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The invention of chromosome conformation capture (3C) techniques, in particular the key method Hi-C providing genome-wide information about chromatin contacts, revolutionized the way we study the three-dimensional organization of the nuclear genome and how it affects transcription, replication, and DNA repair. Because the frequency of chromatin contacts between pairs of genomic segments predictably relates to the distance in the linear genome, the information obtained by Hi-C has also proved useful for scaffolding genomic sequences. Here, we review recent improvements in experimental procedures of Hi-C and its various derivatives, such as Micro-C, HiChIP, and Capture Hi-C. We assess the advantages and limitations of the techniques, and present examples of their use in recent plant studies. We also report on progress in the development of computational tools used in assembling genome sequences. [ABSTRACT FROM AUTHOR]

Eukaryotic genomes are structurally organized via the formation of multiple loops that create gene expression regulatory units called topologically associating domains (TADs). Here we revealed the KSHV TAD structure at 500 bp resolution and constructed a 3D KSHV genomic structural model with 2 kb binning. The latent KSHV genome formed very similar genomic architectures in three different naturally infected PEL cell lines and in an experimentally infected epithelial cell line. The majority of the TAD boundaries were occupied by structural maintenance of chromosomes (SMC1) cohesin complex and CCCTC-binding factor (CTCF), and the KSHV transactivator was recruited to those sites during reactivation. Triggering KSHV gene expression decreased prewired genomic loops within the regulatory unit, while contacts extending outside of regulatory borders increased, leading to formation of a larger regulatory unit with a shift from repressive to active compartments (B to A). The 3D genomic structural model proposes that the immediate early promoter region is localized on the periphery of the 3D viral genome during latency, while highly inducible noncoding RNA regions moved toward the inner space of the structure, resembling the configuration of a "bird cage" during reactivation. The compartment-like properties of viral episomal chromatin structure and its reorganization during the transition from latency may help facilitate viral gene transcription. IMPORTANCE The 3D architecture of chromatin allows for efficient arrangement, expression, and replication of genetic material. The genomes of all organisms studied to date have been found to be organized through some form of tiered domain structures. However, the architectural framework of the genomes of large double-stranded DNA viruses such as the herpesvirus family has not been reported. Prior studies with Kaposi's sarcoma-associated herpesvirus (KSHV) have indicated that the viral chromatin shares many biological properties exhibited by the host cell genome, essentially behaving as a mini human chromosome. Thus, we hypothesized that the KSHV genome may be organized in a similar manner. In this report, we describe the domain structure of the latent and lytic KSHV genome at 500 bp resolution and present a 3D genomic structural model for KSHV under each condition. These results add new insights into the complex regulation of the viral life cycle.

Chromosome conformation capture methodologies have provided insight into the effect of 3D genomic architecture on gene regulation. Capture Hi-C (CHi-C) is a recent extension of Hi-C that improves the effective resolution of chromatin interactions by enriching for defined regions of biological relevance. The varying targeting efficiency between capture regions, however, introduces bias not present in conventional Hi-C, making analysis more complicated. Here we consider salient features of an algorithm that should be considered in evaluating the performance of a program used to analyse CHi-C data in order to infer meaningful interactions. We use the program CHICAGO to analyse promoter capture Hi-C data generated on 28 different cell lines as a case study.

It is now evident that DNA forms an organized nuclear architecture, which is essential to maintain the structural and functional integrity of the genome. Chromatin organization can be systematically studied due to the recent boom in chromosome conformation capture technologies (e.g., 3C and its successors 4C, 5C and Hi-C), which is accompanied by the development of computational pipelines to identify biologically meaningful chromatin contacts in such data. However, not all tools are applicable to all experimental designs and all structural features. Capture Hi-C (CHi-C) is a method that uses an intermediate hybridization step to target and select predefined regions of interest in a Hi-C library, thereby increasing effective sequencing depth for those regions. It allows researchers to investigate fine chromatin structures at high resolution, for instance promoter-enhancer loops, but it introduces additional biases with the capture step, and therefore requires specialized pipelines. Here, we compare multiple analytical pipelines for CHi-C data analysis. We consider the effect of retaining multi-mapping reads and compare the efficiency of different statistical approaches in both identifying reproducible interactions and determining biologically significant interactions. At restriction fragment level resolution, the number of multi-mapping reads that could be rescued was negligible. The number of identified interactions varied widely, depending on the analytical method, indicating large differences in type I and type II error rates. The optimal pipeline depends on the project-specific tolerance level of false positive and false negative chromatin contacts. [ABSTRACT FROM AUTHOR]

