Whole-genome DNA methylation maps for primate tissues
Ontology highlight
ABSTRACT:
ORGANISM(S): Pan troglodytes Homo sapiens Macaca fuscata
PROVIDER: GSE64830 | GEO | 2017/01/22
SECONDARY ACCESSION(S): PRJNA272127
REPOSITORIES: GEO
Similar Datasets
Project description:Whole-genome DNA methylation maps for primate tissues
| PRJNA272127 | ENA
Project description:DNA methylation plays critical roles in gene regulation and cellular specification without altering DNA sequences. The wide application of reduced representation bisulfite sequencing (RRBS) and whole genome bisulfite sequencing (bis-seq) opens the door to study DNA methylation at single CpG site resolution. One challenging question is how best to test for significant methylation differences between groups of biological samples in order to minimize false positive findings.We present a statistical analysis package, methylSig, to analyse genome-wide methylation differences between samples from different treatments or disease groups. MethylSig takes into account both read coverage and biological variation by utilizing a beta-binomial approach across biological samples for a CpG site or region, and identifies relevant differences in CpG methylation. It can also incorporate local information to improve group methylation level and/or variance estimation for experiments with small sample size. A permutation study based on data from enhanced RRBS samples shows that methylSig maintains a well-calibrated type-I error when the number of samples is three or more per group. Our simulations show that methylSig has higher sensitivity compared with several alternative methods. The use of methylSig is illustrated with a comparison of different subtypes of acute leukemia and normal bone marrow samples.methylSig is available as an R package at http://sartorlab.ccmb.med.umich.edu/software.Supplementary data are available at Bioinformatics online.
| S-EPMC4147891 | biostudies-other
Project description:To examine the Boea hygrometrica genome methylation landscape and assess its functional significance during dehydration, we generated the first single-base resolution genome methylation maps for leaf tissues of hydrated, dehydrating to 70% RWC and dehydrating to 10% RWC (desiccating). The overall density of methyl-cytosine was not broadly altered by dehydration. There is, however, a small increase in intensity both for the whole genome and regionally during the first dehydration step (70% RWC) returning to near hydrated levels as leaves desiccate. We identified 5575 differentially methylated region-related genes during dehydration. The finding may provide an important clue for exploring molecular mechanisms and the regulation of desiccation tolerance at the whole-genome level.
2016-07-13 | GSE48904 | GEO
Project description:BACKGROUND:Bisulfite sequencing is widely employed to study the role of DNA methylation in disease; however, the data suffer from biases due to coverage depth variability. Imputation of methylation values at low-coverage sites may mitigate these biases while also identifying important genomic features associated with predictive power. RESULTS:Here we describe BoostMe, a method for imputing low-quality DNA methylation estimates within whole-genome bisulfite sequencing (WGBS) data. BoostMe uses a gradient boosting algorithm, XGBoost, and leverages information from multiple samples for prediction. We find that BoostMe outperforms existing algorithms in speed and accuracy when applied to WGBS of human tissues. Furthermore, we show that imputation improves concordance between WGBS and the MethylationEPIC array at low WGBS depth, suggesting improved WGBS accuracy after imputation. CONCLUSIONS:Our findings support the use of BoostMe as a preprocessing step for WGBS analysis.
| S-EPMC5966887 | biostudies-literature
Project description:It has been thought that epigenetic changes underlie the evolutionary divergence of phenotype between closely related species. To study differences in DNA methylation between humans and chimanzees, we collected methylated DNAs from age- and gender-matched PBL samples of the two species, and deeply sequenced. The map counts were then converted to MEDIPS scores in 100-bp bins, which are related to absolute methylation levels.
2017-01-22 | GSE64828 | GEO
Project description:DNA methylation is essential for normal development and has been implicated in many pathologies including cancer. Our knowledge about the genome-wide distribution of DNA methylation, how it changes during cellular differentiation and how it relates to histone methylation and other chromatin modifications in mammals remains limited. Here we report the generation and analysis of genome-scale DNA methylation profiles at nucleotide resolution in mammalian cells. Using high-throughput reduced representation bisulphite sequencing and single-molecule-based sequencing, we generated DNA methylation maps covering most CpG islands, and a representative sampling of conserved non-coding elements, transposons and other genomic features, for mouse embryonic stem cells, embryonic-stem-cell-derived and primary neural cells, and eight other primary tissues. Several key findings emerge from the data. First, DNA methylation patterns are better correlated with histone methylation patterns than with the underlying genome sequence context. Second, methylation of CpGs are dynamic epigenetic marks that undergo extensive changes during cellular differentiation, particularly in regulatory regions outside of core promoters. Third, analysis of embryonic-stem-cell-derived and primary cells reveals that 'weak' CpG islands associated with a specific set of developmentally regulated genes undergo aberrant hypermethylation during extended proliferation in vitro, in a pattern reminiscent of that reported in some primary tumours. More generally, the results establish reduced representation bisulphite sequencing as a powerful technology for epigenetic profiling of cell populations relevant to developmental biology, cancer and regenerative medicine.
| S-EPMC2896277 | biostudies-literature
Project description:Mammalian development requires cytosine methylation, a heritable epigenetic mark of cellular memory believed to maintain a cell's unique gene expression pattern. However, it remains unclear how dynamic DNA methylation relates to cell type-specific gene expression and animal development. Here, by mapping base-resolution methylomes in 17 adult mouse tissues at shallow coverage, we identify 302,864 tissue-specific differentially methylated regions (tsDMRs) and estimate that >6.7% of the mouse genome is variably methylated. Supporting a prominent role for DNA methylation in gene regulation, most tsDMRs occur at distal cis-regulatory elements. Unexpectedly, some tsDMRs mark enhancers that are dormant in adult tissues but active in embryonic development. These 'vestigial' enhancers are hypomethylated and lack active histone modifications in adult tissues but nevertheless exhibit activity during embryonic development. Our results provide new insights into the role of DNA methylation at tissue-specific enhancers and suggest that epigenetic memory of embryonic development may be retained in adult tissues.
| S-EPMC4095776 | biostudies-literature
Project description:SummaryMethyl-Analyzer is a python package that analyzes genome-wide DNA methylation data produced by the Methyl-MAPS (methylation mapping analysis by paired-end sequencing) method. Methyl-MAPS is an enzymatic-based method that uses both methylation-sensitive and -dependent enzymes covering >80% of CpG dinucleotides within mammalian genomes. It combines enzymatic-based approaches with high-throughput next-generation sequencing technology to provide whole genome DNA methylation profiles. Methyl-Analyzer processes and integrates sequencing reads from methylated and unmethylated compartments and estimates CpG methylation probabilities at single base resolution.Availability and implementationMethyl-Analyzer is available at http://github.com/epigenomics/methylmaps. Sample dataset is available for download at http://epigenomicspub.columbia.edu/methylanalyzer_data.html.Contactfgh3@columbia.eduSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC3150045 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2015-06-03 | GSE64511 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below.
2013-03-28 | GSE39777 | GEO