Before you start

So, you want to add a new pipeline to nf-core - brilliant! Before you start typing, check that you’re happy with the following points:

The main steps involved in adding a new nf-core pipeline covered below are:

  1. Joining the community
  2. Creating a pipeline
  3. Running with test data
  4. Adding to the nf-core organisation
  5. Making your first release
  6. Updates and new releases

Join the community

At its heart, nf-core is a community - to add a pipeline you need to be part of that community! Please join us on Slack, and ask to be added to the GitHub association through the #github-invitations channel. If you feel like it, you can go to the #say-hello channel and introduce yourself to the rest of the community.


It’s good to introduce your idea early on so that it can be discussed, before you spend lots of time coding.

The nf-core guidelines state that no two pipelines should overlap too much in their purpose and results. There may be an existing pipeline that can be extended to give the functionality that you are looking for, or there could be another group working on a similar to the pipeline to the one you’re planning.

To avoid problems at a later date, please come and discuss your plans with the nf-core community as early as possible. Ideally before you make a start on your pipeline!


Not all pipelines are suitable for inclusion in the main nf-core community (eg. bespoke or proprietary workflows). However, we hope that you may still wish to use the nf-core template and/or use components of nf-core code. All nf-core code is under a MIT license and where possible we have endeavoured to make the tools work with any Nextflow pipeline. If this is the case for you, please see the unofficial pipelines tutorial for more details.

All nf-core discussion happens on the nf-core Slack, which you can join here:

These topics are specifically discussed in the #new-pipelines channel:

Create a pipeline from the template

Request a new pipeline

Before you get too carried away, the first task is to request the new pipeline in the nf-core Slack. We have a Slack channel dedicated to this: #new-pipelines.

There, you will find a Workflow bookmarked to top of the Slack window called Pipeline proposal. This gives you a form to fill in with key information about the pipeline you want to write.

The proposal will be discussed and checked for uniqueness (not too much overlap with any existing pipelines) and added to our new pipeline tracking board on GitHub.

Once accepted, someone from the core team will create a Slack channel for your pipeline and you can get started on the next steps.

Create the pipeline

You’ll start by making a new pipeline locally and working with it on your own GitHub account. Once you have a version of the pipeline that runs, ask the core team to move the repo to the nf-core GitHub organisation for final development, review and release.

We generally don’t create repositories within the nf-core organisation from the start, in case development takes longer than expected. This way we avoid having a lot of “empty” pipelines listed which are basically just the template. See Adding your pipeline to the nf-core organisation below for details on this process.

All nf-core pipelines must use the nf-core template. This is done by using the nf-core create command - see the docs for detailed instructions. This tool does lots of things for you: it gives you the correct file structure and boiler plate code and also sets up the required git infrastructure for you to keep your pipeline in sync in the future.

If you already have a working Nextflow pipeline that you’re starting from, it’s usually easier in the long run to start fresh with the nf-core template and copy over your code in the relevant places. Some exceptions can be made - ask the core team on Slack if you’re unsure. You’ll need to set up manual synchronisation, not for the faint hearted!

Once the template for your pipeline is created, make sure to switch branch to the dev branch with git checkout dev. All development should happen on dev (or on other branches that get merged into dev).


Pipeline names must be all lower-case and contain no punctuation.
This is to allow consistent names between platforms.

Push to GitHub

Create an empty repository on GitHub for your new pipeline under your personal account. Do this by going to the GitHub website and clicking + then New Repository.

Make sure not to initialise it with any file, README or LICENSE: you just want an empty repository. You already have these files generated from the nf-core template.

Once created, copy the git URL and add this as a remote to your local git repository. The nf-core create command will have initialised a git repository for you, so all you need to do is add the remote:

## Add a remote called 'origin' - this is the default name for a primary remote
git remote add origin
## Add a remote called 'origin' - this is the default name for a primary remote
git remote add origin

The create command also generated the three standard nf-core branches (master, dev and TEMPLATE), together with an initial commit which is shared between them. This git structure is required for automatic template synchronisation in the future.

You need to push these new branches to the remote GitHub repository:

git push --all origin
git push --all origin

You should now see the vanilla nf-core template and branches in the web interface.

Work on your pipeline

Ok, now you’re all set with your own personal nf-core pipeline! You can now start writing code for real.

Follow usual git development practices, working on the dev branch and committing + pushing code as normal.

Remember to run the nf-core lint command (see docs) to make sure that your workflow passes all of the nf-core compatibility tests. The automated tests on Github Actions also run this, so you should get a notification from GitHub if something breaks.

Running with test data

Whilst the linting tests are good, they’re not sufficient by themselves. It’s also good actually run your pipeline on a minimal dataset. We also automatically run tests with GitHub Actions anytime someone updates the pipeline code (see below). Currently, we don’t usually check the results that are produced, but it often catches syntax errors and other serious problems that cause nextflow to exit with an error.

