Differential DNA Methylation Analysis without a Reference Genome
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA298769 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR2650260.fastq.gz | Fastqsanger.gz | |||
| SRR2650270.fastq.gz | Fastqsanger.gz | |||
| SRR2650280.fastq.gz | Fastqsanger.gz | |||
| SRR2650261.fastq.gz | Fastqsanger.gz | |||
| SRR2650271.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 32 |
Similar Datasets
Project description:Genome-wide DNA methylation mapping uncovers epigenetic changes associated with animal development, environmental adaptation, and species evolution. To address the lack of high-throughput methods for studying DNA methylation in non-model organisms, we developed an integrated approach for studying DNA methylation differences without a reference genome. Experimentally, our method relies on an optimized 96-well protocol for reduced representation bisulfite sequencing (RRBS), which we have validated in nine species (human, mouse, rat, cow, dog, chicken, zebrafish, carp, and sea bass). Bioinformatically, we developed the RefFreeDMA software (http://RefFreeDMA.computational-epigenetics.org) to deduce ad hoc genomes directly from RRBS reads and to pinpoint differentially methylated regions. These regions are interpreted using motif enrichment analysis and/or cross-mapping to annotated genomes. We validated our method by reference-free analysis of cell type-specific DNA methylation in the blood of human, cow, and carp. In summary, we present a cost-effective method for epigenome analysis in ecology and evolution, which enables epigenome-wide association studies in natural populations and species without a reference genome.
2015-12-03 | GSE74026 | GEO
Project description:Genome-wide DNA methylation mapping uncovers epigenetic changes associated with animal development, environmental adaptation, and species evolution. To address the lack of high-throughput methods for DNA methylation analysis in non-model organisms, we developed an integrated approach for studying DNA methylation differences independent of a reference genome. Experimentally, our method relies on an optimized 96-well protocol for reduced representation bisulfite sequencing (RRBS), which we have validated in nine species (human, mouse, rat, cow, dog, chicken, carp, sea bass, and zebrafish). Bioinformatically, we developed the RefFreeDMA software to deduce ad hoc genomes directly from RRBS reads and to pinpoint differentially methylated regions between samples or groups of individuals (http://RefFreeDMA.computational-epigenetics.org). The identified regions are interpreted using motif enrichment analysis and/or cross-mapping to annotated genomes. We validated our method by reference-free analysis of cell-type-specific DNA methylation in the blood of human, cow, and carp. In summary, we present a cost-effective method for epigenome analysis in ecology and evolution, which enables epigenome-wide association studies in natural populations and species without a reference genome.
| S-EPMC4695333 | biostudies-literature
Project description:Differential DNA methylation in Multiple Sclerosis
| PRJNA417506 | ENA
Project description:Aging is a progressive process that results in the accumulation of intra- and extracellular alterations that in turn contribute to a reduction in health. Age-related changes in DNA methylation have been reported before and may be responsible for aging-induced changes in gene expression, although a causal relationship has yet to be shown. Using genome-wide assays, we analyzed age-induced changes in DNA methylation and their effect on gene expression with and without transient induction with the synthetic transcription modulating agent WY14,643. To demonstrate feasibility of the approach, we isolated peripheral blood mononucleated cells (PBMCs) from five young and five old healthy male volunteers and cultured them with or without WY14,643. Infinium 450K BeadChip and Affymetrix Human Gene 1.1 ST expression array analysis revealed significant differential methylation of at least 5 % (M-NM-^TYOM-bM-^@M-^I>M-bM-^@M-^I5 %) at 10,625 CpG sites between young and old subjects, but only a subset of the associated genes were also differentially expressed. Age-related differential methylation of previously reported epigenetic biomarkers of aging including ELOVL2, FHL2, PENK, and KLF14 was confirmed in our study, but these genes did not display an age-related change in gene expression in PBMCs. Bioinformatic analysis revealed that differentially methylated genes that lack an age-related expression change predominantly represent genes involved in carcinogenesis and developmental processes, and expression of most of these genes were silenced in PBMCs. No changes in DNA methylation were found in genes displaying transiently induced changes in gene expression. In conclusion, aging-induced differential methylation often targets developmental genes and occurs mostly without change in gene expression. Peripheral blood mononuclear cells were isolated from 10 volunteers (5 young 5 old), treated with the synthetic PPARalpha agonists WY14,643 or control for 13 hrs, and subjected to genome-wide DNA methylation and gene expression analysis. This entry contains the DNA methylation data.
2014-05-09 | E-GEOD-49064 | biostudies-arrayexpress
Project description:Aging is a progressive process that results in the accumulation of intra- and extracellular alterations that in turn contribute to a reduction in health. Age-related changes in DNA methylation have been reported before and may be responsible for aging-induced changes in gene expression, although a causal relationship has yet to be shown. Using genome-wide assays, we analyzed age-induced changes in DNA methylation and their effect on gene expression with and without transient induction with the synthetic transcription modulating agent WY14,643. To demonstrate feasibility of the approach, we isolated peripheral blood mononucleated cells (PBMCs) from five young and five old healthy male volunteers and cultured them with or without WY14,643. Infinium 450K BeadChip and Affymetrix Human Gene 1.1 ST expression array analysis revealed significant differential methylation of at least 5 % (ΔYO > 5 %) at 10,625 CpG sites between young and old subjects, but only a subset of the associated genes were also differentially expressed. Age-related differential methylation of previously reported epigenetic biomarkers of aging including ELOVL2, FHL2, PENK, and KLF14 was confirmed in our study, but these genes did not display an age-related change in gene expression in PBMCs. Bioinformatic analysis revealed that differentially methylated genes that lack an age-related expression change predominantly represent genes involved in carcinogenesis and developmental processes, and expression of most of these genes were silenced in PBMCs. No changes in DNA methylation were found in genes displaying transiently induced changes in gene expression. In conclusion, aging-induced differential methylation often targets developmental genes and occurs mostly without change in gene expression.
2014-05-09 | GSE49064 | GEO
Project description:Differential methylation profiling of 18 colon tumor samples vs normal colon mucosa using the LogRatios of samples/reference panel
2011-02-07 | GSE21181 | GEO
Project description:We describe a suite of predictive models, coined FASTmC, for non-reference, cost-effective exploration and comparative analysis of context-specific DNA methylation levels. Accurate estimations of true DNA methylation levels can be obtained from as few as several thousand short-reads generated from whole genome bisulfite sequencing. Our models make high-resolution time course or developmental, and large diversity studies practical regardless of species, genome size and availability of a reference genome.
2016-08-17 | GSE72155 | GEO
Project description:The aim of the study was to investigate the differential methylation in psoriatic lesional epidermis compared to non-lesional or healthy epidermis.
2017-11-25 | GSE103038 | GEO
Project description:Genome-wide DNA methylation profiles were determined on a set of fresh 44 bladder cancer tissues using normal blood as control. DNA amplicons were prepared using Differential Methylation Hybridization (DMH) method, subsequently hybridized on to the Agilent Human CpG island Microarray. The goal was to unravel the DNA methylation patterns in different subgropus of bladder cancer along with finding markers for progresssion and early diagnosis. 44 bladder cancer tissues were profiled against commercially available normal human genomic blood DNA (Promega, Madison, WI, USA) as a reference
2012-02-16 | E-GEOD-35824 | biostudies-arrayexpress
Project description:We surveyed the DNA methylation levels of 435,941 CpG sites in samples from 764 children at diagnosis of ALL and from 27 children at relapse. This survey uncovered four characteristic methylation signatures. First, compared with control blood cells, the methylomes of ALL cells shared 9,406 predominantly hypermethylated CpG sites, independent of cytogenetic background. Second, each cytogenetic subtype of ALL displayed a unique set of hyper- and hypomethylated CpG sites. The CpG sites that constituted these two signatures differed in their functional genomic enrichment to regions with marks of active or repressed chromatin. Third, we identified subtype-specific differential methylation in promoter and enhancer regions that were strongly correlated with gene expression. Fourth, a set of 6,612 CpG sites was predominantly hypermethylated in ALL cells at relapse, compared with matched samples at diagnosis. Analysis of relapse-free survival identified CpG sites with subtype-specific differential methylation that divided the patients into different risk groups, depending on their methylation status. The DNA methylation levels of primary pediatric ALL samples taken at diagnosis (n= 764), remission(n=86), first relapse (n=27), second relapse (n=5), fractionated blood cells from healthy blood donors (n=51), and methylation +/- controls (n=11) were analyzed with the Illumina Infinium HumanMethylation 450k BeadChips. One ALL sample was run in duplicate (technical replicate).
2013-09-18 | E-GEOD-49031 | biostudies-arrayexpress