It is now evident that DNA forms an organized nuclear architecture, which is essential to maintain the structural and functional integrity of the genome. Chromatin organization can be systematically studied due to the recent boom in chromosome conformation capture technologies (e.g., 3C and its successors 4C, 5C and Hi-C), which is accompanied by the development of computational pipelines to identify biologically meaningful chromatin contacts in such data. However, not all tools are applicable to all experimental designs and all structural features. Capture Hi-C (CHi-C) is a method that uses an intermediate hybridization step to target and select predefined regions of interest in a Hi-C library, thereby increasing effective sequencing depth for those regions. It allows researchers to investigate fine chromatin structures at high resolution, for instance promoter-enhancer loops, but it introduces additional biases with the capture step, and therefore requires specialized pipelines. Here, we compare multiple analytical pipelines for CHi-C data analysis. We consider the effect of retaining multi-mapping reads and compare the efficiency of different statistical approaches in both identifying reproducible interactions and determining biologically significant interactions. At restriction fragment level resolution, the number of multi-mapping reads that could be rescued was negligible. The number of identified interactions varied widely, depending on the analytical method, indicating large differences in type I and type II error rates. The optimal pipeline depends on the project-specific tolerance level of false positive and false negative chromatin contacts.

Abstract Capture Hi-C (CHi-C) is a new technique for assessing genome organization based on chromosome conformation capture coupled to oligonucleotide capture of regions of interest, such as gene promoters. Chromatin loop detection is challenging because existing Hi-C/4C-like tools, which make different assumptions about the technical biases presented, are often unsuitable. We describe a new approach, ChiCMaxima, which uses local maxima combined with limited filtering to detect DNA looping interactions, integrating information from biological replicates. ChiCMaxima shows more stringency and robustness compared to previously developed tools. The tool includes a GUI browser for flexible visualization of CHi-C profiles alongside epigenomic tracks.

Abstract Background 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. Results 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

Abstract Background Target enrichment combined with chromosome conformation capturing methodologies such as capture Hi-C (CHC) can be used to investigate spatial layouts of genomic regions with high resolution and at scalable costs. A common application of CHC is the investigation of regulatory elements that are in contact with promoters, but CHC can be used for a range of other applications. Therefore, probe design for CHC needs to be adapted to experimental needs, but no flexible tool is currently available for this purpose. Results We present a Java desktop application called GOPHER (Generator Of Probes for capture Hi-C Experiments at high Resolution) that implements three strategies for CHC probe design. GOPHER’s simple approach is similar to the probe design of previous approaches that employ CHC to investigate all promoters, with one probe being placed at each margin of a single digest that overlaps the transcription start site (TSS) of each promoter. GOPHER’s simple-patched approach extends this methodology with a heuristic that improves coverage of viewpoints in which the TSS is located near to one of the boundaries of the digest. GOPHER’s extended approach is intended mainly for focused investigations of smaller gene sets. GOPHER can also be used to design probes for regions other than TSS such as GWAS hits or large blocks of genomic sequence. GOPHER additionally provides a number of features that allow users to visualize and edit viewpoints, and outputs a range of files useful for documentation, ordering probes, and downstream analysis. Conclusion GOPHER is an easy-to-use and robust desktop application for CHC probe design. Source code and a precompiled executable can be downloaded from the GOPHER GitHub page at https://github.com/TheJacksonLaboratory/Gopher.

Hundreds of variants associated with atopic dermatitis (AD) and psoriasis, 2 common inflammatory skin disorders, have previously been discovered through genome-wide association studies (GWASs). The majority of these variants are in noncoding regions, and their target genes remain largely unclear. We sought to understand the effects of these noncoding variants on the development of AD and psoriasis by linking them to the genes that they regulate. We constructed genomic 3-dimensional maps of human keratinocytes during differentiation by using targeted chromosome conformation capture (Capture Hi-C) targeting more than 20,000 promoters and 214 GWAS variants and combined these data with transcriptome and epigenomic data sets. We validated our results with reporter assays, clustered regularly interspaced short palindromic repeats activation, and examination of patient gene expression from previous studies. We identified 118 target genes of 82 AD and psoriasis GWAS variants. Differential expression of 58 of the 118 target genes (49%) occurred in either AD or psoriatic lesions, many of which were not previously linked to any skin disease. We highlighted the genes AFG1L, CLINT1, ADO, LINC00302, and RP1-140J1.1 and provided further evidence for their potential roles in AD and psoriasis. Our work focused on skin barrier pathology through investigation of the interaction profile of GWAS variants during keratinocyte differentiation. We have provided a catalogue of candidate genes that could modulate the risk of AD and psoriasis. Given that only 35% of the target genes are the gene nearest to the known GWAS variants, we expect that our work will contribute to the discovery of novel pathways involved in AD and psoriasis. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: 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. Results: 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. Conclusions: 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. [ABSTRACT FROM AUTHOR]

