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PLoS One. 2013 Nov 12;8(11):e78143. doi: 10.1371/journal.pone.0078143. eCollection 2013.

SomatiCA: identifying, characterizing and quantifying somatic copy number aberrations from cancer genome sequencing data.

Author information

1
Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.

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.

PMID:
24265680
PMCID:
PMC3827077
DOI:
10.1371/journal.pone.0078143
[Indexed for MEDLINE]
Free PMC Article

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