Endonuclease enrichment TAPS for cost-effective genome-wide base-resolution DNA methylation detection
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE148197 | GEO | 2021/04/01
REPOSITORIES: GEO
Similar Datasets
Project description:DNA methylation is an important epigenetic modification involved in many biological processes and diseases. Many studies have mapped DNA methylation changes associated with embryogenesis, cell differentiation and cancer at a genome-wide scale. Our understanding of genome-wide DNA methylation changes in a developmental or disease-related context has been steadily growing. However, the investigation of which CpGs are variably methylated in different normal cell or tissue types is still limited. Here we present an in-depth analysis of 54 single-CpG-resolution DNA methylomes of normal human cell types by integrating high-throughput sequencing-based methylation data. We find that the percentage of unmethylated CpGs is relatively fixed regardless of cell type. However, which CpGs are unmethylated is cell-type specific. We categorize the 26 million human autosomal CpGs based on their methylation levels across multiple cell types to identify variably methylated CpGs. Among all the autosomal CpGs, 22.6% exhibit variable DNA methylation across cell types included in our study. These variably methylated CpGs form 66 thousand variably methylated regions (VMRs), encompassing 11% of the genome. By integrating a multitude of genomic data, we found that VMRs enrich for histone modifications indicative of enhancers, suggesting their role as regulatory elements marking cell type specificity. VMRs enrich for transcription factor binding sites in a tissue-dependent manner. Importantly, they enrich for GWAS variants, suggesting VMRs could potentially be implicated in disease and complex traits. Taken together, our results highlight the link among CpG methylation variation, genetic variation and disease risk in a tissue-specific manner for many human cell types. Processed data includes new Samples and 75 Samples from GSE16368 and 2 Samples from GSE51565.
2016-04-06 | E-GEOD-69894 | biostudies-arrayexpress
Project description:Background: Researching the murine epigenome in disease models has been hampered by the lack of an appropriate and cost-effective DNA methylation array. Until recently, investigators have been limited to the relatively expensive and analysis intensive bisulphite sequencing methods. Here, we performed a comprehensive, comparative analysis between the new Mouse Methylation BeadChip (MMB) and reduced representation bisulphite sequencing (RRBS) in two murine models of colorectal carcinogenesis, providing insight into the utility to each platforms in a real world environment. Results: We captured 1.47x106 CpGs by RRBS and 2.64x105 CpGs by MMB, mapping to 13,778 and 13,365 CpG islands, respectively. RRBS captured significantly more CpGs per island (median 41 for RRBS versus 2 for MMB). We found that 64.4% of intra-island CpG methylation variability can be captured by measuring approximately one quarter of CpG island (CGI) CpGs. MMB was more precise in measuring DNA methylation, especially at sites that had low RRBS coverage. This impacted differential methylation analysis, with more statistically significantly differentially methylated CpG sites identified by MMB in all experimental conditions, however the difference was minute when appropriate thresholding for the magnitude of methylation change (0.2 beta value difference) was applied, providing confidence that both techniques can identify similar differential DNA methylation. Gene ontology enrichment analysis of differentially hypermethylated gene promoters identified similar biological processes and pathways by both RRBS and MMB across two murine model systems. Conclusion: MMB is an effective tool for profiling the murine methylome that performs comparably to RRBS, identifying similar differentially methylated pathways. Although MMB captures a similar proportion of CpG islands, it does so with fewer CpGs per island. We show that subsampling informative CpGs from CpG islands is an appropriate strategy to capture whole island variation. Choice of technology is experiment dependent and will be predicated on the underlying biology being probed.
