Profiling of 27 type 2 diabetes GWAS loci using next-generation (NG) capture C in a human beta-cell model
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PROVIDER: EGAS00001006105 | EGA |
REPOSITORIES: EGA
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Project description:NG Capture-C provides high resolution chromatin conformation capture (3C) interaction maps. NG Capture-C was carried out in erythroid and hESC cells at the Hba, Hbb, Myc, and Slc25a37 locus to generate sequence libraries for analytical software testing.
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Project description:Genome-wide association studies have identified over 70 common variants that are associated with breast cancer risk. Most of these variants map to non-protein-coding regions; several map to gene deserts, regions of several hundred kb lacking protein-coding genes. We hypothesized that gene deserts harbour long-range regulatory elements that can physically interact with target genes to influence their expression. To test this, we developed Capture Hi-C (CHi-C), which by incorporating a sequence capture step into a Hi-C protocol, allows high-resolution analysis of targeted regions of the genome. We used CHi-C to investigate long-range interactions at three breast cancer gene deserts mapping to 2q35, 8q24.21 and 9q31.2. We identified interaction peaks between putative regulatory elements ("bait fragments") within the captured regions and "targets" that included both protein-coding genes and long non-coding (lnc)RNAs, over distances of 6.6 kb to 2.6 Mb. Target protein-coding genes were IGFBP5, KLF4, NSMCE2 and MYC; target lncRNAs included DIRC3, PVT1 and CCDC26. For two gene deserts we were able to define a set of SNPs that were correlated with the published risk variant and that clustered within the bait end of an interaction peak. Preliminary functional analyses implicate one SNP (rs12613955; 2q35) as a potentially functional variant. Capture Hi-C was carried out in BT483, SUM44, and GM06990 cell lines to investigate breast cancer risk loci 2q35, 8q24.21 and 9q31.2.
2014-08-04 | E-GEOD-55634 | biostudies-arrayexpress
Project description:HiCUP is a pipeline for processing sequence data generated by Hi-C, a technique used to investigate the three-dimensional organisation of a genome. The pipeline maps data to a specified reference genome and removes artefacts that would otherwise hinder subsequent analysis. HiCUP also provides an easy-to-interpret yet detailed quality control report that may be used by researchers to refine their experimental protocol for future studies. The software is freely available and has already been used for processing Hi-C data in several recently published peer-reviewed research articles. This experiment investigates the impact of using HiCUP to remove putative PCR amplification products in heavily duplicated Capture Hi-C libraries. Examination of three Capture Hi-C libraries
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Project description:In the article "Fra-1 regulates its target genes via binding to remote enhancers without exerting major control on chromatin architecture in triple negative breast cancers" by Bejjani et al., we used NG Capture-C approach to identify regulatory elements interacting with the promoters of 35 Fra-1 regulated genes in the triple negative breast cancer cell line MDA-MB-231
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Project description:Chromosome conformation capture (3C)-based assays are used to map chromatin interactions genome-wide. Quantitative analyses of chromatin interaction maps can lead to insights into the spatial organization of chromosomes and the mechanisms by which they fold. A number of protocols such as in situ Hi-C and Micro-C are now widely used and these differ in key experimental parameters including cross-linking chemistry and chromatin fragmentation strategy. To understand how the choice of experimental protocol determines the ability to detect and quantify aspects of chromosome folding we have performed a systematic evaluation of experimental parameters of 3C-based protocols. We find that different protocols capture different 3D genome features with different efficiencies. First, the use of crosslinkers such as DSG in addition to formaldehyde improves signal-to-noise allowing detection of thousands of additional loops and strengthening compartment signal. Second, fragmenting chromatin to the level of nucleosomes using MNase allows detection of more loops. On the other hand, protocols that generate larger multi-kb fragments produce stronger compartmentalization signals. We confirmed our results in multiple cell states such as pluripotent and differentiated cells as well as cell cycle stages; Mitosis and G1. Based on these insights we developed Hi-C 3.0, a single protocol that can be used to both efficiently detect chromatin loops and to quantify compartmentalization. Finally, this study produced ultra-deeply sequenced reference interaction maps using in situ Hi-C, Micro-C and Hi-C 3.0 for commonly cell lines in the 4D Nucleome Project.
