Evidence map›Paper›PMID 24451234›Full record

ArticleGenome biology2014

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

Matthew Mort, Timothy Sterne-Weiler, Biao Li, Edward V Ball, David N Cooper, Predrag Radivojac, Jeremy R Sanford, Sean D Mooney

Abstract read
In one paragraph

Article in Genome biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers.

0numbers the graph read from it
0cells of the map it votes in
98citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

98 citing papers in PubMed.

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38 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Matthew Mort
Timothy Sterne-Weiler
Biao Li
Edward V Ball
David N Cooper
Predrag Radivojac
Jeremy R Sanford
Sean D Mooney

Funding

STANFORD COMPOSITE(RMI)U54HG004028 · NHGRI · STANFORD UNIVERSITY · PI MUSEN, MARK A · 2005 to 2014
$36.2M
Interdisciplinary Research Consortium in GeroscienceUL1DE019608 · NIDCR · BUCK INSTITUTE FOR RESEARCH ON AGING · PI LITHGOW, GORDON J · 2008 to 2011
$4.0M
Informatic Profiling of Clinically Relevant MutationR01LM009722 · NLM · UNIVERSITY OF WASHINGTON · PI MOONEY, SEAN DAVID · 2007 to 2014
$3.5M
Controlling toxic RNA with rapamycinR21AG042003 · NIA · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI SANFORD, JEREMY ROBERT · 2013 to 2014
$420k
NHGRI NIH HHS U54 HG004028NHGRI NIH HHS U54-HG004028NIA NIH HHS R21 AG042003NIDCR NIH HHS UL1DE019608NLM NIH HHS R01 LM009722
6 · The paper itself

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.

Indexed as

ExonsGenetic VariationMachine LearningAlternative SplicingGenes, Tumor SuppressorHumansIntronsMutationMutation, MissenseNeoplasmsPolymorphism, Single NucleotideRNA PrecursorsRNA Splice SitesSilencer Elements, TranscriptionalRNA PrecursorsRNA Splice Sites

Identifiers

PMID24451234
PMCPMC4054890

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.