2022-08-31 | E-MTAB-11985 | biostudies-arrayexpress
Project description:Background: Researching the murine epigenome in disease models has been hampered by the lack of an appropriate and cost-effective DNA methylation array. Until recently, investigators have been limited to the relatively expensive and analysis intensive bisulphite sequencing methods. Here, we performed a comprehensive, comparative analysis between the new Mouse Methylation BeadChip (MMB) and reduced representation bisulphite sequencing (RRBS) in two murine models of colorectal carcinogenesis, providing insight into the utility to each platforms in a real world environment. Results: We captured 1.47x106 CpGs by RRBS and 2.64x105 CpGs by MMB, mapping to 13,778 and 13,365 CpG islands, respectively. RRBS captured significantly more CpGs per island (median 41 for RRBS versus 2 for MMB). We found that 64.4% of intra-island CpG methylation variability can be captured by measuring approximately one quarter of CpG island (CGI) CpGs. MMB was more precise in measuring DNA methylation, especially at sites that had low RRBS coverage. This impacted differential methylation analysis, with more statistically significantly differentially methylated CpG sites identified by MMB in all experimental conditions, however the difference was minute when appropriate thresholding for the magnitude of methylation change (0.2 beta value difference) was applied, providing confidence that both techniques can identify similar differential DNA methylation. Gene ontology enrichment analysis of differentially hypermethylated gene promoters identified similar biological processes and pathways by both RRBS and MMB across two murine model systems. Conclusion: MMB is an effective tool for profiling the murine methylome that performs comparably to RRBS, identifying similar differentially methylated pathways. Although MMB captures a similar proportion of CpG islands, it does so with fewer CpGs per island. We show that subsampling informative CpGs from CpG islands is an appropriate strategy to capture whole island variation. Choice of technology is experiment dependent and will be predicated on the underlying biology being probed.
2022-10-15 | E-MTAB-12214 | biostudies-arrayexpress
Project description:DNA methylation is a defining feature of mammalian cellular identity and is essential for normal development. Most cell types, except germ cells and pre-implantation embryos, display relatively stable DNA methylation patterns, with 70-80% of all CpGs being methylated. Despite recent advances we still have a too limited understanding of when, where and how many CpGs participate in genomic regulation. Here we report the in depth analysis of 42 whole genome bisulfite sequencing (WGBS) data sets across 30 diverse human cell and tissue types. We observe dynamic regulation for only 21.8% of autosomal CpGs within a normal developmental context, a majority of which are distal to transcription start sites. These dynamic CpGs co-localize with gene regulatory elements, particularly enhancers and transcription factor binding sites (TFBS), which allow identification of key lineage specific regulators. In addition, differentially methylated regions (DMRs) often harbor SNPs associated with cell type related diseases as determined by GWAS. The results also highlight the general inefficiency of WGBS as 70-80% of the sequencing reads across these data sets provided little or no relevant information regarding CpG methylation. To further demonstrate the utility of our DMR set, we use it to classify unknown samples and identify representative signature regions that recapitulate major DNA methylation dynamics. In summary, although in theory every CpG can change its methylation state, our results confirm that only a fraction does so as part of coordinated regulatory programs. Therefore our selected DMRs can serve as a starting point to help guide novel, more effective reduced representation approaches to capture the most informative fraction of CpGs as well as further pinpoint putative regulatory elements. Analysis of 42 human WGBS libraries comprising 30 distinct primary cell lines, tissues, in vitro derived cell types and cell lines. BiSeq raw sequencing reads were aligned using maq in bisulfite mode (Li et al. 2008) or bsmap 2.7 (Xi et al. 2009) against human genome version hg19/GRCh37, discarding duplicate reads. DNA methylation calling was performed based on an extended custom software pipeline published previously for RRBS (Gu et al., 2010). The bed files contain all seen CpGs within the given library. The number of methylated reads/number of total reads is listed in the score column. This study includes a re-analysis of Samples from the NIH Roadmap Epigenomics Mapping Consortium (REMC; GSE16256, GSE17312), Hodges et al. 2011 (GSE31971), Molaro et al. 2011 (GSE30340), and Xi et al. 2013. WARNING: This submission is incomplete pending further deposits of raw data files from Meissner via EDACC.
