PT - JOURNAL ARTICLE AU - Yu Fan AU - Xi Liu AU - Hughes Daniel S. T. AU - Jianjua Zhang AU - Jianhua Zhang AU - P. Andrew Futreal AU - David A. Wheeler AU - Wang Wenyi TI - Accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling for sequencing data AID - 10.1101/055467 DP - 2016 Jan 01 TA - bioRxiv PG - 055467 4099 - http://biorxiv.org/content/early/2016/05/25/055467.short 4100 - http://biorxiv.org/content/early/2016/05/25/055467.full AB - 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.