ChIA-PET2: a versatile and flexible pipeline for ChIA-PET data analysis

3D landscape of Hepatitis B virus interactions with human chromatins

Hepatitis B viral (HBV) DNAs, including covalently closed circular DNA (cccDNA) and integrated HBV DNA forms, are considered to be primary contributors to the development and progression of HBV-associated liver diseases. However, it remains largely unclear how HBV DNAs communicate with human chromatin. Here we employed a highly sensitive technology, 3C-high-throughput genome-wide translocation sequencing (3C-HTGTS), to globally identify HBV DNA-host DNA contacts in cellular models of HBV infection. HBV DNA does not randomly position in host genome but instead preferentially establishes contacts with the host DNA at active chromatin regions. HBV DNA-host DNA contacts are significantly enriched at H3K4me1-marked regions modified by KMT2C/D; this histone modification is also observed in the HBV cccDNA mini-chromosome and strongly influences HBV transcription. On the other hand, chromatin loop formed by integrated HBV DNA with host genomic DNA was found in transcriptionally active regions. Furthermore, HBV infection influences host gene expression accompanied with HBV DNA-host DNA contacts. Our study provides a 3D landscape of spatial organization of cccDNA and integrated HBV DNA within the human genome, which lays the foundation for a better understanding of the mechanisms how HBV involves in liver disease development and progression.

Systematic screening of CTCF binding partners identifies that BHLHE40 regulates CTCF genome-wide distribution and long-range chromatin interactions

CTCF plays a pivotal role in mediating chromatin interactions, but it does not do so alone. A number of factors have been reported to co-localize with CTCF and regulate CTCF loops, but no comprehensive analysis of binding partners has been performed. This prompted us to identify CTCF loop participants and regulators by co-localization analysis with CTCF. We screened all factors that had ChIP-seq data in humans by co-localization analysis with human super conserved CTCF (hscCTCF) binding sites, and identified many new factors that overlapped with hscCTCF binding sites. Combined with CTCF loop information, we observed that clustered factors could promote CTCF loops. After in-depth mining of each factor, we found that many factors might have the potential to promote CTCF loops. Our data further demonstrated that BHLHE40 affected CTCF loops by regulating CTCF binding. Together, this study revealed that many factors have the potential to participate in or regulate CTCF loops, and discovered a new role for BHLHE40 in modulating CTCF loop formation.

Accurate loop calling for 3D genomic data with cLoops

MOTIVATION

Sequencing-based 3D genome mapping technologies can identify loops formed by interactions between regulatory elements hundreds of kilobases apart. Existing loop-calling tools are mostly restricted to a single data type, with accuracy dependent on a predefined resolution contact matrix or called peaks, and can have prohibitive hardware costs.

RESULTS

Here, we introduce cLoops ('see loops') to address these limitations. cLoops is based on the clustering algorithm cDBSCAN that directly analyzes the paired-end tags (PETs) to find candidate loops and uses a permuted local background to estimate statistical significance. These two data-type-independent processes enable loops to be reliably identified for both sharp and broad peak data, including but not limited to ChIA-PET, Hi-C, HiChIP and Trac-looping data. Loops identified by cLoops showed much less distance-dependent bias and higher enrichment relative to local regions than existing tools. Altogether, cLoops improves accuracy of detecting of 3D-genomic loops from sequencing data, is versatile, flexible, efficient, and has modest hardware requirements.

AVAILABILITY AND IMPLEMENTATION

cLoops with documentation and example data are freely available at: https://github.com/YaqiangCao/cLoops.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

3DeFDR: statistical methods for identifying cell type-specific looping interactions in 5C and Hi-C data

An important unanswered question in chromatin biology is the extent to which long-range looping interactions change across developmental models, genetic perturbations, drug treatments, and disease states. Computational tools for rigorous assessment of cell type-specific loops across multiple biological conditions are needed. We present 3DeFDR, a simple and effective statistical tool for classifying dynamic loops across biological conditions from Chromosome-Conformation-Capture-Carbon-Copy (5C) and Hi-C data. Our work provides a statistical framework and open-source coding libraries for sensitive detection of cell type-specific loops in high-resolution 5C and Hi-C data from multiple cellular conditions.