2013-08-09 | E-GEOD-46644 | biostudies-arrayexpress
Project description:DNA methylation is a defining feature of mammalian cellular identity and is essential for normal development. Most cell types, except germ cells and pre-implantation embryos, display relatively stable DNA methylation patterns, with 70-80% of all CpGs being methylated. Despite recent advances we still have a too limited understanding of when, where and how many CpGs participate in genomic regulation. Here we report the in depth analysis of 42 whole genome bisulfite sequencing (WGBS) data sets across 30 diverse human cell and tissue types. We observe dynamic regulation for only 21.8% of autosomal CpGs within a normal developmental context, a majority of which are distal to transcription start sites. These dynamic CpGs co-localize with gene regulatory elements, particularly enhancers and transcription factor binding sites (TFBS), which allow identification of key lineage specific regulators. In addition, differentially methylated regions (DMRs) often harbor SNPs associated with cell type related diseases as determined by GWAS. The results also highlight the general inefficiency of WGBS as 70-80% of the sequencing reads across these data sets provided little or no relevant information regarding CpG methylation. To further demonstrate the utility of our DMR set, we use it to classify unknown samples and identify representative signature regions that recapitulate major DNA methylation dynamics. In summary, although in theory every CpG can change its methylation state, our results confirm that only a fraction does so as part of coordinated regulatory programs. Therefore our selected DMRs can serve as a starting point to help guide novel, more effective reduced representation approaches to capture the most informative fraction of CpGs as well as further pinpoint putative regulatory elements.
2013-08-09 | GSE46644 | GEO
Project description:DNA methylation is an important epigenetic modification involved in many biological processes and diseases. Many studies have mapped DNA methylation changes associated with embryogenesis, cell differentiation and cancer at a genome-wide scale. Our understanding of genome-wide DNA methylation changes in a developmental or disease-related context has been steadily growing. However, the investigation of which CpGs are variably methylated in different normal cell or tissue types is still limited. Here we present an in-depth analysis of 54 single-CpG-resolution DNA methylomes of normal human cell types by integrating high-throughput sequencing-based methylation data. We find that the percentage of unmethylated CpGs is relatively fixed regardless of cell type. However, which CpGs are unmethylated is cell-type specific. We categorize the 26 million human autosomal CpGs based on their methylation levels across multiple cell types to identify variably methylated CpGs. Among all the autosomal CpGs, 22.6% exhibit variable DNA methylation across cell types included in our study. These variably methylated CpGs form 66 thousand variably methylated regions (VMRs), encompassing 11% of the genome. By integrating a multitude of genomic data, we found that VMRs enrich for histone modifications indicative of enhancers, suggesting their role as regulatory elements marking cell type specificity. VMRs enrich for transcription factor binding sites in a tissue-dependent manner. Importantly, they enrich for GWAS variants, suggesting VMRs could potentially be implicated in disease and complex traits. Taken together, our results highlight the link among CpG methylation variation, genetic variation and disease risk in a tissue-specific manner for many human cell types.
