.of([
+                    [ id: 'FASTQC' ],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastqc/test_fastqc.zip', checkIfExists: true),
+                    file("https://raw.githubusercontent.com/nf-core/seqinspector/1.0.0/assets/multiqc_config.yml", checkIfExists: true),
+                    [],
+                    [],
+                    []
+                ])
                 """
             }
         }
 
         then {
-            assertAll(
-                { assert process.success },
-                { assert process.out.report[0] ==~ ".*/multiqc_report.html" },
-                { assert process.out.data[0] ==~ ".*/multiqc_data" },
-                { assert snapshot(process.out.versions).match("multiqc_versions_config") }
-            )
+            assert process.success
+            assert snapshot(
+                sanitizeOutput(process.out).collectEntries { key, val ->
+                        if (key == "data") {
+                            return [key, val.collect { [path(it[1]).list().collect { file(it.toString()).name }] }]
+                        }
+                        else if (key == "plots") {
+                            return [key, val.collect { [
+                                "pdf",
+                                path("${it[1]}/pdf").list().collect { file(it.toString()).name },
+                                "png",
+                                path("${it[1]}/png").list().collect { file(it.toString()).name },
+                                "svg",
+                                path("${it[1]}/svg").list().collect { file(it.toString()).name }] }]
+                        }
+                        else if (key == "report") {
+                            return [key, file(val[0][1].toString()).name]
+                        }
+                        return [key, val]
+                    }
+            ).match()
+        }
+    }
+
+    test("sarscov2 single-end [fastqc] [multiple configs]") {
+
+        when {
+            process {
+                """
+                input[0] = channel.of([
+                    [ id: 'FASTQC' ],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastqc/test_fastqc.zip', checkIfExists: true),
+                    [
+                        file("https://raw.githubusercontent.com/nf-core/seqinspector/1.0.0/assets/multiqc_config.yml", checkIfExists: true),
+                        file("https://raw.githubusercontent.com/nf-core/seqinspector/1.0.0/assets/multiqc_config.yml", checkIfExists: true)
+                    ],
+                    [],
+                    [],
+                    []
+                ])
+                """
+            }
+        }
+
+        then {
+            assert process.success
+            assert snapshot(
+                sanitizeOutput(process.out).collectEntries { key, val ->
+                        if (key == "data") {
+                            return [key, val.collect { [path(it[1]).list().collect { file(it.toString()).name }] }]
+                        }
+                        else if (key == "plots") {
+                            return [key, val.collect { [
+                                "pdf",
+                                path("${it[1]}/pdf").list().collect { file(it.toString()).name },
+                                "png",
+                                path("${it[1]}/png").list().collect { file(it.toString()).name },
+                                "svg",
+                                path("${it[1]}/svg").list().collect { file(it.toString()).name }] }]
+                        }
+                        else if (key == "report") {
+                            return [key, file(val[0][1].toString()).name]
+                        }
+                        return [key, val]
+                    }
+            ).match()
         }
     }
 
@@ -68,25 +189,23 @@ nextflow_process {
         when {
             process {
                 """
-                input[0] = Channel.of(file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastqc/test_fastqc.zip', checkIfExists: true))
-                input[1] = []
-                input[2] = []
-                input[3] = []
-                input[4] = []
-                input[5] = []
+                input[0] = channel.of([
+                    [ id: 'FASTQC' ],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastqc/test_fastqc.zip', checkIfExists: true),
+                    [],
+                    [],
+                    [],
+                    []
+                ])
                 """
             }
         }
 
         then {
+            assert process.success
             assertAll(
-                { assert process.success },
-                { assert snapshot(process.out.report.collect { file(it).getName() } +
-                                process.out.data.collect { file(it).getName() } +
-                                process.out.plots.collect { file(it).getName() } +
-                                process.out.versions ).match("multiqc_stub") }
+                { assert snapshot(sanitizeOutput(process.out)).match() }
             )
         }
-
     }
 }
diff --git a/modules/nf-core/multiqc/tests/main.nf.test.snap b/modules/nf-core/multiqc/tests/main.nf.test.snap
index 2fcbb5f..4489921 100644
--- a/modules/nf-core/multiqc/tests/main.nf.test.snap
+++ b/modules/nf-core/multiqc/tests/main.nf.test.snap
@@ -1,41 +1,422 @@
 {
-    "multiqc_versions_single": {
+    "sarscov2 single-end [fastqc] [multiple configs]": {
         "content": [
-            [
-                "versions.yml:md5,41f391dcedce7f93ca188f3a3ffa0916"
-            ]
+            {
+                "data": [
+                    [
+                        [
+                            "fastqc-status-check-heatmap.txt",
+                            "fastqc_overrepresented_sequences_plot.txt",
+                            "fastqc_per_base_n_content_plot.txt",
+                            "fastqc_per_base_sequence_quality_plot.txt",
+                            "fastqc_per_sequence_gc_content_plot_Counts.txt",
+                            "fastqc_per_sequence_gc_content_plot_Percentages.txt",
+                            "fastqc_per_sequence_quality_scores_plot.txt",
+                            "fastqc_sequence_counts_plot.txt",
+                            "fastqc_sequence_duplication_levels_plot.txt",
+                            "fastqc_sequence_length_distribution_plot.txt",
+                            "fastqc_top_overrepresented_sequences_table.txt",
+                            "llms-full.txt",
+                            "multiqc.log",
+                            "multiqc.parquet",
+                            "multiqc_citations.txt",
+                            "multiqc_data.json",
+                            "multiqc_fastqc.txt",
+                            "multiqc_general_stats.txt",
+                            "multiqc_sources.txt"
+                        ]
+                    ]
+                ],
+                "plots": [
+                    [
+                        "pdf",
+                        [
+                            "fastqc-status-check-heatmap.pdf",
+                            "fastqc_overrepresented_sequences_plot.pdf",
+                            "fastqc_per_base_n_content_plot.pdf",
+                            "fastqc_per_base_sequence_quality_plot.pdf",
+                            "fastqc_per_sequence_gc_content_plot_Counts.pdf",
+                            "fastqc_per_sequence_gc_content_plot_Percentages.pdf",
+                            "fastqc_per_sequence_quality_scores_plot.pdf",
+                            "fastqc_sequence_counts_plot-cnt.pdf",
+                            "fastqc_sequence_counts_plot-pct.pdf",
+                            "fastqc_sequence_duplication_levels_plot.pdf",
+                            "fastqc_sequence_length_distribution_plot.pdf",
+                            "fastqc_top_overrepresented_sequences_table.pdf"
+                        ],
+                        "png",
+                        [
+                            "fastqc-status-check-heatmap.png",
+                            "fastqc_overrepresented_sequences_plot.png",
+                            "fastqc_per_base_n_content_plot.png",
+                            "fastqc_per_base_sequence_quality_plot.png",
+                            "fastqc_per_sequence_gc_content_plot_Counts.png",
+                            "fastqc_per_sequence_gc_content_plot_Percentages.png",
+                            "fastqc_per_sequence_quality_scores_plot.png",
+                            "fastqc_sequence_counts_plot-cnt.png",
+                            "fastqc_sequence_counts_plot-pct.png",
+                            "fastqc_sequence_duplication_levels_plot.png",
+                            "fastqc_sequence_length_distribution_plot.png",
+                            "fastqc_top_overrepresented_sequences_table.png"
+                        ],
+                        "svg",
+                        [
+                            "fastqc-status-check-heatmap.svg",
+                            "fastqc_overrepresented_sequences_plot.svg",
+                            "fastqc_per_base_n_content_plot.svg",
+                            "fastqc_per_base_sequence_quality_plot.svg",
+                            "fastqc_per_sequence_gc_content_plot_Counts.svg",
+                            "fastqc_per_sequence_gc_content_plot_Percentages.svg",
+                            "fastqc_per_sequence_quality_scores_plot.svg",
+                            "fastqc_sequence_counts_plot-cnt.svg",
+                            "fastqc_sequence_counts_plot-pct.svg",
+                            "fastqc_sequence_duplication_levels_plot.svg",
+                            "fastqc_sequence_length_distribution_plot.svg",
+                            "fastqc_top_overrepresented_sequences_table.svg"
+                        ]
+                    ]
+                ],
+                "report": "multiqc_report.html",
+                "versions": [
+                    [
+                        "MULTIQC",
+                        "multiqc",
+                        "1.35"
+                    ]
+                ]
+            }
         ],
+        "timestamp": "2026-03-17T16:15:42.577775492",
         "meta": {
-            "nf-test": "0.9.0",
-            "nextflow": "24.04.4"
-        },
-        "timestamp": "2024-10-02T17:51:46.317523"
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        }
     },
-    "multiqc_stub": {
+    "sarscov2 single-end [fastqc]": {
         "content": [
-            [
-                "multiqc_report.html",
-                "multiqc_data",
-                "multiqc_plots",
-                "versions.yml:md5,41f391dcedce7f93ca188f3a3ffa0916"
-            ]
+            {
+                "data": [
+                    [
+                        [
+                            "fastqc-status-check-heatmap.txt",
+                            "fastqc_overrepresented_sequences_plot.txt",
+                            "fastqc_per_base_n_content_plot.txt",
+                            "fastqc_per_base_sequence_quality_plot.txt",
+                            "fastqc_per_sequence_gc_content_plot_Counts.txt",
+                            "fastqc_per_sequence_gc_content_plot_Percentages.txt",
+                            "fastqc_per_sequence_quality_scores_plot.txt",
+                            "fastqc_sequence_counts_plot.txt",
+                            "fastqc_sequence_duplication_levels_plot.txt",
+                            "fastqc_sequence_length_distribution_plot.txt",
+                            "fastqc_top_overrepresented_sequences_table.txt",
+                            "llms-full.txt",
+                            "multiqc.log",
+                            "multiqc.parquet",
+                            "multiqc_citations.txt",
+                            "multiqc_data.json",
+                            "multiqc_fastqc.txt",
+                            "multiqc_general_stats.txt",
+                            "multiqc_software_versions.txt",
+                            "multiqc_sources.txt"
+                        ]
+                    ]
+                ],
+                "plots": [
+                    [
+                        "pdf",
+                        [
+                            "fastqc-status-check-heatmap.pdf",
+                            "fastqc_overrepresented_sequences_plot.pdf",
+                            "fastqc_per_base_n_content_plot.pdf",
+                            "fastqc_per_base_sequence_quality_plot.pdf",
+                            "fastqc_per_sequence_gc_content_plot_Counts.pdf",
+                            "fastqc_per_sequence_gc_content_plot_Percentages.pdf",
+                            "fastqc_per_sequence_quality_scores_plot.pdf",
+                            "fastqc_sequence_counts_plot-cnt.pdf",
+                            "fastqc_sequence_counts_plot-pct.pdf",
+                            "fastqc_sequence_duplication_levels_plot.pdf",
+                            "fastqc_sequence_length_distribution_plot.pdf",
+                            "fastqc_top_overrepresented_sequences_table.pdf"
+                        ],
+                        "png",
+                        [
+                            "fastqc-status-check-heatmap.png",
+                            "fastqc_overrepresented_sequences_plot.png",
+                            "fastqc_per_base_n_content_plot.png",
+                            "fastqc_per_base_sequence_quality_plot.png",
+                            "fastqc_per_sequence_gc_content_plot_Counts.png",
+                            "fastqc_per_sequence_gc_content_plot_Percentages.png",
+                            "fastqc_per_sequence_quality_scores_plot.png",
+                            "fastqc_sequence_counts_plot-cnt.png",
+                            "fastqc_sequence_counts_plot-pct.png",
+                            "fastqc_sequence_duplication_levels_plot.png",
+                            "fastqc_sequence_length_distribution_plot.png",
+                            "fastqc_top_overrepresented_sequences_table.png"
+                        ],
+                        "svg",
+                        [
+                            "fastqc-status-check-heatmap.svg",
+                            "fastqc_overrepresented_sequences_plot.svg",
+                            "fastqc_per_base_n_content_plot.svg",
+                            "fastqc_per_base_sequence_quality_plot.svg",
+                            "fastqc_per_sequence_gc_content_plot_Counts.svg",
+                            "fastqc_per_sequence_gc_content_plot_Percentages.svg",
+                            "fastqc_per_sequence_quality_scores_plot.svg",
+                            "fastqc_sequence_counts_plot-cnt.svg",
+                            "fastqc_sequence_counts_plot-pct.svg",
+                            "fastqc_sequence_duplication_levels_plot.svg",
+                            "fastqc_sequence_length_distribution_plot.svg",
+                            "fastqc_top_overrepresented_sequences_table.svg"
+                        ]
+                    ]
+                ],
+                "report": "multiqc_report.html",
+                "versions": [
+                    [
+                        "MULTIQC",
+                        "multiqc",
+                        "1.35"
+                    ]
+                ]
+            }
         ],
+        "timestamp": "2026-03-17T16:21:17.072841555",
         "meta": {
-            "nf-test": "0.9.0",
-            "nextflow": "24.04.4"
-        },
-        "timestamp": "2024-10-02T17:52:20.680978"
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        }
     },
-    "multiqc_versions_config": {
+    "sarscov2 single-end [fastqc] - stub": {
         "content": [
-            [
-                "versions.yml:md5,41f391dcedce7f93ca188f3a3ffa0916"
-            ]
+            {
+                "data": [
+                    [
+                        {
+                            "id": "FASTQC"
+                        },
+                        [
+                            ".stub:md5,d41d8cd98f00b204e9800998ecf8427e"
+                        ]
+                    ]
+                ],
+                "plots": [
+                    [
+                        {
+                            "id": "FASTQC"
+                        },
+                        [
+                            ".stub:md5,d41d8cd98f00b204e9800998ecf8427e"
+                        ]
+                    ]
+                ],
+                "report": [
+                    [
+                        {
+                            "id": "FASTQC"
+                        },
+                        "multiqc_report.html:md5,d41d8cd98f00b204e9800998ecf8427e"
+                    ]
+                ],
+                "versions": [
+                    [
+                        "MULTIQC",
+                        "multiqc",
+                        "1.35"
+                    ]
+                ]
+            }
         ],
+        "timestamp": "2026-02-26T15:14:39.789193051",
         "meta": {
-            "nf-test": "0.9.0",
-            "nextflow": "24.04.4"
-        },
-        "timestamp": "2024-10-02T17:52:09.185842"
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        }
+    },
+    "sarscov2 single-end [fastqc] [config]": {
+        "content": [
+            {
+                "data": [
+                    [
+                        [
+                            "fastqc-status-check-heatmap.txt",
+                            "fastqc_overrepresented_sequences_plot.txt",
+                            "fastqc_per_base_n_content_plot.txt",
+                            "fastqc_per_base_sequence_quality_plot.txt",
+                            "fastqc_per_sequence_gc_content_plot_Counts.txt",
+                            "fastqc_per_sequence_gc_content_plot_Percentages.txt",
+                            "fastqc_per_sequence_quality_scores_plot.txt",
+                            "fastqc_sequence_counts_plot.txt",
+                            "fastqc_sequence_duplication_levels_plot.txt",
+                            "fastqc_sequence_length_distribution_plot.txt",
+                            "fastqc_top_overrepresented_sequences_table.txt",
+                            "llms-full.txt",
+                            "multiqc.log",
+                            "multiqc.parquet",
+                            "multiqc_citations.txt",
+                            "multiqc_data.json",
+                            "multiqc_fastqc.txt",
+                            "multiqc_general_stats.txt",
+                            "multiqc_sources.txt"
+                        ]
+                    ]
+                ],
+                "plots": [
+                    [
+                        "pdf",
+                        [
+                            "fastqc-status-check-heatmap.pdf",
+                            "fastqc_overrepresented_sequences_plot.pdf",
+                            "fastqc_per_base_n_content_plot.pdf",
+                            "fastqc_per_base_sequence_quality_plot.pdf",
+                            "fastqc_per_sequence_gc_content_plot_Counts.pdf",
+                            "fastqc_per_sequence_gc_content_plot_Percentages.pdf",
+                            "fastqc_per_sequence_quality_scores_plot.pdf",
+                            "fastqc_sequence_counts_plot-cnt.pdf",
+                            "fastqc_sequence_counts_plot-pct.pdf",
+                            "fastqc_sequence_duplication_levels_plot.pdf",
+                            "fastqc_sequence_length_distribution_plot.pdf",
+                            "fastqc_top_overrepresented_sequences_table.pdf"
+                        ],
+                        "png",
+                        [
+                            "fastqc-status-check-heatmap.png",
+                            "fastqc_overrepresented_sequences_plot.png",
+                            "fastqc_per_base_n_content_plot.png",
+                            "fastqc_per_base_sequence_quality_plot.png",
+                            "fastqc_per_sequence_gc_content_plot_Counts.png",
+                            "fastqc_per_sequence_gc_content_plot_Percentages.png",
+                            "fastqc_per_sequence_quality_scores_plot.png",
+                            "fastqc_sequence_counts_plot-cnt.png",
+                            "fastqc_sequence_counts_plot-pct.png",
+                            "fastqc_sequence_duplication_levels_plot.png",
+                            "fastqc_sequence_length_distribution_plot.png",
+                            "fastqc_top_overrepresented_sequences_table.png"
+                        ],
+                        "svg",
+                        [
+                            "fastqc-status-check-heatmap.svg",
+                            "fastqc_overrepresented_sequences_plot.svg",
+                            "fastqc_per_base_n_content_plot.svg",
+                            "fastqc_per_base_sequence_quality_plot.svg",
+                            "fastqc_per_sequence_gc_content_plot_Counts.svg",
+                            "fastqc_per_sequence_gc_content_plot_Percentages.svg",
+                            "fastqc_per_sequence_quality_scores_plot.svg",
+                            "fastqc_sequence_counts_plot-cnt.svg",
+                            "fastqc_sequence_counts_plot-pct.svg",
+                            "fastqc_sequence_duplication_levels_plot.svg",
+                            "fastqc_sequence_length_distribution_plot.svg",
+                            "fastqc_top_overrepresented_sequences_table.svg"
+                        ]
+                    ]
+                ],
+                "report": "multiqc_report.html",
+                "versions": [
+                    [
+                        "MULTIQC",
+                        "multiqc",
+                        "1.35"
+                    ]
+                ]
+            }
+        ],
+        "timestamp": "2026-03-17T16:15:30.372239611",
+        "meta": {
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        }
+    },
+    "sarscov2 single-end [fastqc] - custom prefix": {
+        "content": [
+            {
+                "data": [
+                    [
+                        [
+                            "fastqc-status-check-heatmap.txt",
+                            "fastqc_overrepresented_sequences_plot.txt",
+                            "fastqc_per_base_n_content_plot.txt",
+                            "fastqc_per_base_sequence_quality_plot.txt",
+                            "fastqc_per_sequence_gc_content_plot_Counts.txt",
+                            "fastqc_per_sequence_gc_content_plot_Percentages.txt",
+                            "fastqc_per_sequence_quality_scores_plot.txt",
+                            "fastqc_sequence_counts_plot.txt",
+                            "fastqc_sequence_duplication_levels_plot.txt",
+                            "fastqc_sequence_length_distribution_plot.txt",
+                            "fastqc_top_overrepresented_sequences_table.txt",
+                            "llms-full.txt",
+                            "multiqc.log",
+                            "multiqc.parquet",
+                            "multiqc_citations.txt",
+                            "multiqc_data.json",
+                            "multiqc_fastqc.txt",
+                            "multiqc_general_stats.txt",
+                            "multiqc_software_versions.txt",
+                            "multiqc_sources.txt"
+                        ]
+                    ]
+                ],
+                "plots": [
+                    [
+                        "pdf",
+                        [
+                            "fastqc-status-check-heatmap.pdf",
+                            "fastqc_overrepresented_sequences_plot.pdf",
+                            "fastqc_per_base_n_content_plot.pdf",
+                            "fastqc_per_base_sequence_quality_plot.pdf",
+                            "fastqc_per_sequence_gc_content_plot_Counts.pdf",
+                            "fastqc_per_sequence_gc_content_plot_Percentages.pdf",
+                            "fastqc_per_sequence_quality_scores_plot.pdf",
+                            "fastqc_sequence_counts_plot-cnt.pdf",
+                            "fastqc_sequence_counts_plot-pct.pdf",
+                            "fastqc_sequence_duplication_levels_plot.pdf",
+                            "fastqc_sequence_length_distribution_plot.pdf",
+                            "fastqc_top_overrepresented_sequences_table.pdf"
+                        ],
+                        "png",
+                        [
+                            "fastqc-status-check-heatmap.png",
+                            "fastqc_overrepresented_sequences_plot.png",
+                            "fastqc_per_base_n_content_plot.png",
+                            "fastqc_per_base_sequence_quality_plot.png",
+                            "fastqc_per_sequence_gc_content_plot_Counts.png",
+                            "fastqc_per_sequence_gc_content_plot_Percentages.png",
+                            "fastqc_per_sequence_quality_scores_plot.png",
+                            "fastqc_sequence_counts_plot-cnt.png",
+                            "fastqc_sequence_counts_plot-pct.png",
+                            "fastqc_sequence_duplication_levels_plot.png",
+                            "fastqc_sequence_length_distribution_plot.png",
+                            "fastqc_top_overrepresented_sequences_table.png"
+                        ],
+                        "svg",
+                        [
+                            "fastqc-status-check-heatmap.svg",
+                            "fastqc_overrepresented_sequences_plot.svg",
+                            "fastqc_per_base_n_content_plot.svg",
+                            "fastqc_per_base_sequence_quality_plot.svg",
+                            "fastqc_per_sequence_gc_content_plot_Counts.svg",
+                            "fastqc_per_sequence_gc_content_plot_Percentages.svg",
+                            "fastqc_per_sequence_quality_scores_plot.svg",
+                            "fastqc_sequence_counts_plot-cnt.svg",
+                            "fastqc_sequence_counts_plot-pct.svg",
+                            "fastqc_sequence_duplication_levels_plot.svg",
+                            "fastqc_sequence_length_distribution_plot.svg",
+                            "fastqc_top_overrepresented_sequences_table.svg"
+                        ]
+                    ]
+                ],
+                "report": "custom_prefix.html",
+                "versions": [
+                    [
+                        "MULTIQC",
+                        "multiqc",
+                        "1.35"
+                    ]
+                ]
+            }
+        ],
+        "timestamp": "2026-03-17T16:15:18.189023981",
+        "meta": {
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        }
     }
 }
\ No newline at end of file
diff --git a/modules/nf-core/multiqc/tests/nextflow.config b/modules/nf-core/multiqc/tests/nextflow.config
index c537a6a..374dfef 100644
--- a/modules/nf-core/multiqc/tests/nextflow.config
+++ b/modules/nf-core/multiqc/tests/nextflow.config
@@ -1,5 +1,6 @@
 process {
     withName: 'MULTIQC' {
         ext.prefix = null
+        ext.args = '-p'
     }
 }
diff --git a/modules/nf-core/multiqc/tests/tags.yml b/modules/nf-core/multiqc/tests/tags.yml
deleted file mode 100644
index bea6c0d..0000000
--- a/modules/nf-core/multiqc/tests/tags.yml
+++ /dev/null
@@ -1,2 +0,0 @@
-multiqc:
-  - modules/nf-core/multiqc/**
diff --git a/nextflow.config b/nextflow.config
index 3fd3e96..0697a58 100644
--- a/nextflow.config
+++ b/nextflow.config
@@ -1,6 +1,6 @@
 /*
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-    nf-core/dmscore Nextflow config file
+    nf-core/deepmutscan Nextflow config file
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
     Default config options for all compute environments
 ----------------------------------------------------------------------------------------
@@ -9,14 +9,35 @@
 // Global default params, used in configs
 params {
 
-    // TODO nf-core: Specify your pipeline's command line flags
-    // Input options
     input                      = null
 
-    // References
-    genome                     = null
-    igenomes_base              = 's3://ngi-igenomes/igenomes/'
-    igenomes_ignore            = false
+    // Reference and library
+    fasta                      = null
+    reading_frame              = null
+    mutagenesis_type           = 'nnk'
+    custom_codon_library       = null
+
+    // Variant counting
+    base_qual                  = 40
+    min_flank                  = 2
+
+    // Sequencing-error correction
+    error_correction           = 'false_doubles'
+    false_doubles_method       = 'mle'
+    false_doubles_codon_window = 40
+
+    // Library QC
+    sliding_window_size        = 10
+    aimed_cov                  = 100
+    run_seqdepth               = true
+
+    // Fitness estimation
+    fitness                    = false
+    min_counts                 = 10
+    output_pseudocount         = 1
+    dimsum                     = false
+    mutscan                    = false
+    pdb                        = null
 
