MeDIP
Ontology highlight
ABSTRACT:
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR007492_1.fastq.gz | Fastqsanger.gz | |||
| ERR007492_2.fastq.gz | Fastqsanger.gz | |||
| ERR007493_1.fastq.gz | Fastqsanger.gz | |||
| ERR007493_2.fastq.gz | Fastqsanger.gz | |||
| ERR007494_1.fastq.gz | Fastqsanger.gz |
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Project description:Genome-wide DNA methylation profile of human spermatozoa using the meDIP technique and massively parallel sequencing. DNA methylation is an indispensible epigenetic modification required for regulating the expression of mammalian genomes. Immunoprecipitation-based methods for DNA methylome analysis are rapidly shifting the bottleneck in this field from data generation to data analysis, necessitating the development of better analytical tools. In particular, an inability to estimate absolute methylation levels remains a major analytical difficulty associated with immunoprecipitation-based DNA methylation profiling. To address this issue, we developed a cross-platform algorithm-Bayesian tool for methylation analysis (Batman)-for analyzing methylated DNA immunoprecipitation (MeDIP) profiles generated using oligonucleotide arrays (MeDIP-chip) or next-generation sequencing (MeDIP-seq). We developed the latter approach to provide a high-resolution whole-genome DNA methylation profile (DNA methylome) of a mammalian genome. Strong correlation of our data, obtained using mature human spermatozoa, with those obtained using bisulfite sequencing suggest that combining MeDIP-seq or MeDIP-chip with Batman provides a robust, quantitative and cost-effective functional genomic strategy for elucidating the function of DNA methylation. ArrayExpress Release Date: 2008-06-10 Publication Title: A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis Publication Author List: Thomas A Down; Vardhman K Rakyan; Daniel J Turner; Paul Flicek; Heng Li; Eugene Kulesha; Stefan Gräf; Nathan Johnson; Javier Herrero; Eleni M Tomazou; Natalie P Thorne; Liselotte Bäckdahl; Marlis Herberth; Kevin L Howe; David K Jackson; Marcos M Miretti; John C Marioni; Ewan Birney; Tim J P Hubbard; Richard Durbin; Simon Tavaré; Stephan Beck Person Roles: submitter; investigator Person Last Name: Flicek Person First Name: Paul Person Mid Initial Person Email: flicek@ebi.ac.uk Person Address: Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK Person Affiliation: European Bioinformatics Institute
2011-02-05 | GSE25686 | GEO
Project description:Classically, there are two types of endometrial cancer, endometrioid adenocarcinoma (EAC), or Type I; and uterine papillary serous carcinoma (UPSC), or Type II. These two types of cancers exhibit distinct DNA methylation levels in promoters of many genes. In EAC, many tumor suppressor genes were silenced due to DNA hypermethylation at their promoter region. However, promoters of many of these genes remained unmethylated in UPSC. Here, we described complete DNA methylome maps of endometrioid adenocarcinoma, uterine papillary serous carcinoma, and normal endometrium, by applying a combined strategy of methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylation-sensitive restriction enzyme sequencing (MRE-seq). We took a complementary and orthogonal approach to identify DNA methylation changes unique to the two endometrial cancer subtypes in an unbiased fashion. We generated complete DNA methylome maps for endometrioid adenocarcinoma (EAC, three samples), uterine papillary serous carcinomas (UPSC, three samples), and normal endometrium (pooled samples) by integrating data from methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylation-sensitive restriction enzyme sequencing (MRE-seq).
2014-10-12 | E-GEOD-51565 | biostudies-arrayexpress
Project description:To understand cancer biology, it is crucial to unravel malignant- and microenvironmental cell states. Deconvolution of the bulk RNA profile from tumors can provide this insight, but is severely impaired by interpatient heterogeneity of the malignant cells. Bayesian statistics enables to inform RNA-deconvolution with malignant cell fraction estimates from e.g. DNA sequencing. We developed cell fraction-informed deconvolution, and evaluated this experimentally using 46 bulk RNA profiles from blood with 19 CyTOF determined cell fractions from this repository.
2024-05-23 | GSE263756 | GEO
Project description:Unraveling complexity of DNA methylome is essential to decipher DNA methylation mechanism in life. However, this has been subjected to technological constraints to balance between cost and accurate measurement of the DNA methylation level. In this study, by innovatively introducing C-hydroxylmethylated adapters, we have developed MeDIP-Bisulfite sequencing (MB-seq), which could obtain DNA methylome of repertoire CpGs at single-base resolution. We found MB-seq only costs 10% of MethylC-seq, but covers 85% of total CpGs in human genome. Unlike absolute methylation levels determined by MethylC-seq and RRBS, MB-seq presented relative methylation levels that are linearly inflated. This has enlightened us to develop a MB-seq corresponding correction method for methylation level based on ridge regression, which integrates the data of MB-seq and RRBS to predict the methylation level of total 28.2 million CpGs on human genome with high accuracy (Pearson correlation coefficient, PCC=0.90). Moreover, by employing MB-seq, we generated the DNA methylome of an ovarian epithelial cell line (T29) and its oncogenic counterpart (T29H), respectively. After ridge regression, we identified 131,790 differential methylation regions (DMRs) with high accuracy between T29 and T29H, far more than 7,567 obtained from RRBS. Taken together, our result demonstrated that the MB-seq combined with ridge regression is a wide applicable approach for profiling of DNA methylome.
