title: Computational prediction of methylation status in human genomic sequences.
abstract: Epigenetic effects in mammals depend largely on heritable genomic methylation patterns. We describe a computational pattern 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, including K means clustering, linear discriminant analysis, logistic regression, 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 experimentally determined. Using hdfinder, we have depicted the entire genomic methylation patterns for all 22 human autosomes.
authors.affiliation: Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
authors.forename: Rajdeep
authors.initials: R
authors.lastname: Das
authors.forename: Nevenka
authors.initials: N
authors.lastname: Dimitrova
authors.forename: Zhenyu
authors.initials: Z
authors.lastname: Xuan
authors.forename: Robert A
authors.initials: RA
authors.lastname: Rollins
authors.forename: Fatemah
authors.initials: F
authors.lastname: Haghighi
authors.forename: John R
authors.initials: JR
authors.lastname: Edwards
authors.forename: Jingyue
authors.initials: J
authors.lastname: Ju
authors.forename: Timothy H
authors.initials: TH
authors.lastname: Bestor
authors.forename: Michael Q
authors.initials: MQ
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D004247:DNA / Q000737:chemistry* / Q000378:metabolism; D019175:DNA Methylation; D015894:Genome, Human; D006801:Humans; D011237:Predictive Value of Tests
chemical_list: D004247:DNA