Unknown,Transcriptomics,Genomics,Proteomics

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High-resolution genome-wide in vivo footprinting of diverse transcription factors in human cells (ChIP-seq)

ABSTRACT: Using DNaseI hypersensitivity (HS) assays (Dnase-seq), high resolution DNaseI digestion profiles were generated genome-wide in diverse human cell types. We showed that within general regions of DNaseI HS that are known to identify locations of gene regulatory elements, DNaseI digestion patterns allowed us to identify locations of individual transcription factor binding sites that protected against the bound DNA against digestion. To measure the accuracy of these footprints, we also generated ChIP-seq data for the CTCF DNA binding factor in the same cell growths. We found that DNaseI footprints containing the CTCF canonical binding motif show significant ChIP-seq signal while CTCF binding motifs not in footprints show almost no signal providing one measure of valdation of the DNaseI footprints. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Five cell lines representing cervica carcinoma, chronic myeloid leukemia, embryonic stem cells, epidermal keratinocytes, and umbilical vein endothelial cells were analyzed using ChIP-seq.

ORGANISM(S): Homo sapiens

SUBMITTER: Terry Furey

PROVIDER: E-GEOD-25416 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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High-resolution genome-wide in vivo footprinting of diverse transcription factors in human cells.

Boyle Alan P AP Song Lingyun L Lee Bum-Kyu BK London Darin D Keefe Damian D Birney Ewan E Iyer Vishwanath R VR Crawford Gregory E GE Furey Terrence S TS

Genome research 20101124 3

Regulation of gene transcription in diverse cell types is determined largely by varied sets of cis-elements where transcription factors bind. Here we demonstrate that data from a single high-throughput DNase I hypersensitivity assay can delineate hundreds of thousands of base-pair resolution in vivo footprints in human cells that precisely mark individual transcription factor-DNA interactions. These annotations provide a unique resource for the investigation of cis-regulatory elements. We find t ...[more]

PMID: 21106903

Publication: 1/2

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Project description:The human body contains thousands of unique cell types, each with specialized functions. Cell identity is governed in large part by gene transcription programs, which are determined by regulatory elements encoded in DNA. To identify regulatory elements active in seven cell lines representative of diverse human cell types, we used DNase-seq and FAIRE-seq to map M-CM-"M-BM-^@M-BM-^\open chromatinM-CM-"M-BM-^@M-BM-^]. Over 870,000 DNaseI or FAIRE sites, which correspond largely to nucleosome depleted regions (NDRs), were identified across the seven cell lines, covering nearly 9% of the genome. The combination of DNaseI and FAIRE is more effective than either assay alone in identifying likely regulatory elements, as judged by coincidence with transcription factor binding locations determined in the same cells. Open chromatin common to all seven cell types tended to be at or near transcription start sites and encompassed more CTCF binding sites, while open chromatin sites found in only one cell type were typically located away from transcription start sites, and contained DNA motifs recognized by regulators of cell-type identity. As one example of its ability to identify functional DNA, we show that open chromatin regions bound by CTCF are potent insulators. We identified clusters of open regulatory elements (COREs) that were physically near each other and whose appearance was coordinated among one or more cell types. Gene expression and RNA Pol II binding data support the hypothesis that COREs control gene activity required for the maintenance of cell-type identity. This publicly available atlas of regulatory elements may prove valuable in identifying non-coding DNA sequence variants that are causally linked to human disease. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf DNase-seq, FAIRE-seq, and ChIP-seq were performed on seven human cell lines: GM12878 (lymphoblastoid), K562 (leukemia), HepG2 (hepatocellular carcinoma), HelaS3 (cervical carcinoma), HUVEC (human umbilical vein endothelial cells), NHEK (keratinocytes), and H1-ES (embryonic stem cells). For each cell line, two or three replicates were independently grown and split into three, one for each of the three experimental methods. Control ChIP experiments were performed on five of the cell lines with NHEK and H1-ES being excluded due to lack of material.

Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Yijun Ruan mailto:ruanyj@gis.a-star.edu.sg). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track, produced as part of the ENCODE Project, contains deep sequencing DNase data that will be used to identify sites where regulatory factors bind to the genome (footprints). Footprinting is a technique used to define the DNA sequences that interact with and bind DNA-binding proteins, such as transcription factors, zinc-finger proteins, hormone-receptor complexes, and other chromatin-modulating factors like CTCF. The technique depends upon the strength and tight nature of protein-DNA interactions. In their native chromatin state, DNA sequences that interact directly with DNA-binding proteins are relatively protected from DNA degrading endonucleases, while the exposed/unbound portions are readily degraded by such endonucleases. A massively parallel next-generation sequencing technique to define the DNase hypersensitive sites in the genome was adopted. Sequencing these next-generation-sequencing DNase samples to significantly higher depths of 300-fold or greater produces a base-pair level resolution of the DNase susceptibility maps of the native chromatin state. These base-pair resolution maps represent and are dependent upon the nature and the specificity of interaction of the DNA with the regulatory/modulatory proteins binding at specific loci in the genome; thus they represent the native chromatin state of the genome under investigation. The deep sequencing approach has been used to define the footprint landscape of the genome by identifying DNA motifs that interact with known or novel DNA binding proteins. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Cells were grown according to the approved ENCODE cell culture protocols. Digital DNaseI was performed by DNaseI digestion of intact nuclei, followed by isolating DNaseI 'double-hit' fragments as described in Sabo et al. (2006), and direct sequencing of fragment ends (which correspond to in vivo DNaseI cleavage sites) using the Solexa platform (27 bp reads). High-quality reads were mapped to the GRCh37/hg19 human genome using Bowtie 0.12.5 (Eland was used to map to NCBI36/hg18); only unique mappings were kept. DNaseI sensitivity is directly reflected in raw tag density (Signal), which is shown in the track as density of tags mapping within a 150 bp sliding window (at a 20 bp step across the genome). DNaseI hypersensitive zones (HotSpots) were identified using the HotSpot algorithm described in Sabo et al. (2004). False discovery rate thresholds of 1.0% (FDR 0.01) were computed for each cell type by applying the HotSpot algorithm to an equivalent number of random uniquely mapping 36-mers. DNaseI hypersensitive sites (DHSs or Peaks) were identified as signal peaks within 1.0% (FDR 0.01) hypersensitive zones using a peak-finding algorithm. Only DNase Solexa libraries from unique cell types producing the highest quality data, as defined by Percent Tags in Hotspots (PTIH ~40%) were designated for deep sequencing to a depth of over 200 million tags.

Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Richard Sandstrom mailto:sull@u.washington.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track, produced as part of the ENCODE Project, contains deep sequencing DNase data that will be used to identify sites where regulatory factors bind to the genome (footprints). Footprinting is a technique used to define the DNA sequences that interact with and bind DNA-binding proteins, such as transcription factors, zinc-finger proteins, hormone-receptor complexes, and other chromatin-modulating factors like CTCF. The technique depends upon the strength and tight nature of protein-DNA interactions. In their native chromatin state, DNA sequences that interact directly with DNA-binding proteins are relatively protected from DNA degrading endonucleases, while the exposed/unbound portions are readily degraded by such endonucleases. A massively parallel next-generation sequencing technique to define the DNase hypersensitive sites in the genome was adopted. Sequencing these next-generation-sequencing DNase samples to significantly higher depths of 300-fold or greater produces a base-pair level resolution of the DNase susceptibility maps of the native chromatin state. These base-pair resolution maps represent and are dependent upon the nature and the specificity of interaction of the DNA with the regulatory/modulatory proteins binding at specific loci in the genome; thus they represent the native chromatin state of the genome under investigation. The deep sequencing approach has been used to define the footprint landscape of the genome by identifying DNA motifs that interact with known or novel DNA binding proteins. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Cells were grown according to the approved ENCODE cell culture protocols. Digital DNaseI was performed by DNaseI digestion of intact nuclei, followed by isolating DNaseI 'double-hit' fragments as described in Sabo et al. (2006), and direct sequencing of fragment ends (which correspond to in vivo DNaseI cleavage sites) using the Solexa platform (27 bp reads). High-quality reads were mapped to the GRCh37/hg19 human genome using Bowtie 0.12.5 (Eland was used to map to NCBI36/hg18); only unique mappings were kept. DNaseI sensitivity is directly reflected in raw tag density (Signal), which is shown in the track as density of tags mapping within a 150 bp sliding window (at a 20 bp step across the genome). DNaseI hypersensitive zones (HotSpots) were identified using the HotSpot algorithm described in Sabo et al. (2004). False discovery rate thresholds of 1.0% (FDR 0.01) were computed for each cell type by applying the HotSpot algorithm to an equivalent number of random uniquely mapping 36-mers. DNaseI hypersensitive sites (DHSs or Peaks) were identified as signal peaks within 1.0% (FDR 0.01) hypersensitive zones using a peak-finding algorithm. Only DNase Solexa libraries from unique cell types producing the highest quality data, as defined by Percent Tags in Hotspots (PTIH ~40%) were designated for deep sequencing to a depth of over 200 million tags.

Project description:Cellular diversity in a multicellular organism like the human is achieved in part by distinct programs of gene expression at the level of transcription, which in turn are mediated by transcription factors (TFs). However, there are few systematic studies of the genomic binding of different types of TFs across a wide range of human cell types, especially in relation to gene expression. Using chromatin Immunoprecipitation followed by thigh-throughput sequencing (ChIP-seq) we identified an average of 45,000, 30,000 and 8,000 binding sites for CTCF, Pol2 and MYC respectively across eleven cell types.CTCF preferred to bind to intergenic regions while Pol2 and MYC tended to bind to core promoter regions. CTCF sites were highly conserved across diverse cell types, whereas MYC showed the greatest cell-type specificity. MYC co-localized with Pol2 at many of their binding sites and putative target genes. Cell-type specific binding sites, in particular for MYC and Pol II, were associated with cell-type specific functions. Patterns of binding in relation to gene features were generally invariant across different cell types. Pol II occupancy was higher over exons than adjacent introns, likely reflecting a link between transcriptional elongation and splicing. TF binding was positively correlated with the expression levels of their putative target genes, but combinatorial binding, in particular of MYC and Pol II was even more strongly associated with higher gene expression. Our ChIP-seq data sheds considerable light on how these transcription factors of different types function individually and in combination with one another to occupy target genomic loci and shape gene expression programs in a cell-type specific manner. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Keywords: Genome binding/occupancy profiling by high throughput sequencing ChIP-seq were performed on eleven human cell lines: GM12878 (lymphoblastoid), K562 (leukemia), HepG2 (hepatocellular carcinoma), HelaS3 (cervical carcinoma), HUVEC (human umbilical vein endothelial cells), NHEK (keratinocytes), H1-ES (embryonic stem cells), MCF7 (breast adenocarcinoma), FB8470 (Normal child fibroblasts), FB0167P (Progeria fibroblast), and H54 (glioblastoma). For each cell line, two or three replicates were independently grown and split into three, one for each of the three experimental methods. Control ChIP experiments were performed on five of the cell lines with NHEK and H1-ES being excluded due to lack of material. Additional ChIP-Seq data mentioned in the 'overall design' but not included in this Series are contained in the GSE30226 SubSeries.

Dataset Information

High-resolution genome-wide in vivo footprinting of diverse transcription factors in human cells (ChIP-seq)

Publications

High-resolution genome-wide in vivo footprinting of diverse transcription factors in human cells.

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