한빛사논문
- 조회6,277
Abstract
Sangwoo Kim1,7,*, Kyowon Jeong 2,7 , Kunal Bhutani1, Jeong Ho Lee3,6, Anand Patel1, Eric Scott3, Hojung Nam4, Hayan Lee5, Joseph G Gleeson3 and Vineet Bafna1*
1Department of Computer Science and Engineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
2Department of Electrical and Computer Engineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
3Institute for Genomic Medicine, Rady Children's Hospital, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
4School of Information and Communications, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju, 500-712, Republic of Korea
5Department of Computer Science, Stony Brook University, 100 Nicolls Road, NY 11794, USA
6Graduate School of Medical Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea
7These authors contributed equally to this work
* Corresponding author
Abstract (provisional)
Detection of somatic variation using sequence from disease-control matched datasets 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/.
논문정보
- Research article
- 2013년 08월 (BRIC 등록일 2013-09-05)
- 국내+국외 연구진
- 의약학 > 유전 및 유전체의학
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