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Genome Biol. 2011;12(4):R41. doi: 10.1186/gb-2011-12-4-r41. Epub 2011 Apr 28.

GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers.

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  • 1Cancer Program, The Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA.

Abstract

We describe methods with enhanced power and specificity to identify genes targeted by somatic copy-number alterations (SCNAs) that drive cancer growth. By separating SCNA profiles into underlying arm-level and focal alterations, we improve the estimation of background rates for each category. We additionally describe a probabilistic method for defining the boundaries of selected-for SCNA regions with user-defined confidence. Here we detail this revised computational approach, GISTIC2.0, and validate its performance in real and simulated datasets.

© 2011 Mermel et al.; licensee BioMed Central Ltd.

PMID:
21527027
[PubMed - indexed for MEDLINE]
PMCID:
PMC3218867
Free PMC Article

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