     // MultiQC options
     multiqc_config             = null
@@ -32,13 +53,14 @@ params {
     email_on_fail                = null
     plaintext_email              = false
     monochrome_logs              = false
-    hook_url                     = null
     help                         = false
     help_full                    = false
     show_hidden                  = false
     version                      = false
     pipelines_testdata_base_path = 'https://raw.githubusercontent.com/nf-core/test-datasets/'
-    trace_report_suffix          = new java.util.Date().format( 'yyyy-MM-dd_HH-mm-ss')// Config options
+    trace_report_suffix          = new java.util.Date().format( 'yyyy-MM-dd_HH-mm-ss')
+
+    // Config options
     config_profile_name        = null
     config_profile_description = null
 
@@ -51,6 +73,10 @@ params {
     validate_params            = true
 }
 
+// Backwards compatibility for publishDir syntax
+outputDir = params.outdir
+workflow.output.mode = params.publish_dir_mode
+
 // Load base.config by default for all pipelines
 includeConfig 'conf/base.config'
 
@@ -91,7 +117,17 @@ profiles {
         apptainer.enabled       = false
         docker.runOptions       = '-u $(id -u):$(id -g)'
     }
-    arm {
+    arm64 {
+        process.arch            = 'arm64'
+        // For now if you're using arm64 you have to use wave for the sake of the maintainers
+        // wave profile
+        apptainer.ociAutoPull   = true
+        singularity.ociAutoPull = true
+        wave.enabled            = true
+        wave.freeze             = true
+        wave.strategy           = 'conda,container'
+    }
+    emulate_amd64 {
         docker.runOptions       = '-u $(id -u):$(id -g) --platform=linux/amd64'
     }
     singularity {
@@ -148,28 +184,24 @@ profiles {
         wave.freeze             = true
         wave.strategy           = 'conda,container'
     }
-    gitpod {
-        executor.name           = 'local'
-        executor.cpus           = 4
-        executor.memory         = 8.GB
-        process {
-            resourceLimits = [
-                memory: 8.GB,
-                cpus  : 4,
-                time  : 1.h
-            ]
-        }
+    gpu {
+        docker.runOptions       = '-u $(id -u):$(id -g) --gpus all'
+        apptainer.runOptions    = '--nv'
+        singularity.runOptions  = '--nv'
     }
     test      { includeConfig 'conf/test.config'      }
     test_full { includeConfig 'conf/test_full.config' }
 }
 
-// Load nf-core custom profiles from different Institutions
-includeConfig !System.getenv('NXF_OFFLINE') && params.custom_config_base ? "${params.custom_config_base}/nfcore_custom.config" : "/dev/null"
+// Load nf-core custom profiles from different institutions
+
+// If params.custom_config_base is set AND either the NXF_OFFLINE environment variable is not set or params.custom_config_base is a local path, the nfcore_custom.config file from the specified base path is included.
+// Load nf-core/deepmutscan custom profiles from different institutions.
+includeConfig params.custom_config_base && (!System.getenv('NXF_OFFLINE') || !params.custom_config_base.startsWith('http')) ? "${params.custom_config_base}/nfcore_custom.config" : "/dev/null"
+
 
-// Load nf-core/dmscore custom profiles from different institutions.
-// TODO nf-core: Optionally, you can add a pipeline-specific nf-core config at https://github.com/nf-core/configs
-// includeConfig !System.getenv('NXF_OFFLINE') && params.custom_config_base ? "${params.custom_config_base}/pipeline/dmscore.config" : "/dev/null"
+// Load nf-core/deepmutscan custom profiles from different institutions.
+includeConfig params.custom_config_base && (!System.getenv('NXF_OFFLINE') || !params.custom_config_base.startsWith('http')) ? "${params.custom_config_base}/pipeline/deepmutscan.config" : "/dev/null"
 
 // Set default registry for Apptainer, Docker, Podman, Charliecloud and Singularity independent of -profile
 // Will not be used unless Apptainer / Docker / Podman / Charliecloud / Singularity are enabled
@@ -180,9 +212,6 @@ podman.registry       = 'quay.io'
 singularity.registry  = 'quay.io'
 charliecloud.registry = 'quay.io'
 
-// Load igenomes.config if required
-includeConfig !params.igenomes_ignore ? 'conf/igenomes.config' : 'conf/igenomes_ignored.config'
-
 // Export these variables to prevent local Python/R libraries from conflicting with those in the container
 // The JULIA depot path has been adjusted to a fixed path `/usr/local/share/julia` that needs to be used for packages in the container.
 // See https://apeltzer.github.io/post/03-julia-lang-nextflow/ for details on that. Once we have a common agreement on where to keep Julia packages, this is adjustable.
@@ -195,14 +224,14 @@ env {
 }
 
 // Set bash options
-process.shell = """\
-bash
-
-set -e # Exit if a tool returns a non-zero status/exit code
-set -u # Treat unset variables and parameters as an error
-set -o pipefail # Returns the status of the last command to exit with a non-zero status or zero if all successfully execute
-set -C # No clobber - prevent output redirection from overwriting files.
-"""
+process.shell = [
+    "bash",
+    "-C",         // No clobber - prevent output redirection from overwriting files.
+    "-e",         // Exit if a tool returns a non-zero status/exit code
+    "-u",         // Treat unset variables and parameters as an error
+    "-o",         // Returns the status of the last command to exit..
+    "pipefail"    //   ..with a non-zero status or zero if all successfully execute
+]
 
 // Disable process selector warnings by default. Use debug profile to enable warnings.
 nextflow.enable.configProcessNamesValidation = false
@@ -225,64 +254,64 @@ dag {
 }
 
 manifest {
-    name            = 'nf-core/dmscore'
-    author          = """Benjamin Wehnert & Max Stammnitz""" // The author field is deprecated from Nextflow version 24.10.0, use contributors instead
+    name            = 'nf-core/deepmutscan'
     contributors    = [
-        // TODO nf-core: Update the field with the details of the contributors to your pipeline. New with Nextflow version 24.10.0
         [
-            name: 'Benjamin Wehnert & Max Stammnitz',
-            affiliation: '',
-            email: '',
-            github: '',
-            contribution: [], // List of contribution types ('author', 'maintainer' or 'contributor')
-            orcid: ''
+            name: 'Benjamin Wehnert',
+            affiliation: 'Centre for Genomic Regulation (CRG), Barcelona, Spain',
+            email: 'wehnertbenjamin@gmail.com',
+            github: '@BenjaminWehnert1008',
+            contribution: ['author', 'maintainer'],
+            orcid: '0009-0001-8012-6003'
+        ],
+        [
+            name: 'Maximilian R. Stammnitz',
+            affiliation: 'Centre for Genomic Regulation (CRG), Barcelona, Spain',
+            email: 'maximilian.stammnitz@crg.eu',
+            github: '@MaximilianStammnitz',
+            contribution: ['author', 'maintainer'],
+            orcid: '0000-0002-1704-9199'
+        ],
+        [
+            name: 'Fei Sang',
+            affiliation: 'Wellcome Sanger Institute, Hinxton, United Kingdom',
+            email: 'fs18@sanger.ac.uk',
+            github: '@fei-hgi',
+            contribution: ['contributor'],
+            orcid: '0000-0002-8746-5868'
+        ],
+        [
+            name: 'Júlia Mir-Pedrol',
+            affiliation: 'Centre for Genomic Regulation (CRG), Barcelona, Spain',
+            email: 'julia.mir@crg.eu',
+            github: '@mirpedrol',
+            contribution: ['contributor'],
+            orcid: '0000-0001-6104-9260'
+        ],
+        [
+            name: 'Matthias Hörtenhuber',
+            affiliation: 'SciLifeLab, Uppsala University, Uppsala, Sweden',
+            email: 'matthias.hortenhuber@scilifelab.uu.se',
+            github: '@mashehu',
+            contribution: ['contributor'],
+            orcid: '0000-0002-5599-5565'
         ],
     ]
-    homePage        = 'https://github.com/nf-core/dmscore'
-    description     = """Until now, most Deep Mutational Scanning (DMS) experiments relied on variant-specific barcoded libraries for sequencing. This method enabled DMS on large proteins and led to many great publications. Recently, efforts have increased to make use of the classic and more simple random fragmentation-based short-read sequencing (“shotgun-sequencing”). This saves time and money and due to its simpler experimental design is less prone to mistakes. dmscore handles the essential computational steps, processing the raw FASTQ files and generating a count table of variants. Along the way, it provides multiple QC metrics, enabling users to quickly evaluate the success of their experimental setup."""
+    homePage        = 'https://github.com/nf-core/deepmutscan'
+    description     = """Processing and analysis of deep mutational scanning (DMS) data from shotgun and amplicon sequencing: read alignment, variant counting, sequencing-error correction, library QC and fitness estimation"""
     mainScript      = 'main.nf'
     defaultBranch   = 'master'
-    nextflowVersion = '!>=24.04.2'
-    version         = '1.0.0dev'
+    nextflowVersion = '!>=25.10.4'
+    version         = '1.0.0'
     doi             = ''
+    diagram         = 'docs/images/pipeline.png'
 }
 
 // Nextflow plugins
 plugins {
-    id 'nf-schema@2.3.0' // Validation of pipeline parameters and creation of an input channel from a sample sheet
+    id 'nf-schema@2.7.3' // Validation of pipeline parameters and creation of an input channel from a sample sheet
 }
 
-validation {
-    defaultIgnoreParams = ["genomes"]
-    monochromeLogs = params.monochrome_logs
-    help {
-        enabled = true
-        command = "nextflow run nf-core/dmscore -profile <docker/singularity/.../institute> --input samplesheet.csv --outdir <OUTDIR>"
-        fullParameter = "help_full"
-        showHiddenParameter = "show_hidden"
-        beforeText = """
--\033[2m----------------------------------------------------\033[0m-
-                                        \033[0;32m,--.\033[0;30m/\033[0;32m,-.\033[0m
-\033[0;34m        ___     __   __   __   ___     \033[0;32m/,-._.--~\'\033[0m
-\033[0;34m  |\\ | |__  __ /  ` /  \\ |__) |__         \033[0;33m}  {\033[0m
-\033[0;34m  | \\| |       \\__, \\__/ |  \\ |___     \033[0;32m\\`-._,-`-,\033[0m
-                                        \033[0;32m`._,._,\'\033[0m
-\033[0;35m  nf-core/dmscore ${manifest.version}\033[0m
--\033[2m----------------------------------------------------\033[0m-
-"""
-        afterText = """${manifest.doi ? "\n* The pipeline\n" : ""}${manifest.doi.tokenize(",").collect { "    https://doi.org/${it.trim().replace('https://doi.org/','')}"}.join("\n")}${manifest.doi ? "\n" : ""}
-* The nf-core framework
-    https://doi.org/10.1038/s41587-020-0439-x
-
-* Software dependencies
-    https://github.com/nf-core/dmscore/blob/master/CITATIONS.md
-"""
-    }
-    summary {
-        beforeText = validation.help.beforeText
-        afterText = validation.help.afterText
-    }
-}
 
