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Updated: May 4, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
Published on: December 7, 2021
Fundamental diversity of human CpG islands at multiple biological levels
Jia Zeng1, Hema K Nagrajan1, Soojin V Yi1
1School of Biology; Georgia Institute of Technology; Atlanta, GA USA.
Abstract:
CpG islands (CGIs) are commonly used as genomic markers to study the patterns and regulatory consequences of DNA methylation. Interestingly, recent studies reveal a substantial diversity among CGIs: long and short CGIs, for example, exhibit contrasting patterns of gene expression complexity and nucleosome occupancy. Evolutionary origins of CGIs are also highly heterogeneous. In order to systematically evaluate potential diversities among CGIs and ultimately to illuminate the link between diversity of CGIs and their epigenetic variation, we analyzed the nucleotide-resolution DNA methylation maps (methylomes) of multiple cellular origins. We discover novel 'clusters' of CGIs according to their patterns of DNA methylation; the stably hypomethylated CGI cluster (cluster I), sperm-hypomethylated CGI cluster (cluster II), and variably methylated CGI cluster (cluster III). These epigenomic CGI clusters are strikingly distinct at multiple biological features including genomic, evolutionary, and functional characteristics. At the genomic level, the stably hypomethylated CGI cluster tends to be longer and harbors many more CpG dinucleotides than those in other clusters. They are also frequently associated with promoters, while CGI clusters II and III mostly reside in intragenic or intergenic regions and exhibit highly tissue-specific DNA methylation. Functional ontology terms and transcriptional profiles co-vary with CGI clusters, indicating that the regulatory functions of CGIs are tightly linked to their heterogeneity. Finally, CGIs associated with distinctive biological processes, such as diseases, aging, and imprinting, occur disproportionately across CGI clusters. These new findings provide an effective means to combine existing knowledge on CGIs into a genomic context while bringing new insights that elucidate the significance of DNA methylation across different biological conditions and demography.
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