MeDIP sequencing for human samples
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA649931 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR12358949_1.fastq.gz | Fastqsanger.gz | |||
| SRR12358949_2.fastq.gz | Fastqsanger.gz | |||
| SRR12358950_1.fastq.gz | Fastqsanger.gz | |||
| SRR12358950_2.fastq.gz | Fastqsanger.gz | |||
| SRR12358951_1.fastq.gz | Fastqsanger.gz |
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Project description:MeDIP Sequencing for 2 rat liver Samples
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Project description:Aim: We aim to compare current (MeDIP-seq), new (Illumina Infinium 450K BeadChip) and future (PacBio) methods for whole genome DNA methylation analysis. As the interest in determination of disease methylation profiles increases, the scope, advantages and limitations of these methods requires assessment. There are key questions to answer and specific challenges to overcome. For example, how much detail/resolution is sufficient to identify regions of differential methylation and regions of biological/medical significance within a sample? How much coverage of the genome is required for accurate methylation analysis? Is it important to confirm which regions of the genome are unmethylated in addition to focusing on those that are methylated? Loss of methylation may be of equal importance within the cell since this may also contribute to disease pathogenesis. A multi-method (affinity enrichment/bisulphite-conversion based/direct sequencing of methyl-cytosine) and technology platform (Illumina HiSeq/PacBio/Illumina Infinium BeadChip) comparison will enable us to determine the strengths and weakness of each method. We propose to compare four methods using two DNA samples from the Coriell Institute for Cell Repository to assess both current and future capabilities for whole genome methylation analysis in parallel: A) MeDIP-seq using Illumina HiSeq B) Illumina Infinium HumanMethylation 450K BeadChip and C) whole genome methylation sequencing using PacBio. Existing single molecule deep bisulphite sequencing data generated previously from these same samples at the WTSI for targeted regions (30-40 genes) on the human X chromosome will be used to assess performance of each method. The methods selected for this study will generate data covering a range of resolutions from a whole genome scan to array (target defined) resolution and up to single base pair, single molecule resolution; the highest level of detail possible with methods currently available.Samples: DNA from sibling pair GM01240 (female) and GM01240 (male).Requirements: Both samples will be analysed using;A.MeDIP-seq using Illumina HiSeq (one HiSeq lane, 75bp paired end, per sample) B.Illumina Infinium HumanMethylation 450K BeadChipWe are expecting a potentially unnecessary high coverage using one HiSeq lane per sample. However, for the MeDIP procedure we do not have a multiplexing procedure in place. Our requirements for PacBio sequencing have been discussed with and will be supported by the Sequencing Technology Development group.
2013-01-22 | E-ERAD-139 | biostudies-arrayexpress
Project description:Chronic pain is a global public health problem, but the underlying molecular mechanisms are not fully understood. Here we examine genome-wide DNA methylation, first in 50 identical twins discordant for heat pain sensitivity and then in 50 further unrelated individuals. Whole blood DNA methylation was characterized at 5.2 million loci by MeDIP-sequencing and assessed longitudinally to identify differentially methylated regions associated with high or low pain-sensitivity (pain-DMRs). Nine meta-analysis pain-DMRs show robust evidence for association (false discovery rate 5%) with the strongest signal in the pain gene TRPA1 (P=1.2M-CM-^W10-13). Several pain-DMRs show longitudinal stability consistent with susceptibility effects, have similar methylation levels in brain, and altered expression in skin. Our approach identifies epigenetic changes in both novel and established candidate genes that provide molecular insights into pain and may generalize to other complex traits. MeDIP-sequencing in 100 individulas using a 2 stage design: paired-end MeDIP-seq in 50 monozygotic twins and single-end MeDIP-seq in 50 unrelated individuals.
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Project description:we used methylated DNA immunoprecipitation sequencing (MeDIP-Seq) to detect DNA methylation levels and lncRNA-seq to detect lncRNA expression levels in 3 lung cancer samples and 3 matched normal samples. Then 255 differentially methylated genes (DMGs) and 246 differentially expressed lncRNAs (DELs) were identified. Finally, LINC01354 was founded to be differentially methylated and predictive of transcription. Besides, the prognostic value of differential methylation related DELs was analyzed.
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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.
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Project description:MeDIP-seq in human lung cancer tissues
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Project description:To collect human tissue, blood, and fecal samples from patients suffering from Inflammatory Bowel Disease and Colorectal Cancer. The samples will be used to establish biomimetic human organ-on-a-chip technology, as well as study the role of the microbiome in the pathogenesis in human gastrointestinal diseases.
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Project description:We report the application of methylated DNA immunopreciptation (MeDIP)-next generation sequencing for examining methylome in two different breeds of pigs. Two muscle samples from each animal/breed processed to isolated DNA. DNA was isolated from two muscle samples (one per breed) was used for methylated DNA immunoprecipitation (MeDIP). 5 methyl cytosine regions were immunoprecipitated and processed for next generation sequencing in Hiseq 2500 (Illumina). The pre-processed reads were mapped to the Sus scrofa genome v11.1 downloaded from UCSC (http://genome.ucsc.edu/) with Burrows–Wheeler alignment (BWA) (version 0.7.17). The MeDIP data generated was used for identification of differentially methylated regions (DMRs) across the chromosome between the breeds using Burrrows Eheeler Alignment (BWA version 0.7.17. Uniquely mapped reads were processed further with MEDIPS. A total of 1545 and 25 DMRs (P<0.1) were identified across the pig chromosome and mitochondrial DNA respectively.
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