 // Load modules.config for DSL2 module specific options
 includeConfig 'conf/modules.config'
diff --git a/nextflow_schema.json b/nextflow_schema.json
index e4e5523..593de53 100644
--- a/nextflow_schema.json
+++ b/nextflow_schema.json
@@ -1,8 +1,8 @@
 {
     "$schema": "https://json-schema.org/draft/2020-12/schema",
-    "$id": "https://raw.githubusercontent.com/nf-core/dmscore/master/nextflow_schema.json",
-    "title": "nf-core/dmscore pipeline parameters",
-    "description": "Until now, most Deep Mutational Scanning (DMS) experiments relied on variant-specific barcoded libraries for sequencing. This method enabled DMS on large proteins and led to many great publications. Recently, efforts have increased to make use of the classic and more simple random fragmentation-based short-read sequencing (“shotgun-sequencing”). This saves time and money and due to its simpler experimental design is less prone to mistakes. dmscore handles the essential computational steps, processing the raw FASTQ files and generating a count table of variants. Along the way, it provides multiple QC metrics, enabling users to quickly evaluate the success of their experimental setup.",
+    "$id": "https://raw.githubusercontent.com/nf-core/deepmutscan/master/nextflow_schema.json",
+    "title": "nf-core/deepmutscan pipeline parameters",
+    "description": "Processing and analysis of deep mutational scanning (DMS) data from shotgun and amplicon sequencing: read alignment, variant counting, sequencing-error correction, library QC and fitness estimation",
     "type": "object",
     "$defs": {
         "input_output_options": {
@@ -20,7 +20,7 @@
                     "mimetype": "text/csv",
                     "pattern": "^\\S+\\.csv$",
                     "description": "Path to comma-separated file containing information about the samples in the experiment.",
-                    "help_text": "You will need to create a design file with information about the samples in your experiment before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 3 columns, and a header row. See [usage docs](https://nf-co.re/dmscore/usage#samplesheet-input).",
+                    "help_text": "You will need to create a samplesheet with information about the sequencing libraries in your experiment before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 5 columns (`sample`, `type`, `replicate`, `file1`, `file2`) and a header row. See [usage docs](https://nf-co.re/deepmutscan/usage#samplesheet-input).",
                     "fa_icon": "fas fa-file-csv"
                 },
                 "outdir": {
@@ -43,42 +43,154 @@
                 }
             }
         },
-        "reference_genome_options": {
-            "title": "Reference genome options",
+        "reference_options": {
+            "title": "Reference and library options",
             "type": "object",
             "fa_icon": "fas fa-dna",
-            "description": "Reference genome related files and options required for the workflow.",
+            "description": "The reference sequence, the mutagenised reading frame and the programmed codon library.",
             "properties": {
-                "genome": {
-                    "type": "string",
-                    "description": "Name of iGenomes reference.",
-                    "fa_icon": "fas fa-book",
-                    "help_text": "If using a reference genome configured in the pipeline using iGenomes, use this parameter to give the ID for the reference. This is then used to build the full paths for all required reference genome files e.g. `--genome GRCh38`. \n\nSee the [nf-core website docs](https://nf-co.re/usage/reference_genomes) for more details."
-                },
                 "fasta": {
                     "type": "string",
                     "format": "file-path",
                     "exists": true,
                     "mimetype": "text/plain",
                     "pattern": "^\\S+\\.fn?a(sta)?(\\.gz)?$",
-                    "description": "Path to FASTA genome file.",
-                    "help_text": "This parameter is *mandatory* if `--genome` is not specified. If you don't have a BWA index available this will be generated for you automatically. Combine with `--save_reference` to save BWA index for future runs.",
+                    "description": "Path to a FASTA file with the wildtype reference sequence of the mutagenised gene (exactly one sequence).",
+                    "help_text": "The reference may include flanking sequence around the open reading frame (set with `--reading_frame`). A BWA index is built automatically and saved under `intermediate_files/bwa/`.",
                     "fa_icon": "far fa-file-code"
                 },
-                "igenomes_ignore": {
+                "reading_frame": {
+                    "type": "string",
+                    "description": "Nucleotide coordinates of the mutagenised open reading frame within the reference FASTA, in the format 'start-stop' (1-based, inclusive), e.g. '352-1383'.",
+                    "pattern": "^\\d+-\\d+$"
+                },
+                "mutagenesis_type": {
+                    "type": "string",
+                    "description": "Codons programmed at each position of the library. When using 'custom', also provide '--custom_codon_library'.",
+                    "default": "nnk",
+                    "enum": ["nnk", "nns", "nnh", "nnn", "nnk_nns", "nnk_nns_nnh", "custom"]
+                },
+                "custom_codon_library": {
+                    "type": "string",
+                    "format": "file-path",
+                    "description": "Path to a .csv file defining a custom codon library. Required when '--mutagenesis_type custom' is set. Provide either one global comma-separated list of codons without header (e.g. 'AAA,AAC,AAG'), or a position-wise list with a header line containing 'Position', followed by one row per codon position with the position and its allowed codons (e.g. '1,ACG,AAA,ACA' and '2,AAA,TTT,ACA' on separate lines).",
+                    "exists": true,
+                    "mimetype": "text/csv"
+                }
+            },
+            "required": ["fasta", "reading_frame"]
+        },
+        "variant_counting_options": {
+            "title": "Variant counting options",
+            "type": "object",
+            "fa_icon": "fas fa-calculator",
+            "description": "Base-quality and read-edge filters of the variant counter.",
+            "properties": {
+                "base_qual": {
+                    "type": "integer",
+                    "description": "Minimum base quality (Phred) for a mismatch to be counted as a mutation, and for a base to contribute to sequencing coverage.",
+                    "minimum": 0,
+                    "default": 40
+                },
+                "min_flank": {
+                    "type": "integer",
+                    "description": "Minimum distance (nt) a mutation or covered base must be from a read edge; also excludes those bases from coverage. Set to 0 to disable edge trimming.",
+                    "minimum": 0,
+                    "default": 2
+                }
+            }
+        },
+        "error_correction_options": {
+            "title": "Sequencing-error correction options",
+            "type": "object",
+            "fa_icon": "fas fa-eraser",
+            "description": "Correction of single-nucleotide variant counts for sequencing errors.",
+            "properties": {
+                "error_correction": {
+                    "type": "string",
+                    "description": "Sequencing-error correction of variant counts. 'false_doubles' (default) estimates the error rate of every single-nucleotide variant from reads that carry it together with a nearby programmed multi-nucleotide codon variant; 'wildtype' subtracts the background measured by additional deep sequencing of the unmutated template (requires samplesheet rows with type 'wildtype'); 'none' disables correction.",
+                    "enum": ["false_doubles", "none", "wildtype"],
+                    "default": "false_doubles"
+                },
+                "false_doubles_method": {
+                    "type": "string",
+                    "description": "Estimator used by the false-doubles correction. 'mle' (default) is a per-variant maximum-likelihood error rate; 'eb' is an empirical-Bayes estimate that pools across variants and shrinks per substitution class (steadier for sparse variants). Only applies when error_correction = 'false_doubles'.",
+                    "enum": ["mle", "eb"],
+                    "default": "mle"
+                },
+                "false_doubles_codon_window": {
+                    "type": "integer",
+                    "description": "Window (in codons, upstream and downstream) within which programmed multi-nucleotide codon variants are used to estimate the error rate of a single-nucleotide variant during false-doubles correction.",
+                    "minimum": 1,
+                    "default": 40
+                }
+            }
+        },
+        "library_qc_options": {
+            "title": "Library QC options",
+            "type": "object",
+            "fa_icon": "fas fa-chart-bar",
+            "description": "Positional coverage and count profiles, and sequencing-depth rarefaction.",
+            "properties": {
+                "sliding_window_size": {
+                    "type": "integer",
+                    "description": "Sliding-window size (in codons) used to smooth the positional coverage and count profiles in the library QC plots.",
+                    "default": 10,
+                    "minimum": 1
+                },
+                "aimed_cov": {
+                    "type": "integer",
+                    "description": "Targeted number of counts per amino acid variant. Used to draw the sequencing coverage that would be required (assuming an even spread) in the positional coverage plots and the run report.",
+                    "default": 100,
+                    "minimum": 1
+                },
+                "run_seqdepth": {
                     "type": "boolean",
-                    "description": "Do not load the iGenomes reference config.",
-                    "fa_icon": "fas fa-ban",
-                    "hidden": true,
-                    "help_text": "Do not load `igenomes.config` when running the pipeline. You may choose this option if you observe clashes between custom parameters and those supplied in `igenomes.config`."
+                    "default": true,
+                    "description": "Estimate sequencing-depth saturation of every library by closed-form hypergeometric rarefaction (on by default)."
+                }
+            }
+        },
+        "fitness_options": {
+            "title": "Fitness estimation options",
+            "type": "object",
+            "fa_icon": "fas fa-chart-line",
+            "description": "Fitness estimation from matched input and output libraries, optional additional estimators and the 3D variant effect inspection tool.",
+            "properties": {
+                "fitness": {
+                    "type": "boolean",
+                    "description": "Estimate variant fitness from matched 'input' and 'output' libraries of each sample, using the default log-ratio estimator.",
+                    "default": false
+                },
+                "min_counts": {
+                    "type": "integer",
+                    "description": "Minimum number of reads a variant needs in every 'input' replicate to receive a fitness estimate. Applied to the default estimator, DiMSum ('--fitnessMinInputCountAll') and mutscan. Variant counting, error correction and library QC are not filtered.",
+                    "minimum": 1,
+                    "default": 10
+                },
+                "output_pseudocount": {
+                    "type": "integer",
+                    "description": "Pseudocount for 'output' replicates in which a variant observed in the input was not detected (dropouts), so that strongly depleted variants still receive a finite fitness value. Applied to the default estimator and DiMSum ('--fitnessDropoutPseudocount'). Set to 0 to disable; dropout variants then receive no fitness estimate in that replicate.",
+                    "minimum": 0,
+                    "default": 1
                 },
-                "igenomes_base": {
+                "dimsum": {
+                    "type": "boolean",
+                    "description": "Additionally estimate fitness with DiMSum (Faure et al., 2020). Requires '--fitness'.",
+                    "default": false
+                },
+                "mutscan": {
+                    "type": "boolean",
+                    "description": "Additionally estimate variant enrichment with mutscan (Soneson et al., 2023), using edgeR and limma. Requires '--fitness'.",
+                    "default": false
+                },
+                "pdb": {
                     "type": "string",
-                    "format": "directory-path",
-                    "description": "The base path to the igenomes reference files",
-                    "fa_icon": "fas fa-ban",
-                    "hidden": true,
-                    "default": "s3://ngi-igenomes/igenomes/"
+                    "format": "file-path",
+                    "exists": true,
+                    "mimetype": "chemical/x-pdb",
+                    "description": "Optional wildtype 3D structure (PDB). When supplied together with `--fitness`, an interactive variant effect inspection tool (self-contained HTML) is built that projects fitness, counts and error-correction biases onto the structure.",
+                    "help_text": "Use a crystal structure, an AlphaFold DB model, or any precomputed PDB whose sequence matches the wildtype ORF. Structure prediction (nf-core/proteinfold) is not wired in this release."
                 }
             }
         },
@@ -180,13 +292,6 @@
                     "fa_icon": "fas fa-palette",
                     "hidden": true
                 },
-                "hook_url": {
-                    "type": "string",
-                    "description": "Incoming hook URL for messaging service",
-                    "fa_icon": "fas fa-people-group",
-                    "help_text": "Incoming hook URL for messaging service. Currently, MS Teams and Slack are supported.",
-                    "hidden": true
-                },
                 "multiqc_config": {
                     "type": "string",
                     "format": "file-path",
@@ -221,9 +326,17 @@
                 },
                 "trace_report_suffix": {
                     "type": "string",
-                    "fa_icon": "far calendar",
+                    "fa_icon": "far fa-calendar",
                     "description": "Suffix to add to the trace report filename. Default is the date and time in the format yyyy-MM-dd_HH-mm-ss.",
                     "hidden": true
+                },
+                "help_full": {
+                    "type": "boolean",
+                    "description": "Display the full detailed help message."
+                },
+                "show_hidden": {
+                    "type": "boolean",
+                    "description": "Display hidden parameters in the help message (only works when --help or --help_full are provided)."
                 }
             }
         }
@@ -233,7 +346,19 @@
             "$ref": "#/$defs/input_output_options"
         },
         {
-            "$ref": "#/$defs/reference_genome_options"
+            "$ref": "#/$defs/reference_options"
+        },
+        {
+            "$ref": "#/$defs/variant_counting_options"
+        },
+        {
+            "$ref": "#/$defs/error_correction_options"
+        },
+        {
+            "$ref": "#/$defs/library_qc_options"
+        },
+        {
+            "$ref": "#/$defs/fitness_options"
         },
         {
             "$ref": "#/$defs/institutional_config_options"
diff --git a/nf-test.config b/nf-test.config
new file mode 100644
index 0000000..c0c14da
--- /dev/null
+++ b/nf-test.config
@@ -0,0 +1,39 @@
+config {
+    // location for all nf-test tests
+    testsDir = "."
+
+    // nf-test directory including temporary files for each test
+    workDir = System.getenv("NFT_WORKDIR") ?: ".nf-test"
+
+    // location of an optional nextflow.config file specific for executing tests
+    configFile = "tests/nextflow.config"
+
+    // ignore tests coming from the nf-core/modules repo
+    ignore = [
+        'modules/nf-core/**/tests/*',
+        'subworkflows/nf-core/**/tests/*',
+    ]
+
+    // run all test with defined profile(s) from the main nextflow.config
+    profile = "test"
+
+    // list of filenames or patterns that should be trigger a full test run
+    triggers = [
+        '.github/actions/nf-test/action.yml',
+        '.github/workflows/nf-test.yml',
+        'assets/schema_input.json',
+        'bin/*',
+        'conf/test.config',
+        'nextflow.config',
+        'nextflow_schema.json',
+        'nf-test.config',
+        'tests/.nftignore',
+        'tests/nextflow.config',
+    ]
+
+    // load the necessary plugins
+    plugins {
+        load "nft-utils@0.0.3"
+        load "nft-csv@0.1.0"
+    }
+}
diff --git a/ro-crate-metadata.json b/ro-crate-metadata.json
index 766d934..e7b154a 100644
--- a/ro-crate-metadata.json
+++ b/ro-crate-metadata.json
@@ -1,29 +1,23 @@
 {
     "@context": [
-        "https://w3id.org/ro/crate/1.1/context",
-        {
-            "GithubService": "https://w3id.org/ro/terms/test#GithubService",
-            "JenkinsService": "https://w3id.org/ro/terms/test#JenkinsService",
-            "PlanemoEngine": "https://w3id.org/ro/terms/test#PlanemoEngine",
-            "TestDefinition": "https://w3id.org/ro/terms/test#TestDefinition",
-            "TestInstance": "https://w3id.org/ro/terms/test#TestInstance",
-            "TestService": "https://w3id.org/ro/terms/test#TestService",
-            "TestSuite": "https://w3id.org/ro/terms/test#TestSuite",
-            "TravisService": "https://w3id.org/ro/terms/test#TravisService",
-            "definition": "https://w3id.org/ro/terms/test#definition",
-            "engineVersion": "https://w3id.org/ro/terms/test#engineVersion",
-            "instance": "https://w3id.org/ro/terms/test#instance",
-            "resource": "https://w3id.org/ro/terms/test#resource",
-            "runsOn": "https://w3id.org/ro/terms/test#runsOn"
-        }
+        "https://w3id.org/ro/crate/1.3/context",
+        "https://w3id.org/ro/terms/test"
     ],
     "@graph": [
         {
             "@id": "./",
             "@type": "Dataset",
-            "creativeWorkStatus": "InProgress",
-            "datePublished": "2025-01-22T16:52:07+00:00",
-            "description": "<h1>\n  <picture>\n    <source media=\"(prefers-color-scheme: dark)\" srcset=\"docs/images/nf-core-dmscore_logo_dark.png\">\n    <img alt=\"nf-core/dmscore\" src=\"docs/images/nf-core-dmscore_logo_light.png\">\n  </picture>\n</h1>\n\n[![GitHub Actions CI Status](https://github.com/nf-core/dmscore/actions/workflows/ci.yml/badge.svg)](https://github.com/nf-core/dmscore/actions/workflows/ci.yml)\n[![GitHub Actions Linting Status](https://github.com/nf-core/dmscore/actions/workflows/linting.yml/badge.svg)](https://github.com/nf-core/dmscore/actions/workflows/linting.yml)[![AWS CI](https://img.shields.io/badge/CI%20tests-full%20size-FF9900?labelColor=000000&logo=Amazon%20AWS)](https://nf-co.re/dmscore/results)[![Cite with Zenodo](http://img.shields.io/badge/DOI-10.5281/zenodo.XXXXXXX-1073c8?labelColor=000000)](https://doi.org/10.5281/zenodo.XXXXXXX)\n[![nf-test](https://img.shields.io/badge/unit_tests-nf--test-337ab7.svg)](https://www.nf-test.com)\n\n[![Nextflow](https://img.shields.io/badge/nextflow%20DSL2-%E2%89%A524.04.2-23aa62.svg)](https://www.nextflow.io/)\n[![run with conda](http://img.shields.io/badge/run%20with-conda-3EB049?labelColor=000000&logo=anaconda)](https://docs.conda.io/en/latest/)\n[![run with docker](https://img.shields.io/badge/run%20with-docker-0db7ed?labelColor=000000&logo=docker)](https://www.docker.com/)\n[![run with singularity](https://img.shields.io/badge/run%20with-singularity-1d355c.svg?labelColor=000000)](https://sylabs.io/docs/)\n[![Launch on Seqera Platform](https://img.shields.io/badge/Launch%20%F0%9F%9A%80-Seqera%20Platform-%234256e7)](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-core/dmscore)\n\n[![Get help on Slack](http://img.shields.io/badge/slack-nf--core%20%23dmscore-4A154B?labelColor=000000&logo=slack)](https://nfcore.slack.com/channels/dmscore)[![Follow on Twitter](http://img.shields.io/badge/twitter-%40nf__core-1DA1F2?labelColor=000000&logo=twitter)](https://twitter.com/nf_core)[![Follow on Mastodon](https://img.shields.io/badge/mastodon-nf__core-6364ff?labelColor=FFFFFF&logo=mastodon)](https://mstdn.science/@nf_core)[![Watch on YouTube](http://img.shields.io/badge/youtube-nf--core-FF0000?labelColor=000000&logo=youtube)](https://www.youtube.com/c/nf-core)\n\n## Introduction\n\n**nf-core/dmscore** is a bioinformatics pipeline that ...\n\n<!-- TODO nf-core:\n   Complete this sentence with a 2-3 sentence summary of what types of data the pipeline ingests, a brief overview of the\n   major pipeline sections and the types of output it produces. You're giving an overview to someone new\n   to nf-core here, in 15-20 seconds. For an example, see https://github.com/nf-core/rnaseq/blob/master/README.md#introduction\n-->\n\n<!-- TODO nf-core: Include a figure that guides the user through the major workflow steps. Many nf-core\n     workflows use the \"tube map\" design for that. See https://nf-co.re/docs/contributing/design_guidelines#examples for examples.   -->\n<!-- TODO nf-core: Fill in short bullet-pointed list of the default steps in the pipeline -->1. Read QC ([`FastQC`](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/))2. Present QC for raw reads ([`MultiQC`](http://multiqc.info/))\n\n## Usage\n\n> [!NOTE]\n> If you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/usage/installation) on how to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline) with `-profile test` before running the workflow on actual data.\n\n<!-- TODO nf-core: Describe the minimum required steps to execute the pipeline, e.g. how to prepare samplesheets.\n     Explain what rows and columns represent. For instance (please edit as appropriate):\n\nFirst, prepare a samplesheet with your input data that looks as follows:\n\n`samplesheet.csv`:\n\n```csv\nsample,fastq_1,fastq_2\nCONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz\n```\n\nEach row represents a fastq file (single-end) or a pair of fastq files (paired end).\n\n-->\n\nNow, you can run the pipeline using:\n\n<!-- TODO nf-core: update the following command to include all required parameters for a minimal example -->\n\n```bash\nnextflow run nf-core/dmscore \\\n   -profile <docker/singularity/.../institute> \\\n   --input samplesheet.csv \\\n   --outdir <OUTDIR>\n```\n\n> [!WARNING]\n> Please provide pipeline parameters via the CLI or Nextflow `-params-file` option. Custom config files including those provided by the `-c` Nextflow option can be used to provide any configuration _**except for parameters**_; see [docs](https://nf-co.re/docs/usage/getting_started/configuration#custom-configuration-files).\n\nFor more details and further functionality, please refer to the [usage documentation](https://nf-co.re/dmscore/usage) and the [parameter documentation](https://nf-co.re/dmscore/parameters).\n\n## Pipeline output\n\nTo see the results of an example test run with a full size dataset refer to the [results](https://nf-co.re/dmscore/results) tab on the nf-core website pipeline page.\nFor more details about the output files and reports, please refer to the\n[output documentation](https://nf-co.re/dmscore/output).\n\n## Credits\n\nnf-core/dmscore was originally written by Benjamin Wehnert & Max Stammnitz.\n\nWe thank the following people for their extensive assistance in the development of this pipeline:\n\n<!-- TODO nf-core: If applicable, make list of people who have also contributed -->\n\n## Contributions and Support\n\nIf you would like to contribute to this pipeline, please see the [contributing guidelines](.github/CONTRIBUTING.md).\n\nFor further information or help, don't hesitate to get in touch on the [Slack `#dmscore` channel](https://nfcore.slack.com/channels/dmscore) (you can join with [this invite](https://nf-co.re/join/slack)).\n\n## Citations\n\n<!-- TODO nf-core: Add citation for pipeline after first release. Uncomment lines below and update Zenodo doi and badge at the top of this file. -->\n<!-- If you use nf-core/dmscore for your analysis, please cite it using the following doi: [10.5281/zenodo.XXXXXX](https://doi.org/10.5281/zenodo.XXXXXX) -->\n\n<!-- TODO nf-core: Add bibliography of tools and data used in your pipeline -->\n\nAn extensive list of references for the tools used by the pipeline can be found in the [`CITATIONS.md`](CITATIONS.md) file.\n\nYou can cite the `nf-core` publication as follows:\n\n> **The nf-core framework for community-curated bioinformatics pipelines.**\n>\n> Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.\n>\n> _Nat Biotechnol._ 2020 Feb 13. doi: [10.1038/s41587-020-0439-x](https://dx.doi.org/10.1038/s41587-020-0439-x).\n",
+            "conformsTo": [
+                {
+                    "@id": "https://w3id.org/workflowhub/workflow-ro-crate/1.1"
+                },
+                {
+                    "@id": "https://w3id.org/ro/wftest/0.2"
+                }
+            ],
+            "creativeWorkStatus": "Stable",
+            "datePublished": "2026-10-14T08:49:19+00:00",
