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.