GOTHiC, a probabilistic model to resolve complex biases and to identify real interactions in Hi-C data

Annotation of nuclear lncRNAs based on chromatin interactions

The human genome is pervasively transcribed and produces a wide variety of long non-coding RNAs (lncRNAs), constituting the majority of transcripts across human cell types. Some specific nuclear lncRNAs have been shown to be important regulatory components acting locally. As RNA-chromatin interaction and Hi-C chromatin conformation data showed that chromatin interactions of nuclear lncRNAs are determined by the local chromatin 3D conformation, we used Hi-C data to identify potential target genes of lncRNAs. RNA-protein interaction data suggested that nuclear lncRNAs act as scaffolds to recruit regulatory proteins to target promoters and enhancers. Nuclear lncRNAs may therefore play a role in directing regulatory factors to locations spatially close to the lncRNA gene. We provide the analysis results through an interactive visualization web portal at https://fantom.gsc.riken.jp/zenbu/reports/#F6_3D_lncRNA.

Hi-C, a chromatin 3D structure technique advancing the functional genomics of immune cells

The functional performance of immune cells relies on a complex transcriptional regulatory network. The three-dimensional structure of chromatin can affect chromatin status and gene expression patterns, and plays an important regulatory role in gene transcription. Currently available techniques for studying chromatin spatial structure include chromatin conformation capture techniques and their derivatives, chromatin accessibility sequencing techniques, and others. Additionally, the recently emerged deep learning technology can be utilized as a tool to enhance the analysis of data. In this review, we elucidate the definition and significance of the three-dimensional chromatin structure, summarize the technologies available for studying it, and describe the research progress on the chromatin spatial structure of dendritic cells, macrophages, T cells, B cells, and neutrophils.

Comparative study on chromatin loop callers using Hi-C data reveals their effectiveness

BACKGROUND

Chromosome is one of the most fundamental part of cell biology where DNA holds the hierarchical information. DNA compacts its size by forming loops, and these regions house various protein particles, including CTCF, SMC3, H3 histone. Numerous sequencing methods, such as Hi-C, ChIP-seq, and Micro-C, have been developed to investigate these properties. Utilizing these data, scientists have developed a variety of loop prediction techniques that have greatly improved their methods for characterizing loop prediction and related aspects.

RESULTS

In this study, we categorized 22 loop calling methods and conducted a comprehensive study of 11 of them. Additionally, we have provided detailed insights into the methodologies underlying these algorithms for loop detection, categorizing them into five distinct groups based on their fundamental approaches. Furthermore, we have included critical information such as resolution, input and output formats, and parameters. For this analysis, we utilized the GM12878 Hi-C datasets at 5 KB, 10 KB, 100 KB and 250 KB resolutions. Our evaluation criteria encompassed various factors, including memory usages, running time, sequencing depth, and recovery of protein-specific sites such as CTCF, H3K27ac, and RNAPII.

CONCLUSION

This analysis offers insights into the loop detection processes of each method, along with the strengths and weaknesses of each, enabling readers to effectively choose suitable methods for their datasets. We evaluate the capabilities of these tools and introduce a novel Biological, Consistency, and Computational robustness score ( B C C score ) to measure their overall robustness ensuring a comprehensive evaluation of their performance.

The shape of chromatin: insights from computational recognition of geometric patterns in Hi-C data

The three-dimensional organization of chromatin plays a crucial role in gene regulation and cellular processes like deoxyribonucleic acid (DNA) transcription, replication and repair. Hi-C and related techniques provide detailed views of spatial proximities within the nucleus. However, data analysis is challenging partially due to a lack of well-defined, underpinning mathematical frameworks. Recently, recognizing and analyzing geometric patterns in Hi-C data has emerged as a powerful approach. This review provides a summary of algorithms for automatic recognition and analysis of geometric patterns in Hi-C data and their correspondence with chromatin structure. We classify existing algorithms on the basis of the data representation and pattern recognition paradigm they make use of. Finally, we outline some of the challenges ahead and promising future directions.

Delta.EPI: a probabilistic voting-based enhancer-promoter interaction prediction platform

Enhancer promoter interaction (EPI) involves most of gene transcriptional regulation in the high eukaryotes. Predicting the EPIs from given genomic loci or DNA sequences is not a trivial task. The benchmarking work so far for EPI predictors is more or less empirical and lacks quantitative model-based comparisons, posing challenges for molecular biologists to obtain reliable EPI predictions. Here, we present an EPI prediction platform, Delta.EPI. Based on a statistic model of the data integration, Delta.EPI is capable of comprehensively assessing the predictions from four state-of-the-art EPI predictors. Equipped with a user-friendly interface and visualization platform, Delta.EPI presents the sorted results with the confidence of EPI relevance, which may guide the molecular biologists who lack the pre-knowledge of the algorithms of EPI prediction. Last, we showcase the utility of Delta.EPI with a case study. Delta.EPI provides a powerful tool to fuel the gene regulation and 3D genome studies by ease-to-access EPI predictions. Delta.EPI can be freely access at https://ngdc.cncb.ac.cn/deltaEPI/.

PfSET2 Is Involved in Genome Organization of Var Gene Family in Plasmodium falciparum

The three-dimensional (3D) genome structure of human malaria parasite Plasmodium falciparum is highly organized and plays important roles in regulating coordinated expression patterns of specific genes such as virulence genes which are involved in antigenic variation and immune escape. However, the molecular mechanisms that control 3D genome of the parasite remain elusive. Here, by analyzing genome organization of P. falciparum, we identify high-interacting regions (HIRs) with strong chromatin interactions at telomeres and virulence genes loci. Specifically, HIRs are highly enriched with repressive histone marks (H3K36me3 and H3K9me3) and form the transcriptional repressive center. Deletion of PfSET2, which controls H3K36me3 level, results in marked reduction of both intrachromosomal and interchromosomal interactions for HIRs. Importantly, such chromatin reorganization coordinates with dynamic changes in epigenetic feature in HIRs and transcriptional activation of var genes. Additionally, different cluster of var genes based on the pattern of chromatin interactions show distinct transcriptional activation potential after deletion of PfSET2. Our results uncover a fundamental mechanism that the epigenetic factor PfSET2 controls the 3D organization of heterochromatin to regulate the transcription activities of var genes family in P. falciparum. IMPORTANCE PfSET2 has been reported to play key role in silencing var genes in Plasmodium falciparum, while the underlying molecular mechanisms remain unclear. Here, we provide evidence that PfSET2 is essential to maintain 3D genome organization of heterochromatin region to keep var genes in transcription repressive state. These findings can contribute better understanding of the regulation of high-order chromatin structure in P. falciparum.