+            "description": "<h1>\n  <picture>\n    <source media=\"(prefers-color-scheme: dark)\" srcset=\"docs/images/nf-core-deepmutscan_logo_dark.png\">\n    <img alt=\"nf-core/deepmutscan\" src=\"docs/images/nf-core-deepmutscan_logo_light.png\">\n  </picture>\n</h1>\n\n[![GitHub Actions CI Status](https://github.com/nf-core/deepmutscan/actions/workflows/nf-test.yml/badge.svg)](https://github.com/nf-core/deepmutscan/actions/workflows/nf-test.yml)\n[![GitHub Actions Linting Status](https://github.com/nf-core/deepmutscan/actions/workflows/linting.yml/badge.svg)](https://github.com/nf-core/deepmutscan/actions/workflows/linting.yml)[![AWS CI](https://img.shields.io/badge/CI%20tests-full%20size-FF9900?labelColor=000000&logo=Amazon%20AWS)](https://nf-co.re/deepmutscan/results)[![Cite with Zenodo](http://img.shields.io/badge/DOI-10.5281/zenodo.XXXXXXX-1073c8?labelColor=000000)](https://doi.org/10.5281/zenodo.XXXXXXX)\n[![nf-test](https://img.shields.io/badge/unit_tests-nf--test-337ab7.svg)](https://www.nf-test.com)\n\n[![Nextflow](https://img.shields.io/badge/version-%E2%89%A525.10.4-green?style=flat&logo=nextflow&logoColor=white&color=%230DC09D&link=https%3A%2F%2Fnextflow.io)](https://www.nextflow.io/)\n[![nf-core template version](https://img.shields.io/badge/nf--core_template-4.2.2-green?style=flat&logo=nfcore&logoColor=white&color=%2324B064&link=https%3A%2F%2Fnf-co.re)](https://github.com/nf-core/tools/releases/tag/4.2.2)\n[![run with conda](http://img.shields.io/badge/run%20with-conda-3EB049?labelColor=000000&logo=anaconda)](https://docs.conda.io/en/latest/)\n[![run with docker](https://img.shields.io/badge/run%20with-docker-0db7ed?labelColor=000000&logo=docker)](https://www.docker.com/)\n[![run with singularity](https://img.shields.io/badge/run%20with-singularity-1d355c.svg?labelColor=000000)](https://sylabs.io/docs/)\n[![Launch on Seqera Platform](https://img.shields.io/badge/Launch%20%F0%9F%9A%80-Seqera%20Platform-%234256e7)](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-core/deepmutscan)\n\n[![Get help on Slack](http://img.shields.io/badge/slack-nf--core%20%23deepmutscan-4A154B?labelColor=000000&logo=slack)](https://nfcore.slack.com/channels/deepmutscan)[![Follow on Twitter](http://img.shields.io/badge/twitter-%40nf__core-1DA1F2?labelColor=000000&logo=twitter)](https://twitter.com/nf_core)[![Follow on Mastodon](https://img.shields.io/badge/mastodon-nf__core-6364ff?labelColor=FFFFFF&logo=mastodon)](https://mstdn.science/@nf_core)[![Watch on YouTube](http://img.shields.io/badge/youtube-nf--core-FF0000?labelColor=000000&logo=youtube)](https://www.youtube.com/c/nf-core)\n\n## Introduction\n\n**nf-core/deepmutscan** is a workflow designed for the analysis of deep mutational scanning (DMS) data. DMS enables researchers to experimentally measure the fitness effects of thousands of gene variants simultaneously, helping to classify disease-causing mutants in human and other species populations, and to learn the fundamental rules of protein architecture, small-molecule binding, mRNA splicing, viral evolution and many other quantifiable phenotypes.\n\nWhile DNA synthesis and sequencing technologies have advanced substantially, long open reading frame (ORF) targets still present a major challenge for DMS studies. Shotgun DNA sequencing of randomly fragmented variant libraries can greatly speed up the inference of long ORF mutant fitness landscapes, as it avoids the need for library barcoding or multi-tile sequencing. We have designed `nf-core/deepmutscan` to unlock shotgun sequencing-based DMS studies on long ORFs, and to simplify and standardise the bioinformatics steps involved in processing such experiments \u2013 from read alignment to QC reporting, variant count error correction and fitness landscape inference. Amplicon (tile) sequencing data can be processed in the same way.\n\n![nf-core/deepmutscan workflow](docs/images/pipeline.png)\n\n## Major features\n\n- End-to-end processing of DMS libraries from shotgun (randomly fragmented) or amplicon short-read sequencing\n- Light-weight variant counter with base-quality and read-edge filters, replacing `GATK AnalyzeSaturationMutagenesis`\n- Intrinsic sequencing-error correction of single-nucleotide variant counts from read-linked false double mutants (maximum-likelihood or empirical-Bayes estimators), or from additional wildtype template sequencing\n- Read QC with [FastQC](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/), aggregated across all libraries with [MultiQC](https://multiqc.info/)\n- Library quality control: mutant count heatmaps, positional coverage and mutation-type biases, sequencing-depth rarefaction\n- Fitness estimation with a built-in log-ratio estimator, plus optional [DiMSum](https://github.com/lehner-lab/DiMSum) and [mutscan](https://github.com/fmicompbio/mutscan)\n- Support for degenerate codon libraries (NNK, NNS, NNH, NNN and combinations), e.g. from nicking mutagenesis, and for custom (position-specific) codon libraries, e.g. from Twist tiles\n- A single, self-contained HTML report per run, and an optional interactive 3D variant effect inspection tool when a wildtype structure is supplied\n- Containerisation via Docker, Singularity/Apptainer and Conda; scalability across HPC and cloud systems\n\nFor more details on the individual steps, please read the [usage documentation](https://nf-co.re/deepmutscan/usage).\n\n## Pipeline summary\n\n1. Input FASTQ checks ([`fq lint`](https://github.com/stjude-rust-labs/fq)) and raw read QC ([`FastQC`](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/))\n2. Merging of overlapping read pairs into consensus reads for base error reduction ([`vsearch --fastq_mergepairs`](https://github.com/torognes/vsearch))\n3. Alignment of the merged reads to the reference ORF ([`BWA-MEM`](https://github.com/lh3/bwa))\n4. Filtering of unmapped, secondary, supplementary, low mapping-quality and indel-containing alignments, sorting and indexing ([`samtools`](https://www.htslib.org/))\n5. Variant counting (custom counter built on [`pysam`](https://github.com/pysam-developers/pysam) and [`polars`](https://pola.rs))\n6. Annotation and filtering of variant counts against the programmed mutagenesis library\n7. Single-nucleotide variant sequencing-error correction via false double mutants or wildtype sequencing\n8. DMS library quality control and visualisation\n9. _Optional:_ fitness estimation from matched input/output samples (default estimator, [`DiMSum`](https://github.com/lehner-lab/DiMSum), [`mutscan`](https://github.com/fmicompbio/mutscan)) and interactive 3D variant effect inspection tool ([`3Dmol.js`](https://3dmol.csb.pitt.edu/))\n10. An all-in-one `deepmutscan_report.html`\n\n## Usage\n\n> [!NOTE]\n> If you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/get_started/environment_setup/overview) on how to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/get_started/run-your-first-pipeline) with `-profile test` before running the workflow on actual data.\n\nFirst, prepare a samplesheet with your input data. Each row represents one sequencing library as a pair of FASTQ files (paired-end), annotated with the biological sample, its type in the selection experiment (`input`, `output` or `wildtype`) and the replicate number:\n\n```csv title=\"samplesheet.csv\"\nsample,type,replicate,file1,file2\nORF1,input,1,/reads/input1_R1.fastq.gz,/reads/input1_R2.fastq.gz\nORF1,input,2,/reads/input2_R1.fastq.gz,/reads/input2_R2.fastq.gz\nORF1,output,1,/reads/output1_R1.fastq.gz,/reads/output1_R2.fastq.gz\nORF1,output,2,/reads/output2_R1.fastq.gz,/reads/output2_R2.fastq.gz\n```\n\nSecondly, provide the gene or gene region of interest as a reference FASTA file via `--fasta`, and the nucleotide coordinates of the mutagenised open reading frame within it via `--reading_frame` (1-based, inclusive, e.g. `151-450` for 100 codons starting at nucleotide 151).\n\nNow, you can run the pipeline using:\n\n```bash title=\"example_run.sh\"\nnextflow run nf-core/deepmutscan \\\n   -profile <docker/singularity/.../institute> \\\n   --input ./samplesheet.csv \\\n   --fasta ./ref.fa \\\n   --reading_frame 151-450 \\\n   --outdir ./results\n```\n\nAdd `--fitness` to estimate variant fitness from the input and output samples, and see the [usage documentation](https://nf-co.re/deepmutscan/usage) and the [parameter documentation](https://nf-co.re/deepmutscan/parameters) for all other options.\n\n> [!WARNING]\n> Please provide pipeline parameters via the CLI or Nextflow `-params-file` option. Custom config files including those provided by the `-c` Nextflow option can be used to provide any configuration _**except for parameters**_; see [docs](https://nf-co.re/docs/usage/getting_started/configuration#custom-configuration-files).\n\n## Pipeline output\n\nTo see the results of an example test run with a full size dataset refer to the [results](https://nf-co.re/deepmutscan/results) tab on the nf-core website pipeline page.\nFor more details about the output files and reports, please refer to the [output documentation](https://nf-co.re/deepmutscan/output).\n\n## Credits\n\nnf-core/deepmutscan was originally written by [Benjamin Wehnert](https://github.com/BenjaminWehnert1008) and [Maximilian Stammnitz](https://github.com/MaximilianStammnitz) at the [Centre for Genomic Regulation (CRG), Barcelona](https://www.crg.eu/), with the generous support of an EMBO Long-term Postdoctoral Fellowship, the Erasmus+ programme and a Marie Sk\u0142odowska-Curie grant by the European Union.\n\nWe thank the following people for their extensive assistance in the development of this pipeline:\n\n- [Fei Sang](https://github.com/fei-hgi) (Wellcome Sanger Institute) \u2013 original variant counting implementation\n- [J\u00falia Mir-Pedrol](https://github.com/mirpedrol) (CRG) \u2013 nf-core development guidance and code review\n- [Matthias H\u00f6rtenhuber](https://github.com/mashehu) (SciLifeLab) \u2013 nf-core development guidance and code review\n\n## Contributions and Support\n\nIf you would like to contribute to this pipeline, please see the [contributing guidelines](docs/CONTRIBUTING.md).\n\nFor further information or help, don't hesitate to get in touch on the [Slack `#deepmutscan` channel](https://nfcore.slack.com/channels/deepmutscan) (you can join with [this invite](https://nf-co.re/join/slack)). Bug reports and feature requests are welcome as GitHub [issues](https://github.com/nf-core/deepmutscan/issues).\n\nFor scientific discussions around the use of this pipeline (e.g. on experimental design or sequencing data requirements), please feel free to get in touch with us directly:\n\n- Benjamin Wehnert \u2014 wehnertbenjamin@gmail.com\n- Maximilian Stammnitz \u2014 maximilian.stammnitz@crg.eu\n\n## Citations\n\nIf you use `nf-core/deepmutscan` for your analysis, please cite it as follows:\n\n> Wehnert B, et al. _bioRxiv_ preprint (in preparation).\n\n<!-- Add the Zenodo DOI after the first release: -->\n<!-- If you use nf-core/deepmutscan for your analysis, please cite it using the following doi: [10.5281/zenodo.XXXXXX](https://doi.org/10.5281/zenodo.XXXXXX) -->\n\nAn extensive list of references for the tools used by the pipeline can be found in the [`CITATIONS.md`](CITATIONS.md) file.\n\nYou can cite the `nf-core` publication as follows:\n\n> **The nf-core framework for community-curated bioinformatics pipelines.**\n>\n> Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.\n>\n> _Nat Biotechnol._ 2020 Feb 13. doi: [10.1038/s41587-020-0439-x](https://dx.doi.org/10.1038/s41587-020-0439-x).\n",
             "hasPart": [
                 {
                     "@id": "main.nf"
@@ -43,6 +37,9 @@
                 {
                     "@id": "modules/"
                 },
+                {
+                    "@id": "modules/local/"
+                },
                 {
                     "@id": "modules/nf-core/"
                 },
@@ -92,17 +89,17 @@
                     "@id": ".prettierignore"
                 }
             ],
-            "isBasedOn": "https://github.com/nf-core/dmscore",
+            "isBasedOn": "https://github.com/nf-core/deepmutscan",
             "license": "MIT",
             "mainEntity": {
                 "@id": "main.nf"
             },
             "mentions": [
                 {
-                    "@id": "#f9815654-37bd-4781-a133-ab36324210f5"
+                    "@id": "#efd79355-6f62-48b6-83da-33e170beef8c"
                 }
             ],
-            "name": "nf-core/dmscore"
+            "name": "nf-core/deepmutscan"
         },
         {
             "@id": "ro-crate-metadata.json",
@@ -110,14 +107,9 @@
             "about": {
                 "@id": "./"
             },
-            "conformsTo": [
-                {
-                    "@id": "https://w3id.org/ro/crate/1.1"
-                },
-                {
-                    "@id": "https://w3id.org/workflowhub/workflow-ro-crate/1.0"
-                }
-            ]
+            "conformsTo": {
+                "@id": "https://w3id.org/ro/crate/1.3"
+            }
         },
         {
             "@id": "main.nf",
@@ -126,18 +118,49 @@
                 "SoftwareSourceCode",
                 "ComputationalWorkflow"
             ],
+            "author": [
+                {
+                    "@id": "https://orcid.org/0009-0001-8012-6003"
+                },
+                {
+                    "@id": "https://orcid.org/0000-0002-1704-9199"
+                }
+            ],
+            "contributor": [
+                {
+                    "@id": "https://orcid.org/0000-0002-8746-5868"
+                },
+                {
+                    "@id": "https://orcid.org/0000-0001-6104-9260"
+                },
+                {
+                    "@id": "https://orcid.org/0000-0002-5599-5565"
+                }
+            ],
             "dateCreated": "",
-            "dateModified": "2025-01-22T17:52:07Z",
+            "dateModified": "2026-10-14T10:49:19Z",
             "dct:conformsTo": "https://bioschemas.org/profiles/ComputationalWorkflow/1.0-RELEASE/",
             "keywords": [
                 "nf-core",
-                "nextflow"
+                "nextflow",
+                "community-workflow",
+                "deep-mutational-scanning",
+                "genotype-phenotype",
+                "shotgun-sequencing"
             ],
             "license": [
                 "MIT"
             ],
+            "maintainer": [
+                {
+                    "@id": "https://orcid.org/0009-0001-8012-6003"
+                },
+                {
+                    "@id": "https://orcid.org/0000-0002-1704-9199"
+                }
+            ],
             "name": [
-                "nf-core/dmscore"
+                "nf-core/deepmutscan"
             ],
             "programmingLanguage": {
                 "@id": "https://w3id.org/workflowhub/workflow-ro-crate#nextflow"
@@ -146,11 +169,11 @@
                 "@id": "https://nf-co.re/"
             },
             "url": [
-                "https://github.com/nf-core/dmscore",
-                "https://nf-co.re/dmscore/dev/"
+                "https://github.com/nf-core/deepmutscan",
+                "https://nf-co.re/deepmutscan/1.0.0/"
             ],
             "version": [
-                "1.0.0dev"
+                "1.0.0"
             ]
         },
         {
@@ -163,26 +186,44 @@
             "url": {
                 "@id": "https://www.nextflow.io/"
             },
-            "version": "!>=24.04.2"
+            "version": "!>=25.10.4"
+        },
+        {
+            "@id": "https://w3id.org/workflowhub/workflow-ro-crate/1.1",
+            "@type": [
+                "CreativeWork",
+                "Profile"
+            ],
+            "name": "Workflow RO-Crate",
+            "version": "1.1"
         },
         {
-            "@id": "#f9815654-37bd-4781-a133-ab36324210f5",
+            "@id": "#efd79355-6f62-48b6-83da-33e170beef8c",
             "@type": "TestSuite",
             "instance": [
                 {
-                    "@id": "#34dab5c1-0a1b-41b0-80d1-74e2f2d38434"
+                    "@id": "#b4e95fbd-d54e-47ea-9af5-5eb40e2654e5"
                 }
             ],
             "mainEntity": {
                 "@id": "main.nf"
             },
-            "name": "Test suite for nf-core/dmscore"
+            "name": "Test suite for nf-core/deepmutscan"
+        },
+        {
+            "@id": "https://w3id.org/ro/wftest/0.2",
+            "@type": [
+                "CreativeWork",
+                "Profile"
+            ],
+            "name": "Workflow Testing RO-Crate",
+            "version": "0.2"
         },
         {
-            "@id": "#34dab5c1-0a1b-41b0-80d1-74e2f2d38434",
+            "@id": "#b4e95fbd-d54e-47ea-9af5-5eb40e2654e5",
             "@type": "TestInstance",
-            "name": "GitHub Actions workflow for testing nf-core/dmscore",
-            "resource": "repos/nf-core/dmscore/actions/workflows/ci.yml",
+            "name": "GitHub Actions workflow for testing nf-core/deepmutscan",
+            "resource": "repos/nf-core/deepmutscan/actions/workflows/nf-test.yml",
             "runsOn": {
                 "@id": "https://w3id.org/ro/terms/test#GithubService"
             },
@@ -221,6 +262,11 @@
             "@type": "Dataset",
             "description": "Modules used by the pipeline"
         },
+        {
+            "@id": "modules/local/",
+            "@type": "Dataset",
+            "description": "Pipeline-specific modules"
+        },
         {
             "@id": "modules/nf-core/",
             "@type": "Dataset",
@@ -306,6 +352,46 @@
             "@type": "Organization",
             "name": "nf-core",
             "url": "https://nf-co.re/"
+        },
+        {
+            "@id": "https://orcid.org/0009-0001-8012-6003",
+            "@type": "Person",
+            "affiliation": "Centre for Genomic Regulation (CRG), Barcelona, Spain",
+            "email": "wehnertbenjamin@gmail.com",
+            "name": "Benjamin Wehnert",
+            "url": "https://github.com/BenjaminWehnert1008"
+        },
+        {
+            "@id": "https://orcid.org/0000-0002-1704-9199",
+            "@type": "Person",
+            "affiliation": "Centre for Genomic Regulation (CRG), Barcelona, Spain",
+            "email": "maximilian.stammnitz@crg.eu",
+            "name": "Maximilian R. Stammnitz",
+            "url": "https://github.com/MaximilianStammnitz"
+        },
+        {
+            "@id": "https://orcid.org/0000-0002-8746-5868",
+            "@type": "Person",
+            "affiliation": "Wellcome Sanger Institute, Hinxton, United Kingdom",
+            "email": "fs18@sanger.ac.uk",
+            "name": "Fei Sang",
+            "url": "https://github.com/fei-hgi"
+        },
+        {
+            "@id": "https://orcid.org/0000-0001-6104-9260",
+            "@type": "Person",
+            "affiliation": "Centre for Genomic Regulation (CRG), Barcelona, Spain",
+            "email": "julia.mir@crg.eu",
+            "name": "J\u00falia Mir-Pedrol",
+            "url": "https://github.com/mirpedrol"
+        },
+        {
+            "@id": "https://orcid.org/0000-0002-5599-5565",
+            "@type": "Person",
+            "affiliation": "SciLifeLab, Uppsala University, Uppsala, Sweden",
+            "email": "matthias.hortenhuber@scilifelab.uu.se",
+            "name": "Matthias H\u00f6rtenhuber",
+            "url": "https://github.com/mashehu"
         }
     ]
 }
\ No newline at end of file
diff --git a/subworkflows/local/calculate_fitness/main.nf b/subworkflows/local/calculate_fitness/main.nf
new file mode 100644
index 0000000..cd34a57
--- /dev/null
+++ b/subworkflows/local/calculate_fitness/main.nf
@@ -0,0 +1,146 @@
+/*
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    IMPORT MODULES
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+*/
+include { MERGE_COUNTS }             from '../../../modules/local/fitness/merge_counts/main'
+include { EXPDESIGN_FITNESS }        from '../../../modules/local/fitness/fitness_experimental_design/main'
+include { FIND_SYNONYMOUS_MUTATION } from '../../../modules/local/fitness/find_synonymous_mutation/main'
+include { FITNESS_CALCULATION }      from '../../../modules/local/fitness/fitness_calculation/main'
+include { FITNESS_QC }               from '../../../modules/local/fitness/fitness_QC/main'
+include { FITNESS_HEATMAP }          from '../../../modules/local/fitness/fitness_heatmap/main'
+include { RUN_DIMSUM }               from '../../../modules/local/fitness/run_dimsum/main'
+include { RUN_MUTSCAN }              from '../../../modules/local/fitness/run_mutscan/main'
+
+/*
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    SUBWORKFLOW DEFINITION
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+*/
+
+workflow CALCULATE_FITNESS {
+
+    take:
+    ch_fitness_input    // channel: output from COUNTS_TO_FITNESS (grouped by sample/replicate logic)
+    ch_samplesheet_csv  // value channel: path to samplesheet csv
+    ch_fasta            // value channel: tuple([meta], path(fasta))
+    val_reading_frame   // value channel: reading frame param
+    ch_aa_seq           // value channel: tuple([meta], path(aa_seq)) from DMSANALYSIS_AASEQ (for heatmap)
+    val_run_dimsum      // value: boolean, run DiMSum
+    val_run_mutscan     // value: boolean, run mutscan
+
+    main:
+    ch_versions = Channel.empty()
+
+    // 1. Group per biological sample and merge counts
+    ch_fitness_input
+        .map { meta, tsv ->
+            def s  = meta.sample as String
+            def id = meta.id     as String
+            def base = s ? (s.replaceFirst(/_(input|output|quality)\d+$/, ''))
+                       : (id?.tokenize('_')?.first())
+            tuple(base as String, tuple(meta, tsv))
+        }
+        .groupTuple()
+        .map { base, pairs ->
+            // sort by replicate so the staged file order is deterministic (cacheable)
+            def inputs  = pairs.findAll { it[0].type == 'input'  }.sort { it[0].replicate }.collect { it[1] }
+            def outputs = pairs.findAll { it[0].type == 'output' }.sort { it[0].replicate }.collect { it[1] }
+            tuple([sample: base], inputs, outputs)
+        }
+        .filter { smeta, ins, outs -> ins && outs }
+        .set { ch_fitness_bundled }
+
+    // 2. Merge Counts
+    MERGE_COUNTS( ch_fitness_bundled )
+    ch_versions = ch_versions.mix(MERGE_COUNTS.out.versions)
+
+    // 3. Experimental Design
+    EXPDESIGN_FITNESS( ch_samplesheet_csv )
+    ch_versions = ch_versions.mix(EXPDESIGN_FITNESS.out.versions)
+
+    // 4. Find Synonymous Mutations (the reference and the reading frame are value channels)
+    FIND_SYNONYMOUS_MUTATION(
+        MERGE_COUNTS.out.merged_counts,
+        ch_fasta.map { _meta, fasta -> fasta },
+        val_reading_frame
+    )
+    ch_versions = ch_versions.mix(FIND_SYNONYMOUS_MUTATION.out.versions)
+
+
+    // 5. Align Channels for Fitness Calculation
+
+    // Key counts and WT by biological sample name
+    def ch_counts_keyed_d = MERGE_COUNTS.out.merged_counts
+        .map { smp, counts -> tuple(smp.sample as String, smp, counts) }
+
+    def ch_wt_keyed_d = FIND_SYNONYMOUS_MUTATION.out.synonymous_wt
+        .map { smp, wt -> tuple(smp.sample as String, wt) }
+
+    // Join by key
+    def ch_counts_wt_d = ch_counts_keyed_d.join(ch_wt_keyed_d)
+        .map { key, smp, counts, wt -> tuple(smp, counts, wt) }
+
+    // Final aligned channels; the experimental design is a value channel (one samplesheet per run)
+    def ch_run_counts_d = ch_counts_wt_d.map { smp, counts, wt -> tuple(smp, counts) }
+    def ch_run_wt_d     = ch_counts_wt_d.map { smp, counts, wt -> wt }
+    def ch_run_exp_d    = EXPDESIGN_FITNESS.out.experimental_design
+
+    // 6. Run Fitness Calculation & QC
+    FITNESS_CALCULATION(
+        ch_run_counts_d,
+        ch_run_exp_d,
+        ch_run_wt_d,
+        params.min_counts,
+        params.output_pseudocount
+    )
+    ch_versions = ch_versions.mix(FITNESS_CALCULATION.out.versions)
+
+    FITNESS_QC( FITNESS_CALCULATION.out.fitness_estimation )
+    ch_versions = ch_versions.mix(FITNESS_QC.out.versions)
+
+    FITNESS_HEATMAP(
+        FITNESS_CALCULATION.out.fitness_estimation,
+        ch_aa_seq
+    )
+    ch_versions = ch_versions.mix(FITNESS_HEATMAP.out.versions)
+
+    // DiMSum and mutscan are optional, so their artefact channels start empty and are only filled
+    // when the tool actually ran - the summary report then simply omits the section.
+    def ch_dimsum_dir    = Channel.empty()
+    def ch_mutscan_plots = Channel.empty()
+
+    if (val_run_dimsum) {
+        RUN_DIMSUM(
+            ch_run_counts_d,
+            ch_run_wt_d,
+            ch_run_exp_d,
+            params.min_counts,
+            params.output_pseudocount
+        )
+        ch_versions = ch_versions.mix(RUN_DIMSUM.out.versions)
+        ch_dimsum_dir = RUN_DIMSUM.out.results_dir
+    }
+
+    // 8. Run Mutscan
+    if (val_run_mutscan) {
+        RUN_MUTSCAN(
+            ch_run_counts_d,
+            ch_run_wt_d,
+            ch_run_exp_d,
+            params.min_counts
+        )
+        ch_versions = ch_versions.mix(RUN_MUTSCAN.out.versions)
+        ch_mutscan_plots = RUN_MUTSCAN.out.qc_plots
+    }
+
+    emit:
+    fitness_estimation = FITNESS_CALCULATION.out.fitness_estimation
+    // artefacts the all-in-one report embeds
+    fitness_plots      = FITNESS_QC.out.counts_corr_pdf
+                            .mix(FITNESS_QC.out.fitness_corr_pdf)
+                            .mix(FITNESS_HEATMAP.out.fitness_heatmap)
+    mutscan_plots      = ch_mutscan_plots
+    dimsum_dir         = ch_dimsum_dir
+    versions           = ch_versions
+}
diff --git a/subworkflows/local/calculate_fitness/meta.yml b/subworkflows/local/calculate_fitness/meta.yml
new file mode 100644
index 0000000..be44cab
--- /dev/null
+++ b/subworkflows/local/calculate_fitness/meta.yml
@@ -0,0 +1,48 @@
+name: "calculate_fitness"
+description: Consolidates variant counts and calculates fitness scores.
+keywords:
+  - "deep mutational scanning"
+  - "fitness estimation"
+  - "dms"
+  - "enrichment scores"
+components:
+  - "merge/counts"
+  - "fitness/calculation"
+  - "fitness/heatmap"
+  - "fitness/qc"
+  - "run/dimsum"
+  - "run/mutscan"
+  - "expdesign/fitness"
+  - "find/synonymous/mutation"
+input:
+  - ch_fitness_input:
+      type: channel
+      description: Variant count channels
+  - ch_samplesheet_csv:
+      type: channel
+      description: Pipeline samplesheet
+  - ch_fasta:
+      type: channel
+      description: Reference sequence channel
+  - val_reading_frame:
+      type: value
+      description: Reading frame parameter
+  - ch_aa_seq:
+      type: channel
+      description: Amino acid sequence channel
+  - val_run_dimsum:
+      type: value
+      description: Boolean, whether to run DiMSum
+  - val_run_mutscan:
+      type: value
+      description: Boolean, whether to run mutscan
+output:
+  - fitness_estimation:
+      type: channel
+      description: Output fitness metrics
+  - versions:
+      type: channel
+      description: Software versions
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/subworkflows/local/utils_nfcore_deepmutscan_pipeline/main.nf b/subworkflows/local/utils_nfcore_deepmutscan_pipeline/main.nf
new file mode 100644
index 0000000..d52cb02
--- /dev/null
+++ b/subworkflows/local/utils_nfcore_deepmutscan_pipeline/main.nf
@@ -0,0 +1,508 @@
+//
+// Subworkflow with functionality specific to the nf-core/deepmutscan pipeline
+//
+
+/*
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    IMPORT FUNCTIONS / MODULES / SUBWORKFLOWS
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+*/
+
+include { UTILS_NFSCHEMA_PLUGIN     } from '../../nf-core/utils_nfschema_plugin'
+include { paramsSummaryMap          } from 'plugin/nf-schema'
+include { samplesheetToList         } from 'plugin/nf-schema'
+include { completionEmail           } from '../../nf-core/utils_nfcore_pipeline'
+include { completionSummary         } from '../../nf-core/utils_nfcore_pipeline'
+include { UTILS_NFCORE_PIPELINE     } from '../../nf-core/utils_nfcore_pipeline'
+include { UTILS_NEXTFLOW_PIPELINE   } from '../../nf-core/utils_nextflow_pipeline'
+
+/*
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    SUBWORKFLOW TO INITIALISE PIPELINE
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+*/
+
+workflow PIPELINE_INITIALISATION {
+
+    take:
+    version           // boolean: Display version and exit
+    validate_params   // boolean: Boolean whether to validate parameters against the schema at runtime
+    monochrome_logs   // boolean: Do not use coloured log outputs
+    nextflow_cli_args //   array: List of positional nextflow CLI args
+    outdir            //  string: The output directory where the results will be saved
+    input             //  string: Path to input samplesheet
+
+    main:
+
+    ch_versions = Channel.empty()
+
+    //
+    // Print version and exit if required and dump pipeline parameters to JSON file
+    //
+    UTILS_NEXTFLOW_PIPELINE (
+        version,
+        true,
+        outdir,
+        workflow.profile.tokenize(',').intersect(['conda', 'mamba']).size() >= 1
+    )
+
+    //
+    // Validate parameters and generate parameter summary to stdout
+    //
+
+    def before_text = ""
+    def after_text = ""
+    before_text = """
+-\033[2m----------------------------------------------------\033[0m-
+                                        \033[0;32m,--.\033[0;30m/\033[0;32m,-.\033[0m
+\033[0;34m        ___     __   __   __   ___     \033[0;32m/,-._.--~\'\033[0m
+\033[0;34m  |\\ | |__  __ /  ` /  \\ |__) |__         \033[0;33m}  {\033[0m
+\033[0;34m  | \\| |       \\__, \\__/ |  \\ |___     \033[0;32m\\`-._,-`-,\033[0m
+                                        \033[0;32m`._,._,\'\033[0m
+\033[0;35m  nf-core/deepmutscan ${workflow.manifest.version}\033[0m
+-\033[2m----------------------------------------------------\033[0m-
+"""
+    after_text = """${workflow.manifest.doi ? "\n* The pipeline\n" : ""}${workflow.manifest.doi.tokenize(",").collect { doi -> "    https://doi.org/${doi.trim().replace('https://doi.org/','')}"}.join("\n")}${workflow.manifest.doi ? "\n" : ""}
+* The nf-core framework
+    https://doi.org/10.1038/s41587-020-0439-x
+
+* Software dependencies
+    https://github.com/nf-core/deepmutscan/blob/master/CITATIONS.md
+"""
+    if (monochrome_logs) {
+        before_text = before_text.replaceAll(/\033\[[0-9;]*m/, '')
+    }
+
+    command = "nextflow run ${workflow.manifest.name} -profile <docker/singularity/.../institute> --input samplesheet.csv --outdir <OUTDIR>"
+
+    UTILS_NFSCHEMA_PLUGIN (
+        workflow,                                 // input_workflow
+        validate_params,                          // validate_params
+        "${projectDir}/nextflow_schema.json",     // parameters_schema
+        params.help,                              // help
+        params.help_full,                         // help_full
+        params.show_hidden,                       // show_hidden
+        before_text,                              // before_text
+        after_text,                               // after_text
+        command,                                  // command
+        true                                      // cli_typecast: cast CLI strings for validation
+    )
+
+    //
+    // Check config provided to the pipeline
+    //
+    UTILS_NFCORE_PIPELINE (
+        nextflow_cli_args
+    )
+
+    //
+    // Custom validation for pipeline parameters
+    //
+    validateInputParameters()
+
+    //
+    // Create channel from input file provided through params.input
+    //
+    // samplesheetToList() reads the samplesheet with assets/schema_input.json: every row is
+    // validated (FASTQ extensions, both reads present, allowed types, no duplicate
+    // sample/type/replicate) and file paths are returned as file objects.
+    //
+    def samplesheet_rows = samplesheetToList(input, "${projectDir}/assets/schema_input.json")
+        .collect { meta, file1, file2 ->
+            def new_meta = [
+                id        : "${meta.sample}_${meta.type}_${meta.replicate}_pe".toString(),
+                sample    : meta.sample,
+                type      : meta.type,
+                replicate : meta.replicate as int
+            ]
+            [ new_meta, [ file1, file2 ] ]
+        }
+
+    validateSamplesheet(samplesheet_rows.collect { row -> row[0] })
+    validateInputFiles(samplesheet_rows)
+
+    ch_samplesheet = channel.fromList(samplesheet_rows)
+
+    // Emit the samplesheet channel and an empty version channel for use in the workflow
+    emit:
+    samplesheet = ch_samplesheet
+    versions    = ch_versions
+}
+
+/*
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    SUBWORKFLOW FOR PIPELINE COMPLETION
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+*/
+
+workflow PIPELINE_COMPLETION {
+
+    take:
+    email           //  string: email address
+    email_on_fail   //  string: email address sent on pipeline failure
+    plaintext_email // boolean: Send plain-text email instead of HTML
+    outdir          //    path: Path to output directory where results will be published
+    monochrome_logs // boolean: Disable ANSI colour codes in log output
+    multiqc_report  //  string: Path to MultiQC report
+
+    main:
+    summary_params = paramsSummaryMap(workflow, parameters_schema: "nextflow_schema.json")
+    def multiqc_reports = multiqc_report.toList()
+
+    //
+    // Completion email and summary
+    //
+    workflow.onComplete {
+        if (email || email_on_fail) {
+            completionEmail(
+                summary_params,
+                email,
+                email_on_fail,
+                plaintext_email,
+                outdir,
+                monochrome_logs,
+                multiqc_reports.isBound() ? multiqc_reports.getVal() : [],
+            )
+        }
+
+        completionSummary(monochrome_logs)
+
+    }
+
+    workflow.onError {
