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bioRxiv · 10.1101/053306

VERSE: a versatile and efficient RNA-Seq read counting tool

Qin Zhu·Stephen A Fisher·Jamie Shallcross·Junhyong Kim
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Abstract

MotivationRNA-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.\n\nResultsIn 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.\n\nAvailabilityVERSE 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).\n\nContactjunhyong@sas.upenn.edu\n\nSupplementary informationTables and figures illustrating the counting modes implemented in VERSE and the differences between hierarchical and independent assignment.

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Qin Zhu, Stephen A Fisher, Jamie Shallcross, Junhyong Kim. 2016-05-14. VERSE: a versatile and efficient RNA-Seq read counting tool. https://doi.org/10.1101/053306

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