A genome-wide relay of signalling-responsive enhancers drives hematopoietic specification

Developmental control of gene expression critically depends on distal cis-regulatory elements including enhancers which interact with promoters to activate gene expression. To date no global experiments have been conducted that identify their cell type and cell stage-specific activity within one developmental pathway and in a chromatin context. Here, we describe a high-throughput method that identifies thousands of differentially active cis-elements able to stimulate a minimal promoter at five stages of hematopoietic progenitor development from embryonic stem (ES) cells, which can be adapted to any ES cell derived cell type. We show that blood cell-specific gene expression is controlled by the concerted action of thousands of differentiation stage-specific sets of cis-elements which respond to cytokine signals terminating at signalling responsive transcription factors. Our work provides an important resource for studies of hematopoietic specification and highlights the mechanisms of how and where extrinsic signals program a cell type-specific chromatin landscape driving hematopoietic differentiation.

MaxHiC: A robust background correction model to identify biologically relevant chromatin interactions in Hi-C and capture Hi-C experiments

Hi-C is a genome-wide chromosome conformation capture technology that detects interactions between pairs of genomic regions and exploits higher order chromatin structures. Conceptually Hi-C data counts interaction frequencies between every position in the genome and every other position. Biologically functional interactions are expected to occur more frequently than transient background and artefactual interactions. To identify biologically relevant interactions, several background models that take biases such as distance, GC content and mappability into account have been proposed. Here we introduce MaxHiC, a background correction tool that deals with these complex biases and robustly identifies statistically significant interactions in both Hi-C and capture Hi-C experiments. MaxHiC uses a negative binomial distribution model and a maximum likelihood technique to correct biases in both Hi-C and capture Hi-C libraries. We systematically benchmark MaxHiC against major Hi-C background correction tools including Hi-C significant interaction callers (SIC) and Hi-C loop callers using published Hi-C, capture Hi-C, and Micro-C datasets. Our results demonstrate that 1) Interacting regions identified by MaxHiC have significantly greater levels of overlap with known regulatory features (e.g. active chromatin histone marks, CTCF binding sites, DNase sensitivity) and also disease-associated genome-wide association SNPs than those identified by currently existing models, 2) the pairs of interacting regions are more likely to be linked by eQTL pairs and 3) more likely to link known regulatory features including known functional enhancer-promoter pairs validated by CRISPRi than any of the existing methods. We also demonstrate that interactions between different genomic region types have distinct distance distributions only revealed by MaxHiC. MaxHiC is publicly available as a python package for the analysis of Hi-C, capture Hi-C and Micro-C data.

Identification of chromatin loops from Hi-C interaction matrices by CTCF-CTCF topology classification

Genome-wide profiling of long-range interactions has revealed that the CCCTC-Binding factor (CTCF) often anchors chromatin loops and is enriched at boundaries of the so-called Topologically Associating Domains, which suggests that CTCF is essential in the 3D organization of chromatin. However, the systematic topological classification of pairwise CTCF-CTCF interactions has not been yet explored. Here, we developed a computational pipeline able to classify all CTCF-CTCF pairs according to their chromatin interactions from Hi-C experiments. The interaction profiles of all CTCF-CTCF pairs were further structurally clustered using self-organizing feature maps and their functionality characterized by their epigenetic states. The resulting clusters were then input to a convolutional neural network aiming at the de novo detecting chromatin loops from Hi-C interaction matrices. Our new method, called LOOPbit, is able to automatically detect significant interactions with a higher proportion of enhancer-promoter loops compared to other callers. Our highly specific loop caller adds a new layer of detail to the link between chromatin structure and function.

HolistIC: leveraging Hi-C and whole genome shotgun sequencing for double minute chromosome discovery

MOTIVATION

Double minute (DM) chromosomes are acentric extrachromosomal DNA artifacts that are frequently observed in the cells of numerous cancers. They are highly amplified and contain oncogenes and drug-resistance genes, making their presence a challenge for effective cancer treatment. Algorithmic discovery of DM can potentially improve bench-derived therapies for cancer treatment. A hindrance to this task is that DMs evolve, yielding circular chromatin that shares segments from progenitor DMs. This creates DMs with overlapping amplicon coordinates. Existing DM discovery algorithms use whole genome shotgun sequencing (WGS) in isolation, which can potentially incorrectly classify DMs that share overlapping coordinates.

RESULTS

In this study, we describe an algorithm called 'HolistIC' that can predict DMs in tumor genomes by integrating WGS and Hi-C sequencing data. The consolidation of these sources of information resolves ambiguity in DM amplicon prediction that exists in DM prediction with WGS data used in isolation. We implemented and tested our algorithm on the tandem Hi-C and WGS datasets of three cancer datasets and a simulated dataset. Results on the cancer datasets demonstrated HolistIC's ability to predict DMs from Hi-C and WGS data in tandem. The results on the simulated data showed the HolistIC can accurately distinguish DMs that have overlapping amplicon coordinates, an advance over methods that predict extrachromosomal amplification using WGS data in isolation.

AVAILABILITY AND IMPLEMENTATION

Our software, named 'HolistIC', is available at http://www.github.com/mhayes20/HolistIC.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Comparison of Capture Hi-C Analytical Pipelines

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.

