Understanding the regulatory landscape of human cells requires the integration of
genomic and epigenomic maps, capturing combinatorial levels of cell type-specific
and invariant activity states. Here, we segmented whole-genome bisulfite sequencing-derived
methylomes into consecutive blocks of co-methylation (COMETs) to obtain spatial variation
patterns of DNA methylation (DNAm oscillations) integrated with histone modifications
and promoter-enhancer interactions derived from promoter capture Hi-C (PCHi-C) sequencing
of the same purified blood cells. Mapping DNAm oscillations onto regulatory genome
annotation revealed that enhancers are enriched for DNAm hyper-oscillations (>30-fold),
where multiple machine learning models support DNAm as predictive of enhancer location.
Based on this analysis, we report overall predictive power of 99% for DNAm oscillations,
77.3% for DNaseI, 41% for CGIs, 20% for UMRs and 0% for LMRs, demonstrating the power
of DNAm oscillations over other methods for enhancer prediction. Methylomes of activated
and non-activated CD4+ T cells indicate that DNAm oscillations exist in both states
irrespective of activation; hence they can be used to determine the location of latent
enhancers. Our approach advances the identification of tissue-specific regulatory
elements and is a first demonstration of defining enhancer classes based on DNA methylation.