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Accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling for sequencing data

Yu Fan, Xi Liu, Hughes Daniel S. T., Jianjua Zhang, Jianhua Zhang, P. Andrew Futreal, David A. Wheeler, Wang Wenyi
doi: https://doi.org/10.1101/055467
Yu Fan
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, U.S.A.
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Xi Liu
Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Alkek N1419, Houston, TX 77030-3411, U.S.A.
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Hughes Daniel S. T.
Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Alkek N1419, Houston, TX 77030-3411, U.S.A.
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Jianjua Zhang
Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, U.S.A.
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Jianhua Zhang
Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, U.S.A.
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P. Andrew Futreal
Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, U.S.A.
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David A. Wheeler
Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Alkek N1419, Houston, TX 77030-3411, U.S.A.
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Wang Wenyi
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, U.S.A.
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  • For correspondence: wwang7@mdanderson.org
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Abstract

Subclonal mutations reveal important features of the genetic architecture of tumors. However, accurate detection of mutations in genetically heterogeneous tumor cell populations using NGS remains challenging. We developed MuSE (http://bioinformatics.mdanderson.org/main/MuSE), mutation calling using a Markov substitution model for evolution, a novel approach 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 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 sequence.

Footnotes

  • Yu Fan, yfan1{at}mdanderson.ogr, Liu Xi, lxi{at}bcm.edu, Daniel S. T. Hughes, Daniel.Hughes{at}bcm.edu, Jianjun Zhang, JZhang20{at}mdanderson.org, Jianhua Zhang, JZhang22{at}mdanderson.org, P. Andrew Futreal, AFutreal{at}mdanderson.org, David A. Wheeler, wheeler{at}bcm.edu

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted May 25, 2016.
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Accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling for sequencing data
Yu Fan, Xi Liu, Hughes Daniel S. T., Jianjua Zhang, Jianhua Zhang, P. Andrew Futreal, David A. Wheeler, Wang Wenyi
bioRxiv 055467; doi: https://doi.org/10.1101/055467
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Accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling for sequencing data
Yu Fan, Xi Liu, Hughes Daniel S. T., Jianjua Zhang, Jianhua Zhang, P. Andrew Futreal, David A. Wheeler, Wang Wenyi
bioRxiv 055467; doi: https://doi.org/10.1101/055467

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