High-throughput RNA sequencing （RNA-seq） has emerged as a revolutionary and powerful technology for expression profiling. Most proposed methods for detect- ing differentially expressed （DE） genes from RNA-seq are ...High-throughput RNA sequencing （RNA-seq） has emerged as a revolutionary and powerful technology for expression profiling. Most proposed methods for detect- ing differentially expressed （DE） genes from RNA-seq are based on statistics that compare normalized read counts be- tween conditions. However, there are few methods consider- ing the expression measurement uncertainty into DE detec- tion. 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 mea- surement 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 mea- surement uncertainty improves accuracy in detection of DE genes and isoforms. Finally, we develop a GamSeq-BDSeq RNA-seq analysis pipeline to facilitate users.展开更多
文摘High-throughput RNA sequencing （RNA-seq） has emerged as a revolutionary and powerful technology for expression profiling. Most proposed methods for detect- ing differentially expressed （DE） genes from RNA-seq are based on statistics that compare normalized read counts be- tween conditions. However, there are few methods consider- ing the expression measurement uncertainty into DE detec- tion. 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 mea- surement 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 mea- surement uncertainty improves accuracy in detection of DE genes and isoforms. Finally, we develop a GamSeq-BDSeq RNA-seq analysis pipeline to facilitate users.