Putting the test data on GitHub

To avoid bloating the workflow, we don’t keep test data in the same repository as nf-core workflows. Instead, we use the dedicated nf-core/test-datasets repository.

To set this up, make a fork of that repo to your personal account. Clone the repository and check out a new branch for your workflow:

git clone
cd test-datasets
git checkout -b MY_WORKFLOW
git clone
cd test-datasets
git checkout -b MY_WORKFLOW

Now add your test data files - note that they must be very small. GitHub has quite a low file size limit, and the GitHub Actions will time out with anything that’s not tiny. We typically use PhiX / Yeast / part of a chromosome as a reference and aggressively subsampled input data. I.e., as small as possible, as large as necessary. We highly recommend that you ask in the #test-data channel on the nf-core slack for advice before adding any test data!

Once added, push these new files to GitHub:

git add .
git commit -m "Added test data for MY_WORKFLOW"
git push --set-upstream origin MY_WORKFLOW
git add .
git commit -m "Added test data for MY_WORKFLOW"
git push --set-upstream origin MY_WORKFLOW

Finally, make a pull-request against the main nf-core/test-datasets repository with your files. You want this repo to also use a branch with the name of your workflow, so first go to the repository GitHub web page and create this new branch using the UI there. Once created, you can open a pull request and select this as the target branch.

Don’t forget to ask for help if you have any doubts! (Slack,alternatively if you already joined Slack you can go directly to the #help channel)

Setting up a test workflow

Now that your test data is hosted on the web, you can set up a test config profile in your workflow that points to it. A stub test profile should already exist in conf/test.config, so you just need to edit that file. Switch out the example URLs for the ones you added (view the files on GitHub and click ‘Raw’ to get the URL).

Add any other required parameters so that running the pipeline runs with as few extra flags as possible. Note that the test profile can be combined with other profiles such as docker or conda, so your config should not specify a hardware environment.

Have a go at running the pipeline and see if it works:

nextflow run MY_WORKFLOW -profile test,docker --outdir <OUTDIR>
nextflow run MY_WORKFLOW -profile test,docker --outdir <OUTDIR>

Note that if you do need to adjust this nextflow run command, you’ll need to update it in the .github/workflows/ YAML files too.

Adding your pipeline to the nf-core organisation

Ok, so you’re essentially finished. Your pipeline is written, the tests pass and you’re ready to add your workflow to nf-core.

First, go to the settings of your repository. Under the General page, in the ‘Danger Zone’ you should have an option to Transfer Ownership. Transfer this to the nf-core organisation.

You must make sure you are already a part of the nf-core organisation to allow transferring to nf-core. Alternatively you can add a core-team member to your repository, and ask them to do the transfer you.

Once transferred, go to the transferred repository on nf-core, and make a new fork back to your user name or organisation to continue development on a fork.

We prefer transferring over forking to nf-core. If we fork the original repository to nf-core whenever anyone opens a pull-request, it defaults to going to the original user’s fork of the repository, not the nf-core repo. In this case the only way to fix to request manual detachment from GitHub support.

Branch setup

All nf-core pipelines use branches called dev and master. The master branch should contain the code from the latest stable release, dev should have the latest development code. We want people to run the latest development code by default up until the first release. To do this, we set dev as the default repository branch. After an initial release is created, we set the default branch back to master so that the default action is to run the latest stable release code.

Once you have forked the repository, create a new branch called dev for the active development. In the repository settings, set dev to be the default branch.

Repository setup

Remember to configure the repository on the GitHub website with the following:

  • A description, the URL and lots of keywords!
  • Issues enabled, disable Wiki and Projects
  • A protected master branch that requires review and passing tests
  • Write permissions for nf-core/all and admin permissions for nf-core/admin

You can check that all of these settings are done correctly by referring to your pipeline in the nf-core Repository health web page. This reports the status of various checks and also has the option of fixing errors for you via the GitHub API.

Differences to your own fork

The main difference when working with the main nf-core fork of your workflow is that tests for pull-requests against the master branch will fail. This is because the master branch should only ever contain code from the last release. Instead, use the dev branch for new work and always make pull-requests against that. Then the tests should pass.

Making the first release

Reset the default branch

When the code is stable and ready for a release, set the master branch to be the default branch again.

Bump the version

At this point you should bump the version numbers on dev.

When developing the pipeline, the version numbers should be numeric with dev at the end. Use the nf-core bump-version command to do this - there are quite a few locations in the code that need updating and this ensures that it happens in the correct places.

When making a release, version numbers should all be numeric. Use nf-core lint --release when ready - this will check that everything looks correct. Pipeline release numbers MUST use Semantic Versioning.

