A Bayesian model selection approach for identifying differentially expressed transcripts from RNA sequencing data.

ABSTRACT: Recent advances in molecular biology allow the quantification of the transcriptome and scoring transcripts as differentially or equally expressed between two biological conditions. Although these two tasks are closely linked, the available inference methods treat them separately: a primary model is used to estimate expression and its output is post processed by using a differential expression model. In the paper, both issues are simultaneously addressed by proposing the joint estimation of expression levels and differential expression: the unknown relative abundance of each transcript can either be equal or not between two conditions. A hierarchical Bayesian model builds on the BitSeq framework and the posterior distribution of transcript expression and differential expression is inferred

SUBMITTER: Papastamoulis P 

PROVIDER: S-EPMC5763373 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature