Reduced representation bisulfite sequencing of biparental and uniparental human embryonic stem cells and neural progenitor derived cells
Ontology highlight
ABSTRACT:
INSTRUMENT(S): NextSeq 500
ORGANISM(S): Homo sapiens
SUBMITTER: Shay Kinreich
PROVIDER: E-MTAB-14634 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Similar Datasets
Project description:Transcriptome analysis of uniparental and biparental human embryonic stem cells and neural progenitor derived cells
2025-12-02 | E-MTAB-14633 | biostudies-arrayexpress
Project description:RRBS was used to analyse global imprinting gene methylation pattern of offspring from the in vitro derived spermatids like cells and normal control Global imprinting gene methylation pattern comparision of two offspring (one male and one female) from the in vitro derived spermatids like cells and two normal control mice (one male and one female)
2016-02-26 | E-GEOD-76238 | biostudies-arrayexpress
Project description:Differential hyper- and hypo-methylation regions in G0 versus G4/G5 CMP The goal of this study is to evaluate changes in CpG methylation profilings of telomere dysfunctional common myeloid progenitor cells (CMP) as compared to their wild type controls Genomic DNA was extracted from sorted CMP populations isolated from 3 pools of G0 or 2 pools of G5 mice using UltraPure Phenol:Chloroform:Isoamyl Alcohol according to manufacturer’s instructions (Life Technologies). 14,000 to 30,000 cells were available for each sample, resulting in a minimum of 45ng of DNA. Genome-wide DNA methylation profiling was performed by RRBS. Library preparation and sequencing were performed at the UT MD Anderson Cancer Center’s DNA Methylation Analysis Core and Sequencing and Microarray Facility, according to published protocols. RRBS sequencing data were aligned and methylation was called using Bismark v0.7.119. In brief, bisulphite-treated DNA was aligned to UCSC Genome Browser mm10 reference genome using Bowtie. In total 29-38 million reads were generated per sample with alignment rates around 63%. Next, MethylKit10 implemented with Fisher’s exact test was used to compare the cytosine methylation profiles of G0 and G5 CMP. Gene promoter regions were calculated based on RefSeq gene annotations with regions starting 1 kb upstream of the annotated transcription start site (TSS) and extending 500 base pairs downstream of TSS. Exons, introns, and CpG islands coordinates were collected from the UCSC Genome Browser mm10 version.
2015-04-09 | E-GEOD-62392 | 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:Patients with genome-wide paternal uniparental disomy (GWpUPD) usually exhibit clinical features of Beckwith-Wiedemann syndrome (BWS) and a 46,XX karyotype, with all chromosomes showing isodisomy. Here, we report the case of a boy with classical BWS features harboring a 46,XY karyotype. Genetic analysis of autosomes and sex chromosomes revealed two distinct paternal genomes in peripheral blood leukocytes, whereas a normal biparental genome was detected in oral swabs under chimeric conditions. These findings indicated that the patient had genome-wide paternal uniparental heterodisomy (GWpUPhD). To our knowledge, this is the first reported case of a GWpUPhD chimera with a 46,XY karyotype. We therefore propose a potential mechanism underlying this phenomenon.
2026-04-04 | GSE326179 | GEO
Project description:This submission contains the data from https://www.biorxiv.org/content/10.1101/795047v2.full This dataset contains processed data of cell-free reduced representation bisulfite sequencing from 60 pediatric cancer samples, in order to classify them according to histopathological diagnosis. Files are provided in bismark coverage format. Samples were sequenced on a NextSeq 500.
2020-02-27 | E-MTAB-8770 | biostudies-arrayexpress
Project description:DNA methylation and the Polycomb Repression System are epigenetic mechanisms that play important roles in maintaining transcriptional repression. Recent evidence suggests that DNA methylation can attenuate the binding of Polycomb protein components to chromatin and thus plays a role in determining their genomic targeting. However, whether this role of DNA methylation is important in the context of transcriptional regulation is unclear. By genome-wide mapping of the Polycomb Repressive Complex 2 (PRC2)-signature histone mark, H3K27me3, in severely DNA hypomethylated mouse somatic cells, we show that hypomethylation leads to widespread H3K27me3 redistribution, in a manner that reflects the local DNA methylation status in wild-type cells. Unexpectedly, we observe striking loss of H3K27me3 and PRC2 from Polycomb-target gene promoters in DNA hypomethylated cells, including Hox gene clusters. Importantly, we show that many of these genes become ectopically expressed in DNA hypomethylated cells, consistent with loss of Polycomb-mediated repression. An intact DNA methylome is required for appropriate Polycomb-mediated gene repression by constraining PRC2 targeting. These observations identify a previously unappreciated role for DNA methylation in gene regulation and therefore influence our understanding of how this epigenetic mechanism contributes to normal development and disease. comparison of Dnmt1+/+ vs Dnmt1-/- mouse embryonic fibroblasts by Reduced Representation Bisulfite Sequencing
2013-03-01 | E-GEOD-44760 | biostudies-arrayexpress
Project description:Zebrafish Primordial Germ Cells (PGCs) and somatic cells were isolated via FACS from Buc-GFP fish strain. Reduced representation bisulfite sequencing (RRBS) was performed on PGCs and somatic cells at high(6000-7000 cells per replicate), dome (7500 cells per replicate)and prim-5 stages(4500-5000 cells per replicate). Two replicates were collected for each cell type and stage.