Core pipeline review

Ok - now the tough bit - does your workflow stand up to the scrutiny of the nf-core team?! Not to worry, we’re a friendly bunch, just let us know about the new pipeline, when you’re ready, following the process below.

Make a pull-request from the dev branch to master on the nf-core fork. This is a special case and the tests should pass, and once they do you can request a review from the core team.

What happens next depends on the state of your master branch:

  • If you have developed in such a way that your master branch is clean, .i.e. doesn’t have any commits since the inital one, the PR created above will represent all changes associated with the proposed release, and the core team will use it for review and feedback.
  • If your master branch already contains changes associated with the release, the core team may merge your PR and create a pseudo-PR against the first commit in the pipeline. This gives the PR review interface showing all code that you’ve written.

In either case we will go through everything and request changes that we think are necessary until you’re good to go. Common things that are flagged at this point are:

  • A clear, short but descriptive readme
  • Good documentation, especially describing the output files and all parameters
  • Pipeline code

We typically tend to have two reviewers for most of the crucial code changes, e.g. adding new major features to an existing pipeline or making an entirely new pipeline release. You can also ping people from the nf-core core or maintainers team to review your pipeline code by @ing them.

Making the release

Once any requested changes have been made and the associated PR approved, you can go ahead with releasing the pipeline. Put in a basic changelog entry describing the general functionality at release. When you’re ready, follow the instructions in the nf-core release checklist. We recommend you also explicitly tag contributors with their GitHub handles, so each release on GitHub will display their icons.

The nf-core website and helper tools will automatically detect the new release and be updated accordingly.

That’s it, you’re finished! Congratulations!


If you wish to make a publication based on the pipeline, make sure to follow the recommended nf-core guidelines.

Subsequent releases

Once you’ve made your first release you can continue to work on your fork and make pull-requests against the dev branch on the nf-core repository. Now that we have a stable master branch, there should be reviews of each PR against dev before merging.

When ready to make new releases, make sure that the version number is increased and create a pull-request against master. If tests pass, it can be merged and a new release made.

The master branch should always have only the commit from the latest release. This is important because the commit ID is used to reference whether the pipeline is up to date or not.

Adding new pipeline features to existing pipelines

We are an open and inclusive community, welcoming any contributions to pipelines already present in nf-core. In many cases, the original developers might either not have experience with some new fancy method or simply doesn’t have the time to implement everything themselves - so they might be really happy to see you actively contributing!

Basic rules for such contributions:

  • Ask in the Slack channel for the specific pipeline whether there is an open issue on the respective pipeline’s issue tracker for the feature you’re planning to
  • If not, create a new issue there, describing the purpose and ideas you have and wait for someone to comment/discuss
  • If everyone is happy or there is some consensus in the community, start implementing the feature in your fork of the respective pipeline
  • Please do not write to multiple channels in the Slack community, rather collect all of the information in a single GitHub issue, which makes it also much easier to follow up on your proposal

Adding new dependencies to an existing pipeline

Sometimes, especially when adding new features to a pipeline, the dependencies change as well. In such cases, you might want to have an updated Docker Container available before submitting a pull request, in order to have the GitHub Actions tests run through when testing your updated code. To achieve that, please follow these steps:

  • Add only the newly required dependencies to the environment.yml in the pipeline code
  • If you only add new processes to an already existing pipeline however, you can simply specify the container in the nextflow.config file, like so:
process {
    withName:foo {
        container = 'image_name_1'
    withName:bar {
        container = 'image_name_2'
charliecloud {
    enabled = true
process {
    withName:foo {
        container = 'image_name_1'
    withName:bar {
        container = 'image_name_2'
charliecloud {
    enabled = true

An extensive guide on how to handle containers can be found here

  • List this new dependency as something new in the CHANGELOG
  • Create a Pull Request including only these two changes against the dev branch of the pipeline you’re working on

This way, a review process will be very fast and we can merge the changes into the dev branch, updating the Docker Image for that pipeline automatically. After ~30 Minutes, the Docker Image for that pipeline is then updated, and you can open your Pull Request containing your actual pipeline code changes.

nf-core pipeline structure


You will find the following files in each nf-core pipeline. They are automatically generated, when running nf-core create.

  • This is the main nextflow file which will get executed if the pipeline is run. Typically, parameters are initialized and validated in this script before a workflow from the workflows/ directory is called for execution.
  • nextflow.config: The main nextflow configuration file. It contains the default pipeline parameters, nextflow configuration options and information like pipeline and minimum nextflow version, among others. The nextflow.config also defines different configuration profiles that can be used to run the pipeline. See the Configuration docs for more information.
  • Basic information about the pipeline and usage

  • nextflow_schema.json: The JSON schema file is used for pipeline parameter specification. This is automatically created using the nf-core schema build command. It is used for printing command-line help, validating input parameters, building the website docs and for building pipeline launch interfaces (web and cli).

