Journal
GENOME BIOLOGY
Volume 17, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s13059-016-1029-6
Keywords
Somatic mutation calling; Sensitivity and specificity; Bayesian inference; Model-based cutoff finding; Next-generation sequencing
Funding
- Keck Center of the Gulf Coast Consortia for the Computational Cancer Biology Training Program
- Cancer Prevention and Research Institute of Texas (CPRIT) [RP140113]
- National Institutes of Health/National Cancer Institute [U24 CA143883 02S2]
- Integrative Pipeline for Analysis & Translational Application of TCGA Data [5U24CA143883-04]
- Cancer Prevention Research Institute of Texas [RP130090]
- NCI [1R01CA174206-01, P30 CA016672]
- US National Cancer Institute (NCI
- MD Anderson TCGA Genome Data Analysis Center) [CA143883, CA083639, CA183793]
- Cancer Prevention and Research Institute of Texas [R1205 01]
- UT Systems Stars Award [PS100149]
- Welch Foundation Robert A. Welch Distinguished University Chair Award [G-0040]
- MD Anderson Physician Scientist Award
- C.G. Johnson Advanced Scholar Award
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Subclonal mutations reveal important features of the genetic architecture of tumors. However, accurate detection of mutations in genetically heterogeneous tumor cell populations using next-generation sequencing remains challenging. We develop MuSE (http://bioinformatics.mdanderson.org/main/MuSE), Mutation calling using a Markov Substitution model for Evolution, a novel approach for modeling the evolution of the allelic composition of the tumor and normal tissue at each reference base. MuSE adopts a sample-specific error model that reflects the underlying tumor heterogeneity to greatly improve the overall accuracy. We demonstrate the accuracy of MuSE in calling subclonal mutations in the context of large-scale tumor sequencing projects using whole exome and whole genome sequencing.
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