May 14, 2016

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

BioRxiv : the Preprint Server for Biology
Qin ZhuJunhyong Kim

Abstract

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.

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Mentioned in this Paper

Exons
Genome
Genes
Cell Count
Gene Expression
Genomics
Bnk protein, Drosophila
Intergenic Region
Introns
Genome Sequencing

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