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elyadlezmi / RNA2CM

Pipeline for the identification of cancer-related mutations from RNA-seq data
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RNA2CM

RNA2CM is a tool for the identification of cancer-related mutations from RNA-seq data

Pre-requisites

Nextflow and Docker (or Singularity for execution on SLURM-clusters) are the only pre-requisites for the RNA2CM tool. Install both if needed and make sure they are properly running on your system. If the following commands do not generate any error message you are good to go.

nextflow run hello  # test that nextflow is working
docker run hello-world  # test that docker is working if you are working on a local workstation
singularity help  # test that singularity is working in case your are working on a SLURM cluster

Installation

  1. Download the project directory:

    git clone https://github.com/elyadlezmi/RNA2CM.git  # clone the project using git

  2. Download the files CosmicMutantExportCensus.tsv.gz and CosmicCodingMuts.vcf.gz from the COSMIC website (https://cancer.sanger.ac.uk/cosmic/download, login required), then move both files into the project’s subdirectory named data (RNA2CM/data).

  3. Execute the script named setup.nf which is responsible for setting up all the reference data and will complete the installation (this might take a while).

    nextflow run /path/to/RNA2CM/setup.nf  # run the installation script

    The setup.nf script can take three optional arguments:

-profile: Choose the executor profile between a standard dockerized usage on a local workstation or usage on a SLURM cluster (requires Singularity instead of Docker) (standard/cluster, default: standard).

--cpu: The number of threads for multi-threading (int, default 8).

--readLength: The expected Illumina read length for optimal alignment by STAR (int, default 100).

Usage

nextflow run /path/to/RNA2CM --fastq your_sample.fastq.gz  # for single-end reads
nextflow run /path/to/RNA2CM --fastq your_sample_1.fastq.gz --fastq2 your_sample_2.fastq.gz  # for paired-ends reads

Optional arguments (Note that the only required arguments are RNA-seq reads, output is generated into the working directory):

-profile: (standard/cluster, default: standard).

--cpu: (int, default 8).

--prefix: Output files have standard names but a custom prefix can be added (str)

--keepInter: Whether to keep intermediate alignment and VCF files (true/false, default: false).

--filterMouse: Whether to perform mouse contamination cleanup (true/false, default true).

Example for a paired-ends RNA-seq run, using 4 CPUs, keeping intermediate files:

nextflow run /path/to/RNA2CM --fastq esc_1.fastq.gz --fastq2 esc_2.fastq.gz --cpu 4 --keepInter true

Example for a single-end RNA-seq run, skipping mouse read filtration and running on a SLURM cluster (nextflow manages batch jobs, so no need to use sbatch):

nextflow run /path/to/RNA2CM --fastq SRR1234567.fastq.gz --filterMouse false -profile cluster