+        log.error "Pipeline failed. Please refer to troubleshooting docs for common issues: https://nf-co.re/docs/running/troubleshooting"
+    }
+}
+
+/*
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    FUNCTIONS
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+*/
+//
+// Params given on the command line arrive as Strings, so `--fitness false` yields the String
+// "false", which is truthy in Groovy - `if (params.fitness)` would switch fitness ON. Values set
+// in a config or a -params-file keep their real type. These two helpers accept either shape.
+// nf-schema's cast_cli_params only casts for validation; it does not change params itself.
+//
+def asBool(value) {
+    if (value == null) {
+        return false
+    }
+    if (value instanceof Boolean) {
+        return value
+    }
+    return !(value.toString().trim().toLowerCase() in ['false', '0', 'no', 'null', ''])
+}
+
+def asInt(value) {
+    if (value == null) {
+        return null
+    }
+    if (value instanceof Number) {
+        return value as int
+    }
+    return value.toString().trim() as int
+}
+
+//
+// Check and validate pipeline parameters
+//
+def validateInputParameters() {
+    readingFrameError()
+    customCodonLibraryError()
+}
+
+//
+// Check that --reading_frame covers whole codons and lies within the reference FASTA
+//
+def readingFrameError() {
+    if (!params.reading_frame || !params.fasta) {
+        return  // both are required parameters; the schema validation reports them if missing
+    }
+    def (start, stop) = params.reading_frame.tokenize('-').collect { coord -> coord as long }
+    if (start < 1 || stop < start) {
+        error("[--reading_frame ${params.reading_frame}] The reading frame must be given as 'start-stop' with 1 <= start <= stop (1-based, inclusive).")
+    }
+    def length = stop - start + 1
+    if (length % 3 != 0) {
+        error("[--reading_frame ${params.reading_frame}] The reading frame spans ${length} nucleotides, which is not a multiple of 3. 'start' must be the first nucleotide of the first codon and 'stop' the last nucleotide of the last codon.")
+    }
+    def fasta_length = fastaSequenceLength(params.fasta)
+    if (stop > fasta_length) {
+        error("[--reading_frame ${params.reading_frame}] The reading frame ends at nucleotide ${stop}, but the reference sequence in '--fasta' is only ${fasta_length} nucleotides long.")
+    }
+}
+
+//
+// Length of the (first) sequence in a FASTA file, plain or gzipped
+//
+def fastaSequenceLength(fasta_path) {
+    def fasta  = file(fasta_path, checkIfExists: true)
+    def stream = fasta.name.endsWith('.gz') ? new java.util.zip.GZIPInputStream(fasta.newInputStream()) : fasta.newInputStream()
+    def length = 0L
+    def n_records = 0
+    stream.withReader { reader ->
+        reader.eachLine { line ->
+            if (line.startsWith('>')) {
+                n_records += 1
+            } else if (n_records == 1) {
+                length += line.trim().length()
+            }
+        }
+    }
+    return length
+}
+
+//
+// '--mutagenesis_type custom' needs a custom codon library
+//
+def customCodonLibraryError() {
+    if (params.mutagenesis_type == 'custom' && !params.custom_codon_library) {
+        error("[--mutagenesis_type custom] Please provide the custom codon library with '--custom_codon_library <file.csv>'.")
+    }
+}
+
+//
+// Check the samplesheet as a whole (row-level checks are done by assets/schema_input.json)
+//
+def validateSamplesheet(metas) {
+    // One biological sample per run
+    def samples = metas.collect { meta -> meta.sample }.unique()
+    if (samples.size() > 1) {
+        error("[samplesheet] nf-core/deepmutscan v1.0.0 supports one biological sample per run (found: ${samples.join(', ')}). Please split the samplesheet and run the pipeline once per sample.")
+    }
+
+    // Fitness estimation pairs input and output libraries by replicate number
+    if (params.fitness) {
+        def input_reps  = metas.findAll { meta -> meta.type == 'input'  }.collect { meta -> meta.replicate }.sort()
+        def output_reps = metas.findAll { meta -> meta.type == 'output' }.collect { meta -> meta.replicate }.sort()
+        if (!input_reps || !output_reps) {
+            error("[--fitness] Fitness estimation needs at least one 'input' and one 'output' library in the samplesheet (found ${input_reps.size()} input and ${output_reps.size()} output).")
+        }
+        if (input_reps != output_reps) {
+            error("[--fitness] The 'input' and 'output' libraries must have the same replicate numbers, as they are paired by replicate (input: ${input_reps.join(', ')}; output: ${output_reps.join(', ')}).")
+        }
+    }
+
+    // '--error_correction wildtype' needs exactly one wildtype library
+    if (params.error_correction == 'wildtype') {
+        def n_wildtype = metas.count { meta -> meta.type == 'wildtype' }
+        if (n_wildtype == 0) {
+            error(
+                "[--error_correction wildtype] No 'wildtype' library found in the samplesheet.\n" +
+                "  Add a samplesheet row with type 'wildtype' and sample '${samples[0]}' (deep wildtype-only sequencing),\n" +
+                "  or use the internal correction instead (default: --error_correction false_doubles),\n" +
+                "  or disable error correction with --error_correction none."
+            )
+        }
+        if (n_wildtype > 1) {
+            error("[--error_correction wildtype] Found ${n_wildtype} 'wildtype' libraries in the samplesheet; exactly one is supported. Please keep one wildtype library (e.g. the deepest-sequenced one).")
+        }
+    }
+}
+
+//
+// Quick start-up checks of the input FASTQ files, before any process runs. Reads only the first
+// record of each file, so it takes a moment even for remote files. The complete files are checked
+// by FASTQ_CHECK (fq lint) at the start of the workflow. All problems are reported together.
+//
+def validateInputFiles(rows) {
+    def problems = []
+
+    // Each FASTQ file belongs to one library only, and file1 and file2 are different files
+    def libraries_per_file = [:]
+    rows.each { row ->
+        def meta  = row[0]
+        def reads = row[1]
+        reads.each { fastq ->
+            def key = fastq.toUriString()
+            libraries_per_file[key] = (libraries_per_file[key] ?: []) + [meta.id]
+        }
+        if (reads[0].toUriString() == reads[1].toUriString()) {
+            problems << "${meta.id}: file1 and file2 are the same file (${reads[0].name})."
+        }
+    }
+    libraries_per_file.each { path, ids ->
+        def unique_ids = ids.unique(false)
+        if (unique_ids.size() > 1) {
+            problems << "${path.tokenize('/')[-1]} is used by more than one library (${unique_ids.join(', ')})."
+        }
+    }
+
+    // The first record of each file is a valid FASTQ record, and the two files are mates
+    rows.each { row ->
+        def meta  = row[0]
+        def reads = row[1]
+        def first = reads.collect { fastq -> firstFastqRecord(fastq) }
+        first.eachWithIndex { record, i ->
+            if (record.error) {
+                problems << "${meta.id}: file${i + 1} ${reads[i].name} ${record.error}"
+            }
+        }
+        def same_file = reads[0].toUriString() == reads[1].toUriString()
+        if (!same_file && !first[0].error && !first[1].error) {
+            if (first[0].lines == first[1].lines) {
+                problems << "${meta.id}: file1 and file2 start with an identical read (${first[0].name}). The same reads seem to be given twice under two file names."
+            } else if (first[0].name != first[1].name) {
+                problems << "${meta.id}: the first reads of file1 and file2 have different names (${first[0].name} vs ${first[1].name}). Either the mates are not in the same order (re-pair them with 'seqkit pair' (https://github.com/shenwei356/seqkit) or BBTools 'repair.sh' (https://github.com/bbushnell/BBTools)), or the two files do not belong to the same library."
+            }
+        }
+    }
+
+    if (problems) {
+        error(
+            "[samplesheet] Problems with the input FASTQ files:\n" +
+            problems.collect { problem -> "  - ${problem}" }.join('\n') +
+            "\n  Please check these files (e.g. re-download or re-copy them and compare checksums) and the samplesheet."
+        )
+    }
+}
+
+//
+// First FASTQ record of a file (plain or gzipped): [lines: first 4 lines, name: read name] or
+// [error: description of what is wrong]
+//
+def firstFastqRecord(fastq) {
+    def lines = []
+    try {
+        def raw    = fastq.newInputStream()
+        def stream = fastq.name.endsWith('.gz') ? new java.util.zip.GZIPInputStream(raw) : raw
+        stream.withReader { reader ->
+            (1..4).each { _i ->
+                def line = reader.readLine()
+                if (line != null) {
+                    lines << line
+                }
+            }
+        }
+    } catch (java.util.zip.ZipException e) {
+        return [error: "is named '.gz' but is not a valid gzip file (${e.message})."]
+    } catch (java.io.EOFException _e) {
+        return [error: "is empty, or ends before its first read (truncated)."]
+    } catch (Exception e) {
+        return [error: "could not be read (${e.class.simpleName}: ${e.message})."]
+    }
+    if (!lines) {
+        return [error: "contains no reads."]
+    }
+    if (!lines[0].startsWith('@')) {
+        return [error: "is not in FASTQ format: the first line should start with '@', but starts with '${lines[0].take(20)}'."]
+    }
+    if (lines.size() < 4 || !lines[2].startsWith('+') || lines[1].length() != lines[3].length()) {
+        return [error: "is not in FASTQ format: its first record is incomplete, or the sequence and quality lines differ in length."]
+    }
+    // Read name without the '@', anything after the first space and a trailing /1 or /2
+    def name = lines[0].substring(1).tokenize(' \t')[0].replaceAll('/[12]$', '')
+    return [lines: lines, name: name]
+}
+
+//
+// Results of FASTQ_CHECK (fq lint over each complete read pair): stop the run with one message
+// listing every library that failed, otherwise log a short summary
+//
+def checkFastqReports(rows) {
+    def results = rows.collect { row ->
+        def meta   = row[0]
+        def fields = row[2].text.trim().tokenize('\t')
+        [id: meta.id, status: fields[0], detail: fields.size() > 1 ? fields[1] : '']
+    }
+    def failed = results.findAll { result -> result.status != 'PASS' }
+    if (failed) {
+        error(
+            "[FASTQ check] ${failed.size()} of ${results.size()} libraries have faulty input FASTQ files:\n" +
+            failed.collect { result -> "  - ${result.id}: ${result.detail}" }.join('\n') +
+            "\n  Please replace these files (e.g. re-download or re-copy them and compare checksums) and run the pipeline again."
+        )
+    }
+    if (results) {
+        log.info("[FASTQ check] The input FASTQ files of all ${results.size()} libraries are complete and consistent.")
+    }
+    return rows
+}
+
+//
+// Generate methods description for MultiQC
+//
+def toolCitationText() {
+    // Only the tools this run actually used, so the methods text matches the command line.
+    def citations = [
+        "Tools used in the workflow included:",
+        "fq (St. Jude Rust Labs),",
+        "FastQC (Andrews 2010),",
+        "BWA-MEM (Li 2013),",
+        "SAMtools (Danecek et al. 2021),",
+        "VSEARCH (Rognes et al. 2016),",
+        "pysam (Heger et al.),",
+        "Polars (Vink et al.),",
+        "Biopython (Cock et al. 2009),",
+        "Biostrings (Pages et al.),",
+        asBool(params.dimsum) ? "DiMSum (Faure et al. 2020)," : "",
+        asBool(params.mutscan) ? "mutscan (Soneson et al. 2023)," : "",
+        asBool(params.mutscan) ? "edgeR (Robinson et al. 2010)," : "",
+        asBool(params.mutscan) ? "limma (Ritchie et al. 2015)," : "",
+        params.pdb ? "3Dmol.js (Rego and Koes 2015)," : "",
+        "MultiQC (Ewels et al. 2016)",
+        "."
+    ]
+
+    return citations.findAll { entry -> entry != "" }.join(' ').trim()
+}
+
+def toolBibliographyText() {
+    def references = [
+        "<li>St. Jude Rust Labs. fq: FASTQ file manipulation and validation. URL: https://github.com/stjude-rust-labs/fq</li>",
+        "<li>Andrews S, (2010) FastQC, URL: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/.</li>",
+        "<li>Li H. (2013) Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv:1303.3997. doi: 10.48550/arXiv.1303.3997</li>",
+        "<li>Danecek P, Bonfield JK, Liddle J, et al. (2021) Twelve years of SAMtools and BCFtools. Gigascience, 10(2), giab008. doi: 10.1093/gigascience/giab008</li>",
+        "<li>Rognes T, Flouri T, Nichols B, Quince C, Mahe F. (2016) VSEARCH: a versatile open source tool for metagenomics. PeerJ, 4, e2584. doi: 10.7717/peerj.2584</li>",
+        "<li>Heger A, Jacobs K, et al. pysam: a Python module for reading, manipulating and writing genomic data sets. URL: https://github.com/pysam-developers/pysam</li>",
+        "<li>Vink R, et al. Polars: a lightning-fast DataFrame library for Rust and Python. URL: https://github.com/pola-rs/polars</li>",
+        "<li>Cock PJ, Antao T, Chang JT, et al. (2009) Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics, 25(11), 1422-1423. doi: 10.1093/bioinformatics/btp163</li>",
+        "<li>Pages H, Aboyoun P, Gentleman R, DebRoy S. Biostrings: efficient manipulation of biological strings. R package. doi: 10.18129/B9.bioc.Biostrings</li>",
+        asBool(params.dimsum) ? "<li>Faure AJ, Schmiedel JM, Baeza-Centurion P, Lehner B. (2020) DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies. Genome Biology, 21(1), 207. doi: 10.1186/s13059-020-02091-3</li>" : "",
+        asBool(params.mutscan) ? "<li>Soneson C, Bendel AM, Diss G, Stadler MB. (2023) mutscan - a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data. Genome Biology, 24(1), 132. doi: 10.1186/s13059-023-02967-0</li>" : "",
+        asBool(params.mutscan) ? "<li>Robinson MD, McCarthy DJ, Smyth GK. (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139-140. doi: 10.1093/bioinformatics/btp616</li>" : "",
+        asBool(params.mutscan) ? "<li>Ritchie ME, Phipson B, Wu D, et al. (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43(7), e47. doi: 10.1093/nar/gkv007</li>" : "",
+        params.pdb ? "<li>Rego N, Koes D. (2015) 3Dmol.js: molecular visualization with WebGL. Bioinformatics, 31(8), 1322-1324. doi: 10.1093/bioinformatics/btu829</li>" : "",
+        "<li>Ewels, P., Magnusson, M., Lundin, S., & Kaller, M. (2016). MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 32(19), 3047-3048. doi: 10.1093/bioinformatics/btw354</li>"
+    ]
+
+    return references.findAll { entry -> entry != "" }.join(' ').trim()
+}
+
+def methodsDescriptionText(mqc_methods_yaml) {
+    // Convert  to a named map so can be used as with familiar NXF ${workflow} variable syntax in the MultiQC YML file
+    def meta = [:]
+    meta.workflow = workflow.toMap()
+    meta["manifest_map"] = workflow.manifest.toMap()
+
+    // Pipeline DOI
+    if (meta.manifest_map.doi) {
+        // Using a loop to handle multiple DOIs
+        // Removing `https://doi.org/` to handle pipelines using DOIs vs DOI resolvers
+        // Removing ` ` since the manifest.doi is a string and not a proper list
+        def temp_doi_ref = ""
+        def manifest_doi = meta.manifest_map.doi.tokenize(",")
+        manifest_doi.each { doi_ref ->
+            temp_doi_ref += "(doi: <a href=\'https://doi.org/${doi_ref.replace("https://doi.org/", "").replace(" ", "")}\'>${doi_ref.replace("https://doi.org/", "").replace(" ", "")}</a>), "
+        }
+        meta["doi_text"] = temp_doi_ref.substring(0, temp_doi_ref.length() - 2)
+    } else meta["doi_text"] = ""
+    meta["nodoi_text"] = meta.manifest_map.doi ? "" : "<li>If available, make sure to update the text to include the Zenodo DOI of version of the pipeline used. </li>"
+
+    // Tool references
+    meta["tool_citations"] = ""
+    meta["tool_bibliography"] = ""
+
+    meta["tool_citations"] = toolCitationText().replaceAll(", \\.", ".").replaceAll("\\. \\.", ".").replaceAll(", \\.", ".")
+    meta["tool_bibliography"] = toolBibliographyText()
+
+
+    def methods_text = mqc_methods_yaml.text
+
+    def engine =  new groovy.text.SimpleTemplateEngine()
+    def description_html = engine.createTemplate(methods_text).make(meta)
+
+    return description_html.toString()
+}
diff --git a/subworkflows/local/utils_nfcore_deepmutscan_pipeline/meta.yml b/subworkflows/local/utils_nfcore_deepmutscan_pipeline/meta.yml
new file mode 100644
index 0000000..2f5669a
--- /dev/null
+++ b/subworkflows/local/utils_nfcore_deepmutscan_pipeline/meta.yml
@@ -0,0 +1,58 @@
+name: "PIPELINE_INITIALISATION"
+description: Local utility functions, pipeline initialization, completion workflows, and parameter validation wrappers for the deepmutscan pipeline.
+keywords:
+  - "utility"
+  - "validation"
+  - "manifest"
+  - "initialisation"
+  - "completion"
+components:
+  - "utils_nfschema_plugin"
+  - "utils_nfcore_pipeline"
+  - "utils_nextflow_pipeline"
+  - "completionemail"
+  - "completionsummary"
+
+input:
+  - version:
+      type: boolean
+      description: Display version and exit
+  - validate_params:
+      type: boolean
+      description: Boolean whether to validate parameters against the schema at runtime
+  - monochrome_logs:
+      type: boolean
+      description: Do not use coloured log outputs
+  - nextflow_cli_args:
+      type: array
+      description: List of positional nextflow CLI args
+  - outdir:
+      type: string
+      description: The output directory where the results will be saved
+  - input:
+      type: string
+      description: Path to input samplesheet
+  - email:
+      type: string
+      description: Email address for completion summary
+  - email_on_fail:
+      type: string
+      description: Email address sent on pipeline failure
+  - plaintext_email:
+      type: boolean
+      description: Send plain-text email instead of HTML
+  - multiqc_report:
+      type: string
+      description: Path to MultiQC report
+
+output:
+  - samplesheet:
+      type: channel
+      description: Processed samplesheet channel containing metadata objects and file paths
+  - versions:
+      type: channel
+      description: Empty version channel for use in the workflow
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/subworkflows/local/utils_nfcore_dmscore_pipeline/main.nf b/subworkflows/local/utils_nfcore_dmscore_pipeline/main.nf
deleted file mode 100644
index 14eba3c..0000000
--- a/subworkflows/local/utils_nfcore_dmscore_pipeline/main.nf
+++ /dev/null
@@ -1,264 +0,0 @@
-//
-// Subworkflow with functionality specific to the nf-core/dmscore pipeline
-//
-
-/*
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-    IMPORT FUNCTIONS / MODULES / SUBWORKFLOWS
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-*/
-
-include { UTILS_NFSCHEMA_PLUGIN     } from '../../nf-core/utils_nfschema_plugin'
-include { paramsSummaryMap          } from 'plugin/nf-schema'
-include { samplesheetToList         } from 'plugin/nf-schema'
-include { completionEmail           } from '../../nf-core/utils_nfcore_pipeline'
-include { completionSummary         } from '../../nf-core/utils_nfcore_pipeline'
-include { imNotification            } from '../../nf-core/utils_nfcore_pipeline'
-include { UTILS_NFCORE_PIPELINE     } from '../../nf-core/utils_nfcore_pipeline'
-include { UTILS_NEXTFLOW_PIPELINE   } from '../../nf-core/utils_nextflow_pipeline'
-
-/*
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-    SUBWORKFLOW TO INITIALISE PIPELINE
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-*/
-
-workflow PIPELINE_INITIALISATION {
-
-    take:
-    version           // boolean: Display version and exit
-    validate_params   // boolean: Boolean whether to validate parameters against the schema at runtime
-    monochrome_logs   // boolean: Do not use coloured log outputs
-    nextflow_cli_args //   array: List of positional nextflow CLI args
-    outdir            //  string: The output directory where the results will be saved
-    input             //  string: Path to input samplesheet
-
-    main:
-
-    ch_versions = Channel.empty()
-
-    //
-    // Print version and exit if required and dump pipeline parameters to JSON file
-    //
-    UTILS_NEXTFLOW_PIPELINE (
-        version,
-        true,
-        outdir,
-        workflow.profile.tokenize(',').intersect(['conda', 'mamba']).size() >= 1
-    )
-
-    //
-    // Validate parameters and generate parameter summary to stdout
-    //
-    UTILS_NFSCHEMA_PLUGIN (
-        workflow,
-        validate_params,
-        null
-    )
-
-    //
-    // Check config provided to the pipeline
-    //
-    UTILS_NFCORE_PIPELINE (
-        nextflow_cli_args
-    )
-
-    //
-    // Custom validation for pipeline parameters
-    //
-    validateInputParameters()
-
-    //
-    // Create channel from input file provided through params.input
-    //
-
-    Channel
-        .fromList(samplesheetToList(params.input, "${projectDir}/assets/schema_input.json"))
-        .map {
-            meta, fastq_1, fastq_2 ->
-                if (!fastq_2) {
-                    return [ meta.id, meta + [ single_end:true ], [ fastq_1 ] ]
-                } else {
-                    return [ meta.id, meta + [ single_end:false ], [ fastq_1, fastq_2 ] ]
-                }
-        }
-        .groupTuple()
-        .map { samplesheet ->
-            validateInputSamplesheet(samplesheet)
-        }
-        .map {
-            meta, fastqs ->
-                return [ meta, fastqs.flatten() ]
-        }
-        .set { ch_samplesheet }
-
-    emit:
-    samplesheet = ch_samplesheet
-    versions    = ch_versions
-}
-
-/*
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-    SUBWORKFLOW FOR PIPELINE COMPLETION
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-*/
-
-workflow PIPELINE_COMPLETION {
-
-    take:
-    email           //  string: email address
-    email_on_fail   //  string: email address sent on pipeline failure
-    plaintext_email // boolean: Send plain-text email instead of HTML
-    outdir          //    path: Path to output directory where results will be published
-    monochrome_logs // boolean: Disable ANSI colour codes in log output
-    hook_url        //  string: hook URL for notifications
-    multiqc_report  //  string: Path to MultiQC report
-
-    main:
-    summary_params = paramsSummaryMap(workflow, parameters_schema: "nextflow_schema.json")
-    def multiqc_reports = multiqc_report.toList()
-
-    //
-    // Completion email and summary
-    //
-    workflow.onComplete {
-        if (email || email_on_fail) {
-            completionEmail(
-                summary_params,
-                email,
-                email_on_fail,
-                plaintext_email,
-                outdir,
-                monochrome_logs,
-                multiqc_reports.getVal(),
-            )
-        }
-
-        completionSummary(monochrome_logs)
-        if (hook_url) {
-            imNotification(summary_params, hook_url)
-        }
-    }
-
-    workflow.onError {
-        log.error "Pipeline failed. Please refer to troubleshooting docs: https://nf-co.re/docs/usage/troubleshooting"
-    }
-}
-
-/*
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-    FUNCTIONS
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-*/
-//
-// Check and validate pipeline parameters
-//
-def validateInputParameters() {
-    genomeExistsError()
-}
-
-//
-// Validate channels from input samplesheet
-//
-def validateInputSamplesheet(input) {
-    def (metas, fastqs) = input[1..2]
-
-    // Check that multiple runs of the same sample are of the same datatype i.e. single-end / paired-end
-    def endedness_ok = metas.collect{ meta -> meta.single_end }.unique().size == 1
-    if (!endedness_ok) {
-        error("Please check input samplesheet -> Multiple runs of a sample must be of the same datatype i.e. single-end or paired-end: ${metas[0].id}")
-    }
-
-    return [ metas[0], fastqs ]
-}
-//
-// Get attribute from genome config file e.g. fasta
-//
-def getGenomeAttribute(attribute) {
-    if (params.genomes && params.genome && params.genomes.containsKey(params.genome)) {
-        if (params.genomes[ params.genome ].containsKey(attribute)) {
-            return params.genomes[ params.genome ][ attribute ]
-        }
-    }
-    return null
-}
-
-//
-// Exit pipeline if incorrect --genome key provided
-//
-def genomeExistsError() {
-    if (params.genomes && params.genome && !params.genomes.containsKey(params.genome)) {
-        def error_string = "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n" +
-            "  Genome '${params.genome}' not found in any config files provided to the pipeline.\n" +
-            "  Currently, the available genome keys are:\n" +
-            "  ${params.genomes.keySet().join(", ")}\n" +
-            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
-        error(error_string)
-    }
-}
-//
-// Generate methods description for MultiQC
-//
-def toolCitationText() {
-    // TODO nf-core: Optionally add in-text citation tools to this list.
-    // Can use ternary operators to dynamically construct based conditions, e.g. params["run_xyz"] ? "Tool (Foo et al. 2023)" : "",
-    // Uncomment function in methodsDescriptionText to render in MultiQC report
-    def citation_text = [
-            "Tools used in the workflow included:",
-            "FastQC (Andrews 2010),",
-            "MultiQC (Ewels et al. 2016)",
-            "."
-        ].join(' ').trim()
-
-    return citation_text
-}
-
-def toolBibliographyText() {
-    // TODO nf-core: Optionally add bibliographic entries to this list.
-    // Can use ternary operators to dynamically construct based conditions, e.g. params["run_xyz"] ? "<li>Author (2023) Pub name, Journal, DOI</li>" : "",
-    // Uncomment function in methodsDescriptionText to render in MultiQC report
-    def reference_text = [
-            "<li>Andrews S, (2010) FastQC, URL: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/).</li>",
-            "<li>Ewels, P., Magnusson, M., Lundin, S., & Käller, M. (2016). MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics , 32(19), 3047–3048. doi: /10.1093/bioinformatics/btw354</li>"
-        ].join(' ').trim()
-
-    return reference_text
-}
-
-def methodsDescriptionText(mqc_methods_yaml) {
-    // Convert  to a named map so can be used as with familiar NXF ${workflow} variable syntax in the MultiQC YML file
-    def meta = [:]
-    meta.workflow = workflow.toMap()
-    meta["manifest_map"] = workflow.manifest.toMap()
-
-    // Pipeline DOI
-    if (meta.manifest_map.doi) {
-        // Using a loop to handle multiple DOIs
-        // Removing `https://doi.org/` to handle pipelines using DOIs vs DOI resolvers
-        // Removing ` ` since the manifest.doi is a string and not a proper list
-        def temp_doi_ref = ""
-        def manifest_doi = meta.manifest_map.doi.tokenize(",")
-        manifest_doi.each { doi_ref ->
-            temp_doi_ref += "(doi: <a href=\'https://doi.org/${doi_ref.replace("https://doi.org/", "").replace(" ", "")}\'>${doi_ref.replace("https://doi.org/", "").replace(" ", "")}</a>), "
-        }
-        meta["doi_text"] = temp_doi_ref.substring(0, temp_doi_ref.length() - 2)
-    } else meta["doi_text"] = ""
-    meta["nodoi_text"] = meta.manifest_map.doi ? "" : "<li>If available, make sure to update the text to include the Zenodo DOI of version of the pipeline used. </li>"
-
-    // Tool references
-    meta["tool_citations"] = ""
-    meta["tool_bibliography"] = ""
-
-    // TODO nf-core: Only uncomment below if logic in toolCitationText/toolBibliographyText has been filled!
-    // meta["tool_citations"] = toolCitationText().replaceAll(", \\.", ".").replaceAll("\\. \\.", ".").replaceAll(", \\.", ".")
-    // meta["tool_bibliography"] = toolBibliographyText()
-
-
-    def methods_text = mqc_methods_yaml.text
-
-    def engine =  new groovy.text.SimpleTemplateEngine()
-    def description_html = engine.createTemplate(methods_text).make(meta)
-
-    return description_html.toString()
-}
-
diff --git a/subworkflows/nf-core/utils_nextflow_pipeline/main.nf b/subworkflows/nf-core/utils_nextflow_pipeline/main.nf
index d6e593e..37939ac 100644
--- a/subworkflows/nf-core/utils_nextflow_pipeline/main.nf
+++ b/subworkflows/nf-core/utils_nextflow_pipeline/main.nf
@@ -73,11 +73,23 @@ def getWorkflowVersion() {
 def dumpParametersToJSON(outdir) {
     def timestamp = new java.util.Date().format('yyyy-MM-dd_HH-mm-ss')
     def filename  = "params_${timestamp}.json"
-    def temp_pf   = new File(workflow.launchDir.toString(), ".${filename}")
-    def jsonStr   = groovy.json.JsonOutput.toJson(params)
+    def temp_pf       = workflow.launchDir.resolve(".${filename}")
+    def jsonGenerator = new groovy.json.JsonGenerator.Options()
+        .excludeNulls()
+        .addConverter(Path) { Path path -> path.toUriString() }
+        .addConverter(Duration) { Duration duration -> duration.toMillis() }
+        .addConverter(MemoryUnit) { MemoryUnit memory -> memory.toBytes() }
+        .addConverter(nextflow.script.types.VersionNumber) { nextflow.script.types.VersionNumber version -> version.toString() }
+        .build()
+    def jsonStr   = jsonGenerator.toJson(params)
     temp_pf.text  = groovy.json.JsonOutput.prettyPrint(jsonStr)
-
-    nextflow.extension.FilesEx.copyTo(temp_pf.toPath(), "${outdir}/pipeline_info/params_${timestamp}.json")
+    if (outdir instanceof Path) {
+        temp_pf.copyTo(outdir.resolve("pipeline_info/${filename}"))
+    } else if (outdir instanceof String) {
+        temp_pf.copyTo("${outdir}/pipeline_info/params_${timestamp}.json")
+    } else {
+        log.warn("Could not determine type of outdir, parameters JSON file will not be copied to output directory!")
+    }
     temp_pf.delete()
 }
 
