A network-based computational framework to predict and differentiate functions for gene isoforms using exon-level expression data.

ABSTRACT:

Motivation

Alternative splicing makes significant contributions to functional diversity of transcripts and proteins. Many alternatively spliced gene isoforms have been shown to perform specific biological functions under different contexts. In addition to gene-level expression, the advances of high-throughput sequencing offer a chance to estimate isoform-specific exon expression with a high resolution, which is informative for studying splice variants with network analysis.

Results

In this study, we propose a novel network-based analysis framework to predict isoform-specific functions from exon-level RNA-Seq data. In particular, based on exon-level expression data, we firstly propose a unified framework, referred to as Iso-Net, to integrate two new mathematical methods (name

SUBMITTER: Wang D 

PROVIDER: S-EPMC9628273 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature