  DNA methylation is an intensely studied epigenetic mark implicated in many
biological processes of direct clinical relevance. While sequencing based
technologies are increasingly allowing high resolution measurements of DNA
methylation, statistical modelling of such data is still challenging. In
particular, statistical identification of differentially methylated regions
across different conditions poses unresolved challenges in accounting for
spatial correlations within the statistical testing procedure.
  We propose a non-parametric, kernel-based method, M3D, to detect higher-order
changes in methylation profiles, such as shape, across pre-defined regions. The
test statistic explicitly accounts for differences in coverage levels between
samples, thus handling in a principled way a major confounder in the analysis
of methylation data. Empirical tests on real and simulated data sets show an
increased power compared to established methods, as well as considerable
robustness with respect to coverage and replication levels.
  Code is implemented in R, and is freely available via Bioconductor package
M3D.
