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Motivation: DNA methylation is an intensely studied epigenetic mark, yet itsfunctional role is incompletely understood. Attempts to quantitativelyassociate average DNA methylation to gene expression yield poor correlationsoutside of the well-understood methylation-switch at CpG islands.  Results: Here we use probabilistic machine learning to extract higher orderfeatures associated with the methylation profile across a defined region. Thesefeatures quantitate precisely notions of shape of a methylation profile,capturing spatial correlations in DNA methylation across genomic regions. Usingthese higher order features across promoter-proximal regions, we are able toconstruct a powerful machine learning predictor of gene expression,significantly improving upon the predictive power of average DNA methylationlevels. Furthermore, we can use higher order features to clusterpromoter-proximal regions, showing that five major patterns of methylationoccur at promoters across different cell lines, and we provide evidence thatmethylation beyond CpG islands may be related to regulation of gene expression.Our results support previous reports of a functional role of spatialcorrelations in methylation patterns, and provide a mean to quantitate suchfeatures for downstream analyses.  Availability: https://github.com/andreaskapou/BPRMeth

Higher order methylation features for clustering and prediction in epigenomic studies