Background: Target enrichment combined with chromosome conformation capturing methodologies such as capture Hi-C (CHC) can be used to investigate spatial layouts of genomic regions with high resolution and at scalable costs. A common application of CHC is the investigation of regulatory elements that are in contact with promoters, but CHC can be used for a range of other applications. Therefore, probe design for CHC needs to be adapted to experimental needs, but no flexible tool is currently available for this purpose. Results: We present a Java desktop application called GOPHER (Generator Of Probes for capture Hi-C Experiments at high Resolution) that implements three strategies for CHC probe design. GOPHER's simple approach is similar to the probe design of previous approaches that employ CHC to investigate all promoters, with one probe being placed at each margin of a single digest that overlaps the transcription start site (TSS) of each promoter. GOPHER's simple-patched approach extends this methodology with a heuristic that improves coverage of viewpoints in which the TSS is located near to one of the boundaries of the digest. GOPHER's extended approach is intended mainly for focused investigations of smaller gene sets. GOPHER can also be used to design probes for regions other than TSS such as GWAS hits or large blocks of genomic sequence. GOPHER additionally provides a number of features that allow users to visualize and edit viewpoints, and outputs a range of files useful for documentation, ordering probes, and downstream analysis. Conclusion: GOPHER is an easy-to-use and robust desktop application for CHC probe design. Source code and a precompiled executable can be downloaded from the GOPHER GitHub page at https://github.com/TheJacksonLaboratory/Gopher. [ABSTRACT FROM AUTHOR]

Over 500 genetic loci have been associated with risk of cardiovascular diseases (CVDs); however, most loci are located in gene-distal non-coding regions and their target genes are not known. Here, we generated high-resolution promoter capture Hi-C (PCHi-C) maps in human induced pluripotent stem cells (iPSCs) and iPSC-derived cardiomyocytes (CMs) to provide a resource for identifying and prioritizing the functional targets of CVD associations. We validate these maps by demonstrating that promoters preferentially contact distal sequences enriched for tissue-specific transcription factor motifs and are enriched for chromatin marks that correlate with dynamic changes in gene expression. Using the CM PCHi-C map, we linked 1999 CVD-associated SNPs to 347 target genes. Remarkably, more than 90% of SNP-target gene interactions did not involve the nearest gene, while 40% of SNPs interacted with at least two genes, demonstrating the importance of considering long-range chromatin interactions when interpreting functional targets of disease loci.

To produce a diverse antibody repertoire, immunoglobulin heavy-chain (Igh) loci undergo large-scale alterations in structure to facilitate juxtaposition and recombination of spatially separated variable (V H), diversity (D H), and joining (J H) genes. These chromosomal alterations are poorly understood. Uncovering their patterns shows how chromosome dynamics underpins antibody diversity. Using tiled Capture Hi-C, we produce a comprehensive map of chromatin interactions throughout the 2.8-Mb Igh locus in progenitor B cells. We find that the Igh locus folds into semi-rigid subdomains and undergoes flexible looping of the V H genes to its 3′ end, reconciling two views of locus organization. Deconvolution of single Igh locus conformations using polymer simulations identifies thousands of different structures. This heterogeneity may underpin the diversity of V(D)J recombination events. All three immunoglobulin loci also participate in a highly specific, developmentally regulated network of interchromosomal interactions with genes encoding B cell-lineage factors. This suggests a model of interchromosomal coordination of B cell development. [Display omitted] • Capture Hi-C maps intra- and interchromosomal contacts of the immunoglobulin heavy chain • Polymer modeling shows thousands of different Igh structures that underpin diversity • The Ig loci participate in a stage-specific interchromosomal network with B lineage genes Mielczarek et al. use Capture Hi-C to obtain a detailed map of chromosomal interactions within the immunoglobulin heavy-chain locus. Polymer modeling shows that each Igh structure is unique, enabling antibody diversity. The Ig loci associate with key B lineage genes in a developmental-stage-specific interchromosomal network. [ABSTRACT FROM AUTHOR]

There is a strong interrelationship within the cell nucleus between form and function of the genome. This connection is exhibited across multiple hierarchies, ranging from grand-scale positioning of chromosomes and their intersection with specific nuclear functional activities, the segregation of chromosome structure into distinct domains and long-range regulatory contacts that drive spatial and temporal expression patterns of genes. Fifteen years ago, the development of the chromosome conformation capture method placed the nature of specific, long-range regulatory interactions under scrutiny. However, its development and integration with next-generation sequencing technologies has greatly expanded the breadth and scope of what is detected. The sheer scale of data offered by these important advances has come with new and challenging bottlenecks that are both experimental and bioinformatical. Here, we discuss the recent and prospective development and implementation of new methodologies and analytical tools that are allowing an in-depth, yet focussed characterisation of genomic contacts that are associated with functional activities in the nucleus. [ABSTRACT FROM AUTHOR]

Chromosome conformation capture methodologies have provided insight into the effect of 3D genomic architecture on gene regulation. Capture Hi-C (CHi-C) is a recent extension of Hi-C that improves the effective resolution of chromatin interactions by enriching for defined regions of biological relevance. The varying targeting efficiency between capture regions, however, introduces bias not present in conventional Hi-C, making analysis more complicated. Here we consider salient features of an algorithm that should be considered in evaluating the performance of a program used to analyse CHi-C data in order to infer meaningful interactions. We use the program CHICAGO to analyse promoter capture Hi-C data generated on 28 different cell lines as a case study., Competing Interests: No competing interests were disclosed., (Copyright: © 2022 Disney-Hogg L et al.)