2020-12-24 | E-MTAB-9858 | biostudies-arrayexpress
Project description:The samples were collected from the participants of the Finnish Diabetes Prediction and Prevention (DIPP) Study, born between 1995 and 2006. DIPP is a prospective follow-up cohort of children with a moderate or high risk of type 1 diabetes, based on the HLA-DR-DQ genotype. Islet cell autoantibodies (ICA, GADA, IAA, IA2A and ZnT8A) were measured 1 - 4 times per year until age 15 or year 2018. The aim was to study associations between perinatal DNA methylation marks and later progression to type 1 diabetes. Case individuals who became persistently positive for at least two biochemical autoantibodies (GADA, IAA, IA2A or ZnT8A) and/or were diagnosed with type 1 diabetes during the follow-up were compared to the control individuals who remained autoantibody-negative throughout the follow-up. These data were also used in the development of data analysis methodology in bisulfite sequencing studies. To protect the privacy of the study participants, the sequence read data are not publicly available. However, the processed data can be downloaded here. These include two count matrices: \\"methylated_reads\\" and \\"total_reads\\". The matrix \\"methylated_reads\\" contains methylated read counts at each high-coverage CpG site (altogether approximately 2.5 million rows) at each of the 173 samples (173 columns), and the matrix \\"total_reads\\" contains the corresponding total read counts (coverage). Please notice that the methylated read counts are read counts, not percentages. Methylation proportions can be calculated as methylated_reads/total_reads. The row names are the genomic locations of these CpG sites in hg19 (GRCh37) coordinates (1,2). For privacy reasons, all potential SNPs were excluded from these publicly available count matrices. Specifically, we removed all common (minor allele frequency > 1 %) human SNPs, as listed in dbSNP (3). We also removed all SNPs that were detected in one or more samples even with \\"low\\" evidence by BS-SNPer, which is a software for detecting SNPs from bisulfite sequencing data (4). Altogether 204443 out of 2752981 rows were removed from the original coverage-filtered count matrices that were analyzed in the present study. Description of the sample attributes: Individual: The individual-specific identifiers, such as “Subject1”. Since each sample is from a different individual, these correspond to the sample identifiers (Subject1 == Sample 1 etc.) Experimental Group: The variable of interest (called \\"class\\" in the associated publications) with three possible values: 1) case, 2) control and 3) NA (neither case nor control). 1) Case: became persistently positive for at least two biochemical autoantibodies (GADA, IAA, IA2A or ZnT8A) and/or diagnosed with type 1 diabetes during the follow-up. 2) Control: remained autoantibody-negative throughout the follow-up. 3) NA: The remaining 51 individuals with a missing value (“NA”) did not qualify as cases or controls, since they were either persistently positive for only 1 biochemical autoantibody or transiently positive for one or more autoantibodies. We excluded these 51 individuals from the case-control-comparison but included them in the comparison between the sexes. Library preparation batch: The sequencing libraries were prepared in 7 batches. The names of the batches do not have any special meaning. That is, \\"1A\\" is not necessarily more similar to \\"1B\\" than it is to \\"3B\\". We treated this as a categorical technical variable with 7 categories. PC1 and PC2: Projections of the sample-specific methylation proportion vectors on the first two orthonormal principal components. The principal component analysis (PCA) was performed on the original coverage-filtered methylation proportion matrix (methylated/total reads), where missing values at each CpG site were imputed by the median over samples with non-missing values. The original methylation proportion matrix included 2752981 rows, whereas these publicly available matrices include 2548538 rows (all potential SNPs excluded). Hence, PCA on the publicly available data would result in slightly different values for PC1 and PC2. We included these as covariates in the differential methylation analysis to represent technical variation (in addition to the library preparation batches). References 1. Church DM, Schneider VA, Graves T, Auger K, Cunningham F, Bouk N, et al. Modernizing reference genome assemblies. PLoS Biol. 2011 Jul;9(7):e1001091. 2. Genome Reference Consortium. NCBI downloads: https://hgdownload.soe.ucsc.edu/goldenPath/hg19/bigZips/hg19.fa.gz, accessed Feb 10th, 2019 3. NCBI. dbSNP: https://ftp.ncbi.nih.gov/snp/organisms/human_9606_b151_GRCh37p13/VCF/common_all_20180423.vcf.gz, accessed April 29th, 2021 4. Gao S, Zou D, Mao L, Liu H, Song P, Chen Y, et al. BS-SNPer: SNP calling in bisulfite-seq data. Bioinformatics. 2015 Dec 15;31(24):4006–8.
2022-06-06 | E-MTAB-10530 | biostudies-arrayexpress
Project description: Not available
| S-EPMC3883775 | biostudies-literature