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Open Access Method

EMu: probabilistic inference of mutational processes and their localization in the cancer genome

Andrej Fischer, Christopher JR Illingworth, Peter J Campbell and Ville Mustonen*

Author affiliations

Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB10 1SA, Hinxton, Cambridge, UK

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Citation and License

Genome Biology 2013, 14:R39  doi:10.1186/gb-2013-14-4-r39

Published: 29 April 2013

Abstract

The spectrum of mutations discovered in cancer genomes can be explained by the activity of a few elementary mutational processes. We present a novel probabilistic method, EMu, to infer the mutational signatures of these processes from a collection of sequenced tumors. EMu naturally incorporates the tumor-specific opportunity for different mutation types according to sequence composition. Applying EMu to breast cancer data, we derive detailed maps of the activity of each process, both genome-wide and within specific local regions of the genome. Our work provides new opportunities to study the mutational processes underlying cancer development. EMu is available at http://www.sanger.ac.uk/resources/software/emu/ webcite.

Keywords:
cancer genomes; expectation-maximization; chromatin state; breast cancer; mutation clustering