학술논문

Large-scale comparative epigenomics reveals hierarchical regulation of non-CG methylation in Arabidopsis
Document Type
article
Source
Proceedings of the National Academy of Sciences of the United States of America. 115(5)
Subject
Genetics
Biotechnology
Human Genome
Generic health relevance
Arabidopsis
Cluster Analysis
Computational Biology
CpG Islands
DNA Methylation
Epigenesis
Genetic
Epigenomics
Gene Expression Regulation
Plant
Gene Library
Genome
Plant
Heterochromatin
High-Throughput Nucleotide Sequencing
Plants
Genetically Modified
Sequence Analysis
DNA
Sequence Analysis
RNA
Software
epigenetics
DNA methylation
computational biology
Language
Abstract
Genome-wide characterization by next-generation sequencing has greatly improved our understanding of the landscape of epigenetic modifications. Since 2008, whole-genome bisulfite sequencing (WGBS) has become the gold standard for DNA methylation analysis, and a tremendous amount of WGBS data has been generated by the research community. However, the systematic comparison of DNA methylation profiles to identify regulatory mechanisms has yet to be fully explored. Here we reprocessed the raw data of over 500 publicly available Arabidopsis WGBS libraries from various mutant backgrounds, tissue types, and stress treatments and also filtered them based on sequencing depth and efficiency of bisulfite conversion. This enabled us to identify high-confidence differentially methylated regions (hcDMRs) by comparing each test library to over 50 high-quality wild-type controls. We developed statistical and quantitative measurements to analyze the overlapping of DMRs and to cluster libraries based on their effect on DNA methylation. In addition to confirming existing relationships, we revealed unanticipated connections between well-known genes. For instance, MET1 and CMT3 were found to be required for the maintenance of asymmetric CHH methylation at nonoverlapping regions of CMT2 targeted heterochromatin. Our comparative methylome approach has established a framework for extracting biological insights via large-scale comparison of methylomes and can also be adopted for other genomics datasets.