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Comparative Epigenomics Reveals Hierarchical Regulation of Non-CG Methylation in Arabidopsis

Identifiers: SRA: SRP106994
BioProject: PRJNA386485
GEO: GSE98872
Study Type: 
Other
Abstract: Background: 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, methods for the systematic comparison of DNA methylation profiles to identify novel regulatory mechanisms have yet to be established.Results: Here we developed a standardized pipeline and re-analyzed over three hundred publicly available Arabidopsis WGBS libraries from various mutant backgrounds, tissue types, and stress treatments. In total this collection included more than 3,700 Gb (Giga base-pairs) of sequencing data and a large number of wild-type controls. This enabled us to identify “high-confidence Differentially Methylated Regions” (hcDMRs) with high reliability by comparing each 'test' library to each of the 54 controls. We adopted two statistical methods, Statistical Measurements on Overlapping of DMRs (S-MOD) followed by Quantitative Measurements on Overlapping of DMRs (Q-MOD), to compare and cluster libraries based on their impacts on DNA methylation. In addition to confirming existing relationships using this unbiased approach, we revealed novel connections between methylation pathways. For instance, MET1 and CMT3 were found to be required for maintenance of asymmetric CHH methylation at non-overlapping regions of CMT2 targeted heterochromatin.Conclusions: 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 omics datasets. Together, the results demonstrate the effectiveness of data-driven, hypothesis-generating epigenetic research. Overall design: this dataset includes 7 RNA-seq and 1 BS-seq libraries
Center Project: GSE98872
External Link: /pubmed:29339507

Related SRA data

Experiments:
8 ( 8 samples )
Runs:
8 (24.9Gbp; 12.4Gb)
Additional objects:
File type count
fastq 8