Computational prediction of methylation status in human genomic sequences

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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.

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