ARH-seq: identification of differential splicing in 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 amongst 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. ARH-seq works on high-throughput sequencing data and is an extension of the ARH method that was originally developed for exon microarrays. We show that the method has inherent features, such as independence of transcript exon number and independence of differential expression, what makes it particul

SUBMITTER: Rasche A 

PROVIDER: S-EPMC4132698 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature