High-Performance Computing at the NIH
GitHub YouTube @nih_hpc RSS Feed
RSeQC on Biowulf & Helix

RSeQC package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. “Basic modules” quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while “RNA-seq specific modules” investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation

Running on Helix

$ module load rseqc
$ cd /data/$USER/dir
$ bam_stat.py -i myfile.bam &> output

Running a single batch job on Biowulf

1. Create a script file. The file will contain the lines similar to the lines below.


module load rseqc
cd /data/$USER/dir
bam_stat.py -i myfile.bam &> output

2. Submit the script on biowulf:

$ sbatch jobscript

If more momory is required (default 4gb), specify --mem=Mg, for example --mem=10g:

$ sbatch --mem=10g jobscript

Running a swarm of jobs on Biowulf

Setup a swarm command file:

  cd /data/$USER/dir1; bam_stat.py -i myfile.bam &> output
  cd /data/$USER/dir2; bam_stat.py -i myfile.bam &> output
  cd /data/$USER/dir3; bam_stat.py -i myfile.bam &> output

Submit the swarm file, -f specify the swarmfile name, and --module will be loaded the required module for each command line in the file:

  $ swarm -f swarmfile --module rseqc

If more memory is needed for each line of commands, the below example allocate 10g for each command:

  $ swarm -f swarmfile -g 10 --module rseqc

For more information regarding running swarm, see swarm.html

Running an interactive job on Biowulf

It may be useful for debugging purposes to run jobs interactively. Such jobs should not be run on the Biowulf login node. Instead allocate an interactive node as described below, and run the interactive job there.

biowulf$ sinteractive 
salloc.exe: Granted job allocation 16535

cn999$ module load rseqc
cn999$ cd /data/$USER/dir
cn999$ bam_stat.py -i myfile.bam &> output

cn999$ exit


Make sure to exit the job once finished.

If more memory is needed, use --mem. For example

biowulf$ sinteractive --mem=8g