• Systematically differentiating...

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

Title:
Systematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq data.
Authors:
Eksi R; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America., Li HD, Menon R, Wen Y, Omenn GS, Kretzler M, Guan Y
Source:
PLoS computational biology [PLoS Comput Biol] 2013; Vol. 9 (11), pp. e1003314. Date of Electronic Publication: 2013 Nov 07.
Publication Type:
Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
Language:
English
Journal Info:
Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE
Imprint Name(s):
Original Publication: San Francisco, CA : Public Library of Science, [2005]-
MeSH Terms:
Alternative Splicing/*physiology , Computational Biology/*methods , Protein Isoforms/*genetics , Protein Isoforms/*physiology, Algorithms ; Animals ; Cluster Analysis ; Databases, Protein ; Humans ; Mice ; Models, Molecular ; Protein Isoforms/chemistry ; RNA/chemistry ; RNA/genetics ; RNA/physiology ; Reproducibility of Results ; Sequence Analysis, RNA
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Grant Information:
P30 DK081943 United States DK NIDDK NIH HHS; R21 NS082212 United States NS NINDS NIH HHS; R35 GM133346 United States GM NIGMS NIH HHS; NIH 1R21NS082212-01 United States NS NINDS NIH HHS
Substance Nomenclature:
0 (Protein Isoforms)
63231-63-0 (RNA)
Entry Date(s):
Date Created: 20131119 Date Completed: 20140714 Latest Revision: 20211021
Update Code:
20260130
PubMed Central ID:
PMC3820534
DOI:
10.1371/journal.pcbi.1003314
PMID:
24244129
Database:
MEDLINE

*Further Information*

*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.*