Title |
MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing
|
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Published in |
Genome Biology, January 2014
|
DOI | 10.1186/gb-2014-15-1-r19 |
Pubmed ID | |
Authors |
Matthew Mort, Timothy Sterne-Weiler, Biao Li, Edward V Ball, David N Cooper, Predrag Radivojac, Jeremy R Sanford, Sean D Mooney |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 36% |
United Kingdom | 3 | 21% |
Australia | 1 | 7% |
Spain | 1 | 7% |
France | 1 | 7% |
India | 1 | 7% |
Unknown | 2 | 14% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 7 | 50% |
Scientists | 6 | 43% |
Science communicators (journalists, bloggers, editors) | 1 | 7% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 1% |
Germany | 1 | <1% |
Netherlands | 1 | <1% |
Canada | 1 | <1% |
United Kingdom | 1 | <1% |
Spain | 1 | <1% |
Belgium | 1 | <1% |
Unknown | 192 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 55 | 28% |
Researcher | 43 | 22% |
Student > Master | 25 | 13% |
Student > Doctoral Student | 16 | 8% |
Student > Bachelor | 12 | 6% |
Other | 27 | 14% |
Unknown | 22 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 71 | 36% |
Biochemistry, Genetics and Molecular Biology | 53 | 27% |
Computer Science | 23 | 12% |
Medicine and Dentistry | 10 | 5% |
Neuroscience | 5 | 3% |
Other | 10 | 5% |
Unknown | 28 | 14% |