Ontology highlight
ABSTRACT:
OTHER RELATED OMICS DATASETS IN: GSE26386
PROVIDER: PRJNA136829 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Chromatin profiling has emerged as a powerful means for annotating genomic elements and detecting regulatory activity. Here we generate and analyze a compendium of epigenomic maps for nine chromatin marks across nine cell types, in order to systematically characterize cis-regulatory elements, their cell type-specificities, and their functional interactions. We first identify recurrent combinations of histone modifications and use them to annotate diverse regulatory elements including promoters, enhancers, transcripts and insulators in each cell type. We next characterize the dynamics of these elements, revealing meaningful patterns of activity for promoter states and exquisite cell type-selectivity for enhancer states. We define multi-cell activity profiles that reflect the patterns of enhancer state activity across cell types, as well as analogous profiles for gene expression, regulatory motif enrichments, and expression of the corresponding regulators. We use correlations between these profiles to link enhancers to putative target genes, to infer cell type-specific activators and repressors, and to predict and validate functional regulator binding motifs in specific chromatin states. These functional annotations and regulatory predictions enable us to revisit intergenic single-nucleotide polymorphisms (SNPs) associated with human disease in genome-wide association studies (GWAS). We find that for several diseases, top-scoring SNPs are precisely positioned within enhancer elements specifically active in relevant cell types. In several cases a disease variant affects a motif instance for one of the predicted causal regulators, thus providing a potential mechanistic explanation for the disease association. Our study presents a general framework for applying multi-cell chromatin state analysis to decipher cis-regulatory connections and their role in health and disease. 9 human cell types were cultured in duplicate and subject to Affymetrix profiling
2011-03-22 | E-GEOD-26312 | biostudies-arrayexpress
Project description:To investigate the chromatin state into an adult stem cell lineage, we generate cell-type specific chromatin state maps in the adult Drosophila intestine and identify principal chromatin state transitions during lineage determination. we profiled the binding sites of five chromatin-associated proteins from which the previously described five major types of chromatin.
2023-10-26 | GSE224965 | GEO
Project description:To investigate the chromatin state into an adult stem cell lineage, we generate cell-type specific chromatin state maps in the adult Drosophila intestine and identify principal chromatin state transitions during lineage determination. we profiled the binding sites of five chromatin-associated proteins from which the previously described five major types of chromatin.
2023-10-26 | GSE224963 | GEO
Project description:To investigate the chromatin state into an adult stem cell lineage, we generate cell-type specific chromatin state maps in the adult Drosophila intestine and identify principal chromatin state transitions during lineage determination. we profiled the binding sites of five chromatin-associated proteins from which the previously described five major types of chromatin.
2023-10-26 | GSE224962 | GEO
Project description:To establish a systematic approach for the determination of human biological & disease relevance through the generation of epigenome data in cell types of interest. Integration of cell type epigenome data with existing & newly generated reference data from human tissue and cell types to identify assay systems which will provide greater confidence in translating target biology and compound pharmacology to patients. To provide a framework for the identification of optimal cell types for target identification/validation studies and drug discovery programs across multiple therapeutic areas. Development of bioinformatics pipelines and CTTV components for analysis and provision of data
2015-06-09 | E-ERAD-369 | biostudies-arrayexpress
Project description:Chromatin profiling has emerged as a powerful means for annotating genomic elements and detecting regulatory activity. Here we generate and analyze a compendium of epigenomic maps for nine chromatin marks across nine cell types, in order to systematically characterize cis-regulatory elements, their cell type-specificities, and their functional interactions. We first identify recurrent combinations of histone modifications and use them to annotate diverse regulatory elements including promoters, enhancers, transcripts and insulators in each cell type. We next characterize the dynamics of these elements, revealing meaningful patterns of activity for promoter states and exquisite cell type-selectivity for enhancer states. We define multi-cell activity profiles that reflect the patterns of enhancer state activity across cell types, as well as analogous profiles for gene expression, regulatory motif enrichments, and expression of the corresponding regulators. We use correlations between these profiles to link enhancers to putative target genes, to infer cell type-specific activators and repressors, and to predict and validate functional regulator binding motifs in specific chromatin states. These functional annotations and regulatory predictions enable us to revisit intergenic single-nucleotide polymorphisms (SNPs) associated with human disease in genome-wide association studies (GWAS). We find that for several diseases, top-scoring SNPs are precisely positioned within enhancer elements specifically active in relevant cell types. In several cases a disease variant affects a motif instance for one of the predicted causal regulators, thus providing a potential mechanistic explanation for the disease association. Our study presents a general framework for applying multi-cell chromatin state analysis to decipher cis-regulatory connections and their role in health and disease.
2011-03-23 | GSE26312 | GEO
Project description:Chromatin profiling has emerged as a powerful means for annotating genomic elements and detecting regulatory activity. Here we generate and analyze a compendium of epigenomic maps for nine chromatin marks across nine cell types, in order to systematically characterize cis-regulatory elements, their cell type-specificities, and their functional interactions. We first identify recurrent combinations of histone modifications and use them to annotate diverse regulatory elements including promoters, enhancers, transcripts and insulators in each cell type. We next characterize the dynamics of these elements, revealing meaningful patterns of activity for promoter states and exquisite cell type-selectivity for enhancer states. We define multi-cell activity profiles that reflect the patterns of enhancer state activity across cell types, as well as analogous profiles for gene expression, regulatory motif enrichments, and expression of the corresponding regulators. We use correlations between these profiles to link enhancers to putative target genes, to infer cell type-specific activators and repressors, and to predict and validate functional regulator binding motifs in specific chromatin states. These functional annotations and regulatory predictions enable us to revisit intergenic single-nucleotide polymorphisms (SNPs) associated with human disease in genome-wide association studies (GWAS). We find that for several diseases, top-scoring SNPs are precisely positioned within enhancer elements specifically active in relevant cell types. In several cases a disease variant affects a motif instance for one of the predicted causal regulators, thus providing a potential mechanistic explanation for the disease association. Our study presents a general framework for applying multi-cell chromatin state analysis to decipher cis-regulatory connections and their role in health and disease.
2011-03-23 | GSE26320 | GEO
Project description:This track displays a chromatin state segmentation for each of nine human cell types (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?term=GM12878,H1-hESC,HepG2,HUVEC,HMEC,HSMM,K562,NHEK,NHLF). A common set of states across the cell types were learned by computationally integrating ChIP-seq data for nine factors plus input (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?term=CTCF,H3K4me1,H3K4me2,H3K4me3,H3K27ac,H3K9ac,H3K36me3,H4K20me1,H3K27me3,Input) using a Hidden Markov Model (HMM). In total, fifteen states were used to segment the genome, and these states were then grouped and colored to highlight predicted functional elements. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf ChIP-seq data from the Broad Histone (http://hgwdev.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg18&g=wgEncodeBroadChipSeq) track was used to generate this track. Data for nine factors plus input (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?term=CTCF,H3K4me1,H3K4me2,H3K4me3,H3K27ac,H3K9ac,H3K36me3,H4K20me1,H3K27me3,Input) and nine cell types (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?term=GM12878,H1-hESC,HepG2,HUVEC,HMEC,HSMM,K562,NHEK,NHLF) was binarized separately at a 200 base pair resolution based on a Poisson background model. The chromatin states were learned from this binarized data using a multivariate Hidden Markov Model (HMM) that explicitly models the combinatorial patterns of observed modifications (Ernst and Kellis, 2010). To learn a common set of states across the nine cell types, first the genomes were concatenated across the cell types. For each of the nine cell types, each 200 base pair interval was then assigned to its most likely state under the model. Detailed information about the model parameters and state enrichments can be found in (Ernst et al, accepted). This is release 1 (Jun 2011) of this track, and it is based on the NCBI36/hg18 release of the Broad Histone (http://hgwdev.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg18&g=wgEncodeBroadChipSeq) track. This track has also been lifted over to GRCh37/hg19 (http://hgwdev.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=wgEncodeBroadHmm). It is anticipated that the HMM methods will be run on the newer GRCh37/hg19 Broad Histone (http://hgwdev.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=wgEncodeBroadHistone) data and will replace the lifted version.
2012-05-23 | E-GEOD-38163 | biostudies-arrayexpress
Project description:Genome-wide profiling of histone modifications can provide systematic insight into the regulatory elements and programs engaged in a given cell type. However, conventional chromatin immunoprecipitation and sequencing (ChIP-seq) does not capture quantitative information on histone modification levels, requires large amounts of starting material, and involves tedious processing of each individual sample. Here we address these limitations with a technology that leverages DNA barcoding to profile chromatin quantitatively and in multiplexed format. We concurrently map relative levels of multiple histone modifications across multiple samples, each comprising as few as a thousand cells. We demonstrate the technology by monitoring dynamic changes following inhibition of P300, EZH2 or KDM5, by linking altered epigenetic landscapes to chromatin regulator mutations, and by mapping active and repressive marks in purified human hematopoietic stem cells. Hence, this technology enables quantitative studies of chromatin state dynamics across rare cell types, genotypes, environmental conditions and drug treatments.
2015-10-27 | GSE74359 | GEO