Genome-wide analyses of chromatin interactions after the loss of Pol I, Pol II, and Pol III

BACKGROUND

The relationship between transcription and the 3D chromatin structure is debated. Multiple studies have shown that transcription affects global Cohesin binding and 3D genome structures. However, several other studies have indicated that inhibited transcription does not alter chromatin conformations.

RESULTS

We provide the most comprehensive evidence to date to demonstrate that transcription plays a relatively modest role in organizing the local, small-scale chromatin structures in mammalian cells. We show degraded Pol I, Pol II, and Pol III proteins in mESCs cause few or no changes in large-scale 3D chromatin structures, selected RNA polymerases with a high abundance of binding sites or active promoter-associated interactions appear to be relatively more affected after the degradation, transcription inhibition alters local, small loop domains, as indicated by high-resolution chromatin interaction maps, and loops with bound Pol II but without Cohesin or CTCF are identified and found to be largely unchanged after transcription inhibition. Interestingly, Pol II depletion for a longer time significantly affects the chromatin accessibility and Cohesin occupancy, suggesting that RNA polymerases are capable of affecting the 3D genome indirectly. These direct and indirect effects explain the previous inconsistent findings on the influence of transcription inhibition on the 3D genome.

CONCLUSIONS

We conclude that Pol I, Pol II, and Pol III loss alters local, small-scale chromatin interactions in mammalian cells, suggesting that the 3D chromatin structures are pre-established and relatively stable.

MACPET: model-based analysis for ChIA-PET

We present model-based analysis for ChIA-PET (MACPET), which analyzes paired-end read sequences provided by ChIA-PET for finding binding sites of a protein of interest. MACPET uses information from both tags of each PET and searches for binding sites in a two-dimensional space, while taking into account different noise levels in different genomic regions. MACPET shows favorable results compared with MACS in terms of motif occurrence and spatial resolution. Furthermore, significant binding sites discovered by MACPET are involved in a higher number of significant three-dimensional interactions than those discovered by MACS. MACPET is freely available on Bioconductor. ChIA-PET; MACPET; Model-based clustering; Paired-end tags; Peak-calling algorithm.

ChIA-PIPE: A fully automated pipeline for comprehensive ChIA-PET data analysis and visualization

ChIA-PET (chromatin interaction analysis with paired-end tags) enables genome-wide discovery of chromatin interactions involving specific protein factors, with base pair resolution. Interpretation of ChIA-PET data requires a robust analytic pipeline. Here, we introduce ChIA-PIPE, a fully automated pipeline for ChIA-PET data processing, quality assessment, visualization, and analysis. ChIA-PIPE performs linker filtering, read mapping, peak calling, and loop calling and automates quality control assessment for each dataset. To enable visualization, ChIA-PIPE generates input files for two-dimensional contact map viewing with Juicebox and HiGlass and provides a new dockerized visualization tool for high-resolution, browser-based exploration of peaks and loops. To enable structural interpretation, ChIA-PIPE calls chromatin contact domains, resolves allele-specific peaks and loops, and annotates enhancer-promoter loops. ChIA-PIPE also supports the analysis of other related chromatin-mapping data types.

3D genome architecture coordinates trans and cis regulation of differentially expressed ear and tassel genes in maize

BACKGROUND

Maize ears and tassels are two separate types of inflorescence which are initiated by similar developmental processes but gradually develop distinct architectures. However, coordinated trans and cis regulation of differentially expressed genes determining ear and tassel architecture within the 3D genome context is largely unknown.

RESULTS

We identify 56,055 and 52,633 open chromatin regions (OCRs) in developing maize ear and tassel primordia using ATAC-seq and characterize combinatorial epigenome features around these OCRs using ChIP-seq, Bisulfite-seq, and RNA-seq datasets. Our integrative analysis of coordinated epigenetic modification and transcription factor binding to OCRs highlights the cis and trans regulation of differentially expressed genes in ear and tassel controlling inflorescence architecture. We further systematically map chromatin interactions at high-resolution in corresponding tissues using in situ digestion-ligation-only Hi-C (DLO Hi-C). The extensive chromatin loops connecting OCRs and genes provide a 3D view on cis- and trans-regulatory modules responsible for ear- and tassel-specific gene expression. We find that intergenic SNPs tend to locate in distal OCRs, and our chromatin interaction maps provide a potential mechanism for trait-associated intergenic SNPs that may contribute to phenotypic variation by influencing target gene expression through chromatin loops.

CONCLUSIONS

Our comprehensive epigenome annotations and 3D genome maps serve as valuable resource and provide a deep understanding of the complex regulatory mechanisms of genes underlying developmental and morphological diversities between maize ear and tassel.

IDR2D identifies reproducible genomic interactions

Chromatin interaction data from protocols such as ChIA-PET, HiChIP and Hi-C provide valuable insights into genome organization and gene regulation, but can include spurious interactions that do not reflect underlying genome biology. We introduce an extension of the Irreproducible Discovery Rate (IDR) method called IDR2D that identifies replicable interactions shared by chromatin interaction experiments. IDR2D provides a principled set of interactions and eliminates artifacts from single experiments. The method is available as a Bioconductor package for the R community, as well as an online service at https://idr2d.mit.edu.

Multiomic Profiling Identifies cis-Regulatory Networks Underlying Human Pancreatic β Cell Identity and Function

EndoC-βH1 is emerging as a critical human β cell model to study the genetic and environmental etiologies of β cell (dys)function and diabetes. Comprehensive knowledge of its molecular landscape is lacking, yet required, for effective use of this model. Here, we report chromosomal (spectral karyotyping), genetic (genotyping), epigenomic (ChIP-seq and ATAC-seq), chromatin interaction (Hi-C and Pol2 ChIA-PET), and transcriptomic (RNA-seq and miRNA-seq) maps of EndoC-βH1. Analyses of these maps define known (e.g., PDX1 and ISL1) and putative (e.g., PCSK1 and mir-375) β cell-specific transcriptional cis-regulatory networks and identify allelic effects on cis-regulatory element use. Importantly, comparison with maps generated in primary human islets and/or β cells indicates preservation of chromatin looping but also highlights chromosomal aberrations and fetal genomic signatures in EndoC-βH1. Together, these maps, and a web application we created for their exploration, provide important tools for the design of experiments to probe and manipulate the genetic programs governing β cell identity and (dys)function in diabetes.

EndoC-βH1 is becoming an important cellular model to study genes and pathways governing human β cell identity and function, but its (epi)genomic similarity to primary human islets is unknown. Lawlor et al. complete and compare extensive EndoC and primary human islet multiomic maps to identify shared and distinct genomic circuitry.

High resolution discovery of chromatin interactions

Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is a method for the genome-wide de novo discovery of chromatin interactions. Existing computational methods typically fail to detect weak or dynamic interactions because they use a peak-calling step that ignores paired-end linkage information. We have developed a novel computational method called Chromatin Interaction Discovery (CID) to overcome this limitation with an unbiased clustering approach for interaction discovery. CID outperforms existing chromatin interaction detection methods with improved sensitivity, replicate consistency, and concordance with other chromatin interaction datasets. In addition, CID also outperforms other methods in discovering chromatin interactions from HiChIP data. We expect that the CID method will be valuable in characterizing 3D chromatin interactions and in understanding the functional consequences of disease-associated distal genetic variations.

Genome-wide germline correlates of the epigenetic landscape of prostate cancer

Oncogenesis is driven by germline, environmental and stochastic factors. It is unknown how these interact to produce the molecular phenotypes of tumors. We therefore quantified the influence of germline polymorphisms on the somatic epigenome of 589 localized prostate tumors. Predisposition risk loci influence a tumor's epigenome, uncovering a mechanism for cancer susceptibility. We identified and validated 1,178 loci associated with altered methylation in tumoral but not nonmalignant tissue. These tumor methylation quantitative trait loci influence chromatin structure, as well as RNA and protein abundance. One prominent tumor methylation quantitative trait locus is associated with AKT1 expression and is predictive of relapse after definitive local therapy in both discovery and validation cohorts. These data reveal intricate crosstalk between the germ line and the epigenome of primary tumors, which may help identify germline biomarkers of aggressive disease to aid patient triage and optimize the use of more invasive or expensive diagnostic assays.

