PT  - JOURNAL ARTICLE
AU  - Qin Zhu
AU  - Stephen A Fisher
AU  - Jamie Shallcross
AU  - Junhyong Kim
TI  - VERSE: a versatile and efficient RNA-Seq read counting tool
AID  - 10.1101/053306
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 053306
4099  - http://biorxiv.org/content/early/2016/05/14/053306.short
4100  - http://biorxiv.org/content/early/2016/05/14/053306.full
AB  - Motivation RNA-Seq is a powerful technology that delivers digital gene expression data. To measure expression strength at the gene level, one popular approach is direct read counting after aligning the reads to a reference genome/transcriptome. HTSeq is one of the most popular ways of counting reads, yet its slow running speed of poses a bottleneck to many RNA-Seq pipelines. Gene level counting programs also lack a robust scheme for quantifying reads that map to non-exonic genomic features, such as intronic and intergenic regions, even though these reads are prevalent in most RNA-Seq data.Results In this paper we present VERSE, an RNA-Seq read counting tool which builds upon the speed of featureCounts and implements the counting modes of HTSeq. VERSE is more than 30x faster than HTSeq when computing the same gene counts. VERSE also supports a hierarchical assignment scheme, which allows reads to be assigned uniquely and sequentially to different types of features according to user-defined priorities.Availability VERSE is implemented in C. It is built on top of featureCounts. VERSE is open source and can be downloaded freely from Github (https://github.com/qinzhu/VERSE).Contact junhyong{at}sas.upenn.eduSupplementary information Tables and figures illustrating the counting modes implemented in VERSE and the differences between hierarchical and independent assignment.