Computational prediction of methylation status in human genomic sequences.
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.
Das
Rajdeep
R
Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
Dimitrova
Nevenka
N
Xuan
Zhenyu
Z
Rollins
Robert A
RA
Haghighi
Fatemah
F
Edwards
John R
JR
Ju
Jingyue
J
Bestor
Timothy H
TH
Zhang
Michael Q
MQ
D000465:Algorithms; D019295:Computational Biology; D004247:DNA / Q000737:chemistry* / Q000378:metabolism; D019175:DNA Methylation; D015894:Genome, Human; D006801:Humans; D011237:Predictive Value of Tests
D004247:DNA