EnContact: predicting enhancer-enhancer contacts using sequence-based deep learning model

Chromatin contacts between regulatory elements are of crucial importance for the interpretation of transcriptional regulation and the understanding of disease mechanisms. However, existing computational methods mainly focus on the prediction of interactions between enhancers and promoters, leaving enhancer-enhancer (E-E) interactions not well explored. In this work, we develop a novel deep learning approach, named Enhancer-enhancer contacts prediction (EnContact), to predict E-E contacts using genomic sequences as input. We statistically demonstrated the predicting ability of EnContact using training sets and testing sets derived from HiChIP data of seven cell lines. We also show that our model significantly outperforms other baseline methods. Besides, our model identifies finer-mapping E-E interactions from region-based chromatin contacts, where each region contains several enhancers. In addition, we identify a class of hub enhancers using the predicted E-E interactions and find that hub enhancers tend to be active across cell lines. We summarize that our EnContact model is capable of predicting E-E interactions using features automatically learned from genomic sequences.

The Cancer-Associated Genetic Variant Rs3903072 Modulates Immune Cells in the Tumor Microenvironment

Genome-wide association studies (GWAS) have hitherto identified several germline variants associated with cancer susceptibility, but the molecular functions of these risk modulators remain largely uncharacterized. Recent studies have begun to uncover the regulatory potential of noncoding GWAS SNPs using epigenetic information in corresponding cancer cell types and matched normal tissues. However, this approach does not explore the potential effect of risk germline variants on other important cell types that constitute the microenvironment of tumor or its precursor. This paper presents evidence that the breast-cancer-associated variant rs3903072 may regulate the expression of CTSW in tumor-infiltrating lymphocytes. CTSW is a candidate tumor-suppressor gene, with expression highly specific to immune cells and also positively correlated with breast cancer patient survival. Integrative analyses suggest a putative causative variant in a GWAS-linked enhancer in lymphocytes that loops to the 3' end of CTSW through three-dimensional chromatin interaction. Our work thus poses the possibility that a cancer-associated genetic variant could regulate a gene not only in the cell of cancer origin but also in immune cells in the microenvironment, thereby modulating the immune surveillance by T lymphocytes and natural killer cells and affecting the clearing of early cancer initiating cells.

BAT Hi-C maps global chromatin interactions in an efficient and economical way

Chromosome Conformation Capture (3C)-based technologies, such as Hi-C, have represented a significant breakthrough in investigating the structure and function of higher-order genome architecture. However, the mapping of global chromatin interactions remains challenging across many biological conditions due to high background noise and financial constraints, especially for small laboratories. Here, we describe the Bridge linker-Alul-Tn5 Hi-C (BAT Hi-C) method, which is a simple and efficient method for delineating chromatin conformational features of mouse embryonic stem (mES) cells and uncover DNA loops. This protocol combines Alul fragmentation and biotinylated linker-mediated proximity ligation to obtain kilobase (kb) resolution with a marked increase in the amount of unique read pairs. The protocol also includes chromatin isolation to reduce background noise and Tn5 tagmentation to cut down on preparation time. Importantly, with only one-third sequencing depth, our method revealed the same spectrum of chromatin contacts as in situ Hi-C. BAT Hi-C is an economical (i.e., approximately $40 for library preparation) and straightforward (total hands-on time of 3 days) tool that is ideal for the in-depth analysis of long-range chromatin looping events in a genome-wide fashion.

Chromatin loops associated with active genes and heterochromatin shape rice genome architecture for transcriptional regulation

Insight into high-resolution three-dimensional genome organization and its effect on transcription remains largely elusive in plants. Here, using a long-read ChIA-PET approach, we map H3K4me3- and RNA polymerase II (RNAPII)-associated promoter-promoter interactions and H3K9me2-marked heterochromatin interactions at nucleotide/gene resolution in rice. The chromatin architecture is separated into different independent spatial interacting modules with distinct transcriptional potential and covers approximately 82% of the genome. Compared to inactive modules, active modules possess the majority of active loop genes with higher density and contribute to most of the transcriptional activity in rice. In addition, promoter-promoter interacting genes tend to be transcribed cooperatively. In contrast, the heterochromatin-mediated loops form relative stable structure domains in chromatin configuration. Furthermore, we examine the impact of genetic variation on chromatin interactions and transcription and identify a spatial correlation between the genetic regulation of eQTLs and e-traits. Thus, our results reveal hierarchical and modular 3D genome architecture for transcriptional regulation in rice.

