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mutation3D: cancer gene prediction through atomic clustering of coding variants in the structural proteome

Meyer, Michael J., Lapcevic, Ryan, Romero, Alfonso E., Yoon, Mark, Das, Jishnu, Beltrán, Juan Felipe, Mort, Matthew, Stenson, Peter Daniel, Cooper, David Neil ORCID:, Paccanaro, Alberto and Yu, Haiyuan 2016. mutation3D: cancer gene prediction through atomic clustering of coding variants in the structural proteome. Human Mutation 37 (5) , pp. 447-456. 10.1002/humu.22963

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A new algorithm and Web server, mutation3D (, proposes driver genes in cancer by identifying clusters of amino acid substitutions within tertiary protein structures. We demonstrate the feasibility of using a 3D clustering approach to implicate proteins in cancer based on explorations of single proteins using the mutation3D Web interface. On a large scale, we show that clustering with mutation3D is able to separate functional from nonfunctional mutations by analyzing a combination of 8,869 known inherited disease mutations and 2,004 SNPs overlaid together upon the same sets of crystal structures and homology models. Further, we present a systematic analysis of whole-genome and whole-exome cancer datasets to demonstrate that mutation3D identifies many known cancer genes as well as previously underexplored target genes. The mutation3D Web interface allows users to analyze their own mutation data in a variety of popular formats and provides seamless access to explore mutation clusters derived from over 975,000 somatic mutations reported by 6,811 cancer sequencing studies. The mutation3D Web interface is freely available with all major browsers supported.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Medicine
Subjects: Q Science > QH Natural history > QH426 Genetics
R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Publisher: Wiley-Blackwell
ISSN: 1059-7794
Funders: Biotechnology and Biological Sciences Research Council
Date of Acceptance: 14 January 2016
Last Modified: 01 Nov 2022 10:17

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