3D genome organization links non-coding disease-associated variants to genes

Genome sequencing has revealed over 300 million genetic variations in human populations. Over 90% of variants are single nucleotide polymorphisms (SNPs), the remainder include short deletions or insertions, and small numbers of structural variants. Hundreds of thousands of these variants have been associated with specific phenotypic traits and diseases through genome wide association studies which link significant differences in variant frequencies with specific phenotypes among large groups of individuals. Only 5% of disease-associated SNPs are located in gene coding sequences, with the potential to disrupt gene expression or alter of the function of encoded proteins. The remaining 95% of disease-associated SNPs are located in non-coding DNA sequences which make up 98% of the genome. The role of non-coding, disease-associated SNPs, many of which are located at considerable distances from any gene, was at first a mystery until the discovery that gene promoters regularly interact with distal regulatory elements to control gene expression. Disease-associated SNPs are enriched at the millions of gene regulatory elements that are dispersed throughout the non-coding sequences of the genome, suggesting they function as gene regulation variants. Assigning specific regulatory elements to the genes they control is not straightforward since they can be millions of base pairs apart. In this review we describe how understanding 3D genome organization can identify specific interactions between gene promoters and distal regulatory elements and how 3D genomics can link disease-associated SNPs to their target genes. Understanding which gene or genes contribute to a specific disease is the first step in designing rational therapeutic interventions.

Assessing Specific Networks of Chromatin Interactions with HiChIP

The introduction of chromosome conformation capture (3C)-based technologies coupled with next-generation sequencing have significantly advanced our understanding of how the genetic material is organized within the eukaryotic nucleus. Three-dimensional (3D) genomic organization occurs at hierarchical levels, ranging from chromosome territories and subnuclear compartments to smaller self-associated domains and fine-scale chromatin interactions. The latter can be further categorized into different subtypes, such as structural or regulatory, based either on their presumed functionality and/or the factors that mediate their formation. Various enrichment strategies coupled with 3C-based technologies have been developed to prospectively isolate and quantify chromatin interactions around regions occupied by specific proteins or marks of interest. These approaches not only enable high-resolution characterization of the selected chromatin contacts at a cost-effective manner, but also offer important biological insights into their organizational principles and regulatory function. In this chapter, we will focus on the recently developed HiChIP technology with an emphasis on the discovery of putative active enhancers and promoter interactions in cell types of interest. We will describe the specific steps for designing, performing and analyzing successful HiChIP experiments as well as important limitations and considerations.

Seeing the forest through the trees: prioritising potentially functional interactions from Hi-C

Eukaryotic genomes are highly organised within the nucleus of a cell, allowing widely dispersed regulatory elements such as enhancers to interact with gene promoters through physical contacts in three-dimensional space. Recent chromosome conformation capture methodologies such as Hi-C have enabled the analysis of interacting regions of the genome providing a valuable insight into the three-dimensional organisation of the chromatin in the nucleus, including chromosome compartmentalisation and gene expression. Complicating the analysis of Hi-C data, however, is the massive amount of identified interactions, many of which do not directly drive gene function, thus hindering the identification of potentially biologically functional 3D interactions. In this review, we collate and examine the downstream analysis of Hi-C data with particular focus on methods that prioritise potentially functional interactions. We classify three groups of approaches: structural-based discovery methods, e.g. A/B compartments and topologically associated domains, detection of statistically significant chromatin interactions, and the use of epigenomic data integration to narrow down useful interaction information. Careful use of these three approaches is crucial to successfully identifying potentially functional interactions within the genome.

Short- and long-range cis interactions between integrated HPV genomes and cellular chromatin dysregulate host gene expression in early cervical carcinogenesis

Development of cervical cancer is directly associated with integration of human papillomavirus (HPV) genomes into host chromosomes and subsequent modulation of HPV oncogene expression, which correlates with multi-layered epigenetic changes at the integrated HPV genomes. However, the process of integration itself and dysregulation of host gene expression at sites of integration in our model of HPV16 integrant clone natural selection has remained enigmatic. We now show, using a state-of-the-art 'HPV integrated site capture' (HISC) technique, that integration likely occurs through microhomology-mediated repair (MHMR) mechanisms via either a direct process, resulting in host sequence deletion (in our case, partially homozygously) or via a 'looping' mechanism by which flanking host regions become amplified. Furthermore, using our 'HPV16-specific Region Capture Hi-C' technique, we have determined that chromatin interactions between the integrated virus genome and host chromosomes, both at short- (<500 kbp) and long-range (>500 kbp), appear to drive local host gene dysregulation through the disruption of host:host interactions within (but not exceeding) host structures known as topologically associating domains (TADs). This mechanism of HPV-induced host gene expression modulation indicates that integration of virus genomes near to or within a 'cancer-causing gene' is not essential to influence their expression and that these modifications to genome interactions could have a major role in selection of HPV integrants at the early stage of cervical neoplastic progression.

Chrom-Lasso: a lasso regression-based model to detect functional interactions using Hi-C data

Hi-C is a genome-wide assay based on Chromosome Conformation Capture and high-throughput sequencing to decipher 3D chromatin organization in the nucleus. However, computational methods to detect functional interactions utilizing Hi-C data face challenges including the correction for various sources of biases and the identification of functional interactions with low counts of interacting fragments. We present Chrom-Lasso, a lasso linear regression model that removes complex biases assumption-free and identifies functional interacting loci with increased power by combining information of local reads distribution surrounding the area of interest. We showed that interacting regions identified by Chrom-Lasso are more enriched for 5C validated interactions and functional GWAS hits than that of GOTHiC and Fit-Hi-C. To further demonstrate the ability of Chrom-Lasso to detect interactions of functional importance, we performed time-series Hi-C and RNA-seq during T cell activation and exhaustion. We showed that the dynamic changes in gene expression and chromatin interactions identified by Chrom-Lasso were largely concordant with each other. Finally, we experimentally confirmed Chrom-Lasso’s finding that Erbb3 was co-regulated with distinct neighboring genes at different states during T cell activation. Our results highlight Chrom-Lasso’s utility in detecting weak functional interaction between cis-regulatory elements, such as promoters and enhancers.

