Detecting differential expression from RNA-seq data with expression measurement uncertainty  

作　　者：LiZHANG Songcan CHEN Xuejun LIU 

机构地区：[1]College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

出　　处：《Frontiers of Computer Science》2015年第4期652-663,共12页中国计算机科学前沿（英文版）

摘　　要：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.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.

关 键 词：RNA-SEQ Bayesian method differentially ex-pressed genes/isoforms expression measurement uncer-tainty analysis pipeline 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TB9[自动化与计算机技术—控制科学与工程]