This article is part of a special issue on RBPome.

Open Access Highly Accessed Open Badges Software

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

Matthew Mort1*, Timothy Sterne-Weiler45, Biao Li2, Edward V Ball1, David N Cooper1, Predrag Radivojac3, Jeremy R Sanford4 and Sean D Mooney2*

Author Affiliations

1 Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff CF14 4XN, UK

2 Buck Institute for Research on Aging, Novato, CA 94945, USA

3 Department of Computer Science and Informatics, Indiana University, Bloomington, IN 47405, USA

4 Department of Molecular, Cellular and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA 95064, USA

5 Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA

For all author emails, please log on.

Genome Biology 2014, 15:R19  doi:10.1186/gb-2014-15-1-r19

Published: 13 January 2014


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 webcite.