Quantification of global DNA methylation using EPIC array
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PROVIDER: PRJNA1047525 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The level of dNA methylation in BRE80-BRE80-T5 and T47D cells expressing active and inctive DNMT3A was quantified using EPIC array across more than 850,000 CpGs
2024-10-16 | GSE249125 | GEO
Project description:Background: Epigenome-wide association studies (EWAS) have been widely applied to identify methylation CpG sites associated with human disease. To date, the Infinium Methylation EPIC array (EPIC) is commonly used for high-throughput DNA methylation profiling. However, the EPIC array covers only 30% of the human methylome. Methylation Capture bisulfite sequencing (MC-seq) captures target regions of methylome and has advantages of extensive coverage in the methylome at an affordable price. Methods: Epigenome-wide DNA methylation in four peripheral blood mononuclear cell samples was profiled by using SureSelectXT Methyl-Seq for MC-seq and EPIC platforms separately. CpG site-based reproducibility of MC-seq was assessed with DNA sample inputs ranging in quantity of high (> 1000ng), medium (300-1000ng), and low (150ng-300ng). To compare the performance of MC-seq and the EPIC arrays, we conducted a Pearson correlation and methylation value difference at each CpG site that was detected by both MC-seq and EPIC. We compared the percentage and counts in each CpG island and gene annotation between MC-seq and the EPIC array. Results: After quality control, an average of 3,708,550 CpG sites per sample was detected by MC-seq with DNA quantity >1000ng. Reproducibility of MC-seq detected CpG sites was high with strong correlation estimates for CpG methylation among samples with high, medium, and low DNA inputs (r > 0.96). The EPIC array captured an average of 846,464 CpG sites per sample. Compared with the EPIC array, MC-seq detected more CpGs in coding regions and CpG islands. Among the 472,540 CpG sites captured by both platforms, methylation of a majority of CpG sites was highly correlated in the same sample (r: 0.98~0.99). However, methylation for a small proportion of CpGs (N=235) differed significantly between the two platforms, with differences in beta values of greater than 0.5. Conclusions: Our results show that MC-seq is an efficient and reliable platform for methylome profiling with a broader coverage of the methylome than the array-based platform. Although methylation measurements in majority of CpGs are highly correlated, a number of CpG sites show large discrepancy between the two platforms, which warrants further investigation and needs cautious interpretation.
2020-06-11 | GSE152245 | GEO
Project description:Comparison of Methylation Capture Sequencing and Infinium EPIC Methylation Array in Peripheral Blood Mononuclear Cells [EPIC array]
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Project description:EPIC Methylation array profiling of MCF-7 cells
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Project description:Background: Epigenome-wide association studies (EWAS) have been widely applied to identify methylation CpG sites associated with human disease. To date, the Infinium Methylation EPIC array (EPIC) is commonly used for high-throughput DNA methylation profiling. However, the EPIC array covers only 30% of the human methylome. Methylation Capture bisulfite sequencing (MC-seq) captures target regions of methylome and has advantages of extensive coverage in the methylome at an affordable price. Methods: Epigenome-wide DNA methylation in four peripheral blood mononuclear cell samples was profiled by using SureSelectXT Methyl-Seq for MC-seq and EPIC platforms separately. CpG site-based reproducibility of MC-seq was assessed with DNA sample inputs ranging in quantity of high (> 1000ng), medium (300-1000ng), and low (150ng-300ng). To compare the performance of MC-seq and the EPIC arrays, we conducted a Pearson correlation and methylation value difference at each CpG site that was detected by both MC-seq and EPIC. We compared the percentage and counts in each CpG island and gene annotation between MC-seq and the EPIC array. Results: After quality control, an average of 3,708,550 CpG sites per sample was detected by MC-seq with DNA quantity >1000ng. Reproducibility of MC-seq detected CpG sites was high with strong correlation estimates for CpG methylation among samples with high, medium, and low DNA inputs (r > 0.96). The EPIC array captured an average of 846,464 CpG sites per sample. Compared with the EPIC array, MC-seq detected more CpGs in coding regions and CpG islands. Among the 472,540 CpG sites captured by both platforms, methylation of a majority of CpG sites was highly correlated in the same sample (r: 0.98~0.99). However, methylation for a small proportion of CpGs (N=235) differed significantly between the two platforms, with differences in beta values of greater than 0.5. Conclusions: Our results show that MC-seq is an efficient and reliable platform for methylome profiling with a broader coverage of the methylome than the array-based platform. Although methylation measurements in majority of CpGs are highly correlated, a number of CpG sites show large discrepancy between the two platforms, which warrants further investigation and needs cautious interpretation.
2020-06-22 | GSE152922 | GEO
Project description:Illumina Infinium HumanMethylation850 BeadChip (also known as Illumina EPIC array, GPL23976) was used to generate DNA methylation data from synthetic DNA from 3 species. The DNA samples from each species were enzymatically manipulated so that they would exhibit 0%, 25%, 50%, 75% and 100% percent methylation at each CpG location, respectively. The variable “ProportionMethylated” (with ordinal values 0, 0.25, 0.5, 0.75, 1) can be interpreted as a benchmark for each CpG that maps to the respective genome. Thus, the DNA methylation levels of each CpG are expected to have a high positive correlation with ProportionMethylated across the arrays measurement for the human species. The human EPIC array was applied to calibration data from mouse (n=15 EPIC arrays, 3 per methylation level) and rat (n=10, 2 per methylation level). The EPIC array data were normalized using the noob method (R function preprocessNoob in minfi).
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