Information-theoretic approach for detection of differential splicing from RNA-seq data

Abstract

The computational prediction of alternative splicing from high-throughput sequencing data is inherently difficult and necessitates robust statistical measures because the differential splicing signal is overlaid by influencing factors such as gene expression differences and simultaneous expression of multiple isoforms among others. In this work we describe ARH-seq, a discovery tool for differential splicing in case-control studies that is based on the information-theoretic concept of entropy.