MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing

Mort, Matthew, Sterne-Weiler, Timothy, Li, Biao, Ball, Edward, Cooper, David ORCID: https://orcid.org/0000-0002-8943-8484, Radivojac, Predrag, Sanford, Jeremy R. and Mooney, Sean D. 2014. MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing. Genome Biology 15 (1) , R19. 10.1186/gb-2014-15-1-r19

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Abstract

We have developed a novel machine-learning approach, MutPred Splice, for the identification of coding region substitutions that disrupt pre-mRNA splicing. Applying MutPred Splice to human disease-causing exonic mutations suggests that 16% of mutations causing inherited disease and 10 to 14% of somatic mutations in cancer may disrupt pre-mRNA splicing. For inherited disease, the main mechanism responsible for the splicing defect is splice site loss, whereas for cancer the predominant mechanism of splicing disruption is predicted to be exon skipping via loss of exonic splicing enhancers or gain of exonic splicing silencer elements. MutPred Splice is available at http://mutdb.org/mutpredsplice.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Medicine
Subjects:	C Auxiliary Sciences of History > CS Genealogy R Medicine > R Medicine (General)
Publisher:	Genome Biology
ISSN:	1465-6906
Date of First Compliant Deposit:	30 March 2016
Date of Acceptance:	13 January 2014
Last Modified:	15 Feb 2024 15:38
URI:	https://orca.cardiff.ac.uk/id/eprint/79098

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