  High-throughput RNA sequencing (RNA-seq) has emerged as a revolutionary and
powerful technology for expression profiling. Most proposed methods for
detecting differentially expressed (DE) genes from RNA-seq are based on
statistics that compare normalized read counts between conditions. However,
there are few methods considering the expression measurement uncertainty into
DE detection. Moreover, most methods are only capable of detecting DE genes,
and few methods are available for detecting DE isoforms. In this paper, a
Bayesian framework (BDSeq) is proposed to detect DE genes and isoforms with
consideration of expression measurement uncertainty. This expression
measurement uncertainty provides useful information which can help to improve
the performance of DE detection. Three real RAN-seq data sets are used to
evaluate the performance of BDSeq and results show that the inclusion of
expression measurement uncertainty improves accuracy in detection of DE genes
and isoforms. Finally, we develop a GamSeq-BDSeq RNA-seq analysis pipeline to
facilitate users, which is freely available at the website
http://parnec.nuaa.edu.cn/liux/GSBD/GamSeq-BDSeq.html.
