Title |
OncodriveFML: a general framework to identify coding and non-coding regions with cancer driver mutations
|
---|---|
Published in |
Genome Biology, June 2016
|
DOI | 10.1186/s13059-016-0994-0 |
Pubmed ID | |
Authors |
Loris Mularoni, Radhakrishnan Sabarinathan, Jordi Deu-Pons, Abel Gonzalez-Perez, Núria López-Bigas |
Abstract |
Distinguishing the driver mutations from somatic mutations in a tumor genome is one of the major challenges of cancer research. This challenge is more acute and far from solved for non-coding mutations. Here we present OncodriveFML, a method designed to analyze the pattern of somatic mutations across tumors in both coding and non-coding genomic regions to identify signals of positive selection, and therefore, their involvement in tumorigenesis. We describe the method and illustrate its usefulness to identify protein-coding genes, promoters, untranslated regions, intronic splice regions, and lncRNAs-containing driver mutations in several malignancies. |
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Canada | 3 | 8% |
United Kingdom | 2 | 5% |
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France | 1 | 3% |
Argentina | 1 | 3% |
Ecuador | 1 | 3% |
Australia | 1 | 3% |
Other | 7 | 18% |
Unknown | 12 | 30% |
Demographic breakdown
Type | Count | As % |
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Scientists | 27 | 68% |
Members of the public | 11 | 28% |
Science communicators (journalists, bloggers, editors) | 2 | 5% |
Mendeley readers
Geographical breakdown
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Denmark | 1 | <1% |
China | 1 | <1% |
Germany | 1 | <1% |
Unknown | 234 | 99% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 55 | 23% |
Student > Ph. D. Student | 52 | 22% |
Student > Master | 30 | 13% |
Student > Bachelor | 23 | 10% |
Student > Doctoral Student | 9 | 4% |
Other | 31 | 13% |
Unknown | 37 | 16% |
Readers by discipline | Count | As % |
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Medicine and Dentistry | 16 | 7% |
Computer Science | 12 | 5% |
Neuroscience | 3 | 1% |
Other | 14 | 6% |
Unknown | 44 | 19% |