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.

Keywords

Somatic cellBiologyComputational biologyHuman geneticsCopy-number variationGeneticsGeneGenome

MeSH Terms

AlgorithmsComputational BiologyComputer SimulationGene DosageHumansModelsTheoreticalNeoplasmsSoftwareTumor Suppressor Proteins

Affiliated Institutions

Related Publications

Publication Info

Year
2011
Type
article
Volume
12
Issue
4
Pages
R41-R41
Citations
3661
Access
Closed

External Links

Download PDF (Free) View on DOI.org PubMed Semantic Scholar

Social Impact

Altmetric
PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

3661
OpenAlex
323
Influential
2903
CrossRef

Cite This

Craig H. Mermel, Steven E. Schumacher, Barbara Hill et al. (2011). GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome biology , 12 (4) , R41-R41. https://doi.org/10.1186/gb-2011-12-4-r41

Identifiers

DOI
10.1186/gb-2011-12-4-r41
PMID
21527027
PMCID
PMC3218867

Data Quality

Data completeness: 86%