diff --git a/subworkflows/nf-core/utils_nextflow_pipeline/meta.yml b/subworkflows/nf-core/utils_nextflow_pipeline/meta.yml
index e5c3a0a..9043043 100644
--- a/subworkflows/nf-core/utils_nextflow_pipeline/meta.yml
+++ b/subworkflows/nf-core/utils_nextflow_pipeline/meta.yml
@@ -16,11 +16,11 @@ input:
       type: boolean
       description: |
         Dump the parameters of the pipeline to a JSON file
-  - output_directory:
+  - outdir:
       type: directory
       description: Path to output dir to write JSON file to.
       pattern: "results/"
-  - check_conda_channel:
+  - check_conda_channels:
       type: boolean
       description: |
         Check if the conda channel priority is correct.
diff --git a/subworkflows/nf-core/utils_nextflow_pipeline/tests/tags.yml b/subworkflows/nf-core/utils_nextflow_pipeline/tests/tags.yml
deleted file mode 100644
index f847611..0000000
--- a/subworkflows/nf-core/utils_nextflow_pipeline/tests/tags.yml
+++ /dev/null
@@ -1,2 +0,0 @@
-subworkflows/utils_nextflow_pipeline:
-  - subworkflows/nf-core/utils_nextflow_pipeline/**
diff --git a/subworkflows/nf-core/utils_nfcore_pipeline/main.nf b/subworkflows/nf-core/utils_nfcore_pipeline/main.nf
index bfd2587..afca543 100644
--- a/subworkflows/nf-core/utils_nfcore_pipeline/main.nf
+++ b/subworkflows/nf-core/utils_nfcore_pipeline/main.nf
@@ -17,7 +17,7 @@ workflow UTILS_NFCORE_PIPELINE {
     checkProfileProvided(nextflow_cli_args)
 