Targeted chromosome conformation capture (HiCap) is an experimental method for detecting spatial interactions of genomic features such as promoters and/or enhancers. The protocol first describes the design of sequence capture probes. After that, it provides details on the chromosome conformation capture adapted for next-generation sequencing (Hi-C). Finally, the methodology for coupling Hi-C with sequence capture technology is described., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Chromosome conformation capture methodologies have provided insight into the effect of 3D genomic architecture on gene regulation. Capture Hi-C (CHi-C) is a recent extension of Hi-C that improves the effective resolution of chromatin interactions by enriching for defined regions of biological relevance. The varying targeting efficiency between capture regions, however, introduces bias not present in conventional Hi-C, making analysis more complicated. Here we consider salient features of an algorithm that should be considered in evaluating the performance of a program used to analyse CHi-C data in order to infer meaningful interactions. We use the program CHICAGO to analyse promotor capture Hi-C data generated on 28 different cell lines as a case study., Competing Interests: No competing interests were disclosed., (Copyright: © 2020 Disney-Hogg L et al.)

Macrophages are highly plastic immune cells with temporally distinct transcriptome changes upon lipopolysaccride (LPS) activation. However, to what extent transcriptome reprogramming is mediated via spatial chromatin looping is not well studied. We generated high resolution chromatin interaction maps for LPS-stimulated THP-1 macrophages (0 and 2 h) using capture Hi-C. Success of LPS stimulation was validated with transcriptome sequencing. Circa 2900 genes changed their interaction profile upon LPS stimulation and those gaining interactions were enriched for LPS response relevant processes, suggesting a substantial role for distal regulation. Immune and cardiovascular risk variants were enriched within the interacting regions, thereby providing insights into macrophage biology., Competing Interests: Declaration of Competing Interest There is no conflict of interest., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Hi-C, capture Hi-C (CHC) and Capture-C have contributed greatly to our present understanding of the three-dimensional organization of genomes in the context of transcriptional regulation by characterizing the roles of topological associated domains, enhancer promoter loops and other three-dimensional genomic interactions. The analysis is based on counts of chimeric read pairs that map to interacting regions of the genome. However, the processing and quality control presents a number of unique challenges. We review here the experimental and computational foundations and explain how the characteristics of restriction digests, sonication fragments and read pairs can be exploited to distinguish technical artefacts from valid read pairs originating from true chromatin interactions. [ABSTRACT FROM AUTHOR]

Over 500 genetic loci have been associated with risk of cardiovascular diseases (CVDs); however, most loci are located in gene-distal non-coding regions and their target genes are not known. Here, we generated high-resolution promoter capture Hi-C (PCHi-C) maps in human induced pluripotent stem cells (iPSCs) and iPSC-derived cardiomyocytes (CMs) to provide a resource for identifying and prioritizing the functional targets of CVD associations. We validate these maps by demonstrating that promoters preferentially contact distal sequences enriched for tissue-specific transcription factor motifs and are enriched for chromatin marks that correlate with dynamic changes in gene expression. Using the CM PCHi-C map, we linked 1999 CVD-associated SNPs to 347 target genes. Remarkably, more than 90% of SNP-target gene interactions did not involve the nearest gene, while 40% of SNPs interacted with at least two genes, demonstrating the importance of considering long-range chromatin interactions when interpreting functional targets of disease loci., Competing Interests: LM, DS, NS, IA, AJ, GH, GB, IM, EM, MN No competing interests declared, (© 2018, Montefiori et al.)

The introduction of chromosome conformation capture (3C) technologies boosted the field of 3D-genome research and significantly enhanced the available toolset to study chromosomal architecture. 3C technologies not only offer increased resolution compared to the previously dominant cytological approaches but also allow the simultaneous study of genome-wide 3D chromatin contacts, thereby enabling a candidate-free perspective on 3D-genome architecture. Since its introduction in 2002, 3C technologies evolved rapidly and now constitute a collection of tools, each with their strengths and pitfalls with respect to specific research questions. This chapter aims at guiding 3C novices through the labyrinth of potential applications of the various family members, hopefully providing a valuable basis for choosing the appropriate strategy for different research questions.

Capture Hi-C (CHi-C) is a 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. 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.