Identification of methylation states of DNA regions for Illumina methylation BeadChip

• Published in: BMC genomics Vol. 21; no. Suppl 1; p. 672
• Main Authors: Luo, Ximei, Wang, Fang, Wang, Guohua, Zhao, Yuming
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 05.03.2020; BioMed Central; BMC
• Subjects: Chromatin; Criminal investigation; Datasets; Deoxyribonucleic acid; Diseases; DNA; DNA methylation; DNA methylation states and DNA regions; DNA microarrays; DNA sequencing; EPIC; Epigenetic inheritance; Gene expression; Genes; Genetic research; Genomes; Genomics; HM450K; Identification; Markov processes; Methodology; Methods; Methylation; Pyrimidines; Sulfites; China; DNA methylation states and DNA regions; EPIC; HM450K
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-019-6019-0

Methylation of cytosine bases in DNA is a critical epigenetic mark in many eukaryotes and has also been implicated in the development and progression of normal and diseased cells. Therefore, profiling DNA methylation across the genome is vital to understanding the effects of epigenetic. In recent years the Illumina HumanMethylation450 (HM450K) and MethylationEPIC (EPIC) BeadChip have been widely used to profile DNA methylation in human samples. The methods to predict the methylation states of DNA regions based on microarray methylation datasets are critical to enable genome-wide analyses. We report a computational approach based on the two layers two-state hidden Markov model (HMM) to identify methylation states of single CpG site and DNA regions in HM450K and EPIC BeadChip. Using this mothed, all CpGs detected by HM450K and EPIC in H1-hESC and GM12878 cell lines are identified as un-methylated, middle-methylated and full-methylated states. A large number of DNA regions are segmented into three methylation states as well. Comparing the identified regions with the result from the whole genome bisulfite sequencing (WGBS) datasets segmented by MethySeekR, our method is verified. Genome-wide maps of chromatin states show that methylation state is inversely correlated with active histone marks. Genes regulated by un-methylated regions are expressed and regulated by full-methylated regions are repressed. Our method is illustrated to be useful and robust. Our method is valuable for DNA methylation genome-wide analyses. It is focusing on identification of DNA methylation states on microarray methylation datasets. For the features of array datasets, using two layers two-state HMM to identify to methylation states on CpG sites and regions creatively, our method which takes into account the distribution of genome-wide methylation levels is more reasonable than segmentation with a fixed threshold.