     emit:
-    valid_config
+    valid_config = valid_config
 }
 
 /*
@@ -98,7 +98,7 @@ def workflowVersionToYAML() {
 // Get channel of software versions used in pipeline in YAML format
 //
 def softwareVersionsToYAML(ch_versions) {
-    return ch_versions.unique().map { version -> processVersionsFromYAML(version) }.unique().mix(Channel.of(workflowVersionToYAML()))
+    return ch_versions.unique().map { version -> processVersionsFromYAML(version) }.unique().mix(channel.of(workflowVersionToYAML()))
 }
 
 //
@@ -353,67 +353,3 @@ def completionSummary(monochrome_logs=true) {
         log.info("-${colors.purple}[${workflow.manifest.name}]${colors.red} Pipeline completed with errors${colors.reset}-")
     }
 }
-
-//
-// Construct and send a notification to a web server as JSON e.g. Microsoft Teams and Slack
-//
-def imNotification(summary_params, hook_url) {
-    def summary = [:]
-    summary_params
-        .keySet()
-        .sort()
-        .each { group ->
-            summary << summary_params[group]
-        }
-
-    def misc_fields = [:]
-    misc_fields['start']          = workflow.start
-    misc_fields['complete']       = workflow.complete
-    misc_fields['scriptfile']     = workflow.scriptFile
-    misc_fields['scriptid']       = workflow.scriptId
-    if (workflow.repository) {
-        misc_fields['repository'] = workflow.repository
-    }
-    if (workflow.commitId) {
-        misc_fields['commitid']   = workflow.commitId
-    }
-    if (workflow.revision) {
-        misc_fields['revision']   = workflow.revision
-    }
-    misc_fields['nxf_version']    = workflow.nextflow.version
-    misc_fields['nxf_build']      = workflow.nextflow.build
-    misc_fields['nxf_timestamp']  = workflow.nextflow.timestamp
-
-    def msg_fields = [:]
-    msg_fields['version']      = getWorkflowVersion()
-    msg_fields['runName']      = workflow.runName
-    msg_fields['success']      = workflow.success
-    msg_fields['dateComplete'] = workflow.complete
-    msg_fields['duration']     = workflow.duration
-    msg_fields['exitStatus']   = workflow.exitStatus
-    msg_fields['errorMessage'] = (workflow.errorMessage ?: 'None')
-    msg_fields['errorReport']  = (workflow.errorReport ?: 'None')
-    msg_fields['commandLine']  = workflow.commandLine.replaceFirst(/ +--hook_url +[^ ]+/, "")
-    msg_fields['projectDir']   = workflow.projectDir
-    msg_fields['summary']      = summary << misc_fields
-
-    // Render the JSON template
-    def engine       = new groovy.text.GStringTemplateEngine()
-    // Different JSON depending on the service provider
-    // Defaults to "Adaptive Cards" (https://adaptivecards.io), except Slack which has its own format
-    def json_path     = hook_url.contains("hooks.slack.com") ? "slackreport.json" : "adaptivecard.json"
-    def hf            = new File("${workflow.projectDir}/assets/${json_path}")
-    def json_template = engine.createTemplate(hf).make(msg_fields)
-    def json_message  = json_template.toString()
-
-    // POST
-    def post = new URL(hook_url).openConnection()
-    post.setRequestMethod("POST")
-    post.setDoOutput(true)
-    post.setRequestProperty("Content-Type", "application/json")
-    post.getOutputStream().write(json_message.getBytes("UTF-8"))
-    def postRC = post.getResponseCode()
-    if (!postRC.equals(200)) {
-        log.warn(post.getErrorStream().getText())
-    }
-}
diff --git a/subworkflows/nf-core/utils_nfcore_pipeline/meta.yml b/subworkflows/nf-core/utils_nfcore_pipeline/meta.yml
index d08d243..a32b454 100644
--- a/subworkflows/nf-core/utils_nfcore_pipeline/meta.yml
+++ b/subworkflows/nf-core/utils_nfcore_pipeline/meta.yml
@@ -13,10 +13,10 @@ input:
       description: |
         Nextflow CLI positional arguments
 output:
-  - success:
+  - valid_config:
       type: boolean
       description: |
-        Dummy output to indicate success
+        Whether a custom configuration or profile was provided to the pipeline
 authors:
   - "@adamrtalbot"
 maintainers:
diff --git a/subworkflows/nf-core/utils_nfcore_pipeline/tests/main.workflow.nf.test b/subworkflows/nf-core/utils_nfcore_pipeline/tests/main.nf.test
similarity index 100%
rename from subworkflows/nf-core/utils_nfcore_pipeline/tests/main.workflow.nf.test
rename to subworkflows/nf-core/utils_nfcore_pipeline/tests/main.nf.test
diff --git a/subworkflows/nf-core/utils_nfcore_pipeline/tests/main.workflow.nf.test.snap b/subworkflows/nf-core/utils_nfcore_pipeline/tests/main.nf.test.snap
similarity index 100%
rename from subworkflows/nf-core/utils_nfcore_pipeline/tests/main.workflow.nf.test.snap
rename to subworkflows/nf-core/utils_nfcore_pipeline/tests/main.nf.test.snap
diff --git a/subworkflows/nf-core/utils_nfcore_pipeline/tests/tags.yml b/subworkflows/nf-core/utils_nfcore_pipeline/tests/tags.yml
deleted file mode 100644
index ac8523c..0000000
--- a/subworkflows/nf-core/utils_nfcore_pipeline/tests/tags.yml
+++ /dev/null
@@ -1,2 +0,0 @@
-subworkflows/utils_nfcore_pipeline:
-  - subworkflows/nf-core/utils_nfcore_pipeline/**
diff --git a/subworkflows/nf-core/utils_nfschema_plugin/main.nf b/subworkflows/nf-core/utils_nfschema_plugin/main.nf
index 4994303..9ff0681 100644
--- a/subworkflows/nf-core/utils_nfschema_plugin/main.nf
+++ b/subworkflows/nf-core/utils_nfschema_plugin/main.nf
@@ -4,6 +4,7 @@
 
 include { paramsSummaryLog   } from 'plugin/nf-schema'
 include { validateParameters } from 'plugin/nf-schema'
+include { paramsHelp         } from 'plugin/nf-schema'
 
 workflow UTILS_NFSCHEMA_PLUGIN {
 
@@ -15,32 +16,62 @@ workflow UTILS_NFSCHEMA_PLUGIN {
                         //           when this input is empty it will automatically use the configured schema or
                         //           "${projectDir}/nextflow_schema.json" as default. This input should not be empty
                         //           for meta pipelines
+    help                // boolean:  show help message
+    help_full           // boolean:  show full help message
+    show_hidden         // boolean:  show hidden parameters in help message
+    before_text         // string:   text to show before the help message and parameters summary
+    after_text          // string:   text to show after the help message and parameters summary
+    command             // string:   an example command of the pipeline
+    cli_typecast        // boolean:  whether to perform typecasting of CLI parameters. Set this to `null` to use the default behaviour
 
     main:
 
+    if(help || help_full) {
+        help_options = [
+            beforeText: before_text,
+            afterText: after_text,
+            command: command,
+            showHidden: show_hidden,
+            fullHelp: help_full,
+        ]
+        if(parameters_schema) {
+            help_options << [parameters_schema: parameters_schema]
+        }
+        log.info paramsHelp(
+            help_options,
+            (help instanceof String && help != "true") ? help : "",
+        )
+        exit 0
+    }
+
     //
     // Print parameter summary to stdout. This will display the parameters
     // that differ from the default given in the JSON schema
     //
+
+    summary_options = [:]
     if(parameters_schema) {
-        log.info paramsSummaryLog(input_workflow, parameters_schema:parameters_schema)
-    } else {
-        log.info paramsSummaryLog(input_workflow)
+        summary_options << [parameters_schema: parameters_schema]
     }
+    log.info before_text
+    log.info paramsSummaryLog(summary_options, input_workflow)
+    log.info after_text
 
     //
     // Validate the parameters using nextflow_schema.json or the schema
     // given via the validation.parametersSchema configuration option
     //
     if(validate_params) {
+        validateOptions = [:]
         if(parameters_schema) {
-            validateParameters(parameters_schema:parameters_schema)
-        } else {
-            validateParameters()
+            validateOptions << [parameters_schema: parameters_schema]
+        }
+        if(cli_typecast != null) {
+            validateOptions << [cast_cli_params: cli_typecast]
         }
+        validateParameters(validateOptions)
     }
 
     emit:
     dummy_emit = true
 }
-
diff --git a/subworkflows/nf-core/utils_nfschema_plugin/meta.yml b/subworkflows/nf-core/utils_nfschema_plugin/meta.yml
index f7d9f02..1d8c75a 100644
--- a/subworkflows/nf-core/utils_nfschema_plugin/meta.yml
+++ b/subworkflows/nf-core/utils_nfschema_plugin/meta.yml
@@ -25,6 +25,30 @@ input:
         option. When this input is empty it will automatically use the configured schema or
         "${projectDir}/nextflow_schema.json" as default. The schema should not be given in this way
         for meta pipelines.
+  - help:
+      type: boolean, string
+      description: |
+        Show the help message and exit. When a parameter name is given, show the help message for that parameter instead of the general help message.
+  - help_full:
+      type: boolean
+      description: Show the full help message and exit.
+  - show_hidden:
+      type: boolean
+      description: Show hidden parameters in the help message.
+  - before_text:
+      type: string
+      description: Text to show before the parameters summary and help message.
+  - after_text:
+      type: string
+      description: Text to show after the parameters summary and help message.
+  - command:
+      type: string
+      description: An example command to run the pipeline, to show in the help message and the summary.
+  - cli_typecast:
+      type: boolean
+      description: |
+        Whether to apply typecasting to the parameters given via the CLI before validation.
+        Set this to `null` to use the default behavior.
 output:
   - dummy_emit:
       type: boolean
diff --git a/subworkflows/nf-core/utils_nfschema_plugin/tests/main.nf.test b/subworkflows/nf-core/utils_nfschema_plugin/tests/main.nf.test
index 8fb3016..1fd1eac 100644
--- a/subworkflows/nf-core/utils_nfschema_plugin/tests/main.nf.test
+++ b/subworkflows/nf-core/utils_nfschema_plugin/tests/main.nf.test
@@ -25,6 +25,13 @@ nextflow_workflow {
                 input[0] = workflow
                 input[1] = validate_params
                 input[2] = ""
+                input[3] = false
+                input[4] = false
+                input[5] = false
+                input[6] = ""
+                input[7] = ""
+                input[8] = ""
+                input[9] = null
                 """
             }
         }
@@ -51,6 +58,13 @@ nextflow_workflow {
                 input[0] = workflow
                 input[1] = validate_params
                 input[2] = ""
+                input[3] = false
+                input[4] = false
+                input[5] = false
+                input[6] = ""
+                input[7] = ""
+                input[8] = ""
+                input[9] = null
                 """
             }
         }
@@ -77,6 +91,13 @@ nextflow_workflow {
                 input[0] = workflow
                 input[1] = validate_params
                 input[2] = "${projectDir}/subworkflows/nf-core/utils_nfschema_plugin/tests/nextflow_schema.json"
+                input[3] = false
+                input[4] = false
+                input[5] = false
+                input[6] = ""
+                input[7] = ""
+                input[8] = ""
+                input[9] = null
                 """
             }
         }
@@ -103,6 +124,13 @@ nextflow_workflow {
                 input[0] = workflow
                 input[1] = validate_params
                 input[2] = "${projectDir}/subworkflows/nf-core/utils_nfschema_plugin/tests/nextflow_schema.json"
+                input[3] = false
+                input[4] = false
+                input[5] = false
+                input[6] = ""
+                input[7] = ""
+                input[8] = ""
+                input[9] = null
                 """
             }
         }
@@ -114,4 +142,37 @@ nextflow_workflow {
             )
         }
     }
+
+    test("Should create a help message") {
+
+        when {
+
+            params {
+                test_data   = ''
+                outdir      = null
+            }
+
+            workflow {
+                """
+                validate_params = true
+                input[0] = workflow
+                input[1] = validate_params
+                input[2] = "${projectDir}/subworkflows/nf-core/utils_nfschema_plugin/tests/nextflow_schema.json"
+                input[3] = true
+                input[4] = false
+                input[5] = false
+                input[6] = "Before"
+                input[7] = "After"
+                input[8] = "nextflow run test/test"
+                input[9] = null
+                """
+            }
+        }
+
+        then {
+            assertAll(
+                { assert workflow.success }
+            )
+        }
+    }
 }
diff --git a/subworkflows/nf-core/utils_nfschema_plugin/tests/nextflow.config b/subworkflows/nf-core/utils_nfschema_plugin/tests/nextflow.config
index 0907ac5..fd71cb8 100644
--- a/subworkflows/nf-core/utils_nfschema_plugin/tests/nextflow.config
+++ b/subworkflows/nf-core/utils_nfschema_plugin/tests/nextflow.config
@@ -1,8 +1,8 @@
 plugins {
-    id "nf-schema@2.1.0"
+    id "nf-schema@2.7.2"
 }
 
