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      Fine mapping chromatin contacts in capture Hi-C data


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          Hi-C and capture Hi-C (CHi-C) are used to map physical contacts between chromatin regions in cell nuclei using high-throughput sequencing. Analysis typically proceeds considering the evidence for contacts between each possible pair of fragments independent from other pairs. This can produce long runs of fragments which appear to all make contact with the same baited fragment of interest.


          We hypothesised that these long runs could result from a smaller subset of direct contacts and propose a new method, based on a Bayesian sparse variable selection approach, which attempts to fine map these direct contacts. Our model is conceptually novel, exploiting the spatial pattern of counts in CHi-C data. Although we use only the CHi-C count data in fitting the model, we show that the fragments prioritised display biological properties that would be expected of true contacts: for bait fragments corresponding to gene promoters, we identify contact fragments with active chromatin and contacts that correspond to edges found in previously defined enhancer-target networks; conversely, for intergenic bait fragments, we identify contact fragments corresponding to promoters for genes expressed in that cell type. We show that long runs of apparently co-contacting fragments can typically be explained using a subset of direct contacts consisting of <10 % of the number in the full run, suggesting that greater resolution can be extracted from existing datasets.


          Our results appear largely complementary to those from a per-fragment analytical approach, suggesting that they provide an additional level of interpretation that may be used to increase resolution for mapping direct contacts in CHi-C experiments.

          Electronic supplementary material

          The online version of this article (10.1186/s12864-018-5314-5) contains supplementary material, which is available to authorized users.

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          Most cited references14

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          Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

          The three-dimensional folding of chromosomes compartmentalizes the genome and and can bring distant functional elements, such as promoters and enhancers, into close spatial proximity 2-6. Deciphering the relationship between chromosome organization and genome activity will aid in understanding genomic processes, like transcription and replication. However, little is known about how chromosomes fold. Microscopy is unable to distinguish large numbers of loci simultaneously or at high resolution. To date, the detection of chromosomal interactions using chromosome conformation capture (3C) and its subsequent adaptations required the choice of a set of target loci, making genome-wide studies impossible 7-10. We developed Hi-C, an extension of 3C that is capable of identifying long range interactions in an unbiased, genome-wide fashion. In Hi-C, cells are fixed with formaldehyde, causing interacting loci to be bound to one another by means of covalent DNA-protein cross-links. When the DNA is subsequently fragmented with a restriction enzyme, these loci remain linked. A biotinylated residue is incorporated as the 5' overhangs are filled in. Next, blunt-end ligation is performed under dilute conditions that favor ligation events between cross-linked DNA fragments. This results in a genome-wide library of ligation products, corresponding to pairs of fragments that were originally in close proximity to each other in the nucleus. Each ligation product is marked with biotin at the site of the junction. The library is sheared, and the junctions are pulled-down with streptavidin beads. The purified junctions can subsequently be analyzed using a high-throughput sequencer, resulting in a catalog of interacting fragments. Direct analysis of the resulting contact matrix reveals numerous features of genomic organization, such as the presence of chromosome territories and the preferential association of small gene-rich chromosomes. Correlation analysis can be applied to the contact matrix, demonstrating that the human genome is segregated into two compartments: a less densely packed compartment containing open, accessible, and active chromatin and a more dense compartment containing closed, inaccessible, and inactive chromatin regions. Finally, ensemble analysis of the contact matrix, coupled with theoretical derivations and computational simulations, revealed that at the megabase scale Hi-C reveals features consistent with a fractal globule conformation.
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            Randomized Quantile Residuals

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              Capture Hi-C identifies the chromatin interactome of colorectal cancer risk loci

              Multiple regulatory elements distant from their targets on the linear genome can influence the expression of a single gene through chromatin looping. Chromosome conformation capture implemented in Hi-C allows for genome-wide agnostic characterization of chromatin contacts. However, detection of functional enhancer–promoter interactions is precluded by its effective resolution that is determined by both restriction fragmentation and sensitivity of the experiment. Here we develop a capture Hi-C (cHi-C) approach to allow an agnostic characterization of these physical interactions on a genome-wide scale. Single-nucleotide polymorphisms associated with complex diseases often reside within regulatory elements and exert effects through long-range regulation of gene expression. Applying this cHi-C approach to 14 colorectal cancer risk loci allows us to identify key long-range chromatin interactions in cis and trans involving these loci.

                Author and article information

                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                23 January 2019
                23 January 2019
                : 20
                : 77
                [1 ]ISNI 0000000121885934, GRID grid.5335.0, Department of Medicine, University of Cambridge, Addenbrooke’s Hospital, ; Hills Road, Cambridge, UK
                [2 ]ISNI 0000000121885934, GRID grid.5335.0, MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, ; Robinson Way, Cambridge, UK
                [3 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Current address: Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, ; Roosevelt Drive, Oxford, UK
                © The Author(s) 2018

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                : 13 July 2018
                : 27 November 2018
                Funded by: Wellcome Trust
                Award ID: WT107881
                Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MC_UU_00002/4
                Funded by: Medical Research Council
                Award ID: MC_UU_00002/9
                Methodology Article
                Custom metadata
                © The Author(s) 2019

                capture hi-c,chromatin conformation,bayesian statistics,variable selection
                capture hi-c, chromatin conformation, bayesian statistics, variable selection


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