title: SomatiCA: identifying, characterizing and quantifying somatic copy number aberrations from cancer genome sequencing data.
abstract: Whole genome sequencing of matched tumor-normal sample pairs is becoming routine in cancer research. However, analysis of somatic copy-number changes from sequencing data is still challenging because of insufficient sequencing coverage, unknown tumor sample purity and subclonal heterogeneity. Here we describe a computational framework, named SomatiCA, which explicitly accounts for tumor purity and subclonality in the analysis of somatic copy-number profiles. Taking read depths (RD) and lesser allele frequencies (LAF) as input, SomatiCA will output 1) admixture rate for each tumor sample, 2) somatic allelic copy-number for each genomic segment, 3) fraction of tumor cells with subclonal change in each somatic copy number aberration (SCNA), and 4) a list of substantial genomic aberration events including gain, loss and LOH. SomatiCA is available as a Bioconductor R package at http://www.bioconductor.org/packages/2.13/bioc/html/SomatiCA.html.
authors.affiliation: Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.
authors.forename: Mengjie
authors.initials: M
authors.lastname: Chen
authors.forename: Murat
authors.initials: M
authors.lastname: Gunel
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao
mesh_terms: D001499:Bayes Theorem; D056915:DNA Copy Number Variations / Q000235:genetics*; D005189:False Positive Reactions; D005787:Gene Frequency; D023281:Genomics / Q000379:methods*; D005909:Glioblastoma / Q000235:genetics; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide; D017422:Sequence Analysis, DNA / Q000379:methods*