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Project description:Understanding 3D genome structure requires high throughput, genome-wide approaches. However, assays for all vs. all chromatin interaction mapping are expensive and time consuming, which severely restricts their usage for large-scale mutagenesis screens or for mapping the impact of sequence variants. Computational models sophisticated enough to grasp the determinants of chromatin folding provide a unique window into the functional determinants of 3D genome structure as well as the effects of genome variation. A chromatin interaction predictor should work at the base pair level but also incorporate large-scale genomic context to simultaneously capture the large scale and intricate structures of chromatin architecture. Similarly, to be a flexible and generalisable approach it should also be applicable to data it has not been explicitly trained on. To develop a model with these properties, we designed a deep neuronal network (deepC) that utilizes transfer learning to accurately predict chromatin interactions from DNA sequence at megabase scale. The model generalizes well to unseen chromosomes and works across cell types, Hi-C data resolutions and a range of sequencing depths. DeepC integrates DNA sequence context on an unprecedented scale, bridging the different levels of resolution from base pairs to TADs. We demonstrate how this model allows us to investigate sequence determinants of chromatin folding at genome-wide scale and to predict the importance of regulatory elements and the impact of sequence variations.
2020-07-08 | GSE137435 | GEO
Project description:Genome-wide association studies have been tremendously successful in identifying genetic variants associated with complex diseases. The majority of association signals are intergenic and evidence is accumulating that a high proportion of signals lie in enhancer regions.We use Capture Hi-C to investigate, for the first time, the interactions between associated variants for four autoimmune diseases and their functional targets in B- and T-cell lines. Here we report numerous looping interactions and provide evidence that only a minority of interactions are common to both B- and T-cell lines, suggesting interactions may be highly cell-type specific; some disease-associated SNPs do not interact with the nearest gene but with more compelling candidate genes (for example, FOXO1, AZI2) often situated several megabases away; and finally, regions associated with different autoimmune diseases interact with each other and the same promoter suggesting common autoimmune gene targets (for example, PTPRC, DEXI and ZFP36L1). Capture Hi-C was carried out to characterise the interactions of confirmed susceptibility loci for four autoimmune diseases: rheumatoid arthritis (RA), type 1 diabetes (T1D), psoriatic arthritis (PsA) and juvenile idiopathic arthritis (JIA) with the aim of linking associated variants with disease-causing genes. We have tested the interactions in two complementary experiments: the first, Region Capture, targets regions associated with disease; the second, Promoter Capture, provides independent validation through capturing all known promoters within 500kb of lead associated disease SNPs. All experiments were performed in human T-cell (Jurkat) and B-cell (GM12878) lines in duplicate.
2015-11-24 | E-GEOD-69600 | biostudies-arrayexpress
Project description:Chromosome conformation capture (3C)-based assays are used to map chromatin interactions genome-wide. Quantitative analyses of chromatin interaction maps can lead to insights into the spatial organization of chromosomes and the mechanisms by which they fold. A number of protocols such as in situ Hi-C and Micro-C are now widely used and these differ in key experimental parameters including cross-linking chemistry and chromatin fragmentation strategy. To understand how the choice of experimental protocol determines the ability to detect and quantify aspects of chromosome folding we have performed a systematic evaluation of experimental parameters of 3C-based protocols. We find that different protocols capture different 3D genome features with different efficiencies. First, the use of crosslinkers such as DSG in addition to formaldehyde improves signal-to-noise allowing detection of thousands of additional loops and strengthening compartment signal. Second, fragmenting chromatin to the level of nucleosomes using MNase allows detection of more loops. On the other hand, protocols that generate larger multi-kb fragments produce stronger compartmentalization signals. We confirmed our results in multiple cell states such as pluripotent and differentiated cells as well as cell cycle stages; Mitosis and G1. Based on these insights we developed Hi-C 3.0, a single protocol that can be used to both efficiently detect chromatin loops and to quantify compartmentalization. Finally, this study produced ultra-deeply sequenced reference interaction maps using in situ Hi-C, Micro-C and Hi-C 3.0 for commonly cell lines in the 4D Nucleome Project.
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Project description:ING1b and GADD45a are nuclear proteins involved in the regulation of cell growth, apoptosis and DNA repair. We previously found that ING1b is essential to target GADD45a-mediated active DNA-demethylation via TET1 to specific loci. In order to study the physical interaction of distant GADD45a and ING1 bound regions, we performed multiplexed NG Capture-C chromatin conformation capture assay in wildtype and knockout mouse embryonic fibroblasts.
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Project description:Two biological replicates Hi-C and HPV16-specific Region Capture Hi-C libraries were prepared for each of the W12 cell lines. Capture Hi-C was performed using HPV16-specific RNA baits. Hi-C libraries alone were prepared from normal cervix tissue (Ncx).
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