DNA甲基化
生物
差异甲基化区
遗传学
基因组
亚硫酸氢盐测序
表观遗传学
表观遗传学
体育锻炼的表观遗传学
DNA结合位点
人类基因组
甲基化
计算生物学
增强子
基因
CpG站点
DNA测序
发起人
转录因子
基因表达
作者
Michael J. Ziller,Hongcang Gu,Fabian Müller,Julie Donaghey,Linus Tsai,Oliver Kohlbacher,Philip L. De Jager,Evan D. Rosen,David A. Bennett,B Bernstein,Andreas Gnirke,Alexander Meissner
出处
期刊:Nature
[Springer Nature]
日期:2013-08-01
卷期号:500 (7463): 477-481
被引量:1251
摘要
DNA methylation is a defining feature of mammalian cellular identity and is essential for normal development. Most cell types, except germ cells and pre-implantation embryos, display relatively stable DNA methylation patterns, with 70-80% of all CpGs being methylated. Despite recent advances, we still have a limited understanding of when, where and how many CpGs participate in genomic regulation. Here we report the in-depth analysis of 42 whole-genome bisulphite sequencing 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, most of which are distal to transcription start sites. These dynamic CpGs co-localize with gene regulatory elements, particularly enhancers and transcription-factor-binding sites, which allow identification of key lineage-specific regulators. In addition, differentially methylated regions (DMRs) often contain single nucleotide polymorphisms associated with cell-type-related diseases as determined by genome-wide association studies. The results also highlight the general inefficiency of whole-genome bisulphite sequencing, as 70-80% of the sequencing reads across these data sets provided little or no relevant information about CpG methylation. To demonstrate further 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 guide new, 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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