RSeQC package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. Some basic modules quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while RNA-seq specific modules evaluate sequencing saturation, mapped reads distribution, coverage uniformity, strand specificity, transcript level RNA integrity etc.

References:

• https://academic.oup.com/bioinformatics/article/28/16/2184/325191
• https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-0922-z

• http://rseqc.sourceforge.net/

• Module Name: rseqc (see the modules page for more information)

Allocate an interactive session and run the program. Sample session:

[user@biowulf]$ sinteractive
salloc.exe: Pending job allocation 46116226
salloc.exe: job 46116226 queued and waiting for resources
salloc.exe: job 46116226 has been allocated resources
salloc.exe: Granted job allocation 46116226
salloc.exe: Waiting for resource configuration
salloc.exe: Nodes cn3144 are ready for job

[user@cn3144 ~]$ module load rseqc
[user@cn3144 ~]$ bam_stat.py -i input.bam $> output

[user@cn3144 ~]$ exit
salloc.exe: Relinquishing job allocation 46116226
[user@biowulf ~]$

Create a batch input file (e.g. rseqc.sh). For example:

#!/bin/bash
set -e
module load rseqc
bam_stat.py -i input.bam > output

Submit this job using the Slurm sbatch command.

sbatch [--mem=#] rseqc.sh

Create a swarmfile (e.g. rseqc.swarm). For example:

cd dir1;bam_stat.py -i input.bam > output
cd dir2;bam_stat.py -i input.bam > output
cd dir3;bam_stat.py -i input.bam > output

Submit this job using the swarm command.

swarm -f rseqc.swarm [-g #] --module rseqc