STAR task for RNA-seq

This task provides a convenience wrapper around the STAR aligner. It aligns FASTQ reads to a given transcriptome, sorts the resulting BAMs for input into RSEM, and performs flagstat.

Running

We encourage running this task as a Docker image, which is publicly available through GitHub packages. To pull the image, first install Docker, then run

docker pull docker.pkg.github.com/krews-community/rnaseq-star-task/rnaseq-star:latest

The task requires a pre-built STAR index to be provided as a tarball. The STAR indexes available at ENCODE (for example ENCFF912WCJ) are compatible with this task. Alternatively, you can package a STAR index you have built into a compatible tarball by running

tar -cf index.tar -C /path/to/directory/with/index/files .

Then, to align FASTQs, simply run:

docker run \
    --volume /path/to/inputs:/input \
    --volume /path/to/outputs:/output \
    docker.pkg.github.com/krews-community/rnaseq-star-task/rnaseq-star:latest \
    java -jar /app/star.jar --r1 /input/r1.fastq.gz --r2 /input/r2.fastq.gz \
        --index /input/index.tar --output-directory /output

This will produce an output directory containing reads aligned to the genome with associated flagstat QC (output_genome.bam) and reads aligned to the transcriptome with associated flagstat QC (output_anno.bam).

Parameters

This task supports several parameters:

name	description	default
r1	FASTQ containing single-end reads or reads for pair 1	(required)
r2	FASTQ containing reads for pair 2 in a paired-end experiment	(none)
index	path to index tarball; may be gzipped or not	(required)
output-directory	path to output directory	(required)
library-id	name to assign to reads in the BAM file	(empty string)
output-prefix	prefix to use when naming output files	output
cores	number of cores available to the task	1
ram-gb	gigabytes of RAM available to the task	16

For developers

The task provides integrated unit testing, which runs a simple alignment of a small number of reads to a human mitochondrial index and checks that the outputs match expected values. To run the tests, first install Docker and Java, then clone this repo, then run

scripts/test.sh

Contributions to the code are welcome via pull request.