nf-core/rnaseq: Contributing guidelines

Hi there! Thanks for taking an interest in improving nf-core/rnaseq.

This page describes the recommended nf-core way to contribute to both nf-core/rnaseq and nf-core pipelines in general, including:

• General contribution guidelines: common procedures or guides across all nf-core pipelines.
• Pipeline-specific contribution guidelines: procedures or guides specific to the development conventions of nf-core/rnaseq.

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 for instructions.
• Check whether a GitHub issue about your idea already exists. If an issue does not exist, create one so that others are aware you are working on it.
• Fork the nf-core/rnaseq repository to your GitHub account.
• Create a branch on your forked repository and make your changes following pipeline conventions (if applicable).
• To fix major bugs, name your branch patch and follow the 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 for more information.
• Lint 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 or Git resources 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 the blog post for a statement from the nf-core/core team.

Getting help

For further information and help, see the nf-core/rnaseq documentation or ask on the nf-core #rnaseq Slack channel (join our Slack here).

GitHub Codespaces

You can contribute to nf-core/rnaseq without installing a local development environment on your machine by using GitHub Codespaces.

GitHub 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.

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.

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:

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 which all pipelines must follow. To enforce these, run linting with nf-core/tools:

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.

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

• 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/rnaseq 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:

• Define the corresponding input channel 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), 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.
• Add new or updated parameters and relevant help text to nextflow_schema.json with nf-core/tools.
• 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 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 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 section below. For more information about pipeline contributions, see 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:

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. 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 the ${task.cpus} and ${task.memory} Nextflow variables in the script: block of a module (see an example in the modules repository).

Nextflow version bumping

If you use a new feature from core Nextflow, bump the minimum required Nextflow version in the pipeline with:

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.

Pipeline specific contribution guidelines

A few conventions that are specific to nf-core/rnaseq and tend to surprise new contributors:

Test profiles

The pipeline ships three test profile families: test (CPU smoke test, ~15 GB resourceLimits), test_prokaryotic (composes on top of test, swaps in the bacterial/archaeal samplesheet and bowtie2_salmon aligner), and test_gpu (~30 GB resourceLimits for GPU CI). nf-test.config deliberately does not set a default profile - you must always pass one explicitly (e.g. --profile +test,docker).

GPU/license-server-bound CI cases are gated on env vars so they don't run by default on contributor laptops:

• SKIP_GPU=1 skips Parabricks and GPU ribodetector tests
• SKIP_SENTIEON=1 skips Sentieon STAR tests
• SKIP_PARABRICKS=1 is a finer-grained subset of SKIP_GPU

Test data

Eukaryotic test data is pulled from the iGenomes S3 mirror (pipelines_testdata_base_path = s3://ngi-igenomes/testdata/nf-core/pipelines/rnaseq/3.15/); prokaryotic test data is pulled from nf-core/test-datasets on GitHub (Salmonella SL1344 subset). Both are pinned in conf/test.config and conf/test_prokaryotic.config; if you need to add a new fixture, prefer extending one of those rather than introducing a new bucket.

nf-core modules and subworkflows

Modules and subworkflows under modules/nf-core/ and subworkflows/nf-core/ are managed by the nf-core tooling - install or update them with nf-core modules install / nf-core subworkflows update, do not edit them directly. Edits should go through a PR to nf-core/modules first; the pipeline then picks up the new SHA via a modules.json bump. Pipeline-specific code (anything not portable to other pipelines) lives under modules/local/ and subworkflows/local/.

Module configs

Per-tool publishDir, ext.args, and ext.prefix settings are split into one file per logical group under conf/modules/ (e.g. conf/modules/align_star.config, conf/modules/quantify_rsem.config) and included from nextflow.config. When you add a new local module, add or extend the matching file rather than dropping settings into nextflow.config directly.

Version reporting

Modules emit their versions onto the versions channel topic so the calling workflow does not have to thread a ch_versions through every process (PR #1689). Modules that still also declare a path "versions.yml", emit: versions output do so because they are templated (the .r/.py template script writes the YAML); those modules populate the topic too and don't need migrating - leave them alone.

--genome reference catalogues

--genome <key> resolves a bundle of reference paths from the params.genomes map. The pipeline ships the iGenomes catalogue out of the box, but the same mechanism works with a user-authored catalogue - that is the recommended path for modern reference data, since the iGenomes annotations are stale. If you add new fields to genome map entries, make them optional and gate behaviour on their presence (see e.g. the star_legacy flag in conf/igenomes.config).

Snapshots

Non-deterministic outputs (STAR, Salmon, Kallisto, RSEM, HISAT2 indices; qualimap reports) are snapshotted by file-name-only (getSnapshot() filtered) rather than content. Deterministic text outputs are snapshotted by md5. Verbose JSON test output (e.g. helper-function tests) should snapshot .md5() of the result rather than inlining the JSON. Don't snapshot timestamps or paths that contain hash directories.

.nftignore

tests/.nftignore (and tests/.nftignore_rustqc for the RustQC variant) is a list of glob patterns that nf-test excludes from ${outputDir} snapshots at the pipeline-test level. It is the right place to drop outputs that are content-stable but not byte-stable (e.g. files that include a timestamp, paths under multiqc/multiqc_data, log files where ordering varies), or that are already covered elsewhere. If a pipeline-level snapshot is fluttering on a file you don't actually need to assert on, add it here rather than rerunning until you get lucky.

CHANGELOG

One-line entry per PR under the unreleased section, focused on the what, not the implementation history. A ### Software dependencies table at the end of each release section captures tool version bumps (Old → New).