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installation.md

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nf-core/rnaseq: Installation

To start using the nf-core/rnaseq pipeline, follow the steps below:

Install NextFlow

Nextflow runs on most POSIX systems (Linux, Mac OSX etc). It can be installed by running the following commands:

# Make sure that Java v8+ is installed:
java -version

# Install Nextflow
curl -fsSL get.nextflow.io | bash

# Add Nextflow binary to your PATH:
mv nextflow ~/bin/
# OR system-wide installation:
# sudo mv nextflow /usr/local/bin

See nextflow.io for further instructions on how to install and configure Nextflow.

Install the pipeline

Automatic

This pipeline itself needs no installation - NextFlow will automatically fetch it from GitHub if nf-core/rnaseq is specified as the pipeline name.

Offline

The above method requires an internet connection so that Nextflow can download the pipeline files. If you're running on a system that has no internet connection, you'll need to download and transfer the pipeline files manually:

wget https://github.com/nf-core/rnaseq/archive/master.zip
mkdir -p ~/my-pipelines/nf-core/
unzip master.zip -d ~/my-pipelines/nf-core/
cd ~/my_data/
nextflow run ~/my-pipelines/nf-core/rnaseq-master

To stop nextflow from looking for updates online, you can tell it to run in offline mode by specifying the following environment variable in your ~/.bashrc file:

export NXF_OFFLINE='TRUE'

Development

If you would like to make changes to the pipeline, it's best to make a fork on GitHub and then clone the files. Once cloned you can run the pipeline directly as above.

Pipeline configuration

By default, the pipeline loads a basic server configuration conf/base.config This uses a number of sensible defaults for process requirements and is suitable for running on a simple (if powerful!) local server.

Be warned of two important points about this default configuration:

  1. The default profile uses the local executor
    • All jobs are run in the login session. If you're using a simple server, this may be fine. If you're using a compute cluster, this is bad as all jobs will run on the head node.
    • See the nextflow docs for information about running with other hardware backends. Most job scheduler systems are natively supported.
  2. Nextflow will expect all software to be installed and available on the PATH
    • It's expected to use an additional config profile for docker, singularity or conda support. See below.

Docker

First, install docker on your system: Docker Installation Instructions

Then, running the pipeline with the option -profile docker tells Nextflow to enable Docker for this run. An image containing all of the software requirements will be automatically fetched and used from dockerhub (https://hub.docker.com/r/nfcore/rnaseq).

Singularity

If you're not able to use Docker then Singularity is a great alternative. The process is very similar: running the pipeline with the option -profile singularity tells Nextflow to enable singularity for this run. An image containing all of the software requirements will be automatically fetched and used from singularity hub.

Running offline

If running offline with Singularity, you'll need to download and transfer the Singularity image first:

singularity pull --name nf-core-rnaseq-1.3.img docker://nf-core/rnaseq:1.3

NB: The "tag" at the end of this command corresponds to the pipeline version. Here, we're pulling the docker image for version 1.3 of the nf-core/rnaseq pipeline Make sure that this tag corresponds to the version of the pipeline that you're using

Once transferred, use -with-singularity and specify the path to the image file:

nextflow run /path/to/nf-core-rnaseq -with-singularity /path/to/nf-core-rnaseq-1.3.img

Remember to pull updated versions of the singularity image if you update the pipeline.

Conda

If you're not able to use Docker or Singularity, you can instead use conda to manage the software requirements. This is slower and less reproducible than the above, but is still better than having to install all requirements yourself! The pipeline ships with a conda environment file and nextflow has built-in support for this. To use it first ensure that you have conda installed (we recommend miniconda), then follow the same pattern as above and use the flag -profile conda

Configuration profiles

See docs/configuration/adding_your_own.md

Reference genomes

See docs/configuration/reference_genomes.md