Chromatin Interaction Analysis with Updated ChIA-PET Tool (V3)

Understanding chromatin interactions is important because they create chromosome conformation and link the cis- and trans- regulatory elements to their target genes for transcriptional regulation. Chromatin Interaction Analysis with Paired-End Tag (ChIA-PET) sequencing is a genome-wide high-throughput technology that detects chromatin interactions associated with a specific protein of interest. We developed ChIA-PET Tool for ChIA-PET data analysis in 2010. Here, we present the updated version of ChIA-PET Tool (V3) as a computational package to process the next-generation sequence data generated from ChIA-PET experiments. It processes short-read and long-read ChIA-PET data with multithreading and generates statistics of results in an HTML file. In this paper, we provide a detailed demonstration of the design of ChIA-PET Tool V3 and how to install it and analyze RNA polymerase II (RNAPII) ChIA-PET data from human K562 cells with it. We compared our tool with existing tools, including ChiaSig, MICC, Mango and ChIA-PET2, by using the same public data set in the same computer. Most peaks detected by the ChIA-PET Tool V3 overlap with those of other tools. There is higher enrichment for significant chromatin interactions from ChIA-PET Tool V3 in aggregate peak analysis (APA) plots. The ChIA-PET Tool V3 is publicly available at GitHub.

Pervasive Chromatin-RNA Binding Protein Interactions Enable RNA-Based Regulation of Transcription

Increasing evidence suggests that transcriptional control and chromatin activities at large involve regulatory RNAs, which likely enlist specific RNA-binding proteins (RBPs). Although multiple RBPs have been implicated in transcription control, it has remained unclear how extensively RBPs directly act on chromatin. We embarked on a large-scale RBP ChIP-seq analysis, revealing widespread RBP presence in active chromatin regions in the human genome. Like transcription factors (TFs), RBPs also show strong preference for hotspots in the genome, particularly gene promoters, where their association is frequently linked to transcriptional output. Unsupervised clustering reveals extensive co-association between TFs and RBPs, as exemplified by YY1, a known RNA-dependent TF, and RBM25, an RBP involved in splicing regulation. Remarkably, RBM25 depletion attenuates all YY1-dependent activities, including chromatin binding, DNA looping, and transcription. We propose that various RBPs may enhance network interaction through harnessing regulatory RNAs to control transcription.

DeepTACT: predicting 3D chromatin contacts via bootstrapping deep learning

Interactions between regulatory elements are of crucial importance for the understanding of transcriptional regulation and the interpretation of disease mechanisms. Hi-C technique has been developed for genome-wide detection of chromatin contacts. However, unless extremely deep sequencing is performed on a very large number of input cells, which is technically limited and expensive, current Hi-C experiments do not have high enough resolution to resolve contacts between regulatory elements. Here, we develop DeepTACT, a bootstrapping deep learning model, to integrate genome sequences and chromatin accessibility data for the prediction of chromatin contacts between regulatory elements. DeepTACT can infer not only promoter-enhancer interactions, but also promoter-promoter interactions. In tests based on promoter capture Hi-C data, DeepTACT shows better performance over existing methods. DeepTACT analysis also identifies a class of hub promoters, which are correlated with transcriptional activation across cell lines, enriched in housekeeping genes, functionally related to fundamental biological processes, and capable of reflecting cell similarity. Finally, the utility of chromatin contacts in the study of human diseases is illustrated by the association of IFNA2 to coronary artery disease via an integrative analysis of GWAS data and interactions predicted by DeepTACT.

ChIAPoP: a new tool for ChIA-PET data analysis

Chromatin Interaction Analysis by Paired-End Tag Sequencing (ChIA-PET) is a popular assay method for studying genome-wide chromatin interactions mediated by a protein of interest. The main goal of ChIA-PET data analysis is to detect interactions between DNA regions. Here, we propose a new method and the associated data analysis pipeline, ChIAPoP, to detect chromatin interactions from ChIA-PET data. We compared ChIAPoP with other popular methods, including a hypergeometric model (used in ChIA-PET tool), MICC (used in ChIA-PET2), ChiaSig and mango. The results showed that ChIA-PoP performed better than or at least as well as these top existing methods in detecting true chromatin interactions. ChIAPoP is freely available to the public at https://github.com/wh90999/ChIAPoP.

