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Bioinformatics. 2016 Mar 15;32(6):926-8. doi: 10.1093/bioinformatics/btv676. Epub 2015 Nov 16.

Global copy number profiling of cancer genomes.

Author information

1
Department of Family, Population & Preventive Medicine, Stony Brook University, Stony Brook, NY 11794, USA, Department of Pathology, Yale School of Medicine, New Haven, CT 06520, USA.
2
Departments of Biostatistics and Genetics, University of North Carolina, Chapel Hill, NC 27599, USA.
3
Department of Biostatistics.
4
Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, Department of Pathology, Yale School of Medicine, New Haven, CT 06520, USA.
5
Department of Statistics, University of California, Davis, CA 9516, USA.
6
Department of Statistics, The Wharton School, University of Pennsylvania, PA 19104, USA and.
7
Department of Pathology, Stony Brook University, Stony Brook, NY 11794, USA.

Abstract

In this article, we introduce a robust and efficient strategy for deriving global and allele-specific copy number alternations (CNA) from cancer whole exome sequencing data based on Log R ratios and B-allele frequencies. Applying the approach to the analysis of over 200 skin cancer samples, we demonstrate its utility for discovering distinct CNA events and for deriving ancillary information such as tumor purity.

AVAILABILITY AND IMPLEMENTATION:

https://github.com/xfwang/CLOSE CONTACT: xuefeng.wang@stonybrook.edu or michael.krauthammer@yale.edu

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
26576652
PMCID:
PMC4907391
DOI:
10.1093/bioinformatics/btv676
[Indexed for MEDLINE]
Free PMC Article
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