Compute the Significant Interactions of Hi-C data through HOMER
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE93834 | GEO | 2017/05/09
SECONDARY ACCESSION(S): PRJNA362564
REPOSITORIES: GEO
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Project description:Millions of cis-regulatory sequences have recently been found in the human genome, but the function of most cis-elements are not yet clear, in part due to the difficulty in determining their regulatory targets, which are often located millions of base pairs away and separated by one or more unrelated genes. To address this problem, the Hi-C method has been developed to identify long-range looping interactions in a genome-wide, unbiased fashion. However, current data analysis of Hi-C datasets cannot fully resolve regulatory interactions between enhancers and promoters due to the low resolution. Here, we generated a high-depth Hi-C dataset and applied a new analysis method that offers improved resolution permitting genome-wide identification of nearly one million chromatin interactions. We demonstrated the use of Hi-C to identify target promoters of enhancers regulated by NF-M-NM-:B signaling and signal-dependent dynamic chromatin interaction at these enhancers in human cells. Surprisingly, our results showed that most NF-M-NM-:B binding sites are looped to their regulatory targets prior to activation of the signaling pathway, and appear to undergo little change during signaling. This observation suggests that the chromatin organization landscape, once established in a cell type, is rather stable and may influence the selection and activation of target genes by a transcription factor. We performed Hi-C analysis using a human fibroblast cell line IMR90 before and after NF-M-NM-:B activation. In the meantime, we also performed ChIP-seq experiments to map the location of NF-M-NM-:B p65 subunit, RNA polymerase II, p300, and several histone modifications (including H3K4me1, H3K4me3, H3K27ac and H3K36me3) in IMR90 cells before and after transient TNF-M-NM-1 stimulation. Additionally, to monitor the dynamic transcription profiles, we also performed Global Run-On sequencing (GRO-seq).
2013-10-20 | E-GEOD-43070 | biostudies-arrayexpress
Project description:In this work, we generated hundreds of millions of Hi-C paired end sequence reads for three different human cells (RL follicular lymphoma cell line, primary tumor B-cells from an acute lymphoblastic leukemia patient, and MHH-CALL-4 B-cell acute lymphoblastic leukemia cell line) using the Hi-C technique. An in-house Bioinformatics software pipeline was developed and applied to map sequence reads to the human reference genome, producing a large data set of high-quality and high-resolution chromosome contacts. Our computational analysis on these data reveal some interesting properties of human genome conformation, including conformational conservation and variation of the genomes of different cells, intra- and inter-chromosomal interactions, aberrant chromosomal translocation, spatial gene clusters, spatial gene-gene interactions, and spatial gene-regulatory-element interaction. Furthermore, we derived spatial interactions between functional elements (genes, transcription factor binding sites) from the chromosomal interaction data. The data were then used to generate chromosome-/genome-wide gene-gene interaction networks, transcription factor binding site (TFBS) â TFBS networks, and gene-TFBS networks. Remarkably, the connectivity in both networks shows the hallmark features of scale-free networks, suggesting that spatial interactions of gene-gene, gene-TFBS, and TFBS-TFBS in a genome are far from random. Three different human cells (RL follicular lymphoma cell line, primary tumor B-cells from an acute lymphoblastic leukemia patient, and MHH-CALL-4 B-cell acute lymphoblastic leukemia cell line) were analyzed
2015-11-01 | E-GEOD-73924 | biostudies-arrayexpress
Project description:Epithelial cells in 2D and 3D culture exhibit significant differences in long-range genomic interactions [Hi-C]
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Project description:Large scale study on human fibroblast, IMR90, by top-down proteomics on a benchtop Orbitrap QExactive HF instrument. RAW files were collected in three main modes: (i) data-dependent top-2 with high resolution for both MS1 and MS2, or hi/hi; (ii) in hi/hi using the in-house written instrument control software AUTOPILOT; (iii) in med/hi mode, where MS1 scans are acquired with short transients.
2016-07-15 | MSV000079913 | MassIVE
Project description:Compute the Significant Interactions of Hi-C data through HOMER
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Project description:Recent studies have characterized the genomic structures of many eukaryotic cells, often with a focus on their relation to gene expression. So far, these studies have largely only investigated cells grown in 2D culture, although the transcriptomes of 3D cultured cells are generally closer to their in vivo phenotype. To examine the effects of spatial constraints on chromosome conformation, we investigated the genomic architecture of mouse hepatocytes grown in 2D and 3D cultures using in situ Hi-C. Our results reveal significant differences in long-range genomic interactions, notably in compartment identity and strength as well as in TAD-TAD interactions, but only minor differences at the TAD level. RNA-seq analysis reveals an up-regulation in the 3D cultured cells of those genes involved in physiological hepatocyte functions. We find that these genes are associated with only a subset of the structural changes, suggesting that the differences in genomic structure are indeed critically important for transcriptional regulation but also that there are major structural differences owing to other functions than gene expression. Overall, our results indicate that growth in 3D significantly alters longer-range genomic interactions, which may be consequential for a subset of genes that are important for the physiological functioning of the cell.
2020-09-30 | E-MTAB-8303 | biostudies-arrayexpress
Project description:We provide here an unprecedented analysis of the three-dimensional re-organization of the genome during cellular reprograming of one cell type into another by the ectopic expression of one single transcription factor. By integrating Hi-C analysis with ChIPseq-based detection of MYOD genome-wide binding and RNAseq profiling of gene expression, we discovered that MYOD causes profound alterations in the high order chromatin structure during fibroblast reprogramming into skeletal muscle cells. We also used CTCF ChIPSeq in IMR90 GSE31477 (GSM935404); CTCF ChIPSeq in human myoblasts (hMB) GSE29611 (GSM733762); CTCF ChIPSeq human myotubes (hMT) GSE29611 (GSM733783); H3K27Ac in IMR90 GSE43070 (GSM1055818); H3K27Ac in hMB GSE29611 (GSM733755); H3K27Ac in hMT GSE29611 (GSM733666) Same number of reads was used for H3K27Ac ChIP comparison. ChIPSeq p300 IMR90 GSE43070 (GSM1055812). We also used RNASeq from GSE90895 (GSM2417196, GSM2417197, GSM2417198, GSM2417204, GSM2417203)
2019-09-10 | GSE98509 | GEO
Project description:The spatial organization of the genome is intimately linked to its biological function, yet our understanding of higher order genomic structure is limited. Here we present high-resolution analyses of chromosomal organization in pluripotent and differentiated human and murine cell types determined using the Hi-C technique. We find that the mammalian genome is composed of over 1000 topological domains, which are stable among different cell types and highly conserved across species. These megabase-long genomic structures, defined by prominent local chromatin interactions and separated by narrow boundary regions, likely function to restrict regulatory interactions between cis-acting elements and limit the spreading of heterochromatin during cellular differentiation. Interestingly, the boundaries are enriched not only for binding sites of the insulator protein CTCF, but also for promoters of house keeping genes, supporting a role for both CTCF and housekeeping genes in the establishment or maintenance of the topological domains. Hi-C experiment were conducted in mESC, hESC and IMR90 cells
2012-04-10 | E-GEOD-35156 | biostudies-arrayexpress
Project description:Capture Hi-C (CHi-C) is a state-of-the art method for profiling chromosomal interactions involving targeted regions of interest (such as gene promoters) globally and at high resolution. Signal detection in CHi-C data involves a number of statistical challenges that are not observed when using other Hi-C-like techniques. We present a background model, and algorithms for normalisation and multiple testing that are specifically adapted to CHi-C experiments, in which many spatially dispersed regions are captured, such as in Promoter CHi-C. We implement these procedures in CHiCAGO (http://regulatorygenomicsgroup.org/chicago), an open-source package for robust interaction detection in CHi-C. We validate CHiCAGO by showing that promoter-interacting regions detected with this method are enriched for regulatory features and disease-associated SNPs. Three human CHi-C biological replicates were generated (comprising 1, 2and 3 technical replicates). Two mouse CHi-C biological replicates were generated (both comprising three technical replicates) and a mouse Hi-C dataset. The publicly available HiCUP pipeline (doi: 10.12688/f1000research.7334.1) was used to process the raw sequencing reads. This pipeline was used to map the read pairs against the mouse (mm9) and human (hg19) genomes, to filter experimental artefacts (such as circularized reads and re-ligations), and to remove duplicate reads. For the CHi-C data, the resulting BAM files were processed into CHiCAGO input files, retaining only those read pairs that mapped, at least on one end, to a captured bait. CHiCAGO then identified Hi-C restriction fragments interacting, with statistical significant, to captured baits.
2016-05-18 | E-GEOD-81503 | biostudies-arrayexpress
Project description:We here systematically studied the interaction network of bone marrow cells. To this end, we micro-dissected many small interacting structures (cell doublets, triplets etc.) into single cells, and sequenced their mRNAs, to infer cell identity. After grouping the cells into cell types (based on the single-cell transcriptomes), we identified actual physical interactions that occurred more, or less, than what would be expected by chance.
2017-11-01 | GSE89378 | GEO