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

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 add...

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Detalles Bibliográficos
Autores principales: Mermel, Craig H, Schumacher, Steven E, Hill, Barbara, Meyerson, Matthew L, Beroukhim, Rameen, Getz, Gad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3218867/
https://www.ncbi.nlm.nih.gov/pubmed/21527027
http://dx.doi.org/10.1186/gb-2011-12-4-r41
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author Mermel, Craig H
Schumacher, Steven E
Hill, Barbara
Meyerson, Matthew L
Beroukhim, Rameen
Getz, Gad
author_facet Mermel, Craig H
Schumacher, Steven E
Hill, Barbara
Meyerson, Matthew L
Beroukhim, Rameen
Getz, Gad
author_sort Mermel, Craig H
collection PubMed
description 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.
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spelling pubmed-32188672011-11-18 GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers Mermel, Craig H Schumacher, Steven E Hill, Barbara Meyerson, Matthew L Beroukhim, Rameen Getz, Gad Genome Biol Method 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. BioMed Central 2011 2011-04-28 /pmc/articles/PMC3218867/ /pubmed/21527027 http://dx.doi.org/10.1186/gb-2011-12-4-r41 Text en Copyright ©2011 Mermel et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Method
Mermel, Craig H
Schumacher, Steven E
Hill, Barbara
Meyerson, Matthew L
Beroukhim, Rameen
Getz, Gad
GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers
title GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers
title_full GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers
title_fullStr GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers
title_full_unstemmed GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers
title_short GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers
title_sort gistic2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3218867/
https://www.ncbi.nlm.nih.gov/pubmed/21527027
http://dx.doi.org/10.1186/gb-2011-12-4-r41
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