MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data-Reference-Cited by-同舟云学术

MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data

Published:2016-08-24 Issue:1 Volume:17 Page:
ISSN:1474-760X
Container-title:Genome Biology
language:en
Short-container-title:Genome Biol

Author:

Fan Yu,Xi Liu,Hughes Daniel S. T.,Zhang Jianjun,Zhang Jianhua,Futreal P. Andrew,Wheeler David A.,Wang Wenyi^ORCID

Funder

Cancer Prevention and Research Institute of Texas (US)

National Cancer Institute (US)

UT Systems Stars Award

Welch Foundation Robert A. Welch Distinguished University Chair Award

MD Anderson Physician Scientist Award

C.G. Johnson Advanced Scholar Award

Publisher

Springer Science and Business Media LLC

Link

http://link.springer.com/content/pdf/10.1186/s13059-016-1029-6

Reference25 articles.

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3. Roth A, Ding J, Morin R, Crisan A, Ha G, Giuliany R, et al.JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data. Bioinformatics (Oxford, England). 2012; 28(7):907–13.

4. Saunders CT, Wong WSW, Swamy S, Becq J, Murray LJ, Cheetham RK. Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics (Oxford, England). 2012; 28(14):1811–7.

5. Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, et al.Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013; 31(3):213–9.

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