2016-04-06 | GSE69894 | GEO
Project description:DNA methylation in CpG context is fundamental to the epigenetic regulation of gene expression in high eukaryotes. Disorganization of methylation status is implicated in many diseases, cellular differentiation, imprinting, and other biological processes. Techniques that enrich for biologically relevant CpG-rich genomic regions are desired since, depending on the size of an oragnism's methylome, the depth of sequencing required to cover all CpGs can be prohibitively expensive. Currently, restriction-enzyme based Reduced Representation Bisulfite Sequencing and its modified protocols are widely used to study methylation differences. Recently, Agilent Technologies and Roche NimbleGen have aimed to both reduce sequencing costs and capture CpGs of known biological relevance by marketing in-solution custom-capture hybridization platforms. These three methods target approximately 10-13% of the human methylome. For each platform - restriction-enzyme based enhanced reduced representation (ERRBS), capture based Agilent SureSelect Methyl-seq (SSMethylseq), and capture based Roche NimbleGen SeqCap Epi CpGiant (CpGiant) - we used human lung fibroblast cell line IMR90 DNA to make libraries according to each protocol and sequenced to equivalent depth. Overall, SSMethylSeq and CpGiant covered >95% of their designed capture regions whereas ERRBS covered 70% of its expected MspI regions. Methylation levels were concordant across the platforms. The concordance of annotations of CpG units for genomic features, displayed roughly the same proportions of genomic features. SSMethylSeq and CpGiant are most similar and cover marginally more annotated regions than ERRBS. However, the number of CpG units shared by all methods was low, ~26% of any platform. We conclude that captured based methods are largely consistent in terms of covered CpG loci although ERRBS provides comparable data at a significantly reduced price. Furthermore, library preparation for ERRBS can be performed with as little as 75ngs of starting material, whereas micrograms are needed for the capture hybridization techniques. Libraries were made from human lung fibroblast cell line IMR90 DNA for each protocol of ERRBS, Agilent SureSelect Methyl-seq, Roche NimbleGen SeqCap Epi CpGiant, and WGBS, then sequenced as paired-end 100bp on an Illumina HiSeq 2500.
2016-06-22 | E-GEOD-83595 | biostudies-arrayexpress
Project description:We have previously developed an approach that fractionates genomic DNA fragments depending on their CpG density (methyl-CpG-immunoprecipitation, MCIp), and adapted this approach to identify regions that are differentially methylated in the two closely related regulatory T-cells (Treg cells) and conventional T-cells (Tconv cells). Because Treg cells naturally occur at a relatively low frequency, we used a previously established protocol to expand Treg cells from a stable naïve Treg population that is characterized by the co-expression of CD4, CD25 and CD45RA. We separated gDNA of both expanded T cell lineages (Tregexp and Tconvexp) into unmethylated (CpG) and methylated pools (mCpG) using MCIp and compared cell type-specific differences in DNA methylation by co-hybridization of the two umethylated or the two methylated DNA subpopulations of Treg and Tconv, respectively, to these locus-wide custom tiling arrays. As enriched DNA-fragments from a cell type in the methylated fraction should be depleted in the unmethylated fraction, the signal intensities in CpG pool and mCpG pool hybridizations should complement themselves (âMirror-Imageâ approach) and thereby allow the identification of differentially methylated regions (DMR). Because we expected to find lineage-specific methylation differences with greater probability in regions associated with differential transcriptional activity, we limited our analysis to gene loci that showed cell type-specific gene expression in Treg versus Tconv cells plus a handful of control regions that were equally expressed in both cell types. The microarray used in this study covered 12 megabases of the human genome and contained 69 regions (with a median size of 100.000 kb) and 128 proximal promoter regions and 181 genes, which included a number of well known and functionally relevant genes like CD40LG, IFNG, FOXP3, IL2RA and CTLA4. Keywords: MCIp-on-chip; comparative genomic hybridization With MCIp gDNA from Treg or Tconv cells was separated into hypo- and hypermethylated pools. On each array, wheather the two hypomethylated fractions- one from Treg, the other from Tconv cells- or the two hypermethylated fractions were cohybridized. Two biological replicates.
