Measuring DNA Copy Number Variation Using High-Density Methylation Microarrays

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
Soonweng ChoLeslie M Cope

Abstract

Genetic and epigenetic changes drive carcinogenesis, and their integrated analysis provides insights into mechanisms of cancer development. Computational methods have been developed to measure copy number variation (CNV) from methylation array data, including ChAMP-CNV, CN450K, and, introduced here, Epicopy. Using paired single nucleotide polymorphism (SNP) and methylation array data from the public The Cancer Genome Atlas repository, we optimized CNV calling and benchmarked the performance of these methods. We optimized the thresholds of all three methods and showed comparable performance across methods. Using Epicopy as a representative analysis of Illumina450K array, we show that Illumina450K-derived CNV methods achieve a sensitivity of 0.7 and a positive predictive value of 0.75 in identifying CNVs, which is similar to results achieved when comparing competing SNP microarray platforms with each other.

References

Oct 12, 2004·Biostatistics·Adam B OlshenMichael Wigler
Mar 14, 2008·The New England Journal of Medicine·Manel Esteller
Apr 11, 2009·Nature·Michael R StrattonP Andrew Futreal
Dec 10, 2009·BMC Genomics·Christina CurtisCarlos Caldas
Mar 19, 2011·BMC Bioinformatics·Xavier RobinMarkus Müller
Dec 1, 2011·Epigenomics·Sarah DedeurwaerderFrançois Fuks
Jul 14, 2012·Cancer Cell·Jueng Soo You, Peter A Jones
Sep 11, 2012·Nature·UNKNOWN Cancer Genome Atlas Research Network
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Software Mentioned

R
CN450K
GISTIC2
Epicopy
ChAMP
Locfit
modeest
SNP
minfi
GISTIC

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