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Open Access Method

Virmid: accurate detection of somatic mutations with sample impurity inference

Sangwoo Kim1*, Kyowon Jeong2, Kunal Bhutani1, Jeong Ho Lee36, Anand Patel1, Eric Scott3, Hojung Nam4, Hayan Lee5, Joseph G Gleeson3 and Vineet Bafna1*

Author affiliations

1 Department of Computer Science and Engineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA

2 Department of Electrical and Computer Engineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA

3 Institute for Genomic Medicine, Rady Children's Hospital, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA

4 School of Information and Communications, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju, 500-712, Republic of Korea

5 Department of Computer Science, Stony Brook University, 100 Nicolls Road, NY 11794, USA

6 Graduate School of Medical Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea

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Citation and License

Genome Biology 2013, 14:R90  doi:10.1186/gb-2013-14-8-r90

Published: 29 August 2013

Abstract

Detection of somatic variation using sequence from disease-control matched data sets is a critical first step. In many cases including cancer, however, it is hard to isolate pure disease tissue, and the impurity hinders accurate mutation analysis by disrupting overall allele frequencies. Here, we propose a new method, Virmid, that explicitly determines the level of impurity in the sample, and uses it for improved detection of somatic variation. Extensive tests on simulated and real sequencing data from breast cancer and hemimegalencephaly demonstrate the power of our model. A software implementation of our method is available at http://sourceforge.net/projects/virmid/ webcite.