  • Information about the changes made to the pipeline for each release.

  • LICENSE: The license - should be MIT

  • The nf-core code of conduct.

  • All citations needed when using the pipeline

  • .gitattributes: Git settings, primarily getting the .config files to render with Nextflow syntax highlighting on <>

  • .gitignore: Files that should be ignored by git.

  • .editorconfig: Editorconfig file that helps assuring consistent coding style

  • .prettierrc.yml: Prettier lint configuration file to assure consistent markdown files

  • .prettierignore: Files that should be ignored by prettier

  • modules.json: This file holds information (e.g. version) about all the modules in the pipeline that have been installed from nf-core/modules

  • .nf-core.yml: Indicates the type of repository (pipeline or modules repo)

  • .gitpod.yml: Config file to define online working environment with

  • pyproject.toml: Config file for Python. Mostly used to configure linting of bin/ with Black


  • .devcontainer: Configuration to work with the GitHub Codespaces online editing environments.

  • .github/: Other GitHub specific files, e.g. for specifying templates and GitHub actions

  • assets/: Any additional files needed for the pipeline

  • bin/: Directory for scripts that must be directly accessible within a pipeline process. Anything in this directory can be directly called from within Nextflow processes.

  • conf/: Configuration files, including a base.config file which is always loaded into nextflow.config and describes basic pipeline configurations, like CPU and memory usage for processes with low, medium and high requirements. Additionally, most pipelines also have a igenomes.config file which describes the locations of popular genomes that can be automatically downloaded for a pipeline run. Finally, the test.config and test_full.config files are test configurations that are loaded during test runs. Since DSL2, it also contains a modules.config file, which defines module-specific configurations and is explained further down in the “DSL2 and modules” section.

  • docs/: Markdown files for documenting the pipeline

  • lib/: The lib directory contains Groovy utility functions. These are called from within the nf-core pipeline to do common pipeline tasks (e.g. parameter schema validation) and to hold Groovy functions that may be useful in the pipeline context (e.g. to validate pipeline-specific parameters). Currently, the following files are included:

    • NfcoreSchema.groovy - Functions to validate input parameters using the pipeline JSON schema
    • NfcoreTemplate.groovy - Additional nf-core specific pipeline functions (sending emails, checking nf-core config profiles and more)
    • Utils.groovy - Additional generic pipeline functions (checking conda config and more)
    • WorkflowMain.groovy - Startup functions for the main pipeline (printing logs, custom params initialisation and more)
    • WorkflowPipeline.groovy - Functions for pipeline subworkflows
    • nfcore_external_java_deps.jar - Bundled Groovy dependencies so that pipelines work offline (mostly for JSON schema validation - see imports in NfcoreSchema.groovy)
  • modules/: Contains pipeline-specific and common nf-core modules

  • workflows/: Contains the main pipeline workflows to be executed in the file

  • subworkflows/: Contains smaller subworkflows that typically consist out of a few modules chained together

Continuous integration testing

To assure that nf-core pipelines don’t break after some change is made to the code, we use automated continuous integration (CI) testing. This is done via GitHub actions, which are defined in the .github/workflows directory. Parameters and file paths are set in the conf/test.config and conf/test_full.config. Please see also here for how to set-up the test workflow for your pipeline.

DSL2 and modules

Nextflow DSL2 allows for a more modularized design of pipelines and the reuse of components. The nf-core team has developed a set of design patterns on how to best implement DSL2 pipelines, which should be used by all nf-core pipelines in order to assure standardization and the reuse of components. The following is meant to help understand certain design choices and how a nf-core DSL2 pipeline should be build.


Each pipeline has a modules directory which contains all the module code. A module here depicts a single process which involves - if possible - only a single tool/software. The modules directory is furthermore divided into localand nf-core sub-directories, where local contains the Modules contained in the local directory are specific to the pipeline, whereas nf-core modules are installed from the nf-core/modules repository. For instance, most pipelines that involve FastQ files will run the FastQC tool for quality control. The module needed for this can be easily reused from the nf-core/modules directory using the nf-core/toolspackage.

For more informations and a comprehensive guide on the guidelines of how to implement modules in pipelines please refer to the DSL 2 Modules page

Sample meta information

In nf-core DSL2 pipelines, every channel that contains sample data in some way should also contain a metavariable, which must contain the fields, meta.single_end and meta.strandedness. The meta variable can be passed down to processes as a tuple of the channel containing the actual samples, e.g. FastQ files, and the meta variable. This meta information can easily be extracted from a samplesheet which specifies the input files. For an example process that reads a samplesheet, creates a meta variable and returns it along with the filepaths, have a look at the rnaseq pipeline.