Systematic determination and analysis of chromatin state dynamics in nine human cell types
Ontology highlight
ABSTRACT:
OTHER RELATED OMICS DATASETS IN: PRJNA343958PRJNA136829
ORGANISM(S): Homo sapiens
PROVIDER: GSE26386 | GEO | 2011/03/23
SECONDARY ACCESSION(S): PRJNA136829
REPOSITORIES: GEO
Similar Datasets
Project description: Not available
| S-EPMC3088773 | biostudies-literature
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: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: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:This is a dataset generated by the Drosophila Regulatory Elements modENCODE Project led by Kevin P. White at the University of Chicago. It contains ChIP-seq data generated on Solexa Genome Analyzer for 6 Histone modifications (H3K9me3, H3K27me3, H3K4me3, H3K4me1, H3K27Ac, H3K9Ac), PolII and CBP/p300. Each factor has been studied for 12 different time-points of Drosophila development. Keywords: Epigenetics For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf For each combination of time-point and antibody, triplicate ChIP experiments have been performed and hybridized on Agilent 244K arrays. The hybridizations have been verified by sequencing one replicate of IP and one replicate of Input following Solexa sequencing procedure.
2009-05-12 | E-GEOD-16013 | biostudies-arrayexpress
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:This is a dataset generated by the Drosophila Regulatory Elements modENCODE Project led by Kevin P. White at the University of Chicago. It contains ChIP-seq data generated on Solexa Genome Analyzer for several Transcription Factors and Chromatin associated Proteins. This dataset is called February_1st_datafreeze and is part of the regular datafreezes we produce for the modENCODE project. Keywords: Epigenetics For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf For each experiment, at least 2 replicates of ChIP have been performed (except Caudal in Adult Female and E4-8h) and compared to an Input sample(s). The factors analyzed in this freeze are: CTCF in S2 cells, Caudal in embryos 0-4h, embryos 4-8h, L3 larvae and Adult Female, EcR in White Pre-Pupae (10 to 11 hours and 30 to 33 hours) and HDAC1, HDAC3, HDAC6, HDAC4a and HDAC11 in Embryos 0-12h. ChIP samples have been sequenced on the Solexa Genome Analyser (36 bp reads) following the standard Illumina protocol.
2010-01-26 | E-GEOD-20000 | biostudies-arrayexpress
Project description:This is a dataset generated by the Drosophila Regulatory Elements modENCODE Project led by Kevin P. White at the University of Chicago. It contains ChIP-seq data generated on Solexa Genome Analyzer for 6 Histone modifications (H3K9me3, H3K27me3, H3K4me3, H3K4me1, H3K27Ac, H3K9Ac), PolII and CBP/p300. Each factor has been studied for 12 different time-points of Drosophila development. Keywords: Epigenetics For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf
2009-05-12 | GSE16013 | GEO
Project description:This is a dataset generated by the Drosophila Regulatory Elements modENCODE Project led by Kevin P. White at the University of Chicago. It contains ChIP-seq data generated on Solexa Genome Analyzer for several Transcription Factors and Chromatin associated Proteins. This dataset is called February_1st_datafreeze and is part of the regular datafreezes we produce for the modENCODE project. Keywords: Epigenetics For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf
2010-01-26 | GSE20000 | GEO
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (mailto:jernst@mit.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). 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
2012-05-24 | GSE38163 | GEO