Isoform-specific and signaling-dependent propagation of acute myeloid leukemia by Wilms tumor 1

Acute myeloid leukemia (AML) is caused by recurrent mutations in members of the gene regulatory and signaling machinery that control hematopoietic progenitor cell growth and differentiation. Here, we show that the transcription factor WT1 forms a major node in the rewired mutation-specific gene regulatory networks of multiple AML subtypes. WT1 is frequently either mutated or upregulated in AML, and its expression is predictive for relapse. The WT1 protein exists as multiple isoforms. For two main AML subtypes, we demonstrate that these isoforms exhibit differential patterns of binding and support contrasting biological activities, including enhanced proliferation. We also show that WT1 responds to oncogenic signaling and is part of a signaling-responsive transcription factor hub that controls AML growth. WT1 therefore plays a central and widespread role in AML biology.

Identifying high-confidence capture Hi-C interactions using CHiCANE

The ability to identify regulatory interactions that mediate gene expression changes through distal elements, such as risk loci, is transforming our understanding of how genomes are spatially organized and regulated. Capture Hi-C (CHi-C) is a powerful tool to delineate such regulatory interactions. However, primary analysis and downstream interpretation of CHi-C profiles remains challenging and relies on disparate tools with ad-hoc input/output formats and specific assumptions for statistical modeling. Here we present a data processing and interaction calling toolkit (CHiCANE), specialized for the analysis and meaningful interpretation of CHi-C assays. In this protocol, we demonstrate applications of CHiCANE to region capture Hi-C (rCHi-C) and promoter capture Hi-C (pCHi-C) libraries, followed by quality assessment of interaction peaks, as well as downstream analysis specific to rCHi-C and pCHi-C to aid functional interpretation. For a typical rCHi-C/pCHi-C dataset this protocol takes up to 3 d for users with a moderate understanding of R programming and statistical concepts, although this is dependent on dataset size and compute power available. CHiCANE is freely available at https://cran.r-project.org/web/packages/chicane .

3D reconstruction of genomic regions from sparse interaction data

Chromosome conformation capture (3C) technologies measure the interaction frequency between pairs of chromatin regions within the nucleus in a cell or a population of cells. Some of these 3C technologies retrieve interactions involving non-contiguous sets of loci, resulting in sparse interaction matrices. One of such 3C technologies is Promoter Capture Hi-C (pcHi-C) that is tailored to probe only interactions involving gene promoters. As such, pcHi-C provides sparse interaction matrices that are suitable to characterize short- and long-range enhancer-promoter interactions. Here, we introduce a new method to reconstruct the chromatin structural (3D) organization from sparse 3C-based datasets such as pcHi-C. Our method allows for data normalization, detection of significant interactions and reconstruction of the full 3D organization of the genomic region despite of the data sparseness. Specifically, it builds, with as low as the 2-3% of the data from the matrix, reliable 3D models of similar accuracy of those based on dense interaction matrices. Furthermore, the method is sensitive enough to detect cell-type-specific 3D organizational features such as the formation of different networks of active gene communities.

Molecular and computational approaches to map regulatory elements in 3D chromatin structure

Epigenetic marks do not change the sequence of DNA but affect gene expression in a cell-type specific manner by altering the activities of regulatory elements. Development of new molecular biology assays, sequencing technologies, and computational approaches enables us to profile the human epigenome in three-dimensional structure genome-wide. Here we describe various molecular biology techniques and bioinformatic tools that have been developed to measure the activities of regulatory elements and their chromatin interactions. Moreover, we list currently available three-dimensional epigenomic data sets that are generated in various human cell types and tissues to assist in the design and analysis of research projects.

Inferring time series chromatin states for promoter-enhancer pairs based on Hi-C data

BACKGROUND

Co-localized combinations of histone modifications ("chromatin states") have been shown to correlate with promoter and enhancer activity. Changes in chromatin states over multiple time points ("chromatin state trajectories") have previously been analyzed at promoter and enhancers separately. With the advent of time series Hi-C data it is now possible to connect promoters and enhancers and to analyze chromatin state trajectories at promoter-enhancer pairs.

RESULTS

We present TimelessFlex, a framework for investigating chromatin state trajectories at promoters and enhancers and at promoter-enhancer pairs based on Hi-C information. TimelessFlex extends our previous approach Timeless, a Bayesian network for clustering multiple histone modification data sets at promoter and enhancer feature regions. We utilize time series ATAC-seq data measuring open chromatin to define promoters and enhancer candidates. We developed an expectation-maximization algorithm to assign promoters and enhancers to each other based on Hi-C interactions and jointly cluster their feature regions into paired chromatin state trajectories. We find jointly clustered promoter-enhancer pairs showing the same activation patterns on both sides but with a stronger trend at the enhancer side. While the promoter side remains accessible across the time series, the enhancer side becomes dynamically more open towards the gene activation time point. Promoter cluster patterns show strong correlations with gene expression signals, whereas Hi-C signals get only slightly stronger towards activation. The code of the framework is available at https://github.com/henriettemiko/TimelessFlex .

CONCLUSIONS

TimelessFlex clusters time series histone modifications at promoter-enhancer pairs based on Hi-C and it can identify distinct chromatin states at promoter and enhancer feature regions and their changes over time.

An Empirical Bayes approach for the identification of long-range chromosomal interaction from Hi-C data

Hi-C experiments have become very popular for studying the 3D genome structure in recent years. Identification of long-range chromosomal interaction, i.e., peak detection, is crucial for Hi-C data analysis. But it remains a challenging task due to the inherent high dimensionality, sparsity and the over-dispersion of the Hi-C count data matrix. We propose EBHiC, an empirical Bayes approach for peak detection from Hi-C data. The proposed framework provides flexible over-dispersion modeling by explicitly including the "true" interaction intensities as latent variables. To implement the proposed peak identification method (via the empirical Bayes test), we estimate the overall distributions of the observed counts semiparametrically using a Smoothed Expectation Maximization algorithm, and the empirical null based on the zero assumption. We conducted extensive simulations to validate and evaluate the performance of our proposed approach and applied it to real datasets. Our results suggest that EBHiC can identify better peaks in terms of accuracy, biological interpretability, and the consistency across biological replicates. The source code is available on Github (https://github.com/QiZhangStat/EBHiC).

High-resolution single-cell 3D-models of chromatin ensembles during Drosophila embryogenesis

Single-cell chromatin studies provide insights into how chromatin structure relates to functions of individual cells. However, balancing high-resolution and genome wide-coverage remains challenging. We describe a computational method for the reconstruction of large 3D-ensembles of single-cell (sc) chromatin conformations from population Hi-C that we apply to study embryogenesis in Drosophila. With minimal assumptions of physical properties and without adjustable parameters, our method generates large ensembles of chromatin conformations via deep-sampling. Our method identifies specific interactions, which constitute 5-6% of Hi-C frequencies, but surprisingly are sufficient to drive chromatin folding, giving rise to the observed Hi-C patterns. Modeled sc-chromatins quantify chromatin heterogeneity, revealing significant changes during embryogenesis. Furthermore, >50% of modeled sc-chromatin maintain topologically associating domains (TADs) in early embryos, when no population TADs are perceptible. Domain boundaries become fixated during development, with strong preference at binding-sites of insulator-complexes upon the midblastula transition. Overall, high-resolution 3D-ensembles of sc-chromatin conformations enable further in-depth interpretation of population Hi-C, improving understanding of the structure-function relationship of genome organization.

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.

RUNX1-ETO Depletion in t(8;21) AML Leads to C/EBPα- and AP-1-Mediated Alterations in Enhancer-Promoter Interaction

Acute myeloid leukemia (AML) is associated with mutations in transcriptional and epigenetic regulator genes impairing myeloid differentiation. The t(8;21)(q22;q22) translocation generates the RUNX1-ETO fusion protein, which interferes with the hematopoietic master regulator RUNX1. We previously showed that the maintenance of t(8;21) AML is dependent on RUNX1-ETO expression. Its depletion causes extensive changes in transcription factor binding, as well as gene expression, and initiates myeloid differentiation. However, how these processes are connected within a gene regulatory network is unclear. To address this question, we performed Promoter-Capture Hi-C assays, with or without RUNX1-ETO depletion and assigned interacting cis-regulatory elements to their respective genes. To construct a RUNX1-ETO-dependent gene regulatory network maintaining AML, we integrated cis-regulatory element interactions with gene expression and transcription factor binding data. This analysis shows that RUNX1-ETO participates in cis-regulatory element interactions. However, differential interactions following RUNX1-ETO depletion are driven by alterations in the binding of RUNX1-ETO-regulated transcription factors.

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.

MHiC, an integrated user-friendly tool for the identification and visualization of significant interactions in Hi-C data

Background

Hi-C is a molecular biology technique to understand the genome spatial structure. However, data obtained from Hi-C experiments is biased. Therefore, several methods have been developed to model Hi-C data and identify significant interactions. Each method receives its own Hi-C data structure and only work on specific operating systems.

Results

We introduce MHiC (Multi-function Hi-C data analysis tool), a tool to identify and visualize statistically signifiant interactions from Hi-C data. The MHiC tool (i) works on different operating systems, (ii) accepts various Hi-C data structures from different Hi-C analysis tools such as HiCUP or HiC-Pro, (iii) identify significant Hi-C interactions with GOTHiC, HiCNorm and Fit-Hi-C methods and (iv) visualizes interactions in Arc or Heatmap diagram. MHiC is an open-source tool which is freely available for download on https://github.com/MHi-C.

Conclusions

MHiC is an integrated tool for the analysis of high-throughput chromosome conformation capture (Hi-C) data.

RADICL-seq identifies general and cell type-specific principles of genome-wide RNA-chromatin interactions

Mammalian genomes encode tens of thousands of noncoding RNAs. Most noncoding transcripts exhibit nuclear localization and several have been shown to play a role in the regulation of gene expression and chromatin remodeling. To investigate the function of such RNAs, methods to massively map the genomic interacting sites of multiple transcripts have been developed; however, these methods have some limitations. Here, we introduce RNA And DNA Interacting Complexes Ligated and sequenced (RADICL-seq), a technology that maps genome-wide RNA–chromatin interactions in intact nuclei. RADICL-seq is a proximity ligation-based methodology that reduces the bias for nascent transcription, while increasing genomic coverage and unique mapping rate efficiency compared with existing methods. RADICL-seq identifies distinct patterns of genome occupancy for different classes of transcripts as well as cell type–specific RNA-chromatin interactions, and highlights the role of transcription in the establishment of chromatin structure.

Mammalian genomes encode tens of thousands of ncRNAs that have important roles in regulation of gene expression and chromatin organization. Here, the authors present RADICLseq to map RNA-chromatin interactions in intact nuclei to shed light on these fine-tuned processes.

Identifying statistically significant chromatin contacts from Hi-C data with FitHiC2

Fit-Hi-C is a programming application to compute statistical confidence estimates for Hi-C contact maps to identify significant chromatin contacts. By fitting a monotonically non-increasing spline, Fit-Hi-C captures the relationship between genomic distance and contact probability without any parametric assumption. The spline fit together with the correction of contact probabilities with respect to bin- or locus-specific biases accounts for previously characterized covariates impacting Hi-C contact counts. Fit-Hi-C is best applied for the study of mid-range (e.g., 20 kb-2 Mb for human genome) intra-chromosomal contacts; however, with the latest reimplementation, named FitHiC2, it is possible to perform genome-wide analysis for high-resolution Hi-C data, including all intra-chromosomal distances and inter-chromosomal contacts. FitHiC2 also offers a merging filter module, which eliminates indirect/bystander interactions, leading to significant reduction in the number of reported contacts without sacrificing recovery of key loops such as those between convergent CTCF binding sites. Here, we describe how to apply the FitHiC2 protocol to three use cases: (i) 5-kb resolution Hi-C data of chromosome 5 from GM12878 (a human lymphoblastoid cell line), (ii) 40-kb resolution whole-genome Hi-C data from IMR90 (human lung fibroblast), and (iii) budding yeast whole-genome Hi-C data at a single restriction cut site (EcoRI) resolution. The procedure takes ~12 h with preprocessing when all use cases are run sequentially (~4 h when run parallel). With the recent improvements in its implementation, FitHiC2 (8 processors and 16 GB memory) is also scalable to genome-wide analysis of the highest resolution (1 kb) Hi-C data available to date (~48 h with 32 GB peak memory). FitHiC2 is available through Bioconda, GitHub and the Python Package Index.

CHROMATIX: computing the functional landscape of many-body chromatin interactions in transcriptionally active loci from deconvolved single cells

Chromatin interactions are important for gene regulation and cellular specialization. Emerging evidence suggests many-body spatial interactions play important roles in condensing super-enhancer regions into a cohesive transcriptional apparatus. Chromosome conformation studies using Hi-C are limited to pairwise, population-averaged interactions; therefore unsuitable for direct assessment of many-body interactions. We describe a computational model, CHROMATIX, which reconstructs ensembles of single-cell chromatin structures by deconvolving Hi-C data and identifies significant many-body interactions. For a diverse set of highly active transcriptional loci with at least 2 super-enhancers, we detail the many-body functional landscape and show DNase accessibility, POLR2A binding, and decreased H3K27me3 are predictive of interaction-enriched regions.

MIA-Sig: multiplex chromatin interaction analysis by signal processing and statistical algorithms

The single-molecule multiplex chromatin interaction data are generated by emerging 3D genome mapping technologies such as GAM, SPRITE, and ChIA-Drop. These datasets provide insights into high-dimensional chromatin organization, yet introduce new computational challenges. Thus, we developed MIA-Sig, an algorithmic solution based on signal processing and information theory. We demonstrate its ability to de-noise the multiplex data, assess the statistical significance of chromatin complexes, and identify topological domains and frequent inter-domain contacts. On chromatin immunoprecipitation (ChIP)-enriched data, MIA-Sig can clearly distinguish the protein-associated interactions from the non-specific topological domains. Together, MIA-Sig represents a novel algorithmic framework for multiplex chromatin interaction analysis.

GenomeDISCO: a concordance score for chromosome conformation capture experiments using random walks on contact map graphs

Motivation

The three-dimensional organization of chromatin plays a critical role in gene regulation and disease. High-throughput chromosome conformation capture experiments such as Hi-C are used to obtain genome-wide maps of three-dimensional chromatin contacts. However, robust estimation of data quality and systematic comparison of these contact maps is challenging due to the multi-scale, hierarchical structure of chromatin contacts and the resulting properties of experimental noise in the data. Measuring concordance of contact maps is important for assessing reproducibility of replicate experiments and for modeling variation between different cellular contexts.

Results

We introduce a concordance measure called DIfferences between Smoothed COntact maps (GenomeDISCO) for assessing the similarity of a pair of contact maps obtained from chromosome conformation capture experiments. The key idea is to smooth contact maps using random walks on the contact map graph, before estimating concordance. We use simulated datasets to benchmark GenomeDISCO's sensitivity to different types of noise that affect chromatin contact maps. When applied to a large collection of Hi-C datasets, GenomeDISCO accurately distinguishes biological replicates from samples obtained from different cell types. GenomeDISCO also generalizes to other chromosome conformation capture assays, such as HiChIP.

Availability and implementation

Software implementing GenomeDISCO is available at https://github.com/kundajelab/genomedisco.

Supplementary information

Supplementary data are available at Bioinformatics online.

ChiCMaxima: a robust and simple pipeline for detection and visualization of chromatin looping in Capture Hi-C

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.

Differential methylation of enhancer at IGF2 is associated with abnormal dopamine synthesis in major psychosis

Impaired neuronal processes, including dopamine imbalance, are central to the pathogenesis of major psychosis, but the molecular origins are unclear. Here we perform a multi-omics study of neurons isolated from the prefrontal cortex in schizophrenia and bipolar disorder (n = 55 cases and 27 controls). DNA methylation, transcriptomic, and genetic-epigenetic interactions in major psychosis converged on pathways of neurodevelopment, synaptic activity, and immune functions. We observe prominent hypomethylation of an enhancer within the insulin-like growth factor 2 (IGF2) gene in major psychosis neurons. Chromatin conformation analysis revealed that this enhancer targets the nearby tyrosine hydroxylase (TH) gene responsible for dopamine synthesis. In patients, we find hypomethylation of the IGF2 enhancer is associated with increased TH protein levels. In mice, Igf2 enhancer deletion disrupts the levels of TH protein and striatal dopamine, and induces transcriptional and proteomic abnormalities affecting neuronal structure and signaling. Our data suggests that epigenetic activation of the enhancer at IGF2 may enhance dopamine synthesis associated with major psychosis.

An atlas of genetic influences on osteoporosis in humans and mice

Osteoporosis is a common aging-related disease diagnosed primarily using bone mineral density (BMD). We assessed genetic determinants of BMD as estimated by heel quantitative ultrasound in 426,824 individuals, identifying 518 genome-wide significant loci (301 novel), explaining 20% of its variance. We identified 13 bone fracture loci, all associated with estimated BMD (eBMD), in ~1.2 million individuals. We then identified target genes enriched for genes known to influence bone density and strength (maximum odds ratio (OR) = 58, P = 1 × 10-75) from cell-specific features, including chromatin conformation and accessible chromatin sites. We next performed rapid-throughput skeletal phenotyping of 126 knockout mice with disruptions in predicted target genes and found an increased abnormal skeletal phenotype frequency compared to 526 unselected lines (P < 0.0001). In-depth analysis of one gene, DAAM2, showed a disproportionate decrease in bone strength relative to mineralization. This genetic atlas provides evidence linking associated SNPs to causal genes, offers new insight into osteoporosis pathophysiology, and highlights opportunities for drug development.

Subtype-specific regulatory network rewiring in acute myeloid leukemia

Acute myeloid leukemia (AML) is a heterogeneous disease caused by a variety of alterations in transcription factors, epigenetic regulators and signaling molecules. To determine how different mutant regulators establish AML subtype-specific transcriptional networks, we performed a comprehensive global analysis of cis-regulatory element activity and interaction, transcription factor occupancy and gene expression patterns in purified leukemic blast cells. Here, we focused on specific subgroups of subjects carrying mutations in genes encoding transcription factors (RUNX1, CEBPα), signaling molecules (FTL3-ITD, RAS) and the nuclear protein NPM1). Integrated analysis of these data demonstrates that each mutant regulator establishes a specific transcriptional and signaling network unrelated to that seen in normal cells, sustaining the expression of unique sets of genes required for AML growth and maintenance.

Hi-C analysis: from data generation to integration

In the epigenetics field, large-scale functional genomics datasets of ever-increasing size and complexity have been produced using experimental techniques based on high-throughput sequencing. In particular, the study of the 3D organization of chromatin has raised increasing interest, thanks to the development of advanced experimental techniques. In this context, Hi-C has been widely adopted as a high-throughput method to measure pairwise contacts between virtually any pair of genomic loci, thus yielding unprecedented challenges for analyzing and handling the resulting complex datasets. In this review, we focus on the increasing complexity of available Hi-C datasets, which parallels the adoption of novel protocol variants. We also review the complexity of the multiple data analysis steps required to preprocess Hi-C sequencing reads and extract biologically meaningful information. Finally, we discuss solutions for handling and visualizing such large genomics datasets.

Divergent wiring of repressive and active chromatin interactions between mouse embryonic and trophoblast lineages

The establishment of the embryonic and trophoblast lineages is a developmental decision underpinned by dramatic differences in the epigenetic landscape of the two compartments. However, it remains unknown how epigenetic information and transcription factor networks map to the 3D arrangement of the genome, which in turn may mediate transcriptional divergence between the two cell lineages. Here, we perform promoter capture Hi-C experiments in mouse trophoblast (TSC) and embryonic (ESC) stem cells to understand how chromatin conformation relates to cell-specific transcriptional programmes. We find that key TSC genes that are kept repressed in ESCs exhibit interactions between H3K27me3-marked regions in ESCs that depend on Polycomb repressive complex 1. Interactions that are prominent in TSCs are enriched for enhancer-gene contacts involving key TSC transcription factors, as well as TET1, which helps to maintain the expression of TSC-relevant genes. Our work shows that the first developmental cell fate decision results in distinct chromatin conformation patterns establishing lineage-specific contexts involving both repressive and active interactions.

Capture Hi-C Library Generation and Analysis to Detect Chromatin Interactions

Chromosome conformation capture (3C), coupled with next-generation sequencing (Hi-C), provides a means for deciphering not only the principles underlying genome folding and architecture, but more broadly, the role 3D chromatin structure plays in gene regulation and the replication and repair of DNA. The recently implemented modification, in situ Hi-C, maintains nuclear integrity during digestion and ligation steps, reducing random ligation of Hi-C fragments. Although Hi-C allows for genome-wide characterization of chromatin contacts, it requires high-depth sequencing to discover significant contacts. To address this, Capture Hi-C (CHi-C) enriches standard Hi-C libraries for regions of biological interest, for example by specifically targeting gene promoters, aiding identification of biologically significant chromatin interactions compared to conventional Hi-C, for an equivalent number of sequence reads. Illustrating the application of CHi-C applied to genome-wide analysis of chromatin interactions with promoters, we detail the protocols for in situ Hi-C and CHi-C library generation for sequencing, as well as the bioinformatics tools for data analysis. © 2018 by John Wiley & Sons, Inc.

Computational 3D genome modeling using Chrom3D

Chrom3D is a computational platform for 3D genome modeling that simulates the spatial positioning of chromosome domains relative to each other and relative to the nuclear periphery. In Chrom3D, chromosomes are modeled as chains of contiguous beads, in which each bead represents a genomic domain. In this protocol, a bead represents a topologically associated domain (TAD) mapped from ensemble Hi-C data. Chrom3D takes as input data significant pairwise TAD-TAD interactions determined from a Hi-C contact matrix, and TAD interactions with the nuclear periphery, determined by ChIP-sequencing of nuclear lamins to define lamina-associated domains (LADs). Chrom3D is based on Monte Carlo simulations initiated from a starting random bead configuration. During the optimization process, TAD-TAD interactions constrain bead positions relative to each other, whereas LAD information constrains the corresponding bead toward the nuclear periphery. Optimization can be repeated many times to generate an ensemble of 3D genome models. Analyses of the models enable estimations of the radial positioning of genomic sites in the nucleus across cells in a population. Chrom3D provides opportunities to reveal spatial relationships between TADs and LADs. More generally, predictions from Chrom3D models can be experimentally tested in the laboratory. We describe the entire Chrom3D protocol for modeling a 3D diploid human genome, from the creation of input files to the final rendering of 3D genome structures. The procedure takes ∼18 h. Chrom3D is freely available on GitHub.

Computational methods for analyzing genome-wide chromosome conformation capture data

In all organisms, chromatin is packed to fulfil structural constraints and functional requirements. The hierarchical model of chromatin organization in the 3D nuclear space encompasses different topologies at diverse scale lengths, with chromosomes occupying distinct volumes, further organized in compartments, inside which the chromatin fibers fold into large domains and short-range loops. In the recent years, the combination of chromosome conformation capture (3C) techniques and high-throughput sequencing allowed probing chromatin spatial organization at the whole genome-scale. 3C-based methods produce enormous amounts of genomic data that are analyzed using ad-hoc computational procedures. Here, we review the common pipelines and methods for the analysis of genome-wide chromosome conformation capture data, highlighting recent developments in key steps for the identification of chromatin structures.