2010-05-18 | E-GEOD-14233 | biostudies-arrayexpress
Project description:DNA methylation in CpG context is fundamental to the epigenetic regulation of gene expression in high eukaryotes. Disorganization of methylation status is implicated in many diseases, cellular differentiation, imprinting, and other biological processes. Techniques that enrich for biologically relevant CpG-rich genomic regions are desired since, depending on the size of an oragnism's methylome, the depth of sequencing required to cover all CpGs can be prohibitively expensive. Currently, restriction-enzyme based Reduced Representation Bisulfite Sequencing and its modified protocols are widely used to study methylation differences. Recently, Agilent Technologies and Roche NimbleGen have aimed to both reduce sequencing costs and capture CpGs of known biological relevance by marketing in-solution custom-capture hybridization platforms. These three methods target approximately 10-13% of the human methylome. For each platform - restriction-enzyme based enhanced reduced representation (ERRBS), capture based Agilent SureSelect Methyl-seq (SSMethylseq), and capture based Roche NimbleGen SeqCap Epi CpGiant (CpGiant) - we used human lung fibroblast cell line IMR90 DNA to make libraries according to each protocol and sequenced to equivalent depth. Overall, SSMethylSeq and CpGiant covered >95% of their designed capture regions whereas ERRBS covered 70% of its expected MspI regions. Methylation levels were concordant across the platforms. The concordance of annotations of CpG units for genomic features, displayed roughly the same proportions of genomic features. SSMethylSeq and CpGiant are most similar and cover marginally more annotated regions than ERRBS. However, the number of CpG units shared by all methods was low, ~26% of any platform. We conclude that captured based methods are largely consistent in terms of covered CpG loci although ERRBS provides comparable data at a significantly reduced price. Furthermore, library preparation for ERRBS can be performed with as little as 75ngs of starting material, whereas micrograms are needed for the capture hybridization techniques.
2016-06-22 | GSE83595 | GEO
Project description:We conducted a genome-wide DNA methylation analysis in CD19+ B-cells from CLL patient and normal control samples using reduced representation bisulfite sequencing (RRBS). The methylation status of 1.8-2.3 million CpGs in the CLL genome was determined; about 45% of these CpGs were located in more than 23,000 CpG islands (CGIs). While global CpG methylation was similar between CLL and normal B-cells, 1764 gene promoters were identified as being differentially methylated between the two groups. Aberrant hypermethylation was found in all HOX gene clusters and a significant number of WNT signaling pathway genes. The genes that were frequently hypermethylated were typically associated with histone H3 lysine 27 tri-methylation or bivalent domains in normal B-cells. An additional 152 genes were found to be differentially methylated between normal naïve and memory B-cells. Of these 152 genes, 123 were hypomethylated in memory B-cells when compared to naïve B-cells. Overall, CLL B-cells had methylation patterns more similar to memory B-cells than naïve B-cells. Cluster analysis showed that the tissue-specific methylated genes separated CLL samples into two groups with differential ZAP70 methylation status. Hypomethylation occurred more frequently in the gene body including introns, exons, and 3'-UTRs in CLL. The hypomethylation in the NFATc1 P2 promoter and first intron correlated with up-regulation of both NFATc1 RNA and protein expression levels in CLL suggesting that an epigenetic mechanism is involved in the constitutive activation of NFAT activity in CLL cells. This comprehensive DNA methylation map will further our understanding of the epigenetic contribution to cellular dysfunction in CLL. To perform a genome-wide analysis of DNA methylation in CLL, we applied the Reduced Representation Bisulfite Sequencing (RRBS) to CD19+ B-cells isolated from normal control and CLL peripheral blood samples. The genomic DNA from each sample was digested with the methylation-insensitive restriction enzyme MspI (restriction site, CCGG) and ligated to Illumina sequencing adaptors containing methylated cytosine residues. The ligated MspI fragments were size-selected, treated with sodium bisulfite, and amplified by PCR. The PCR products were purified and sequenced using Illumina GAIIx sequencer with a read length of 52 or 76bp. 11 CLL B-cell samples, 3 normal control samples including one each of normal CD19+, CD19+/ IgD+ naïve, and CD19+/CD27+ memory B-cell sample and three CLL cell lines (Mec-1, Mec-2, and Wac-3) were used. We generated 20-30 million Illumina sequencing reads for each sample.
2012-05-10 | E-GEOD-32698 | biostudies-arrayexpress