Guan Lab

Department of Computational Medicine & Bioinformatics
Thu, 01/16/2014 - 11:47 -- gyuanfan
TitleSystematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq data.
Publication TypeJournal Article
Year of Publication2013
AuthorsEksi R, Li H-D, Menon R, Wen Y, Omenn GS, Kretzler M, Guan Y
JournalPLoS Comput Biol
Volume9
Issue11
Paginatione1003314
Date Published2013 Nov
ISSN1553-7358
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 is effective in assigning functions to genes, especially the ones with multiple isoforms, and robust to gene expression levels and removal of homologous gene pairs. We identified genes in the mouse whose isoforms are predicted to have disparate functionalities and experimentally validated the 'responsible' isoforms using data from mammary tissue. With protein structure modeling and experimental evidence, we further validated the predicted isoform functional differences for the genes Cdkn2a and Anxa6. Our generic framework is the first to predict and differentiate functions for alternatively spliced isoforms, instead of genes, using genomic data. It is extendable to any base machine learner and other species with alternatively spliced isoforms, and shifts the current gene-centered function prediction to isoform-level predictions.

DOI10.1371/journal.pcbi.1003314
Alternate JournalPLoS Comput. Biol.
PubMed ID24244129
PubMed Central IDPMC3820534
Grant ListNIH 1R21NS082212-01 / NS / NINDS NIH HHS / United States
R21 NS082212 / NS / NINDS NIH HHS / United States