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

作者：Fan Yu; Xi Liu; Hughes Daniel S T; Zhang Jianjun; Zhang Jianhua; Futreal P Andrew; Wheeler David A; Wang Wenyi^*

来源：Genome Biology, 2016, 17(1): 178.

DOI：10.1186/s13059-016-1029-6

• Somatic mutation calling
• Sensitivity and specificity
• Bayesian inference
• Model-based cutoff finding
• Next-generation sequencing

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.

• 出版日期2016-8-24