2015-10-01 | GSE62277 | GEO
Project description:Whole blood is a highly convenient and informative tissue from which to sample DNA and RNA in epigenomic and functional genomic studies, but it is comprised of multiple distinct cell types and this complexity significantly impairs our ability to interpret downstream differential methylation and/or differential expression results. In this multiple sclerosis (MS)-focused study we utilised an application of current statistical deconvolution methods to interrogate whole blood DNA methylation data thereby enabling the methylome of several immune cell types to be analysed independently. Methylome profiling on cell type-purified blood samples revealed optimal CpG sets for use as robust immune cell markers in the statistical deconvolution process. We show that it is possible to identify differentially methylated (DM) loci in a cell type specific manner using statistical deconvolution. Finally, we demonstrate that deconvolution improved the biological relevance and interpretability of our DM results, significantly enhancing concordance of the identified DM loci with loci previously shown to be genetically or epigenetically associated with MS.
2017-06-30 | GSE88824 | GEO
Project description:We report the 6mA methylation profiling in two Phytophthora species and one psdamt3 mutant. Genomic DNA of Phytophthora sojae P6497 mycelium (3-days old), Phytophthora infestans T30-4 mycelium and psdamt3 mutant T9 (lost 374bp by CRISPR/Cas9) was extracted and sonicated to 200-400bp using Biorupter UCD-600. The antibody sysy 202003 was used to immunoprecipitation. We find that 6mA is associated with lowly expressed genes in two Phytophthora species. 6mA methylome is preferentially associated with TEs, the genes and intergenic regions that form the gene-sparse compartments of Phytophthora genomes. In the psdamt3 mutant, 6mA level reduced in TEs and illustrates the uneven reduction pattern around the TSS.
2018-09-29 | GSE116089 | GEO
Project description:Methylated DNA enrichment is a key step in a microarray based genome-wide methylation profiling study, and even for future high-throughput sequencing based methylome analysis. In order to evaluate the sensitivity and accuracy of methylated DNA enrichment, we investigated and optimized a number of important parameters to improve the performance of several enrichment assays, including differential methylation hybridization (DMH), microarray-based methylation assessment of single samples (MMASS), and methylated DNA immunoprecipitation (MeDIP). With advantages and disadvantages unique to each approach, we found that assays based on methylation-sensitive enzyme digestion and those based on immunoprecipitation detected different methylated DNA fragments, indicating that they are complementary in their relative ability to detect methylation differences. Our study provides the first comprehensive evaluation for widely used methodologies for methylated DNA enrichment, and could be helpful for developing a cost effective approach for DNA methylation profiling.
2010-01-22 | GSE19974 | GEO
Project description:We report a high-throughput analysis of human breast cancer cells using methylated DNA immunoprecipitation-sequencing.in this study to reveal the difference in methylation between breast cancer drug-resistant cells and sensitive cells. A total of 55076 differentially methylated genes (DMGs) were detected, including 21061 hypermethylated DMGs and 34015 hypomethylated DMGs. Moreover, Gene Ontology (GO) analysis and KEGG pathway analysis reveal the function and pathway of screening genes. These results indicate that DNA methylation may be involved in regulating the occurrence and development of breast cancer.
2020-08-01 | GSE155485 | GEO
Project description:ChIP-seq has become the method of choice for studying functional DNA-protein interactions on a genome wide scale. The method is based on co-immunoprecipitation of DNA binding proteins with formaldehyde cross-linked DNA, followed by deep sequencing of immunoprecipitated chromatin fragments, allowing for the identification of binding sites with high accuracy [1-5]. Traditionally, genome-wide tiling path microarrays were used for the detection of specifically immunoprecipitated DNA fragments, but current next-generation sequencers now generate sufficient data to assay multiple samples in a single sequencing run, making this method more time and cost effective compared to microarray-based approaches [2]. However, due to limitations in read length of next generation sequencing technologies (50-76bp), it is not possible to sequence the complete length of immunoprecipitated DNA fragments which can be up to 2kb long reflecting biology in case of big protein complexes. As a consequence only the ends of the immunoprecipitated DNA fragments are sequenced. Deconvolution of sequencing reads mapping to the positive and negative strand is required to identify the real DNA binding site [4-9]. This limitation is most obvious in case of large complex regions where multiple binding sites of various regulatory elements are clustered close to each other. Deconvolution of such regions and the exact identification of individual binding positions is very challenging [4]. Similar problems can occur when studying histone positioning in cases where the length of ChIP fragments is bigger than the average distance of 2 neighboring nucleosomes. In addition frequently only narrow size range of immunoprecipitated fragments is selected for sequencing and thus possible bias towards binding regions within the selected range can be expected by loosing larger fragments possible originated from protein complexes interacting with DNA. To circumvent these complications, we modified the procedure for the preparation of ChIP-seq samples by introducing an additional extensive fragmentation round after isolation of immunoprecipitated chromatin to generate 70-110 bp long DNA fragments. For testing, we choose Tcf4 protein which is a well-studied downstream element of the Wnt-pathway for which the genome wide binding site profile is known was already known from ChIP-on-CHIP experiments [10]. Using the modified approach, we were able to identify Tcf4 binding sites at near nucleotide resolution without computational deconvolution. Furthermore, high-resolution information on genome-wide binding site regions allowed for the identification of potential novel Tcf4 co-factors as well as target genes. 5 samples + 3 input samples
2010-11-01 | E-GEOD-18481 | biostudies-arrayexpress
Project description:Malignant melanoma is the most fatal skin cancer with a high degree of genetic and epigenetic aberrations. To investigate the role of DNA methylation on melanoma heterogeneity, we performed methylated DNA immunoprecipitation (MeDIP) microarray analysis of 10 primary melanoma cell cultures.
2014-05-28 | GSE57971 | GEO