Discerning novel splice junctions derived from RNA-seq alignment: a deep learning approach.

ABSTRACT:

Background

Exon splicing is a regulated cellular process in the transcription of protein-coding genes. Technological advancements and cost reductions in RNA sequencing have made quantitative and qualitative assessments of the transcriptome both possible and widely available. RNA-seq provides unprecedented resolution to identify gene structures and resolve the diversity of splicing variants. However, currently available ab initio aligners are vulnerable to spurious alignments due to random sequence matches and sample-reference genome discordance. As a consequence, a significant set of false positive exon junction predictions would be introduced, which will further confuse downstream analyses of splice variant discovery and abundance estimation.

Results

In this work, we presen

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC6307148 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature