title: Virmid: accurate detection of somatic mutations with sample impurity inference.
abstract: Detection of somatic variation using sequence from disease-control matched data sets is a critical first step. In many cases including cancer, however, it is hard to isolate pure disease tissue, and the impurity hinders accurate mutation analysis by disrupting overall allele frequencies. Here, we propose a new method, Virmid, that explicitly determines the level of impurity in the sample, and uses it for improved detection of somatic variation. Extensive tests on simulated and real sequencing data from breast cancer and hemimegalencephaly demonstrate the power of our model. A software implementation of our method is available at http://sourceforge.net/projects/virmid/.
authors.forename: Sangwoo
authors.initials: S
authors.lastname: Kim
authors.forename: Kyowon
authors.initials: K
authors.lastname: Jeong
authors.forename: Kunal
authors.initials: K
authors.lastname: Bhutani
authors.forename: Jeong
authors.initials: J
authors.lastname: Lee
authors.forename: Anand
authors.initials: A
authors.lastname: Patel
authors.forename: Eric
authors.initials: E
authors.lastname: Scott
authors.forename: Hojung
authors.initials: H
authors.lastname: Nam
authors.forename: Hayan
authors.initials: H
authors.lastname: Lee
authors.forename: Joseph G
authors.initials: JG
authors.lastname: Gleeson
authors.forename: Vineet
authors.initials: V
authors.lastname: Bafna
mesh_terms: D000483:Alleles; D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics*; D059472:Exome; D005260:Female; D005787:Gene Frequency; D065705:Hemimegalencephaly / Q000175:diagnosis / Q000235:genetics*; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D016013:Likelihood Functions; D009154:Mutation; D009363:Neoplasm Proteins / Q000235:genetics*; D012984:Software; D059016:Tumor Microenvironment / Q000235:genetics*
chemical_list: D009363:Neoplasm Proteins