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

Journal: Methods (San Diego, Calif.)
Published Date:

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.

Authors

  • Dingjie Wang
    Department of Biomedical Informatics, The Ohio State University, OH 43210, USA; School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China; Computational Science Hubei Key Laboratory, Wuhan University, Wuhan 430072, China.
  • Xiufen Zou
  • Kin Fai Au
    Department of Biomedical Informatics, The Ohio State University, OH 43210, USA. Electronic address: kinfai.au@osumc.edu.