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

Genome biology

Volumn 17, Issue 1, 2016, Pages 178-

  Fan, Yu ^a   Xi, Liu ^a   Hughes, Daniel S T ^a   Zhang, Jianjun ^a   Zhang, Jianhua ^a   Futreal, P Andrew ^a   Wheeler, David A ^a   Wang, Wenyi ^a  

^a UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

Author keywords

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

Indexed keywords

ALGORITHM; ALLELE; BIOLOGY; EXOME; GENETIC HETEROGENEITY; GENETICS; HIGH THROUGHPUT SEQUENCING; HUMAN; MUTATION; NEOPLASM; SENSITIVITY AND SPECIFICITY; SOFTWARE;

ALGORITHMS; ALLELES; COMPUTATIONAL BIOLOGY; EXOME; GENETIC HETEROGENEITY; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; MUTATION; NEOPLASMS; SENSITIVITY AND SPECIFICITY; SOFTWARE;

EID: 85021317147     PISSN: None     EISSN: 1474760X     Source Type: Journal    
DOI: 10.1186/s13059-016-1029-6     Document Type: Article

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