Large-scale comparative epigenomics reveals hierarchical regulation of non-CG methylation in Arabidopsis

• DOI: 10.1073/pnas.1716300115
• Publication Date: 2018-01-16T16:39:22Z
• Authors: Yu Zhang; C. Jake Harris; Qikun Liu; Wanlu Liu; Israel Ausin; Yanping Long; Lidan Xiao; Li Feng; Xu Chen; Yubin Xie; Xinyuan Chen; Lingyu Zhan; Suhua Feng; Jingyi Jessica Li; Haifeng Wang; Jixian Zhai; Steven E. Jacobsen
• Keywords: Epigenomics; 0301 basic medicine; Arabidopsis; Genetically Modified; Epigenesis, Genetic; 03 medical and health sciences; computational biology; Genetic; Gene Expression Regulation, Plant; Heterochromatin; Genetics; Cluster Analysis; Gene Library; Genome; DNA methylation; epigenetics; Sequence Analysis, RNA; Human Genome; Computational Biology; High-Throughput Nucleotide Sequencing; Plant; DNA; Sequence Analysis, DNA; Plants; DNA Methylation; Plants, Genetically Modified; Gene Expression Regulation; PNAS Plus; RNA; CpG Islands; Generic health relevance; Sequence Analysis; Genome, Plant; Software; Biotechnology; Epigenesis

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.