Charting a dynamic DNA methylation landscape of the human genome
Tsai, Linus T.-Y.
De Jager, Phil L.
Bennett, David A.
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CitationZiller, M. J., H. Gu, F. Müller, J. Donaghey, L. T. Tsai, O. Kohlbacher, P. L. De Jager, et al. 2013. “Charting a dynamic DNA methylation landscape of the human genome.” Nature 500 (7463): 10.1038/nature12433. doi:10.1038/nature12433. http://dx.doi.org/10.1038/nature12433.
AbstractDNA methylation is a defining feature of mammalian cellular identity and essential for normal development1,2. Most cell types, except germ cells and pre-implantation embryos3–5, display relatively stable DNA methylation patterns with 70–80% of all CpGs being methylated6. Despite recent advances we still have a too limited understanding of when, where and how many CpGs participate in genomic regulation. Here we report the in depth analysis of 42 whole genome bisulfite sequencing (WGBS) data sets across 30 diverse human cell and tissue types. We observe dynamic regulation for only 21.8% of autosomal CpGs within a normal developmental context, a majority of which are distal to transcription start sites. These dynamic CpGs co-localize with gene regulatory elements, particularly enhancers and transcription factor binding sites (TFBS), which allow identification of key lineage specific regulators. In addition, differentially methylated regions (DMRs) often harbor SNPs associated with cell type related diseases as determined by GWAS. The results also highlight the general inefficiency of WGBS as 70–80% of the sequencing reads across these data sets provided little or no relevant information regarding CpG methylation. To further demonstrate the utility of our DMR set, we use it to classify unknown samples and identify representative signature regions that recapitulate major DNA methylation dynamics. In summary, although in theory every CpG can change its methylation state, our results suggest that only a fraction does so as part of coordinated regulatory programs. Therefore our selected DMRs can serve as a starting point to help guide novel, more effective reduced representation approaches to capture the most informative fraction of CpGs as well as further pinpoint putative regulatory elements.
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