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