SparseIso: a novel Bayesian approach to identify alternatively spliced isoforms from RNA-seq data.

ABSTRACT:

Motivation

Recent advances in high-throughput RNA sequencing (RNA-seq) technologies have made it possible to reconstruct the full transcriptome of various types of cells. It is important to accurately assemble transcripts or identify isoforms for an improved understanding of molecular mechanisms in biological systems.

Results

We have developed a novel Bayesian method, SparseIso, to reliably identify spliced isoforms from RNA-seq data. A spike-and-slab prior is incorporated into the Bayesian model to enforce the sparsity for isoform identification, effectively alleviating the problem of overfitting. A Gibbs sampling procedure is further developed to simultaneously identify and quantify transcripts from RNA-seq data. With the sampling approach, SparseIso estimates the joint di

SUBMITTER: Shi X 

PROVIDER: S-EPMC5870564 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature