Ontology highlight
ABSTRACT:
PROVIDER: PRJNA272798 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Data tracks from bisulfite sequencing (BS-seq) experiments were sorted by tissue or cell types and processed using an in-house algorithm that provides normalization functionality followed by generation of a track average. Re-analysis of Roadmap Epigenomics DNA methylation datasets using an in-house algorithm to create an average data track.
2015-01-20 | E-GEOD-64577 | biostudies-arrayexpress
Project description:Data tracks from chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq) experiments were sorted by tissue or cell types and processed using an in-house algorithm that provides normalization functionality followed by generation of a track average. Re-analysis of Roadmap Epigenomics H3K4me3 ChIP-seq datasets using an in-house algorithm to create an average data track.
2015-01-20 | E-GEOD-65049 | biostudies-arrayexpress
Project description:Track normalization and averaging of H3K4me3 ChIP-seq data across various cell and tissue types.
| PRJNA272800 | ENA
Project description:Track normalization and averaging of bislufite-seq DNA methylation data across various cell and tissue types
| PRJNA271345 | ENA
Project description:Track normalization and averaging of H3K4me3 ChIP-seq data across various cell and tissue types from Mouse ENCODE.
| PRJNA298126 | ENA
Project description:<p>For the NIH Roadmap Epigenomics project, we applied ChlP-Seq, HTBS and WGBS pipelines to generate comprehensive high-resolution maps of chromatin state and DNA methylation for 100 diverse cell types. Cell types were selected for their biological and medical importance, and for their potential to maximize the comprehensiveness of acquired epigenomic data. They include human ES cells, ES-derived cells, mesenchymal stem cells, reprogrammed stem cells and primary tissues. Comprehensive characterization of epigenetic marks ("the epigenome") is a critical step towards a global understanding of the human genome in health and disease. In this study we provide unprecedented views of the human epigenetic landscape and its variation across cell states, which offer fundamental insight into the functions and interrelationships of epigenetic marks, and provide a framework for future studies of normal and diseased epigenomes.</p> <p><b>The Roadmap Epigenomics Broad cohort is utilized in the following dbGaP sub-study.</b> To view molecular data and derived variables collected in this sub-study, please click on the following sub-study below or in the "Sub-studies" box located on the right hand side of this top-level study page <a href="study.cgi?study_id=phs000700">phs000700</a> the Roadmap Epigenomics Broad cohort. <ul> <li><a href="study.cgi?study_id=phs000610">phs000610</a> RM_Epigenomics_Broad_Alz</li> </ul> </p>
| phs000700 | dbGaP
Project description:Data tracks from bisulfite sequencing (BS-seq) experiments were sorted by tissue or cell types and processed using an in-house algorithm that provides normalization functionality followed by generation of a track average.
2015-01-20 | GSE64577 | GEO
Project description:Data tracks from chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq) experiments were sorted by tissue or cell types and processed using an in-house algorithm that provides normalization functionality followed by generation of a track average.
2015-01-20 | GSE65049 | GEO
Project description:Recently, various groups managed to isolate naïve human embryonic stem cell (hESC) state in vitro has come into acceptance. However, a thorough epigenetic characterization of this human ground state, defined as a state without any epigenetic restrictions, and how that compares to mouse is currently lacking. Also, the epigenomic remodeling required to obtain the ground state, and the important transient processes occurring during the remodeling, have remained elusive in human. Here, we address these issues by using an untargeted mass spectrometry-based (MS) approach to profile the histone epigenome in a time-resolved experimental design. Special care was given to defining the naïve hESC state that was reached over 12 passages (P12, 37 days) in feeder-free conditions in this study. We found that conversion is a multi-staged process with a prominent cellular disturbance after stimulation (P3), an increase in cell proliferation between P3 and P6 and a naïve cell state stabilizing between P9 and P12. In total, 20 different histone post-translational modifications (hPTMs) changed significantly over time from primed to naïve hESCs. Most notably, H3K27me3 is the most prominently increasing hPTM in naïve hESCs, in line with what we recently described in mouse. Essentially, we present a first roadmap of the changing human histone epigenome from primed to naïve state and emphasize that the overlap with mouse hints at a conserved Mammalian epigenetic signature of the ground state.
2019-11-27 | PXD013067 | Pride
Project description:Thousands of epigenomic datasets have been generated in the past decade, but it is difficult for researchers to effectively utilize all the data relevant to their projects. Systematic integrative analysis can help meet this need, and the VISION project was established for ValIdated Systematic IntegratiON of epigenomic data in hematopoiesis. Here, we systematically integrated extensive data recording epigenetic features and transcriptomes from many sources, including individual laboratories and consortia, to produce a comprehensive view of the regulatory landscape of differentiating hematopoietic cell types in mouse. By employing IDEAS as our Integrative and Discriminative Epigenome Annotation System, we identified and assigned epigenetic states simultaneously along chromosomes and across cell types, precisely and comprehensively. Combining nuclease accessibility and epigenetic states produced a set of over 200,000 candidate cis-regulatory elements (cCREs) that efficiently capture enhancers and promoters. The transitions in epigenetic states of these cCREs across cell types provided insights into mechanisms of regulation, including decreases in numbers of active cCREs during differentiation of most lineages, transitions from poised to active or inactive states, and shifts in nuclease accessibility of CTCF-bound elements. Regression modeling of epigenetic states at cCREs and gene expression produced a versatile resource to improve selection of cCREs potentially regulating target genes. These resources are available from our VISION website (usevision.org) to aid research in genomics and hematopoiesis.
2020-01-09 | GSE143261 | GEO