adding_your_own.md

radseqQC: Configuration for other clusters

It is entirely possible to run this pipeline on other clusters, though you will need to set up your own config file so that the pipeline knows how to work with your cluster.

If you think that there are other people using the pipeline who would benefit from your configuration (eg. other common cluster setups), please let us know. We can add a new configuration and profile which can used by specifying -profile <name> when running the pipeline.

If you are the only person to be running this pipeline, you can create your config file as ~/.nextflow/config and it will be applied every time you run Nextflow. Alternatively, save the file anywhere and reference it when running the pipeline with -c path/to/config (see the Nextflow documentation for more).

A basic configuration comes with the pipeline, which runs by default (the standard config profile - see conf/base.config). This means that you only need to configure the specifics for your system and overwrite any defaults that you want to change.

Cluster Environment

By default, pipeline uses the local Nextflow executor - in other words, 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.

To specify your cluster environment, add the following line to your config file:

process {
  executor = 'YOUR_SYSTEM_TYPE'
}

Many different cluster types are supported by Nextflow. For more information, please see the Nextflow documentation.

Note that you may need to specify cluster options, such as a project or queue. To do so, use the clusterOptions config option:

process {
  executor = 'SLURM'
  clusterOptions = '-A myproject'
}

Software Requirements

To run the pipeline, several software packages are required. How you satisfy these requirements is essentially up to you and depends on your system. If possible, we highly recommend using either Docker or Singularity.

Docker

Docker is a great way to run radseqQC, as it manages all software installations and allows the pipeline to be run in an identical software environment across a range of systems.

Nextflow has excellent integration with Docker, and beyond installing the two tools, not much else is required.

First, install docker on your system: Docker Installation Instructions

Then, simply run the analysis pipeline:

nextflow run remiolsen/nf-core-radseq -profile docker --reads '<path to your reads>'

Nextflow will recognise remiolsen/nf-core-radseq and download the pipeline from GitHub. The -profile docker configuration lists the remiolsen/nf-core-radseq image that we have created and is hosted at dockerhub, and this is downloaded.

The public docker images are tagged with the same version numbers as the code, which you can use to ensure reproducibility. When running the pipeline, specify the pipeline version with -r, for example -r v1.3. This uses pipeline code and docker image from this tagged version.

To add docker support to your own config file (instead of using the docker profile, which runs locally), add the following:

docker {
  enabled = true
}
process {
  container = wf_container
}

The variable wf_container is defined dynamically and automatically specifies the image tag if Nextflow is running with -r.

A test suite for docker comes with the pipeline, and can be run by moving to the tests directory and running ./run_test.sh. This will download a small yeast genome and some data, and attempt to run the pipeline through docker on that small dataset. This is automatically run using Travis whenever changes are made to the pipeline.

Singularity image

Many HPC environments are not able to run Docker due to security issues. Singularity is a tool designed to run on such HPC systems which is very similar to Docker. Even better, it can use create images directly from dockerhub.

To use the singularity image for a single run, use -with-singularity 'docker://remiolsen/nf-core-radseq'. This will download the docker container from dockerhub and create a singularity image for you dynamically.

To specify singularity usage in your pipeline config file, add the following:

singularity {
  enabled = true
}
process {
  container = "docker://$wf_container"
}

The variable wf_container is defined dynamically and automatically specifies the image tag if Nextflow is running with -r.

If you intend to run the pipeline offline, nextflow will not be able to automatically download the singularity image for you. Instead, you'll have to do this yourself manually first, transfer the image file and then point to that.

First, pull the image file where you have an internet connection:

singularity pull --name nf-core-radseq.img docker://remiolsen/nf-core-radseq

Then transfer this file and run the pipeline with this path:

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

Manual Installation

As a last resort, you may need to install the required software manually. We recommend using Bioconda to do this. The following instructions are an example only and will not be updated with the pipeline.

1) Install miniconda in your home directory

cd
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

2) Add the bioconda conda channel (and others)

conda config --add channels anaconda
conda config --add channels conda-forge
conda config --add channels defaults
conda config --add channels r
conda config --add channels bioconda
conda config --add channels salilab

3) Create a conda environment, with all necessary packages:

conda create --name nf-core-radseq_py2.7 python=2.7
source activate nf-core-radseq_py2.7
conda install --yes \
    fastqc \
    multiqc

(Feel free to adjust versions as required.)

4) Usage

Once created, the conda environment can be activated before running the pipeline and deactivated afterwards:

source activate nf-core-radseq_py2.7
# run pipeline
source deactivate