Title | MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data. |
Publication Type | Journal Article |
Year of Publication | 2016 |
Authors | Fan, Y, Xi, L, Hughes, DST, Zhang, J, Zhang, J, P Futreal, A, Wheeler, DA, Wang, W |
Journal | Genome Biol |
Volume | 17 |
Issue | 1 |
Pagination | 178 |
Date Published | 2016 Aug 24 |
ISSN | 1474-760X |
Keywords | Algorithms, Alleles, Computational Biology, Exome, Genetic Heterogeneity, High-Throughput Nucleotide Sequencing, Humans, Mutation, Neoplasms, Sensitivity and Specificity, Software |
Abstract | 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. |
DOI | 10.1186/s13059-016-1029-6 |
Alternate Journal | Genome Biol |
PubMed ID | 27557938 |
PubMed Central ID | PMC4995747 |
Grant List | P30 CA016672 / CA / NCI NIH HHS / United States R01 CA183793 / CA / NCI NIH HHS / United States P50 CA083639 / CA / NCI NIH HHS / United States R01 CA174206 / CA / NCI NIH HHS / United States U24 CA143883 / CA / NCI NIH HHS / United States |
MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data.
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