Methylation dynamics of mouse neuron by MeDIP-seq and MRE-seq
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE53694 | GEO | 2015/02/20
SECONDARY ACCESSION(S): PRJNA232650
REPOSITORIES: GEO
Similar Datasets
Project description:Study DNA methylation of mouse neuron using MeDIP-seq and MRE-seq DNA methylation of mouse neuron using MeDIP-seq and MRE-seq
2015-02-20 | E-GEOD-53694 | biostudies-arrayexpress
Project description:Understanding the impact of DNA methylation within different disease contexts often requires accurate assessment of these modifications in a genome-wide fashion. Frequently, patient-derived tissue stored in long-term hospital tissue banks have been preserved using formalin-fixation paraffin-embedding (FFPE). While these samples can comprise valuable resources for studying disease, the fixation process ultimately compromises the DNA’s integrity and leads to degradation. Degraded DNA can complicate CpG methylome profiling using traditional techniques, particularly when performing methylation sensitive restriction enzyme sequencing (MRE-seq), yielding high backgrounds and resulting in lowered library complexity. Here, we provide results using our new MRE-seq protocol (Capture MRE-seq), tailored to preserving unmethylated CpG information when using samples with highly degraded DNA. The results using Capture MRE-seq correlate well (0.92) with traditional MRE-seq calls when profiling non-degraded samples, and can recover unmethylated regions in highly degraded samples when traditional MRE-seq fails, which we validate using bisulfite sequencing-based data (WGBS) as well as methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq).
2023-03-01 | GSE155968 | GEO
Project description:DNA CpG methylation is a widespread epigenetic mark in high eukaryotes including mammals. DNA methylation plays key roles in diverse biological processes such as X chromosome inactivation, transposable element repression, genomic imprinting, and control of gene expression. Recent advancements in sequencing-based DNA methylation profiling methods provide an unprecedented opportunity to measure DNA methylation in a genome-wide fashion, making it possible to comprehensively investigate the role of DNA methylation. Several methods have been developed, such as Whole Genome Bisulfite Sequencing (WGBS), Reduced Representation Bisulfite Sequencing (RRBS), and enrichment-based methods including Methylation Dependent ImmunoPrecipitation followed by sequencing (MeDIP-seq), methyl-CpG binding domain (MBD) protein-enriched genome sequencing (MBD-seq), methyltransferase-directed Transfer of Activated Groups followed by sequencing (mTAG), and Methylation-sensitive Restriction Enzyme digestion followed by sequencing (MRE-seq). These methods differ by their genomic CpG coverage, resolution, quantitative accuracy, cost, and software for analyzing the data. Among these, WGBS is considered the gold standard. However, it is still a cost-prohibitive technology for a typical laboratory due to the required sequencing depth. We found that by integrating two enrichment-based methods that are complementary in nature (i.e., MeDIP-seq and MRE-seq), we can significantly increase the efficiency of whole DNA methylome profiling. By using two recently developed computational algorithms (i.e., M&M and methylCRF), the combination of MeDIP-seq and MRE-seq produces genome-wide CpG methylation measurement at high coverage and high resolution, and robust predictions of differentially methylated regions. Thus, the combination of the two enrichment-based methods provides a cost-effective alternative to WGBS. In this article we describe both the experimental protocols for performing MeDIP-seq and MRE-seq, and the computational protocols for running M&M and methylCRF.
| S-EPMC4300244 | biostudies-literature
Project description:Understanding the role of DNA methylation often requires accurate assessment and comparison of these modifications in a genome-wide fashion. Sequencing-based DNA methylation profiling provides an unprecedented opportunity to map and compare complete DNA CpG methylomes. These include whole genome bisulfite sequencing (WGBS), Reduced-Representation Bisulfite-Sequencing (RRBS), and enrichment-based methods such as MeDIP-seq, MBD-seq, and MRE-seq. An investigator needs a method that is flexible with the quantity of input DNA, provides the appropriate balance among genomic CpG coverage, resolution, quantitative accuracy, and cost, and comes with robust bioinformatics software for analyzing the data. In this chapter, we describe four protocols that combine state-of-the-art experimental strategies with state-of-the-art computational algorithms to achieve this goal. We first introduce two experimental methods that are complementary to each other. MeDIP-seq, or methylation-dependent immunoprecipitation followed by sequencing, uses an anti-methylcytidine antibody to enrich for methylated DNA fragments, and uses massively parallel sequencing to reveal identity of enriched DNA. MRE-seq, or methylation-sensitive restriction enzyme digestion followed by sequencing, relies on a collection of restriction enzymes that recognize CpG containing sequence motifs, but only cut when the CpG is unmethylated. Digested DNA fragments enrich for unmethylated CpGs at their ends, and these CpGs are revealed by massively parallel sequencing. The two computational methods both implement advanced statistical algorithms that integrate MeDIP-seq and MRE-seq data. M&M is a statistical framework to detect differentially methylated regions between two samples. methylCRF is a machine learning framework that predicts CpG methylation levels at single CpG resolution, thus raising the resolution and coverage of MeDIP-seq and MRE-seq to a comparable level of WGBS, but only incurring a cost of less than 5% of WGBS. Together these methods form an effective, robust, and affordable platform for the investigation of genome-wide DNA methylation.
| S-EPMC6007844 | biostudies-literature
Project description:We have identified a MORC3-regulated DNA element (MRE) that regulates the activation of IFNB1 upon loss of MORC3. To study the global transcriptional effects of the MRE, we deleted the MRE in STAT1–/– STAT2–/– and STAT1–/– STAT2–/– MORC3–/– BLaER1 monocytes and performed RNA-seq.
