Incorporating exon-exon junction reads enhances differential splicing detection

MotivationRNA sequencing (RNA-seq) is a gold standard technology for studying gene and transcript expression. Different transcripts from the same gene are usually determined by varying combinations of exons within the gene, formed by splicing events. One method of studying differential alternative splicing between groups in short-read RNA-seq experiments is through differential exon usage (DEU) analyses, which use exon-level read counts along with downstream statistical testing strategies. Popular pipelines for these analyses include Rsubread for read alignment and exon-level count quantification, and edgeR and limma for statistical testing of differential splicing. However, the standard Rsubread::featureCounts count summarization method does not consider exon-junction information, which may reduce the statistical power in differential splicing analyses. ResultsA new feature quantification approach is proposed to incorporate both exon and exon-junction reads into the popular Rsubread-edgeR and Rsubread-limma frameworks for detecting differential splicing events. This new Differential Exon-Junction Usage (DEJU) analysis pipeline demonstrates increased statistical power compared to existing popular methods while effectively controlling the false discovery rate. Availability and implementationData and code to reproduce the results shown in this article are available from https://github.com/TamPham271299/DEJU.