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ExonImpact: prioritizing pathogenic alternative splicing events

Li, Meng, Feng, Weixing, Zhang, Xinjun, Yang, Yuedong, Wang, Kejun, Mort, Matthew, Cooper, David Neil, Wang, Yue, Zhou, Yaoqi and Liu, Yunlong 2017. ExonImpact: prioritizing pathogenic alternative splicing events. Human Mutation 38 (1) , pp. 16-24. 10.1002/humu.23111

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Abstract

Alternative splicing (AS) is a closely regulated process that allows a single gene to encode multiple protein isoforms, thereby contributing to the diversity of the proteome. Dysregulation of the splicing process has been found to be associated with many inherited diseases. However, among the pathogenic AS events, there are numerous “passenger” events whose inclusion or exclusion does not lead to significant changes with respect to protein function. In this study, we evaluate the secondary and tertiary structural features of proteins associated with disease-causing and neutral AS events, and show that several structural features are strongly associated with the pathological impact of exon inclusion. We further develop a machine-learning-based computational model, ExonImpact, for prioritizing and evaluating the functional consequences of hitherto uncharacterized AS events. We evaluated our model using several strategies including cross-validation, and data from the Gene-Tissue Expression (GTEx) and ClinVar databases. ExonImpact is freely available at http://watson.compbio.iupui.edu/ExonImpact.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Medicine
Subjects: R Medicine > R Medicine (General)
Uncontrolled Keywords: alternative splicing; exon impaction; disease; machine learning
Publisher: Wiley-Blackwell
ISSN: 1059-7794
Date of First Compliant Deposit: 6 February 2018
Date of Acceptance: 30 August 2016
Last Modified: 07 Feb 2018 17:19
URI: http://orca.cf.ac.uk/id/eprint/96858

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