SomatiCA: Identifying,Characterizing and Quantifying Somatic Copy Number Aberrations from Cancer Genome Sequencing Data |
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| Authors: | Mengjie Chen Murat Gunel Hongyu Zhao |
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| Institution: | 1. Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.; 2. Department of Genetics, Yale University, New Haven, Connecticut, United States of America.; 3. Department of Neurosurgery, Yale University, New Haven, Connecticut, United States of America.; 4. Department of Biostatistics, Yale University, New Haven, Connecticut, United States of America.; Deutsches Krebsforschungszentrum, Germany, |
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| 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. |
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