Systematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq data.

ABSTRACT: Integrating large-scale functional genomic data has significantly accelerated our understanding of gene functions. However, no algorithm has been developed to differentiate functions for isoforms of the same gene using high-throughput genomic data. This is because standard supervised learning requires 'ground-truth' functional annotations, which are lacking at the isoform level. To address this challenge, we developed a generic framework that interrogates public RNA-seq data at the transcript level to differentiate functions for alternatively spliced isoforms. For a specific function, our algorithm identifies the 'responsible' isoform(s) of a gene and generates classifying models at the isoform level instead of at the gene level. Through cross-validation, we demonstrated that our algorithm

SUBMITTER: Eksi R 

PROVIDER: S-EPMC3820534 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature