RT info:eu-repo/semantics/article
T1 mutation3D: cancer gene prediction through atomic clustering of coding variants in the structural proteome
A1 Meyer, Michael J.
A1 Lapcevic, Ryan
A1 Romero, Alfonso E.
A1 Yoon, Mark
A1 Das, Jishnu
A1 Beltrán, Juan Felipe
A1 Mort, Matthew
A1 Stenson, Peter D.
A1 Cooper, David N.
A1 Paccanaro, Alberto
A1 Yu, Haiyuan
A2 Universidad Católica Nuestra Señora de la Asunción
AB A new algorithm and Web server, mutation3D (http://mutation3d.org), 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.
PB John Wiley & Sons Ltd.
YR 2016
FD 2016-02-03
LK http://hdl.handle.net/20.500.14066/2830
UL http://hdl.handle.net/20.500.14066/2830
LA eng
NO Meyer, M. J., Lapcevic, R., Romero, A. E., Yoon, M., Das, J., Beltrán, J. F., Mort, M., Stenson, P. D., Cooper, D. N., Paccanaro, A., & Yu, H. (2016). mutation3D: Cancer Gene Prediction Through Atomic Clustering of Coding Variants in the Structural Proteome. Human Mutation, 37(5), 447-456. https://doi.org/10.1002/humu.22963
NO Correspondence to: Haiyuan Yu, 335 Weill Hall, 237 Tower Road, Ithaca, NY 14853, USA. E-mail: haiyuan.yu@cornell.edu.
NO Consejo Nacional de Ciencia y Tecnología
DS MINDS@UW
RD 04-sep-2026