bioRxivGenomicsPreprint
DNA methylation oscillation defines classes of enhancers
Emanuele Libertini, Rifat HamoudiiD, Simon Charles HeathiD, Lee LancashireiD, Arcadio Rubio GarcíaiD, Luigi GrassiiD, Kate DownesiD, Willem Hendrik OuwehandiD, Biola M. JavierreiD, Jonathan CairnsiD, Steven WingettiD, Dirk S. PauliD, Marta GutiD, Ivo Glynne GutiD, Joost H.A. MartensiD, Alexander E. Ivliev, Hendrik G. StunnenbergiD, Mattia FrontiniiD, Mikhail SpivakoviD, Peter FraseriD, Antony J. CutleriD, Chris WallaceiD, Stephan BeckiD
Abstract
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.\n\nHere, 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.\n\nMapping 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.\n\nOur approach advances the identification of tissue-specific regulatory elements and outperforms previous approaches defining enhancer classes based on DNA methylation.
- organism: human
- assay: DNA methylation
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 2
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