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Hum Mutat. 2016 May;37(5):447-56. doi: 10.1002/humu.22963. Epub 2016 Feb 18.

mutation3D: Cancer Gene Prediction Through Atomic Clustering of Coding Variants in the Structural Proteome.

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Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, 14853.
Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853.
Tri-Institutional Training Program in Computational Biology and Medicine, New York, New York, 10065.
Department of Computer Science and Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Egham TW20 0EX, UK.
Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff CF14 4XN, UK.


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.


Web tool; cancer; clustering; protein structures; somatic mutations

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