 validation {
     parametersSchema = "${projectDir}/subworkflows/nf-core/utils_nfschema_plugin/tests/nextflow_schema.json"
     monochromeLogs = true
-}
\ No newline at end of file
+}
diff --git a/tests/.nftignore b/tests/.nftignore
new file mode 100644
index 0000000..af85a80
--- /dev/null
+++ b/tests/.nftignore
@@ -0,0 +1,33 @@
+.DS_Store
+multiqc/multiqc_data/fastqc_top_overrepresented_sequences_table.txt
+multiqc/multiqc_data/multiqc.parquet
+multiqc/multiqc_data/multiqc.log
+multiqc/multiqc_data/multiqc_data.json
+multiqc/multiqc_data/multiqc_sources.txt
+multiqc/multiqc_data/multiqc_software_versions.txt
+multiqc/multiqc_data/llms-full.txt
+multiqc/multiqc_plots/{svg,pdf,png}/*.{svg,pdf,png}
+multiqc/multiqc_report.html
+fastqc/*_fastqc.{html,zip}
+pipeline_info/*.{html,json,txt,yml}
+library_QC/**/*.pdf
+fitness/DiMSum_results/**/*.pdf
+fitness/DiMSum_results/**/dimsum_results_workspace.RData
+fitness/DiMSum_results/**/report.html
+fitness/DiMSum_results/**/*.png
+**/*.pdf
+**/*.RData
+**/*_error_correction_report.html
+**/variant_effect_inspection_tool.html
+deepmutscan_report.html
+**/structure_data.json
+**/fitness_estimation_mutscan_edgeR.tsv
+**/fitness_estimation_mutscan_limma.tsv
+**/dimsum_results_*_merge.tsv
+**/fitness_synonymous.txt
+**/*_fitness_input.tsv
+**/counts_merged.tsv
+**/fitness_estimation.tsv
+**/annotated_variantCounts.csv
+**/library_completed_variantCounts*.csv
+**/variantCounts_filtered_by_library*.csv
diff --git a/tests/default.nf.test b/tests/default.nf.test
new file mode 100644
index 0000000..0cf5c0b
--- /dev/null
+++ b/tests/default.nf.test
@@ -0,0 +1,97 @@
+nextflow_pipeline {
+
+    name "Test pipeline"
+    script "../main.nf"
+    tag "pipeline"
+
+    test("-profile test") {
+
+        when {
+            params {
+                outdir = "$outputDir"
+            }
+        }
+
+        then {
+            // stable_path: All files + folders in ${params.outdir}/ with a stable path (including file name)
+            // DiMSum's diagnostic plots are excluded from the path list as well as from the content
+            // hashes (tests/.nftignore): which of them DiMSum emits depends on its graphics stack, so
+            // their mere presence is not a stable contract to assert on.
+            def stable_path = getAllFilesFromDir(params.outdir, relative: true, includeDir: true, ignore: ['pipeline_info/*.{html,json,txt}', 'multiqc/multiqc_plots', 'multiqc/multiqc_plots/**', 'fitness/DiMSum_results/**/reports/*.{pdf,png}'])
+            // stable_content: All files in ${params.outdir}/ with stable content
+            def stable_content = getAllFilesFromDir(params.outdir, ignoreFile: 'tests/.nftignore')
+            // Parse the unstable TSVs to snapshot their structure instead of their hashes
+            def edger_tsv = path("$outputDir/fitness/mutscan_results/fitness_estimation_mutscan_edgeR.tsv").csv(sep: '\t')
+            def limma_tsv = path("$outputDir/fitness/mutscan_results/fitness_estimation_mutscan_limma.tsv").csv(sep: '\t')
+            assert workflow.success
+            assertAll(
+                { assert snapshot(
+                    // pipeline versions.yml file for multiqc from which Nextflow version is removed because we test pipelines on multiple Nextflow versions
+                    removeNextflowVersion("$outputDir/pipeline_info/nf_core_deepmutscan_software_mqc_versions.yml"),
+                    // All stable path name, with a relative path
+                    stable_path,
+                    // All files with stable contents
+                    stable_content,
+                    // Partial structural snapshot of the unstable TSV files
+                    [
+                        edger_rowCount: edger_tsv.rowCount,
+                        limma_rowCount: limma_tsv.rowCount
+                    ]
+                ).match() }
+            )
+        }
+    }
+
+    test("-profile test, without fitness, error correction, SeqDepth and structure") {
+
+        // The other branches of the workflow: --fitness false (no fitness modules at all),
+        // --error_correction none, --run_seqdepth false and no --pdb (no inspection tool)
+
+        when {
+            params {
+                outdir           = "$outputDir"
+                fitness          = false
+                error_correction = 'none'
+                run_seqdepth     = false
+                pdb              = null
+            }
+        }
+
+        then {
+            def stable_path = getAllFilesFromDir(params.outdir, relative: true, includeDir: true, ignore: ['pipeline_info/*.{html,json,txt}', 'multiqc/multiqc_plots', 'multiqc/multiqc_plots/**'])
+            def stable_content = getAllFilesFromDir(params.outdir, ignoreFile: 'tests/.nftignore')
+            assert workflow.success
+            assertAll(
+                { assert snapshot(
+                    removeNextflowVersion("$outputDir/pipeline_info/nf_core_deepmutscan_software_mqc_versions.yml"),
+                    stable_path,
+                    stable_content
+                ).match() }
+            )
+        }
+    }
+
+    test("-profile test -stub") {
+
+        // Runs every module's stub block, so that stub outputs stay in step with the real outputs
+
+        options "-stub"
+
+        when {
+            params {
+                outdir = "$outputDir"
+            }
+        }
+
+        then {
+            def stable_path = getAllFilesFromDir(params.outdir, relative: true, includeDir: true, ignore: ['pipeline_info/*.{html,json,txt}', 'multiqc/multiqc_plots', 'multiqc/multiqc_plots/**', 'fitness/DiMSum_results/**/reports/*.{pdf,png}'])
+            assert workflow.success
+            assertAll(
+                { assert snapshot(
+                    removeNextflowVersion("$outputDir/pipeline_info/nf_core_deepmutscan_software_mqc_versions.yml"),
+                    stable_path
+                ).match() }
+            )
+        }
+    }
+}
diff --git a/tests/default.nf.test.snap b/tests/default.nf.test.snap
new file mode 100644
index 0000000..084c8de
--- /dev/null
+++ b/tests/default.nf.test.snap
@@ -0,0 +1,940 @@
+{
+    "-profile test, without fitness, error correction, SeqDepth and structure": {
+        "content": [
+            {
+                "BWA_INDEX": {
+                    "bwa": "0.7.19-r1273"
+                },
+                "DMSANALYSIS_AASEQ": {
+                    "r-base": "4.5.1",
+                    "r-Biostrings": "2.78.0"
+                },
+                "DMSANALYSIS_POSSIBLE_MUTATIONS": {
+                    "r-base": "4.5.1",
+                    "biostrings": "2.78.0"
+                },
+                "DMSANALYSIS_PROCESS_VARIANT_COUNTS": {
+                    "r-base": "4.5.1",
+                    "r-dplyr": "1.1.4",
+                    "r-Biostrings": "2.78.0",
+                    "r-stringr": "1.6.0",
+                    "r-ggplot2": "4.0.2",
+                    "r-tidyr": "1.3.1",
+                    "r-reshape2": "1.4.4",
+                    "r-scales": "1.4.0"
+                },
+                "FASTQC": {
+                    "fastqc": "0.13.0"
+                },
+                "FASTQ_CHECK": {
+                    "fq": "0.12.0"
+                },
+                "MERGE_ALIGN": {
+                    "bwa": "0.7.19-r1273",
+                    "samtools": 1.21,
+                    "vsearch": "2.30.0"
+                },
+                "VARIANTCOUNTING": {
+                    "python": "3.12.13",
+                    "pysam": "0.24.0",
+                    "polars": "1.33.1",
+                    "pyarrow": "24.0.0",
+                    "numpy": "2.5.1",
+                    "biopython": 1.87
+                },
+                "VISUALIZATION_COUNTS_HEATMAP": {
+                    "r-base": "4.5.1",
+                    "r-dplyr": "1.1.4",
+                    "r-ggplot2": "4.0.2"
+                },
+                "VISUALIZATION_COUNTS_PER_COV": {
+                    "r-base": "4.5.1",
+                    "r-dplyr": "1.1.4",
+                    "r-ggplot2": "4.0.2"
+                },
+                "VISUALIZATION_GLOBAL_POS_BIASES_COUNTS": {
+                    "r-base": "4.5.1",
+                    "r-dplyr": "1.1.4",
+                    "r-ggplot2": "4.0.2",
+                    "r-scales": "1.4.0",
+                    "r-zoo": "1.8.15"
+                },
+                "VISUALIZATION_GLOBAL_POS_BIASES_COV": {
+                    "r-base": "4.5.1",
+                    "r-dplyr": "1.1.4",
+                    "r-ggplot2": "4.0.2",
+                    "r-scales": "1.4.0",
+                    "r-zoo": "1.8.15"
+                },
+                "VISUALIZATION_LOGDIFF": {
+                    "r-base": "4.5.1",
+                    "r-dplyr": "1.1.4",
+                    "r-ggplot2": "4.0.2",
+                    "r-scales": "1.4.0"
+                },
+                "Workflow": {
+                    "nf-core/deepmutscan": "v1.0.0"
+                }
+            },
+            [
+                "deepmutscan_report.html",
+                "fastqc",
+                "fastqc/GID1A_input_1_pe_1_fastqc.html",
+                "fastqc/GID1A_input_1_pe_1_fastqc.zip",
+                "fastqc/GID1A_input_1_pe_2_fastqc.html",
+                "fastqc/GID1A_input_1_pe_2_fastqc.zip",
+                "fastqc/GID1A_input_2_pe_1_fastqc.html",
+                "fastqc/GID1A_input_2_pe_1_fastqc.zip",
+                "fastqc/GID1A_input_2_pe_2_fastqc.html",
+                "fastqc/GID1A_input_2_pe_2_fastqc.zip",
+                "fastqc/GID1A_output_1_pe_1_fastqc.html",
+                "fastqc/GID1A_output_1_pe_1_fastqc.zip",
+                "fastqc/GID1A_output_1_pe_2_fastqc.html",
+                "fastqc/GID1A_output_1_pe_2_fastqc.zip",
+                "fastqc/GID1A_output_2_pe_1_fastqc.html",
+                "fastqc/GID1A_output_2_pe_1_fastqc.zip",
+                "fastqc/GID1A_output_2_pe_2_fastqc.html",
+                "fastqc/GID1A_output_2_pe_2_fastqc.zip",
+                "intermediate_files",
+                "intermediate_files/aa_seq.txt",
+                "intermediate_files/bam_files",
+                "intermediate_files/bam_files/sorted",
+                "intermediate_files/bam_files/sorted/GID1A_input_1_pe.sorted.bam",
+                "intermediate_files/bam_files/sorted/GID1A_input_1_pe.sorted.bam.bai",
+                "intermediate_files/bam_files/sorted/GID1A_input_2_pe.sorted.bam",
+                "intermediate_files/bam_files/sorted/GID1A_input_2_pe.sorted.bam.bai",
+                "intermediate_files/bam_files/sorted/GID1A_output_1_pe.sorted.bam",
+                "intermediate_files/bam_files/sorted/GID1A_output_1_pe.sorted.bam.bai",
+                "intermediate_files/bam_files/sorted/GID1A_output_2_pe.sorted.bam",
+                "intermediate_files/bam_files/sorted/GID1A_output_2_pe.sorted.bam.bai",
+                "intermediate_files/bwa",
+                "intermediate_files/bwa/GID1A.amb",
+                "intermediate_files/bwa/GID1A.ann",
+                "intermediate_files/bwa/GID1A.bwt",
+                "intermediate_files/bwa/GID1A.pac",
+                "intermediate_files/bwa/GID1A.sa",
+                "intermediate_files/merged_reads",
+                "intermediate_files/merged_reads/GID1A_input_1_pe.vsearch_mergepairs.log",
+                "intermediate_files/merged_reads/GID1A_input_2_pe.vsearch_mergepairs.log",
+                "intermediate_files/merged_reads/GID1A_output_1_pe.vsearch_mergepairs.log",
+                "intermediate_files/merged_reads/GID1A_output_2_pe.vsearch_mergepairs.log",
+                "intermediate_files/possible_mutations.csv",
+                "intermediate_files/processed_variant_counts",
+                "intermediate_files/processed_variant_counts/GID1A_input_1_pe",
+                "intermediate_files/processed_variant_counts/GID1A_input_1_pe/annotated_variantCounts.csv",
+                "intermediate_files/processed_variant_counts/GID1A_input_1_pe/library_completed_variantCounts.csv",
+                "intermediate_files/processed_variant_counts/GID1A_input_1_pe/variantCounts_filtered_by_library.csv",
+                "intermediate_files/processed_variant_counts/GID1A_input_1_pe/variantCounts_for_heatmaps.csv",
+                "intermediate_files/processed_variant_counts/GID1A_input_2_pe",
+                "intermediate_files/processed_variant_counts/GID1A_input_2_pe/annotated_variantCounts.csv",
+                "intermediate_files/processed_variant_counts/GID1A_input_2_pe/library_completed_variantCounts.csv",
+                "intermediate_files/processed_variant_counts/GID1A_input_2_pe/variantCounts_filtered_by_library.csv",
+                "intermediate_files/processed_variant_counts/GID1A_input_2_pe/variantCounts_for_heatmaps.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_1_pe",
+                "intermediate_files/processed_variant_counts/GID1A_output_1_pe/annotated_variantCounts.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_1_pe/library_completed_variantCounts.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_1_pe/variantCounts_filtered_by_library.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_1_pe/variantCounts_for_heatmaps.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_2_pe",
+                "intermediate_files/processed_variant_counts/GID1A_output_2_pe/annotated_variantCounts.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_2_pe/library_completed_variantCounts.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_2_pe/variantCounts_filtered_by_library.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_2_pe/variantCounts_for_heatmaps.csv",
+                "intermediate_files/variant_counts",
+                "intermediate_files/variant_counts/GID1A_input_1_pe",
+                "intermediate_files/variant_counts/GID1A_input_1_pe/GID1A_input_1_pe.variant_counts.tsv",
+                "intermediate_files/variant_counts/GID1A_input_1_pe/variant_counts_columns.tsv",
+                "intermediate_files/variant_counts/GID1A_input_2_pe",
+                "intermediate_files/variant_counts/GID1A_input_2_pe/GID1A_input_2_pe.variant_counts.tsv",
+                "intermediate_files/variant_counts/GID1A_input_2_pe/variant_counts_columns.tsv",
+                "intermediate_files/variant_counts/GID1A_output_1_pe",
+                "intermediate_files/variant_counts/GID1A_output_1_pe/GID1A_output_1_pe.variant_counts.tsv",
+                "intermediate_files/variant_counts/GID1A_output_1_pe/variant_counts_columns.tsv",
+                "intermediate_files/variant_counts/GID1A_output_2_pe",
+                "intermediate_files/variant_counts/GID1A_output_2_pe/GID1A_output_2_pe.variant_counts.tsv",
+                "intermediate_files/variant_counts/GID1A_output_2_pe/variant_counts_columns.tsv",
+                "library_QC",
+                "library_QC/GID1A_input_1_pe",
+                "library_QC/GID1A_input_1_pe/counts_heatmap.pdf",
+                "library_QC/GID1A_input_1_pe/counts_per_cov_heatmap.pdf",
+                "library_QC/GID1A_input_1_pe/logdiff_plot.pdf",
+                "library_QC/GID1A_input_1_pe/logdiff_varying_bases.pdf",
+                "library_QC/GID1A_input_1_pe/rolling_counts.pdf",
+                "library_QC/GID1A_input_1_pe/rolling_counts_per_cov.pdf",
+                "library_QC/GID1A_input_1_pe/rolling_coverage.pdf",
+                "library_QC/GID1A_input_2_pe",
+                "library_QC/GID1A_input_2_pe/counts_heatmap.pdf",
+                "library_QC/GID1A_input_2_pe/counts_per_cov_heatmap.pdf",
+                "library_QC/GID1A_input_2_pe/logdiff_plot.pdf",
+                "library_QC/GID1A_input_2_pe/logdiff_varying_bases.pdf",
+                "library_QC/GID1A_input_2_pe/rolling_counts.pdf",
+                "library_QC/GID1A_input_2_pe/rolling_counts_per_cov.pdf",
+                "library_QC/GID1A_input_2_pe/rolling_coverage.pdf",
+                "library_QC/GID1A_output_1_pe",
+                "library_QC/GID1A_output_1_pe/counts_heatmap.pdf",
+                "library_QC/GID1A_output_1_pe/counts_per_cov_heatmap.pdf",
+                "library_QC/GID1A_output_1_pe/logdiff_plot.pdf",
+                "library_QC/GID1A_output_1_pe/logdiff_varying_bases.pdf",
+                "library_QC/GID1A_output_1_pe/rolling_counts.pdf",
+                "library_QC/GID1A_output_1_pe/rolling_counts_per_cov.pdf",
+                "library_QC/GID1A_output_1_pe/rolling_coverage.pdf",
+                "library_QC/GID1A_output_2_pe",
+                "library_QC/GID1A_output_2_pe/counts_heatmap.pdf",
+                "library_QC/GID1A_output_2_pe/counts_per_cov_heatmap.pdf",
+                "library_QC/GID1A_output_2_pe/logdiff_plot.pdf",
+                "library_QC/GID1A_output_2_pe/logdiff_varying_bases.pdf",
+                "library_QC/GID1A_output_2_pe/rolling_counts.pdf",
+                "library_QC/GID1A_output_2_pe/rolling_counts_per_cov.pdf",
+                "library_QC/GID1A_output_2_pe/rolling_coverage.pdf",
+                "multiqc",
+                "multiqc/multiqc_data",
+                "multiqc/multiqc_data/fastqc-status-check-heatmap.txt",
+                "multiqc/multiqc_data/fastqc_adapter_content_plot.txt",
+                "multiqc/multiqc_data/fastqc_overrepresented_sequences_plot.txt",
+                "multiqc/multiqc_data/fastqc_per_base_n_content_plot.txt",
+                "multiqc/multiqc_data/fastqc_per_base_sequence_quality_plot.txt",
+                "multiqc/multiqc_data/fastqc_per_sequence_gc_content_plot_Counts.txt",
+                "multiqc/multiqc_data/fastqc_per_sequence_gc_content_plot_Percentages.txt",
+                "multiqc/multiqc_data/fastqc_per_sequence_quality_scores_plot.txt",
+                "multiqc/multiqc_data/fastqc_sequence_counts_plot.txt",
+                "multiqc/multiqc_data/fastqc_sequence_duplication_levels_plot.txt",
+                "multiqc/multiqc_data/fastqc_top_overrepresented_sequences_table.txt",
+                "multiqc/multiqc_data/llms-full.txt",
+                "multiqc/multiqc_data/multiqc.log",
+                "multiqc/multiqc_data/multiqc.parquet",
+                "multiqc/multiqc_data/multiqc_citations.txt",
+                "multiqc/multiqc_data/multiqc_data.json",
+                "multiqc/multiqc_data/multiqc_fastqc.txt",
+                "multiqc/multiqc_data/multiqc_general_stats.txt",
+                "multiqc/multiqc_data/multiqc_software_versions.txt",
+                "multiqc/multiqc_data/multiqc_sources.txt",
+                "multiqc/multiqc_report.html",
+                "pipeline_info",
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+                "intermediate_files/variant_counts/GID1A_input_1_pe/variant_counts_columns.tsv",
+                "intermediate_files/variant_counts/GID1A_input_2_pe",
+                "intermediate_files/variant_counts/GID1A_input_2_pe/GID1A_input_2_pe.variant_counts.tsv",
+                "intermediate_files/variant_counts/GID1A_input_2_pe/variant_counts_columns.tsv",
+                "intermediate_files/variant_counts/GID1A_output_1_pe",
+                "intermediate_files/variant_counts/GID1A_output_1_pe/GID1A_output_1_pe.variant_counts.tsv",
+                "intermediate_files/variant_counts/GID1A_output_1_pe/variant_counts_columns.tsv",
+                "intermediate_files/variant_counts/GID1A_output_2_pe",
+                "intermediate_files/variant_counts/GID1A_output_2_pe/GID1A_output_2_pe.variant_counts.tsv",
+                "intermediate_files/variant_counts/GID1A_output_2_pe/variant_counts_columns.tsv",
+                "library_QC",
+                "library_QC/GID1A_input_1_pe",
+                "library_QC/GID1A_input_1_pe/SeqDepth.pdf",
+                "library_QC/GID1A_input_1_pe/counts_heatmap.pdf",
+                "library_QC/GID1A_input_1_pe/counts_per_cov_heatmap.pdf",
+                "library_QC/GID1A_input_1_pe/logdiff_plot.pdf",
+                "library_QC/GID1A_input_1_pe/logdiff_varying_bases.pdf",
+                "library_QC/GID1A_input_1_pe/rolling_counts.pdf",
+                "library_QC/GID1A_input_1_pe/rolling_counts_per_cov.pdf",
+                "library_QC/GID1A_input_1_pe/rolling_coverage.pdf",
+                "library_QC/GID1A_input_1_pe/seqdepth_curve.csv",
+                "library_QC/GID1A_input_2_pe",
+                "library_QC/GID1A_input_2_pe/SeqDepth.pdf",
+                "library_QC/GID1A_input_2_pe/counts_heatmap.pdf",
+                "library_QC/GID1A_input_2_pe/counts_per_cov_heatmap.pdf",
+                "library_QC/GID1A_input_2_pe/logdiff_plot.pdf",
+                "library_QC/GID1A_input_2_pe/logdiff_varying_bases.pdf",
+                "library_QC/GID1A_input_2_pe/rolling_counts.pdf",
+                "library_QC/GID1A_input_2_pe/rolling_counts_per_cov.pdf",
+                "library_QC/GID1A_input_2_pe/rolling_coverage.pdf",
+                "library_QC/GID1A_input_2_pe/seqdepth_curve.csv",
+                "library_QC/GID1A_output_1_pe",
+                "library_QC/GID1A_output_1_pe/SeqDepth.pdf",
+                "library_QC/GID1A_output_1_pe/counts_heatmap.pdf",
+                "library_QC/GID1A_output_1_pe/counts_per_cov_heatmap.pdf",
+                "library_QC/GID1A_output_1_pe/logdiff_plot.pdf",
+                "library_QC/GID1A_output_1_pe/logdiff_varying_bases.pdf",
+                "library_QC/GID1A_output_1_pe/rolling_counts.pdf",
+                "library_QC/GID1A_output_1_pe/rolling_counts_per_cov.pdf",
+                "library_QC/GID1A_output_1_pe/rolling_coverage.pdf",
+                "library_QC/GID1A_output_1_pe/seqdepth_curve.csv",
+                "library_QC/GID1A_output_2_pe",
+                "library_QC/GID1A_output_2_pe/SeqDepth.pdf",
+                "library_QC/GID1A_output_2_pe/counts_heatmap.pdf",
+                "library_QC/GID1A_output_2_pe/counts_per_cov_heatmap.pdf",
+                "library_QC/GID1A_output_2_pe/logdiff_plot.pdf",
+                "library_QC/GID1A_output_2_pe/logdiff_varying_bases.pdf",
+                "library_QC/GID1A_output_2_pe/rolling_counts.pdf",
+                "library_QC/GID1A_output_2_pe/rolling_counts_per_cov.pdf",
+                "library_QC/GID1A_output_2_pe/rolling_coverage.pdf",
+                "library_QC/GID1A_output_2_pe/seqdepth_curve.csv",
+                "library_QC/error_correction_report.html",
+                "multiqc",
+                "multiqc/multiqc_data",
+                "multiqc/multiqc_data/.stub",
+                "multiqc/multiqc_report.html",
+                "pipeline_info",
+                "pipeline_info/nf_core_deepmutscan_software_mqc_versions.yml",
+                "variant_effect_inspection_tool",
+                "variant_effect_inspection_tool/structure_data.json",
+                "variant_effect_inspection_tool/variant_effect_inspection_tool.html"
+            ]
+        ],
+        "timestamp": "2026-11-06T19:31:52.175022",
+        "meta": {
+            "nf-test": "0.9.4",
+            "nextflow": "25.10.4"
+        }
+    }
+}
\ No newline at end of file
diff --git a/tests/nextflow.config b/tests/nextflow.config
new file mode 100644
index 0000000..31fc1fb
--- /dev/null
+++ b/tests/nextflow.config
@@ -0,0 +1,30 @@
+/*
+========================================================================================
+    Nextflow config file for running nf-test tests
+========================================================================================
+*/
+
+process {
+    resourceLimits = [
+        cpus: 4,
+        memory: '8.GB',
+        time: '1.h'
+    ]
+}
+
+params {
+    modules_testdata_base_path = 'https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/'
+    pipelines_testdata_base_path = 'https://raw.githubusercontent.com/nf-core/test-datasets/refs/heads/deepmutscan/'
+
+    // Input data
+    input = params.pipelines_testdata_base_path + 'samplesheet/GID1A_test.csv'
+    fasta = params.pipelines_testdata_base_path + 'testdata/GID1A.fasta'
+    reading_frame = '352-1383'
+    min_counts = 1
+    mutagenesis_type = 'nnk_nns'
+    run_seqdepth = true
+    fitness = true
+}
+
+// Fixes S3 access issues on self-hosted runners
+aws.client.anonymous = true
diff --git a/workflows/deepmutscan.nf b/workflows/deepmutscan.nf
new file mode 100644
index 0000000..1d80611
--- /dev/null
+++ b/workflows/deepmutscan.nf
@@ -0,0 +1,495 @@
+/*
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    IMPORT MODULES / SUBWORKFLOWS / FUNCTIONS
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+*/
+include { FASTQC                 } from '../modules/nf-core/fastqc/main'
+include { FASTQ_CHECK            } from '../modules/local/fastq_check/main'
+include { MULTIQC                } from '../modules/nf-core/multiqc/main'
+include { BWA_INDEX              } from '../modules/nf-core/bwa/index/main'
+include { MERGE_ALIGN            } from '../modules/local/bamprocessing/merge_align/main'
+include { VARIANTCOUNTING            } from '../modules/local/variantcounting/main'
+include { DMSANALYSIS_AASEQ      } from '../modules/local/dmsanalysis/aa_seq/main'
+include { DMSANALYSIS_POSSIBLE_MUTATIONS      } from '../modules/local/dmsanalysis/possible_mutations/main'
+include { DMSANALYSIS_PROCESS_VARIANT_COUNTS      } from '../modules/local/dmsanalysis/process_variant_counts/main'
+include { DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES } from '../modules/local/dmsanalysis/error_correction_false_doubles/main'
+include { DMSANALYSIS_ERROR_CORRECTION_WILDTYPE      } from '../modules/local/dmsanalysis/error_correction_wildtype/main'
+include { VISUALIZATION_COUNTS_PER_COV      } from '../modules/local/visualization/counts_per_cov/main'
+include { VISUALIZATION_COUNTS_HEATMAP      } from '../modules/local/visualization/counts_heatmap/main'
+include { VISUALIZATION_GLOBAL_POS_BIASES_COUNTS      } from '../modules/local/visualization/global_pos_biases_counts/main'
+include { VISUALIZATION_GLOBAL_POS_BIASES_COV      } from '../modules/local/visualization/global_pos_biases_cov/main'
+include { VISUALIZATION_LOGDIFF      } from '../modules/local/visualization/logdiff/main'
+include { VISUALIZATION_SEQDEPTH      } from '../modules/local/visualization/seqdepth/main'
+include { VISUALIZATION_ERROR_CORRECTION_REPORT } from '../modules/local/visualization/error_correction_report/main'
+include { VISUALIZATION_SUMMARY_REPORT } from '../modules/local/visualization/summary_report/main'
+include { VARIANT_EFFECT_INSPECTION_TOOL } from '../modules/local/structure/variant_effect_inspection_tool/main'
+include { COUNTS_TO_FITNESS          } from '../modules/local/fitness/counts_to_fitness/main'
+
+include { CALCULATE_FITNESS } from '../subworkflows/local/calculate_fitness/main'
+
+include { paramsSummaryMap       } from 'plugin/nf-schema'
+include { paramsSummaryMultiqc   } from '../subworkflows/nf-core/utils_nfcore_pipeline'
+include { softwareVersionsToYAML } from '../subworkflows/nf-core/utils_nfcore_pipeline'
+include { methodsDescriptionText } from '../subworkflows/local/utils_nfcore_deepmutscan_pipeline'
+include { asBool                 } from '../subworkflows/local/utils_nfcore_deepmutscan_pipeline'
+include { asInt                  } from '../subworkflows/local/utils_nfcore_deepmutscan_pipeline'
+include { checkFastqReports      } from '../subworkflows/local/utils_nfcore_deepmutscan_pipeline'
+
+/*
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    RUN MAIN WORKFLOW
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+*/
+
+workflow DEEPMUTSCAN {
+
+    take:
+    ch_samplesheet // channel: samplesheet read in from --input
+    multiqc_config
+    multiqc_logo
+    multiqc_methods_description
+    outdir
+
+    main:
+
+    def ch_versions = Channel.empty()
+    def ch_multiqc_files = Channel.empty()
+
+    // Define input channels from parameters. The reference and the samplesheet are one file per run,
+    // so they are value channels: every task that needs them reads the same value, however many
+    // libraries there are. Outputs of processes whose inputs are all value channels (BWA_INDEX,
+    // DMSANALYSIS_AASEQ, DMSANALYSIS_POSSIBLE_MUTATIONS, EXPDESIGN_FITNESS) are value channels too.
+    def ch_fasta = Channel.value(tuple([id: 'ref'], file(params.fasta, checkIfExists: true)))
+    def ch_fasta_file = ch_fasta.map { _meta, fasta -> fasta }               // path(fasta)
+
+    // CLI params arrive as Strings; normalise once here (see asBool/asInt)
+    def run_fitness     = asBool(params.fitness)
+    def run_dimsum      = asBool(params.dimsum)
+    def run_mutscan     = asBool(params.mutscan)
+    def run_seqdepth    = asBool(params.run_seqdepth)
+
+    def reading_frame_ch        = Channel.value(params.reading_frame)
+    def custom_codon_library_ch = params.custom_codon_library ? Channel.value(file(params.custom_codon_library, checkIfExists: true)) : Channel.value([])
+    def mutagenesis_type_ch     = Channel.value(params.mutagenesis_type)
+    def sliding_window_size_ch  = Channel.value(asInt(params.sliding_window_size))
+    def aimed_cov_ch            = Channel.value(asInt(params.aimed_cov))
+    def base_qual_ch            = Channel.value(asInt(params.base_qual))
+    def min_flank_ch            = Channel.value(asInt(params.min_flank))
+
+    // Raw samplesheet path for downstream subworkflows
+    def ch_samplesheet_csv      = Channel.value(file(params.input, checkIfExists: true))
+
+    // '--dimsum' only has an effect together with '--fitness'
+    if (run_dimsum && !run_fitness) {
+        log.warn("'--dimsum true' only works together with '--fitness true'. DiMSum will be skipped.")
+    }
+
+    // Samplesheet-level checks (one sample per run, replicate pairing, one wildtype library for
+    // '--error_correction wildtype') run at start-up in PIPELINE_INITIALISATION.
+
+    // Nudge fitness users who did not supply a structure towards the interactive tool
+    if (run_fitness && !params.pdb) {
+        log.info(
+            "\n" +
+            "  ┌─ Optional: interactive variant effect inspection tool ───────────────────┐\n" +
+            "     Supply a wildtype structure with '--pdb <structure.pdb>' to also build an\n" +
+            "     interactive HTML tool that projects fitness, counts and error-correction\n" +
+            "     biases onto the 3D structure to support data interpretation.\n" +
+            "  └───────────────────────────────────────────────────────────────────────────┘\n"
+        )
+    }
+
+    //
+    // MODULE: Check that the input FASTQ files are complete and consistent (fq lint). Every later
+    // step waits for all libraries to pass; otherwise the run stops with one message listing every
+    // faulty library. Quick checks of the first record of each file already ran at start-up.
+    //
+    FASTQ_CHECK(ch_samplesheet)
+
+    def ch_reads = ch_samplesheet
+        .join(FASTQ_CHECK.out.report)                                         // [meta, reads, report]
+        .toList()
+        .map { rows -> checkFastqReports(rows) }                              // stops the run on any FAIL
+        .flatMap { rows -> rows.collect { meta, reads, _report -> [ meta, reads ] } }
+
+    //
+    // MODULE: Run FastQC
+    //
+    FASTQC(ch_reads)
+    ch_multiqc_files = ch_multiqc_files.mix(FASTQC.out.zip.map{ _meta, file -> file })
+
+    //
+    // MODULE: BWA Index
+    //
+    BWA_INDEX (
+        ch_fasta
+    )
+
+    //
+    // MODULE: Merge each raw read pair into a consensus read (vsearch), align the merged reads
+    // (BWA-MEM), filter the alignments, and coordinate-sort and index them (samtools), streamed in
+    // one task per library. The sorted, indexed BAM is what the read-based counter needs.
+    //
+    MERGE_ALIGN(
+      ch_reads,                        // tuple(val(meta), path(reads)): checked raw paired-end FASTQ
+      BWA_INDEX.out.index              // tuple(val(meta2), path(index)), a value channel
+    )
+
+    // One task per library (the sorted, indexed BAMs); the other inputs are value channels
+    VARIANTCOUNTING(
+      MERGE_ALIGN.out.bam, // tuple(val(meta), path(bam), path(bai))
+      ch_fasta_file,       // path(fasta)
+      reading_frame_ch,    // val(reading_frame string, 1-based inclusive)
+      base_qual_ch,        // val(base_qual)
+      min_flank_ch         // val(min_flank)
+    )
+    ch_versions = ch_versions.mix(VARIANTCOUNTING.out.versions)
+
+    DMSANALYSIS_AASEQ (
+      ch_fasta,
+      reading_frame_ch
+    )
+    ch_versions = ch_versions.mix(DMSANALYSIS_AASEQ.out.versions)
+
+    DMSANALYSIS_POSSIBLE_MUTATIONS(
+      ch_fasta,
+      reading_frame_ch,           // pos_range (as val)
+      mutagenesis_type_ch,        // mutagenesis_type (as val)
+      custom_codon_library_ch     // custom_codon_library (path, or [] if not given)
+    )
+    ch_versions = ch_versions.mix(DMSANALYSIS_POSSIBLE_MUTATIONS.out.versions)
+
+    // Run-level reference tables, as value channels (paths only)
+    def ch_aa_seq             = DMSANALYSIS_AASEQ.out.aa_seq.map { _meta, f -> f }
+    def ch_possible_mutations = DMSANALYSIS_POSSIBLE_MUTATIONS.out.possible_mutations.map { _meta, f -> f }
+
+    // One item per library
+    def ch_vc = VARIANTCOUNTING.out.variant_counts   // tuple(val(meta), path)
+
+    DMSANALYSIS_PROCESS_VARIANT_COUNTS(
+      ch_vc,                        // tuple(val(meta), path(variantCounts))
+      ch_possible_mutations,        // path(possible_mutations)
+      ch_aa_seq                     // path(aa_seq)
+    )
+
+    ch_versions = ch_versions.mix(DMSANALYSIS_PROCESS_VARIANT_COUNTS.out.versions)
+
+    def annotated_variantCounts_ch           = DMSANALYSIS_PROCESS_VARIANT_COUNTS.out.processed_variantCounts.map { meta, a, b, c, d -> tuple(meta, a) }
+    def variantCounts_filtered_by_library_ch = DMSANALYSIS_PROCESS_VARIANT_COUNTS.out.processed_variantCounts.map { meta, a, b, c, d -> tuple(meta, b) }
+    def library_completed_variantCounts_ch   = DMSANALYSIS_PROCESS_VARIANT_COUNTS.out.processed_variantCounts.map { meta, a, b, c, d -> tuple(meta, c) }
+    def variantCounts_for_heatmaps_ch        = DMSANALYSIS_PROCESS_VARIANT_COUNTS.out.processed_variantCounts.map { meta, a, b, c, d -> tuple(meta, d) }
+
+    //
+    // Sequencing-error correction of single-codon counts (default: false_doubles).
+    // Replaces the filtered + heatmap channels with corrected versions; downstream fitness
+    // picks up correction via the `counts_corrected` column, count heatmaps via canonical names.
+    //
+    if (params.error_correction == 'false_doubles') {
+      // per sample: counts + library-filtered counts + completed library table
+      def ch_fd_in       = ch_vc.join(variantCounts_filtered_by_library_ch).join(library_completed_variantCounts_ch)  // (meta, raw, filtered, completed)
+      // the reference the reads were aligned to (base_mut is in this fasta's coordinate frame);
+      // the estimator and the mode/window are run-level constants.
+      DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES(
+        ch_fd_in, ch_fasta_file, ch_aa_seq,
+        params.false_doubles_method, asInt(params.false_doubles_codon_window)
+      )
+      ch_versions = ch_versions.mix(DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES.out.versions)
+      variantCounts_filtered_by_library_ch = DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES.out.corrected.map { meta, filt, heat, comp -> tuple(meta, filt) }
+      variantCounts_for_heatmaps_ch        = DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES.out.corrected.map { meta, filt, heat, comp -> tuple(meta, heat) }
+      library_completed_variantCounts_ch   = DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES.out.corrected.map { meta, filt, heat, comp -> tuple(meta, comp) }
+    }
+    else if (params.error_correction == 'wildtype') {
+      // pair each non-wildtype sample with the wildtype sample of the same `sample` base
+      def filt_comp = variantCounts_filtered_by_library_ch.join(library_completed_variantCounts_ch)  // (meta, filtered, completed)
+      def wt_ch     = filt_comp.filter { meta, f, c -> meta.type == 'wildtype' }.map { meta, f, c -> tuple(meta.sample, f) }
+      def tgt_ch    = filt_comp.filter { meta, f, c -> meta.type != 'wildtype' }.map { meta, f, c -> tuple(meta.sample, meta, f, c) }
+      def ch_wt_in  = tgt_ch.combine(wt_ch, by: 0).map { _base, meta, filt, comp, wtf -> tuple(meta, filt, wtf, comp) }  // (meta, filtered, wt_filtered, completed)
+      DMSANALYSIS_ERROR_CORRECTION_WILDTYPE( ch_wt_in, ch_aa_seq )
+      ch_versions = ch_versions.mix(DMSANALYSIS_ERROR_CORRECTION_WILDTYPE.out.versions)
+      variantCounts_filtered_by_library_ch = DMSANALYSIS_ERROR_CORRECTION_WILDTYPE.out.corrected.map { meta, filt, heat, comp -> tuple(meta, filt) }
+      variantCounts_for_heatmaps_ch        = DMSANALYSIS_ERROR_CORRECTION_WILDTYPE.out.corrected.map { meta, filt, heat, comp -> tuple(meta, heat) }
+      library_completed_variantCounts_ch   = DMSANALYSIS_ERROR_CORRECTION_WILDTYPE.out.corrected.map { meta, filt, heat, comp -> tuple(meta, comp) }
+    }
+    // else 'none': keep the uncorrected channels
+
+    // Artefacts the all-in-one report embeds, collected as [[kind, group, name], file]. Optional
+    // parts simply never mix anything in, and the report drops the corresponding section.
+    def ch_report_assets = Channel.empty()
+
+    //
+    // MODULE: per-sample error-correction report (only when correction was applied)
+    //
+    if (params.error_correction != 'none') {
+      // One run-level report over every file. Sorting first keeps the id list and the staged files in
+      // the same order, which is the only thing tying a column of numbers to the file it came from.
+      def ch_ec = variantCounts_filtered_by_library_ch.toSortedList { a, b -> a[0].id <=> b[0].id }
+
+      VISUALIZATION_ERROR_CORRECTION_REPORT(
+        ch_ec.map { rows -> rows[0][0].sample ?: 'run' },
+        ch_ec.map { rows -> groovy.json.JsonOutput.toJson(rows.collect { [id: it[0].id, type: it[0].type, replicate: it[0].replicate] }).bytes.encodeBase64().toString() },
+        ch_ec.map { rows -> rows.collect { it[1] } },
+        params.error_correction,
+        params.false_doubles_method,
+        file("${projectDir}/assets/error_correction_report/ec_report_template.html", checkIfExists: true)
+      )
+      ch_versions = ch_versions.mix(VISUALIZATION_ERROR_CORRECTION_REPORT.out.versions)
+      ch_report_assets = ch_report_assets.mix(
+        VISUALIZATION_ERROR_CORRECTION_REPORT.out.report.map { f -> [['report', 'ec', 'Error correction'], f] }
+      )
+    }
+
+    VISUALIZATION_COUNTS_PER_COV(
+      variantCounts_for_heatmaps_ch
+    )
+
+    VISUALIZATION_COUNTS_HEATMAP(
+      variantCounts_for_heatmaps_ch
+    )
+
+    VISUALIZATION_GLOBAL_POS_BIASES_COUNTS(
+      variantCounts_filtered_by_library_ch,
+      ch_aa_seq,
+      sliding_window_size_ch
+    )
+
+    VISUALIZATION_GLOBAL_POS_BIASES_COV(
+      variantCounts_filtered_by_library_ch,
+      ch_aa_seq,
+      sliding_window_size_ch,
+      aimed_cov_ch
+    )
+
+    VISUALIZATION_LOGDIFF(
+      library_completed_variantCounts_ch
+    )
+
+    ch_versions = ch_versions
+      .mix(VISUALIZATION_COUNTS_PER_COV.out.versions)
+      .mix(VISUALIZATION_COUNTS_HEATMAP.out.versions)
+      .mix(VISUALIZATION_GLOBAL_POS_BIASES_COUNTS.out.versions)
+      .mix(VISUALIZATION_GLOBAL_POS_BIASES_COV.out.versions)
+      .mix(VISUALIZATION_LOGDIFF.out.versions)
+
+    // library QC plots (original R PDFs) + the tables the report re-plots interactively
+    ch_report_assets = ch_report_assets
+      .mix(VISUALIZATION_GLOBAL_POS_BIASES_COV.out.rolling_coverage.map          { meta, f -> [['qc', meta.id, 'rolling_coverage.pdf'], f] })
+      .mix(VISUALIZATION_GLOBAL_POS_BIASES_COUNTS.out.rolling_counts.map         { meta, f -> [['qc', meta.id, 'rolling_counts.pdf'], f] })
+      .mix(VISUALIZATION_GLOBAL_POS_BIASES_COUNTS.out.rolling_counts_per_cov.map { meta, f -> [['qc', meta.id, 'rolling_counts_per_cov.pdf'], f] })
+      .mix(VISUALIZATION_COUNTS_HEATMAP.out.counts_heatmap.map                   { meta, f -> [['qc', meta.id, 'counts_heatmap.pdf'], f] })
+      .mix(VISUALIZATION_COUNTS_PER_COV.out.counts_per_cov_heatmap.map           { meta, f -> [['qc', meta.id, 'counts_per_cov_heatmap.pdf'], f] })
+      .mix(VISUALIZATION_LOGDIFF.out.logdiff_plot.map                            { meta, f -> [['qc', meta.id, 'logdiff_plot.pdf'], f] })
+      .mix(VISUALIZATION_LOGDIFF.out.logdiff_varying_bases.map                   { meta, f -> [['qc', meta.id, 'logdiff_varying_bases.pdf'], f] })
+      .mix(variantCounts_filtered_by_library_ch.map                              { meta, f -> [['filtered', meta.id, 'counts.csv'], f] })
+      .mix(variantCounts_for_heatmaps_ch.map                                     { meta, f -> [['heatmap', meta.id, 'heatmap.csv'], f] })
+      // Fixes the ORF axis at the wildtype length: positions with no library coverage still have to
+      // occupy a slot, or every rolling window downstream is computed over the wrong neighbourhood.
+      .mix(ch_aa_seq.map                                                         { f -> [['aa_seq', 'run', 'aa_seq.txt'], f] })
+      // Needed to name the exact nucleotide change behind each heatmap cell: the fitness table gives
+      // the variant ORFs, and the wildtype to diff them against only exists in the reference.
+      .mix(ch_fasta.map                                                          { _meta, f -> [['fasta', 'run', 'reference.fasta'], f] })
+
+    if (run_seqdepth) {
+      VISUALIZATION_SEQDEPTH(
+        variantCounts_filtered_by_library_ch,
+        ch_possible_mutations
+      )
+      ch_versions = ch_versions.mix(VISUALIZATION_SEQDEPTH.out.versions)
+      // The rarefaction curve travels to the summary report, which re-plots it per file under
+      // Sequencing QC (the PDF is still written to the results folder for the record).
+      ch_report_assets = ch_report_assets.mix(
+        VISUALIZATION_SEQDEPTH.out.curve.map { meta, f -> [['seqdepth', meta.id, 'seqdepth.csv'], f] }
+      )
+    }
+
+    // Execution of fitness estimation, if --fitness true
+    if (run_fitness) {
+
+        // Per-library count tables in DiMSum input format, merged by CALCULATE_FITNESS
+        COUNTS_TO_FITNESS(
+          variantCounts_filtered_by_library_ch, // tuple(val(meta), path)
+          ch_fasta_file,                        // path(fasta)
+          reading_frame_ch                      // val(reading_frame)
+        )
+        ch_versions = ch_versions.mix(COUNTS_TO_FITNESS.out.versions)
+
+        CALCULATE_FITNESS (
+            COUNTS_TO_FITNESS.out.fitness_input, // Input from previous step
+            ch_samplesheet_csv,                   // Path to samplesheet
+            ch_fasta,                             // The original Fasta tuple
+            reading_frame_ch,                     // Reading frame value channel
+            DMSANALYSIS_AASEQ.out.aa_seq,         // Amino Acid Sequence (for Heatmap)
+            run_dimsum,                           // Boolean: run DiMSum
+            run_mutscan                           // Boolean: run mutscan
+        )
+
+        // Collect versions
+        ch_versions = ch_versions.mix(CALCULATE_FITNESS.out.versions)
+
+        ch_report_assets = ch_report_assets
+          .mix(CALCULATE_FITNESS.out.fitness_estimation.first().map { _m, f -> [['fitness_table', 'default', 'fitness_estimation.tsv'], f] })
+          .mix(CALCULATE_FITNESS.out.fitness_plots.map { _m, f -> [['fitness_pdf', 'default', f.name], f] })
+          // mutscan emits all of its plots as one list, so flatten to a file per entry
+          .mix(CALCULATE_FITNESS.out.mutscan_plots.transpose().map { _m, f -> [['mutscan_pdf', 'mutscan', f.name], f] })
+          .mix(CALCULATE_FITNESS.out.dimsum_dir.flatten().filter { it.name == 'report.html' }.map { f -> [['report', 'dimsum', 'DiMSum'], f] })
+
+        //
+        // MODULE: interactive variant effect inspection tool (one self-contained HTML per sample).
+        // Runs only when a wildtype structure is supplied via --pdb. Projects fitness, counts and
+        // error-correction biases onto the 3D structure. (Structure prediction is not wired here.)
+        //
+        if (params.pdb) {
+            def ch_structure = Channel
+                .fromPath(params.pdb, checkIfExists: true)
+                .map { pdb -> tuple([id: 'wildtype'], pdb) }
+            def ch_viewer_input = CALCULATE_FITNESS.out.fitness_estimation
+                .combine(ch_structure)
+                .map { smeta, fitness_tsv, _stmeta, pdb -> tuple(smeta, pdb, fitness_tsv) }
+            VARIANT_EFFECT_INSPECTION_TOOL(
+                ch_viewer_input,
+                variantCounts_filtered_by_library_ch.map { _meta, file -> file }.collect(),
+                ch_aa_seq,
+                file("${projectDir}/assets/variant_effect_inspection_tool/3Dmol-min.js",         checkIfExists: true),
+                file("${projectDir}/assets/variant_effect_inspection_tool/viewer_template.html", checkIfExists: true),
+            )
+            ch_versions = ch_versions.mix(VARIANT_EFFECT_INSPECTION_TOOL.out.versions)
+            ch_report_assets = ch_report_assets.mix(
+                VARIANT_EFFECT_INSPECTION_TOOL.out.viewer.map { _meta, f -> [['report', 'viewer', 'Variant effect inspection tool'], f] }
+            )
+            // The raw structure also travels to the summary report, which builds its own slow-spinning
+            // fitness-coloured viewer on the Variant effects page from it.
+            ch_report_assets = ch_report_assets.mix(
+                ch_structure.map { _meta, pdb -> [['pdb', 'structure', 'structure.pdb'], pdb] }
+            )
+        }
+    }
+
+    //
+    // Collate and save software versions
+    //
+    def topic_versions = channel.topic("versions")
+        .distinct()
+        .branch { entry ->
+            versions_file: entry instanceof Path
+            versions_tuple: true
+        }
+
+    def topic_versions_string = topic_versions.versions_tuple
+        .map { process, tool, version ->
+            [ process[process.lastIndexOf(':')+1..-1], "  ${tool}: ${version}" ]
+        }
+        .groupTuple(by:0)
+        .map { process, tool_versions ->
+            tool_versions.unique().sort()
+            "${process}:\n${tool_versions.join('\n')}"
+        }
+
+    def ch_collated_versions = softwareVersionsToYAML(ch_versions.mix(topic_versions.versions_file))
+        .mix(topic_versions_string)
+        .collectFile(
+            storeDir: "${outdir}/pipeline_info",
+            name: 'nf_core_'  +  'deepmutscan_software_'  + 'mqc_'  + 'versions.yml',
+            sort: true,
+            newLine: true
+        )
+
+    //
+    // MODULE: MultiQC
+    //
+    ch_multiqc_files = ch_multiqc_files.mix(ch_collated_versions)
+    def ch_summary_params = paramsSummaryMap(workflow, parameters_schema: "nextflow_schema.json")
+    def ch_workflow_summary = channel.value(paramsSummaryMultiqc(ch_summary_params))
+    ch_multiqc_files = ch_multiqc_files.mix(ch_workflow_summary.collectFile(name: 'workflow_summary_mqc.yaml'))
+    def ch_multiqc_custom_methods_description = multiqc_methods_description
+        ? file(multiqc_methods_description, checkIfExists: true)
+        : file("${projectDir}/assets/methods_description_template.yml", checkIfExists: true)
+    def ch_methods_description = channel.value(methodsDescriptionText(ch_multiqc_custom_methods_description))
+    ch_multiqc_files = ch_multiqc_files.mix(ch_methods_description.collectFile(name: 'methods_description_mqc.yaml', sort: true))
+
+    MULTIQC(
+        ch_multiqc_files.flatten().collect().map { files ->
+            [
+                [id: 'deepmutscan'],
+                files,
+                multiqc_config
+                    ? file(multiqc_config, checkIfExists: true)
+                    : file("${projectDir}/assets/multiqc_config.yml", checkIfExists: true),
+                multiqc_logo ? file(multiqc_logo, checkIfExists: true) : [],
+                [],
+                [],
+            ]
+        }
+    )
+
+    //
+    // MODULE: the single all-in-one report. Runs last, because it embeds every other artefact -
+    // the MultiQC report included - as a base64 data URI, so the file stands alone when shared.
+    //
+    ch_report_assets = ch_report_assets.mix(
+        MULTIQC.out.report.map { _meta, f -> [['report', 'multiqc', 'MultiQC'], f] }
+    )
+    // The collated software versions drive the Citation page: it cites only the tools this run used.
+    ch_report_assets = ch_report_assets.mix(
+        ch_collated_versions.map { f -> [['versions', 'run', 'versions.yml'], f] }
+    )
+
+    // Sorting keeps the manifest and the staged files in one deterministic, matching order.
+    def ch_summary_input = ch_report_assets
+        .toSortedList { a, b -> a[0].join('|') <=> b[0].join('|') }
+        .map { rows ->
+            def manifest = rows.collect { [kind: it[0][0], group: it[0][1], name: it[0][2]] }
+            tuple(
+                groovy.json.JsonOutput.toJson(manifest).bytes.encodeBase64().toString(),
+                rows.collect { it[1] },
+            )
+        }
+
+    def ch_run_meta = variantCounts_filtered_by_library_ch
+        .first()
+        .map { meta, _f ->
+            tuple(
+                [id: 'run', sample: meta.sample],
+                groovy.json.JsonOutput.toJson([
+                    sample: meta.sample,
+                    reading_frame: params.reading_frame,
+                    mutagenesis_type: params.mutagenesis_type,
+                    error_correction: params.error_correction,
+                    version: workflow.manifest.version,
+                    // The report smooths client-side with the same window the R plots use, and draws
+                    // the same required-coverage reference line, so both must travel with the run.
+                    sliding_window_size: asInt(params.sliding_window_size),
+                    aimed_cov: asInt(params.aimed_cov),
+                    fitness: run_fitness,
+                    dimsum: run_dimsum,
+                    mutscan: run_mutscan,
+                    outdir: params.outdir,
+                    // Lets the Overview link straight to Nextflow's own execution report + timeline,
+                    // which sit next to this file as execution_{report,timeline}_<suffix>.html.
+                    trace_report_suffix: params.trace_report_suffix,
+                    false_doubles_method: params.false_doubles_method,
+                ]).bytes.encodeBase64().toString(),
+            )
+        }
+
+    VISUALIZATION_SUMMARY_REPORT(
+        ch_run_meta,
+        ch_summary_input.map { manifest, _files -> manifest },
+        ch_summary_input.map { _manifest, files -> files },
+        file("${projectDir}/assets/summary_report/summary_report_template.html", checkIfExists: true),
+        file("${projectDir}/assets/variant_effect_inspection_tool/3Dmol-min.js", checkIfExists: true),
+    )
+    // Deliberately NOT mixed into ch_versions: this process consumes the collated versions file
+    // (it embeds it to build the Citations page), so feeding its own versions back in would make
+    // the collectFile above wait on a process that is waiting on the collectFile.
+
+    emit:
+    multiqc_report = MULTIQC.out.report.map { _meta, report -> [report] }.toList() // channel: /path/to/multiqc_report.html
+    versions       = ch_versions                 // channel: [ path(versions.yml) ]
+    bwa_index      = BWA_INDEX.out.index
+    merged_bam     = MERGE_ALIGN.out.bam
+}
+
+/*
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    THE END
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+*/
diff --git a/workflows/dmscore.nf b/workflows/dmscore.nf
deleted file mode 100644
index 041e503..0000000
--- a/workflows/dmscore.nf
+++ /dev/null
@@ -1,97 +0,0 @@
-/*
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-    IMPORT MODULES / SUBWORKFLOWS / FUNCTIONS
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-*/
-include { FASTQC                 } from '../modules/nf-core/fastqc/main'
-include { MULTIQC                } from '../modules/nf-core/multiqc/main'
-include { paramsSummaryMap       } from 'plugin/nf-schema'
-include { paramsSummaryMultiqc   } from '../subworkflows/nf-core/utils_nfcore_pipeline'
-include { softwareVersionsToYAML } from '../subworkflows/nf-core/utils_nfcore_pipeline'
-include { methodsDescriptionText } from '../subworkflows/local/utils_nfcore_dmscore_pipeline'
-
-/*
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-    RUN MAIN WORKFLOW
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-*/
-
-workflow DMSCORE {
-
-    take:
-    ch_samplesheet // channel: samplesheet read in from --input
-    main:
-
-    ch_versions = Channel.empty()
-    ch_multiqc_files = Channel.empty()
-    //
-    // MODULE: Run FastQC
-    //
-    FASTQC (
-        ch_samplesheet
-    )
-    ch_multiqc_files = ch_multiqc_files.mix(FASTQC.out.zip.collect{it[1]})
-    ch_versions = ch_versions.mix(FASTQC.out.versions.first())
-
-    //
-    // Collate and save software versions
-    //
-    softwareVersionsToYAML(ch_versions)
-        .collectFile(
-            storeDir: "${params.outdir}/pipeline_info",
-            name: 'nf_core_'  +  'dmscore_software_'  + 'mqc_'  + 'versions.yml',
-            sort: true,
-            newLine: true
-        ).set { ch_collated_versions }
-
-
-    //
-    // MODULE: MultiQC
-    //
-    ch_multiqc_config        = Channel.fromPath(
-        "$projectDir/assets/multiqc_config.yml", checkIfExists: true)
-    ch_multiqc_custom_config = params.multiqc_config ?
-        Channel.fromPath(params.multiqc_config, checkIfExists: true) :
-        Channel.empty()
-    ch_multiqc_logo          = params.multiqc_logo ?
-        Channel.fromPath(params.multiqc_logo, checkIfExists: true) :
-        Channel.empty()
-
-    summary_params      = paramsSummaryMap(
-        workflow, parameters_schema: "nextflow_schema.json")
-    ch_workflow_summary = Channel.value(paramsSummaryMultiqc(summary_params))
-    ch_multiqc_files = ch_multiqc_files.mix(
-        ch_workflow_summary.collectFile(name: 'workflow_summary_mqc.yaml'))
-    ch_multiqc_custom_methods_description = params.multiqc_methods_description ?
-        file(params.multiqc_methods_description, checkIfExists: true) :
-        file("$projectDir/assets/methods_description_template.yml", checkIfExists: true)
-    ch_methods_description                = Channel.value(
-        methodsDescriptionText(ch_multiqc_custom_methods_description))
-
-    ch_multiqc_files = ch_multiqc_files.mix(ch_collated_versions)
-    ch_multiqc_files = ch_multiqc_files.mix(
-        ch_methods_description.collectFile(
-            name: 'methods_description_mqc.yaml',
-            sort: true
-        )
-    )
-
-    MULTIQC (
-        ch_multiqc_files.collect(),
-        ch_multiqc_config.toList(),
-        ch_multiqc_custom_config.toList(),
-        ch_multiqc_logo.toList(),
-        [],
-        []
-    )
-
-    emit:multiqc_report = MULTIQC.out.report.toList() // channel: /path/to/multiqc_report.html
-    versions       = ch_versions                 // channel: [ path(versions.yml) ]
-
-}
-
-/*
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-    THE END
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-*/