An alternative CTCF isoform antagonizes canonical CTCF occupancy and changes chromatin architecture to promote apoptosis

CTCF plays key roles in gene regulation, chromatin insulation, imprinting, X chromosome inactivation and organizing the higher-order chromatin architecture of mammalian genomes. Previous studies have mainly focused on the roles of the canonical CTCF isoform. Here, we explore the functions of an alternatively spliced human CTCF isoform in which exons 3 and 4 are skipped, producing a shorter isoform (CTCF-s). Functionally, we find that CTCF-s competes with the genome binding of canonical CTCF and binds a similar DNA sequence. CTCF-s binding disrupts CTCF/cohesin binding, alters CTCF-mediated chromatin looping and promotes the activation of IFI6 that leads to apoptosis. This effect is caused by an abnormal long-range interaction at the IFI6 enhancer and promoter. Taken together, this study reveals a non-canonical function for CTCF-s that antagonizes the genomic binding of canonical CTCF and cohesin, and that modulates chromatin looping and causes apoptosis by stimulating IFI6 expression.

CTCF plays key roles in gene regulation, chromatin insulation and organizing the higher-order chromatin architecture of mammalian genomes. Here the authors investigate the function an alternatively spliced shorter CTCF isoform, finding that this isoform antagonizes canonical CTCF occupancy and changes chromatin architecture to promote apoptosis.

Deciphering Hi-C: from 3D genome to function

Hi-C is a commonly used technology in 3D genomics which can depict global chromatin interactions across eukaryotic genome. Integrating with different datasets, it can also be applied to studying various biological questions, such as nuclear organization, gene transcription regulation, spatiotemporal development, genome assembly, and cancer genomics. During the last decade, the development and application of Hi-C have dramatically changed the view of genome architecture, chromatin conformation, and gene interaction. So far, Hi-C-related studies remain vivacious and controversial; thus, a unified standard of library construction and bioinformatics analysis are urgently needed. In this review, we have summarized its history, development, methodologies, advances, applications, shortages, and future perspectives. We discuss a few limitations of the current Hi-C technologies and future directions for improvement and highlight how Hi-C can bridge 3D structure to gene function. This review will be helpful for scientists who want to engage in the 3D genomics field; it also shows some future tracks.

Predicting CTCF-mediated chromatin interactions by integrating genomic and epigenomic features

The CCCTC-binding zinc-finger protein (CTCF)-mediated network of long-range chromatin interactions is important for genome organization and function. Although this network has been considered largely invariant, we find that it exhibits extensive cell-type-specific interactions that contribute to cell identity. Here, we present Lollipop, a machine-learning framework, which predicts CTCF-mediated long-range interactions using genomic and epigenomic features. Using ChIA-PET data as benchmark, we demonstrate that Lollipop accurately predicts CTCF-mediated chromatin interactions both within and across cell types, and outperforms other methods based only on CTCF motif orientation. Predictions are confirmed computationally and experimentally by Chromatin Conformation Capture (3C). Moreover, our approach identifies other determinants of CTCF-mediated chromatin wiring, such as gene expression within the loops. Our study contributes to a better understanding about the underlying principles of CTCF-mediated chromatin interactions and their impact on gene expression.

CTCF mediates long-range chromatin interactions which are important for genome organization and function. Here, the authors demonstrate that CTCF-mediated interactome exhibits extensive plasticity and present Lollipop, a machine-learning framework which predicts CTCF-mediated long-range interactions using genomic and epigenomic features.

Enhancing the Promise of Drug Repositioning through Genetics

The development of new drugs has become challenging as the necessary investments in time and money have increased while drug approval rates have decreased. A potential solution to this problem is drug repositioning which aims to use existing drugs to treat conditions for which they were not originally intended. One approach that may enhance the likelihood of success is to reposition drugs against a target that has a genetic basis. The multitude of genome-wide association studies (GWASs) conducted in recent years represents a large potential pool of novel targets for drug repositioning. Although trait-associated variants identified from GWAS still need to be causally linked to a target gene, recently developed functional genomic techniques, databases, and workflows are helping to remove this bottleneck. The pre-clinical validation of repositioning against these targets also needs to be carefully performed to ensure that findings are not confounded by off-target effects or limitations of the techniques used. Nevertheless, the approaches described in this review have the potential to provide a faster, cheaper and more certain route to clinical approval.

