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htseq-count-cluster

A cli wrapper for running htseq’s htseq-count on a cluster.

View documentation.

Install

Requires Python 3.9 or higher.

pipinstallHTSeqCountCluster

Features

  • For use with large datasets (we’ve previously used a dataset of 120 different human samples)

  • For use with SGE/SGI cluster systems

  • Submits multiple jobs

  • Command line interface/script

  • Merges counts files into one counts table/csv file

  • Uses accepted_hits.bam file output of tophat

Examples

Run htseq-count-cluster

After generating bam output files from tophat, instead of using HTSeq’s htseq-count, you can use our htseq-count-cluster script. This script is intended for use with clusters that are using pbs (qsub) for job monitoring.

Our default htseq-count command is htseq-count -f bam -s no file.bam file.gtf -o htseq.out. This command does not take into account any strandedness (-s no) for the input bamfiles (-f bam) and uses the default union mode. For the default mode union, only the aligned read determines how the read pair is counted.

Legacy mode (still supported):

htseq-count-cluster-ppath/to/bam-files/-fsamples.csv-ggenes.gtf-opath/to/cluster-output/

New subcommand mode:

htseq-count-clusterrun-ppath/to/bam-files/-fsamples.csv-ggenes.gtf-opath/to/cluster-output/

Argument

Description

Required

-p

This is the path of your .bam files. Presently, this script looks for a folder that is the sample name and searches for an accepted_hits.bam file (tophat output).

Yes

-f

You should have a csv file list of your samples or folder names (no header).

Yes

-g

This should be the path to your genes.gtf file.

Yes

-o

This should be an existing directory for your output counts files.

Yes

-e

Email address to send script completion notifications to.

No

This script uses logzero so there will be color coded logging information to your shell.

A common linux practice is to use screen to create a new shell and run a program so that if it does produce output to the stdout/shell, the user can exit that particular shell without the program ending and utilize another shell.

Help message output for htseq-count-cluster
usage: htseq-count-cluster [-h] COMMAND ...
This is a command line wrapper around htseq-count.
positional arguments:
 COMMAND
 run Run htseq-count jobs on a cluster
 merge Merge multiple counts tables into one CSV file
optional arguments:
 -h, --help show this help message and exit
*Ensure that htseq-count is in your path.

For the run subcommand:

usage: htseq-count-cluster run [-h] -p INPATH -f INFILE -g GTF -o OUTPATH [-e EMAIL]
Submit multiple htseq-count jobs to a cluster.
optional arguments:
 -h, --help show this help message and exit
 -p INPATH, --inpath INPATH
 Path of your samples/sample folders.
 -f INFILE, --infile INFILE
 Name or path to your input csv file.
 -g GTF, --gtf GTF Name or path to your gtf/gff file.
 -o OUTPATH, --outpath OUTPATH
 Directory of your output counts file. The counts file
 will be named.
 -e EMAIL, --email EMAIL
 Email address to send script completion to.

Merge output counts files

In order to prep your data for DESeq2, limma or edgeR, it’s best to have 1 merged counts file instead of multiple files produced from the htseq-count-cluster script.

Using the merge subcommand:

htseq-count-clustermerge-dpath/to/cluster-output/

Or using the standalone command (still available):

merge-counts-dpath/to/cluster-output/
Help message for merge subcommand
usage: htseq-count-cluster merge [-h] -d DIRECTORY
Merge multiple counts tables into 1 counts .csv file.
Your output file will be named: merged_counts_table.csv
optional arguments:
 -h, --help show this help message and exit
 -d DIRECTORY, --directory DIRECTORY
 Path to folder of counts files.

ToDo

  • Monitor jobs.

  • Enhance wrapper input for other use cases.

  • Add example data.

Maintainers

Shaurita Hutchins | @sdhutchins |
Rob Gilmore | @grabear |

Help

Please feel free to open an issue if you have a question/feedback/problem or submit a pull request to add a feature/refactor/etc. to this project.

Citation

Simon Anders, Paul Theodor Pyl, Wolfgang Huber; HTSeq—a Python framework to work with high-throughput sequencing data, Bioinformatics, Volume 31, Issue 2, 15 January 2015, Pages 166–169, https://doi.org/10.1093/bioinformatics/btu638

Documentation

Indices and tables