All nf-core pipelines use Nextflow, so this must be installed on the system where you launch your analysis.

We recommend using a personal installation of Nextflow where possible, instead of using a system-wide installation. This makes it easier to update and control versions.

We generally recommend following the official installation instructions from the Nextflow documentation. However on this page we provide ‘quick start’ version of these instructions, as well as instructions for installing via Conda, and on Windows operating systems.

If in doubt, see the official Nextflow installation documentation. Any instructions on this page are provided for convinence, and may not be up-to-date.


You don’t need to install the nf-core command line tools to run nf-core pipelines, you only need Nextflow. However, they offer a number of helpful functions and are essential for pipeline development using nf-core components. See the tools page for more information.

Official Nextflow installation.

The Nextflow installation docs can be found here for the latest instructions.

Quick-start installation

Nextflow runs on most POSIX systems (Linux, macOS, etc) and can typically be installed by running these commands:

java -version                           # Check that Java v11+ is installed
curl -s | bash  # Download Nextflow
chmod +x nextflow                       # Make executable
mv nextflow ~/bin/                      # Add to user's $PATH

Bioconda installation

You can also install Nextflow using Bioconda. First, set up Bioconda according to the Bioconda documentation, notably setting up channels:

conda config --add channels bioconda
conda config --add channels conda-forge

This is important - if not done, dependencies (such as Java) will be installed from the wrong channels and things may break in strange ways.

A best practice with conda is to use a dedicated conda environment. This can help to prevent version conflicts and keep everything clean:

conda create --name env_nf nextflow
conda activate env_nf

To deactivate the conda environment, run:

conda deactivate

Installation on Windows

For Windows the installation procedure is more complex and is fully described on the Nextflow website.

The main steps will be the following:

  • Install Windows PowerShell
  • Configure the Windows Subsystem for Linux (WSL2)
  • Install a Linux distribution (on WSL2)

The step to install Nextflow in itself will afterwards be the same as previously mentioned.

Updating Nextflow

Updating nextflow is as simple as running:

nextflow self-update

or conda update nextflow, depending on how it was installed.

Specific Nextflow versions

You can install a specific version of Nextflow by using the NXF_VER environment version. Nextflow will automatically install that version at the start of the next command (be it nextflow self-update or nextflow run).

You can export this bash env variable, which is good to do in analysis scripts for full reproducibility:

export NXF_VER=23.10.1
nextflow run hello-world

Or even just prepend it to a Nextflow command:

NXF_VER=23.10.1 nextflow run hello-world

Edge releases

Nextflow has two stable releases per year and monthly edge releases. Some pipelines may require the use of Nextflow edge releases in order to exploit cutting edge features.

Nextflow installs the latest stable version by default. You can get an edge release either by defining the exact version with NXF_VER or by using the NXF_EDGE environment variable:

NXF_VER=24.02.0-edge nextflow run hello-world
NXF_EDGE=1 nextflow self-update

Pipeline software

An analysis pipeline chains the execution of multiple tools together. Historically, all tools would have to be manually installed — often a source of great frustration and a key step where reproducibility between analyses is lost. nf-core pipelines utilise the built-in support for software packaging that Nextflow offers: all can work with Docker and Singularity, and most pipelines also support Conda.

To use any of the below, simply run your nf-core pipeline with -profile <type>. For example, -profile docker or -profile conda.

  • Docker

    • Typically used locally, on single-user servers, and the cloud
    • Analysis runs in a container, which behaves like an isolated operating system
    • Typically requires system root access, though a “rootless mode” is available
  • Singularity

    • Often used as an alternative to Docker on HPC systems
    • Also runs containers, and can optionally create these from Docker images
    • Does not need root access or any daemon processes
  • Apptainer

    • Open source version of Singularity (split from Singularity in 2021)

    • Warning

      Currently, nf-core pipelines run with -profile apptainer will build using docker containers instead of using pre-built singularity containers.

      To use the singularity containers, use -profile singularity instead. This works because apptainer simply defines singularity as an alias to the apptainer command.

  • Podman, Charliecloud and Shifter

    • All alternatives to Docker, often used on HPC systems
  • Conda

    • Packaging system that manages environments instead of running analysis in containers
    • Poorer reproducibility than Docker / Singularity
      • There can be changes in low-level package dependencies over time
      • The software still runs in your native operating system environment and so core system functions can differ
  • Mamba

    • A faster reimplementation of Conda

Pipeline code


The pipeline needs no installation - Nextflow will automatically fetch it from GitHub if nf-core/<pipeline-name> is specified as the pipeline name.

This method requires an internet connection. If you’re running on a system that has no internet connection, please see Running Offline.


If you would like to make changes to the pipeline, fork the GitHub repository and then clone the files. Once cloned, you can run the pipeline with nextflow run <path-to-repo>.

Note that you should only do this if you intend to make significant changes to the pipeline. All configuration options can be changed without editing the pipeline code. Forking the pipeline repositories means that you cannot use stable releases and you will fall behind new updates.