nf-core tools

The documentation for the {{ name }} pipeline is organized into several key sections to help you understand its operation and results:

• Usage: Provides an overview of the pipeline workflow, instructions on how to execute it, and a detailed reference for all available command-line flags.
• Output: Explains the various results and files produced by the pipeline and provides guidance on how to interpret them.

If this is an official nf-core pipeline, additional comprehensive guides for installing, configuring, and running nf-core pipelines can be found at https://nf-co.re.

The pipeline produces results in a dedicated results directory. All output paths are relative to the top-level results directory. The specific contents depend on which modules (like FastQC or MultiQC) are enabled in the pipeline configuration.

Common output categories include:

• QC Results: Individual tool reports (e.g., FastQC).
• Aggregate Reports: Summarized reports (e.g., MultiQC).
• Pipeline Information: Execution metrics, software versions, and parameters used during the run.

The .nf-core.yml file located at the root of an nf-core repository defines how nf-core tools behave for that specific repository. The configuration is loaded via load_tools_config() and must adhere to the NFCoreYamlConfig schema. The configuration behavior changes based on the repository_type specified.

# Example for a pipeline repository
repository_type: pipeline
nf_core_version: "4.0.2"

To contribute code to an nf-core pipeline, follow these steps:

1. Prerequisites: Ensure Nextflow, nf-core tools, and nf-test are installed.
2. Issue Tracking: Check for an existing GitHub issue regarding your change. If none exists, create one.
3. Fork & Branch: Fork the repository and create a new branch. For major bug fixes, name your branch patch and follow the patch release process.
4. Development: Make changes following pipeline conventions (e.g., updating documentation, using nf-core/tools to update nextflow_schema.json, and updating CITATIONS.md).
5. Testing: Run and update tests using nf-test.
6. Linting: Use nf-core/tools to lint your code.
7. Pull Request: Submit a PR against the dev branch and request a review.

Because the Nextflow process must remain running until the workflow is finished, you can use the -bg flag to launch Nextflow in the background. This detaches the process from your terminal so the workflow continues even if you log out. Logs are saved to a file.

Alternatively, you can use terminal multiplexers like screen or tmux, or submit the Nextflow process itself as a job to your cluster's job scheduler.

nextflow run <pipeline> -bg

You can install the nf-core package using pip from the Python Package Index (PyPI).

pip install nf-core

If you are contributing to the repository, follow these steps to set up your local development environment using uv:

1. Synchronize the project dependencies:

   uv sync

2. You can either activate the virtual environment:

   source .venv/bin/activate

3. Or run commands directly without activation using uv run:

   uv run nf-core --help

uv sync

# Activate the virtual environment
source .venv/bin/activate

# Or use uv run to run commands without activating
uv run nf-core --help

To prevent the Nextflow Java virtual machines from requesting excessive amounts of memory, it is recommended to set the NXF_OPTS environment variable. You can add this to your ~/.bashrc or ~/.bash_profile.

NXF_OPTS='-Xms1g -Xmx4g'

Each pipeline step has default requirements for CPUs, memory, and time. If a job fails with specific error codes, the pipeline automatically attempts resubmission with increased resources (2x original, then 3x original) before stopping.

To override these defaults with your own resource requirements, you should use Nextflow configuration files. For detailed implementation, refer to the nf-core documentation on customizing process resources.