2021-11-01 | GSE182753 | GEO
Project description:Genome-wide maps of cytosine methylation, cytosine hydroxylmethylation and small non coding RNAs in mouse ES cells and upon guided differentiation to mesoendoderm cells. Mouse embryonic stem cells (E14) were guided differentiated into mesoendoderm lineages by activin-A induction. cells in three time points (day0, day4 and day6) were collected. The genome-wide studies on three cell types were summerized as following: cytosine methylation data were generated using methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq) and DNA digestion by methyl-sensitive restriction enzymes followed by sequencing (MRE-seq); DNA product for 5-hmC_ChIP-seq is generated by a selctive chemical labeling method (Nat. Biotechnol. 2011, 29, 68-72). E14 Day0 data for MRE-seq and MeDIP-seq are released first in previous publication and included in prior series GSE36114 ChIP-seq, 5-hmC-seq, MeDIP-Seq, MRE-Seq, ncRNA-Seq, and RNA-seq on activin-induced differentiating ES cells at 3 time points.
2012-09-22 | E-GEOD-38596 | biostudies-arrayexpress
Project description:We generated two types 5-methylcytosine (5-mC) data in E14 mouse embryonic stem cells, using methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq) and DNA digestion by methyl-sensitive restriction enzymes followed by sequencing (MRE-seq).
2014-01-09 | GSE34953 | GEO
Project description:Methylation dynamics of mouse neuron by MeDIP-seq and MRE-seq
| PRJNA232650 | ENA
Project description:In this study, we combined the MeDIP-Seq and MRE-Seq data of 6 ADU (active disease, untreated) and 5 HC (health control) samples to obtatin the differentially methylated regions (DMRs) between ADU and HC using the repMnM R package, and observed that those DMRs can successfully classify the two groups.
2019-09-28 | GSE92749 | GEO
Project description:BackgroundDNA methylation is an important regulator of gene expression and chromatin structure. Methylated DNA immunoprecipitation sequencing (MeDIP-Seq) is commonly used to identify regions of DNA methylation in eukaryotic genomes. Within MeDIP-Seq libraries, methylated cytosines can be found in both double-stranded (symmetric) and single-stranded (asymmetric) genomic contexts. While symmetric CG methylation has been relatively well-studied, asymmetric methylation in any dinucleotide context has received less attention. Importantly, no currently available software for processing MeDIP-Seq reads is able to resolve these strand-specific DNA methylation signals. Here we introduce DISMISS, a new software package that detects strand-associated DNA methylation from existing MeDIP-Seq analyses.ResultsUsing MeDIP-Seq datasets derived from Apis mellifera (honeybee), an invertebrate species that contains more asymmetric- than symmetric- DNA methylation, we demonstrate that DISMISS can identify strand-specific DNA methylation signals with similar accuracy as bisulfite sequencing (BS-Seq; single nucleotide resolution methodology). Specifically, DISMISS is able to confidently predict where DNA methylation predominates (plus or minus DNA strands - asymmetric DNA methylation; plus and minus DNA stands - symmetric DNA methylation) in MeDIP-Seq datasets derived from A. mellifera samples. When compared to DNA methylation data derived from BS-Seq analysis of A. mellifera worker larva, DISMISS-mediated identification of strand-specific methylated cytosines is 80 % accurate. Furthermore, DISMISS can correctly (p <0.0001) detect the origin (sense vs antisense DNA strands) of DNA methylation at splice site junctions in A. mellifera MeDIP-Seq datasets with a precision close to BS-Seq analysis. Finally, DISMISS-mediated identification of DNA methylation signals associated with upstream, exonic, intronic and downstream genomic loci from A. mellifera MeDIP-Seq datasets outperforms MACS2 (Model-based Analysis of ChIP-Seq2; a commonly used MeDIP-Seq analysis software) and closely approaches the results achieved by BS-Seq.ConclusionsWhile asymmetric DNA methylation is increasingly being found in growing numbers of eukaryotic species and is the predominant pattern observed in some invertebrate genomes, it has been difficult to detect in MeDIP-Seq datasets using existing software. DISMISS now enables more sensitive examinations of MeDIP-Seq datasets and will be especially useful for the study of genomes containing either low levels of DNA methylation or for genomes containing relatively high amounts of asymmetric methylation.
| S-EPMC4966778 | biostudies-literature