Detecting differential usage of exons from RNA-seq data

• DOI: 10.1101/gr.133744.111
• Publication Date: 2012-06-22T16:22:07Z
• Authors: Simon Anders; Alejandro Reyes; Wolfgang Huber
• Keywords: 0301 basic medicine; 0303 health sciences; Models, Genetic; Pan troglodytes; Bioinformatics; Method; Computational Biology; Exons; Genomics; Genetics & Genomics; Cell Line; Alternative Splicing; 03 medical and health sciences; Animals; Humans; RNA; Databases, Nucleic Acid; Algorithms

RNA-seq is a powerful tool for the study of alternative splicing and other forms of alternative isoform expression. Understanding the regulation of these processes requires sensitive and specific detection of differential isoform abundance in comparisons between conditions, cell types, or tissues. We presentDEXSeq, a statistical method to test for differential exon usage in RNA-seq data.DEXSequses generalized linear models and offers reliable control of false discoveries by taking biological variation into account.DEXSeqdetects with high sensitivity genes, and in many cases exons, that are subject to differential exon usage. We demonstrate the versatility ofDEXSeqby applying it to several data sets. The method facilitates the study of regulation and function of alternative exon usage on a genome-wide scale. An implementation ofDEXSeqis available as an R/Bioconductor package.