Detailed modeling of positive selection improves detection of cancer driver genes

Siming Zhao; Jun Liu; Pranav Nanga; Yuwen Liu; A. Ercument Cicek; Nicholas Knoblauch; Chuan He; Matthew Stephens; Xin He

doi:10.1038/s41467-019-11284-9

Nature Communications (Jul 2019)

Detailed modeling of positive selection improves detection of cancer driver genes

Siming Zhao,
Jun Liu,
Pranav Nanga,
Yuwen Liu,
A. Ercument Cicek,
Nicholas Knoblauch,
Chuan He,
Matthew Stephens,
Xin He

Affiliations

Siming Zhao: Department of Human Genetics, University of Chicago
Jun Liu: Department of Chemistry, Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, Howard Hughes Medical Institute, University of Chicago
Pranav Nanga: Department of Computer Science, University of Chicago
Yuwen Liu: Department of Human Genetics, University of Chicago
A. Ercument Cicek: Computer Engineering Department, Bilkent University
Nicholas Knoblauch: Department of Human Genetics, University of Chicago
Chuan He: Department of Chemistry, Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, Howard Hughes Medical Institute, University of Chicago
Matthew Stephens: Department of Human Genetics, University of Chicago
Xin He: Department of Human Genetics, University of Chicago

DOI: https://doi.org/10.1038/s41467-019-11284-9
Journal volume & issue: Vol. 10, no. 1
pp. 1 – 13

Abstract

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Finding driver genes sheds lights on the biological mechanisms propelling the development of a tumour, and can suggest therapeutic strategies. Here, the authors develop driverMAPS, a model-based approach to identify driver genes, and apply it to TCGA datasets.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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