rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data

TLDR

Alternative splicing is a key mechanism of eukaryotic gene regulation, and deep RNA‑sequencing has become a powerful tool for its quantitative profiling, especially as studies increasingly include multiple biological replicates. The authors developed rMATS to robustly and flexibly detect differential alternative splicing from replicate RNA‑Seq data. rMATS employs a statistical framework that handles both unpaired and paired replicates, including a model tailored for case–control matched pairs in clinical datasets. rMATS is expected to be useful for genome‑wide AS studies and provides new insights into optimal experimental design for RNA‑Seq studies of alternative splicing.

Abstract

Significance Alternative splicing (AS) is an important mechanism of eukaryotic gene regulation. Deep RNA sequencing (RNA-Seq) has become a powerful approach for quantitative profiling of AS. With the increasing capacity of high-throughput sequencers, it has become common for RNA-Seq studies of AS to examine multiple biological replicates. We developed rMATS, a new statistical method for robust and flexible detection of differential AS from replicate RNA-Seq data. Besides the analysis of unpaired replicates, rMATS includes a model specifically designed for paired replicates, such as case–control matched pairs in clinical RNA-Seq datasets. We expect rMATS will be useful for genome-wide studies of AS in diverse research projects. Our data also provide new insights about the experimental design for RNA-Seq studies of AS.