BL-Hi-C is an efficient and sensitive approach for capturing structural and regulatory chromatin interactions

In human cells, DNA is hierarchically organized and assembled with histones and DNA-binding proteins in three dimensions. Chromatin interactions play important roles in genome architecture and gene regulation, including robustness in the developmental stages and flexibility during the cell cycle. Here we propose in situ Hi-C method named Bridge Linker-Hi-C (BL-Hi-C) for capturing structural and regulatory chromatin interactions by restriction enzyme targeting and two-step proximity ligation. This method improves the sensitivity and specificity of active chromatin loop detection and can reveal the regulatory enhancer-promoter architecture better than conventional methods at a lower sequencing depth and with a simpler protocol. We demonstrate its utility with two well-studied developmental loci: the beta-globin and HOXC cluster regions.

Chromatin interactions and genome architecture are key regulators of gene expression. Here the authors present Bridge-Linker-Hi-C to map active chromatin loops and enhancer-promoter interactions.

Novel insights into chromosomal conformations in cancer

Exploring gene function is critical for understanding the complexity of life. DNA sequences and the three-dimensional organization of chromatin (chromosomal interactions) are considered enigmatic factors underlying gene function, and interactions between two distant fragments can regulate transactivation activity via mediator proteins. Thus, a series of chromosome conformation capture techniques have been developed, including chromosome conformation capture (3C), circular chromosome conformation capture (4C), chromosome conformation capture carbon copy (5C), and high-resolution chromosome conformation capture (Hi-C). The application of these techniques has expanded to various fields, but cancer remains one of the major topics. Interactions mediated by proteins or long noncoding RNAs (lncRNAs) are typically found using 4C-sequencing and chromatin interaction analysis by paired-end tag sequencing (ChIA-PET). Currently, Hi-C is used to identify chromatin loops between cancer risk-associated single-nucleotide polymorphisms (SNPs) found by genome-wide association studies (GWAS) and their target genes. Chromosomal conformations are responsible for altered gene regulation through several typical mechanisms and contribute to the biological behavior and malignancy of different tumors, particularly prostate cancer, breast cancer and hematologic neoplasms. Moreover, different subtypes may exhibit different 3D-chromosomal conformations. Thus, C-tech can be used to help diagnose cancer subtypes and alleviate cancer progression by destroying specific chromosomal conformations. Here, we review the fundamentals and improvements in chromosome conformation capture techniques and their clinical applications in cancer to provide insight for future research.

[Computer methods of analysis of chromosome contacts in the cell nucleus based on sequencing technology data]

The study spatial chromosome structure and chromosome folding in the interphase cell nucleus is an important challenge of world science. Detection of eukaryotic genome regions that physically interact with each other could be done by modern sequencing technologies. A basic method of chromosome folding by total sequencing of contacting DNA fragments is HI-C. Long-range chromosomal interactions play an important role in gene transcription and regulation. The study of chromosome interactions, 3D (three-dimensional) genome structure and its effect on gene transcription allows revealing fundamental biological processes from a viewpoint of structural regulation and are important for cancer research. The technique of chromatin immunoprecipitation and subsequent sequencing (ChIP-seq) make possible to determine binding sites of transcription factors that regulate expression of eukaryotic genes; genome transcription factors binding maps have been. The ChIA-PET technology allows exploring not only target protein binding sites, but also pairs of such sites on proximally located and interacting with each other chromosomes co-located in three-dimensional space of the cell nucleus. Here we discuss the principles of the construction of genomic maps and matrices of chromosome contacts according to ChIA-PET and Hi-C data that capture the chromosome conformation and overview existing software for 3D genome analysis including in house programs of gene location analysis in topological domains.

Three-dimensional genome architecture and emerging technologies: looping in disease

Genome compaction is a universal feature of cells and has emerged as a global regulator of gene expression. Compaction is maintained by a multitude of architectural proteins, long non-coding RNAs (lncRNAs), and regulatory DNA. Each component comprises interlinked regulatory circuits that organize the genome in three-dimensional (3D) space to manage gene expression. In this review, we update the current state of 3D genome catalogues and focus on how recent technological advances in 3D genomics are leading to an enhanced understanding of disease mechanisms. We highlight the use of genome-wide chromatin conformation capture (Hi-C) coupled with oligonucleotide capture technology (capture Hi-C) to map interactions between gene promoters and distal regulatory elements such as enhancers that are enriched for disease variants from genome-wide association studies (GWASs). We discuss how aberrations in architectural units are associated with various pathological outcomes, and explore how recent advances in genome and epigenome editing show great promise for a systematic understanding of complex genetic disorders. Our growing understanding of 3D genome architecture—coupled with the ability to engineer changes in it—may create novel therapeutic opportunities.