Bayesian nonparametric discovery of isoforms and individual specific quantification.

ABSTRACT: Most human protein-coding genes can be transcribed into multiple distinct mRNA isoforms. These alternative splicing patterns encourage molecular diversity, and dysregulation of isoform expression plays an important role in disease etiology. However, isoforms are difficult to characterize from short-read RNA-seq data because they share identical subsequences and occur in different frequencies across tissues and samples. Here, we develop BIISQ, a Bayesian nonparametric model for isoform discovery and individual specific quantification from short-read RNA-seq data. BIISQ does not require isoform reference sequences but instead estimates an isoform catalog shared across samples. We use stochastic variational inference for efficient posterior estimates and demonstrate superior precision and rec

SUBMITTER: Aguiar D 

PROVIDER: S-EPMC5923247 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature