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compuationalPredictMethyStatusHumanGeno_06pnas

compuationalPredictMethyStatusHumanGeno_06pnas -...

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Computational prediction of methylation status in human genomic sequences Rajdeep Das*, Nevenka Dimitrova , Zhenyu Xuan*, Robert A. Rollins , Fatemah Haghighi § , John R. Edwards §¶ , Jingyue Ju §¶ , Timothy H. Bestor , and Michael Q. Zhang* *Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724; Philips Research, 345 Scarborough Road, Briarcliff Manor, NY 10510; Department of Genetics and Development, College of Physicians and Surgeons of Columbia University, New York, NY 10032; and § Columbia Genome Center and Department of Chemical Engineering, Columbia University, New York, NY 10032 Communicated by Michael H. Wigler, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, April 12, 2006 (received for review October 25, 2005) Epigenetic effects in mammals depend largely on heritable genomic methylation patterns. We describe a computational pat- tern recognition method that is used to predict the methylation landscape of human brain DNA. This method can be applied both to CpG islands and to non-CpG island regions. It computes the methylation propensity for an 800-bp region centered on a CpG dinucleotide based on specific sequence features within the region. We tested several classifiers for classification performance, includ- ing K means clustering, linear discriminant analysis, logistic regres- sion, and support vector machine. The best performing classifier used the support vector machine approach. Our program (called HDFINDER ) presently has a prediction accuracy of 86%, as validated with CpG regions for which methylation status has been experi- mentally determined. Using HDFINDER , we have depicted the entire genomic methylation patterns for all 22 human autosomes. DNA methylation epigenomics methylation prediction CpG islands A lthough progress recently has been made toward whole- genome DNA methylation profiling by using molecular techniques, computational epigenomics is still in its infancy (1). Global analyses of DNA methylation have been focused mainly on two themes: the discovery of methylated CpG islands (CGI) and allele-specific cytosine methylation. Computational predic- tion of CGIs was introduced in 1987 by Gardiner-Garden et al. (2). They defined CGIs as regions of 200 bp with G C content of 0.5 and the observed expected CpG ratio 0.6. Takai and Jones (3) later proposed a more stringent definition that requires CGIs to be 500 bp long, CG content 55%, and the CpG ratio 0.65. This latter method is successful in excluding Alu repeats, many of which were annotated as CGIs when the former criteria were used. Matsuo et al. (4) have provided statistical evidence for erosion of mouse CGIs as compared with human ones. They suggested that an accumulation of TpGs and CpAs observed in mouse, presumably due to the higher rate of deamination of the methylated CpGs, results in a lower CpG ratio in mouse.
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