Computational Methods for Assessing Chromatin Hierarchy

The hierarchical organization of chromatin is known to associate with diverse cellular functions; however, the precise mechanisms and the 3D structure remain to be determined. With recent advances in high-throughput next generation sequencing (NGS) techniques, genome-wide profiling of chromatin structures is made possible. Here, we provide a comprehensive overview of NGS-based methods for profiling "higher-order" and "primary-order" chromatin structures from both experimental and computational aspects. Experimental requirements and considerations specific for each method were highlighted. For computational analysis, we summarized a common analysis strategy for both levels of chromatin assessment, focusing on the characteristic computing steps and the tools. The recently developed single-cell level techniques based on Hi-C and ATAC-seq present great potential to reveal cell-to-cell variability in chromosome architecture. A brief discussion on these methods in terms of experimental and data analysis features is included. We also touch upon the biological relevance of chromatin organization and how the combination with other techniques uncovers the underlying mechanisms. We conclude with a summary and our prospects on necessary improvements of currently available methods in order to advance understanding of chromatin hierarchy. Our review brings together the analyses of both higher- and primary-order chromatin structures, and serves as a roadmap when choosing appropriate experimental and computational methods for assessing chromatin hierarchy.

The Epstein-Barr Virus Episome Maneuvers between Nuclear Chromatin Compartments during Reactivation

The human genome is structurally organized in three-dimensional space to facilitate functional partitioning of transcription. We learned that the latent episome of the human Epstein-Barr virus (EBV) preferentially associates with gene-poor chromosomes and avoids gene-rich chromosomes. Kaposi's sarcoma-associated herpesvirus behaves similarly, but human papillomavirus does not. Contacts on the EBV side localize to OriP, the latent origin of replication. This genetic element and the EBNA1 protein that binds there are sufficient to reconstitute chromosome association preferences of the entire episome. Contacts on the human side localize to gene-poor and AT-rich regions of chromatin distant from transcription start sites. Upon reactivation from latency, however, the episome moves away from repressive heterochromatin and toward active euchromatin. Our work adds three-dimensional relocalization to the molecular events that occur during reactivation. Involvement of myriad interchromosomal associations also suggests a role for this type of long-range association in gene regulation.

IMPORTANCE The human genome is structurally organized in three-dimensional space, and this structure functionally affects transcriptional activity. We set out to investigate whether a double-stranded DNA virus, Epstein-Barr virus (EBV), uses mechanisms similar to those of the human genome to regulate transcription. We found that the EBV genome associates with repressive compartments of the nucleus during latency and with active compartments during reactivation. This study advances our knowledge of the EBV life cycle, adding three-dimensional relocalization as a novel component to the molecular events that occur during reactivation. Furthermore, the data add to our understanding of nuclear compartments, showing that disperse interchromosomal interactions may be important for regulating transcription.

CTCF-Mediated Enhancer-Promoter Interaction Is a Critical Regulator of Cell-to-Cell Variation of Gene Expression

Recent studies indicate that even a homogeneous population of cells display heterogeneity in gene expression and response to environmental stimuli. Although promoter structure critically influences the cell-to-cell variation of gene expression in bacteria and lower eukaryotes, it remains unclear what controls the gene expression noise in mammals. Here we report that CTCF decreases cell-to-cell variation of expression by stabilizing enhancer-promoter interaction. We show that CTCF binding sites are interwoven with enhancers within topologically associated domains (TADs) and a positive correlation is found between CTCF binding and the activity of the associated enhancers. Deletion of CTCF sites compromises enhancer-promoter interactions. Using single-cell flow cytometry and single-molecule RNA-FISH assays, we demonstrate that knocking down of CTCF or deletion of a CTCF binding site results in increased cell-to-cell variation of gene expression, indicating that long-range promoter-enhancer interaction mediated by CTCF plays important roles in controlling the cell-to-cell variation of gene expression in mammalian cells.

The second decade of 3C technologies: detailed insights into nuclear organization

The relevance of three-dimensional (3D) genome organization for transcriptional regulation and thereby for cellular fate at large is now widely accepted. Our understanding of the fascinating architecture underlying this function is based on microscopy studies as well as the chromosome conformation capture (3C) methods, which entered the stage at the beginning of the millennium. The first decade of 3C methods rendered unprecedented insights into genome topology. Here, we provide an update of developments and discoveries made over the more recent years. As we discuss, established and newly developed experimental and computational methods enabled identification of novel, functionally important chromosome structures. Regulatory and architectural chromatin loops throughout the genome are being cataloged and compared between cell types, revealing tissue invariant and developmentally dynamic loops. Architectural proteins shaping the genome were disclosed, and their mode of action is being uncovered. We explain how more detailed insights into the 3D genome increase our understanding of transcriptional regulation in development and misregulation in disease. Finally, to help researchers in choosing the approach best tailored for their specific research question, we explain the differences and commonalities between the various 3C-derived methods.

Capturing genomic relationships that matter

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.

The three-dimensional genome: principles and roles of long-distance interactions

The linear sequence of eukaryotic genomes is arranged in a specific manner within the three-dimensional nuclear space. Interactions between distant sites partition the genome into domains of highly associating chromatin. Interaction domains are found in many organisms, but their properties and the principles governing their establishment vary between different species. Topologically associating domains (TADs) extending over large genomic regions are found in mammals and Drosophila melanogaster, whereas other types of contact domains exist in lower eukaryotes. Here we review recent studies that explore the mechanisms by which long distance chromatin interactions determine the 3D organization of the genome and the relationship between this organization and the establishment of specific patterns of gene expression.

GenomicInteractions: An R/Bioconductor package for manipulating and investigating chromatin interaction data

Background

Precise quantitative and spatiotemporal control of gene expression is necessary to ensure proper cellular differentiation and the maintenance of homeostasis. The relationship between gene expression and the spatial organisation of chromatin is highly complex, interdependent and not completely understood. The development of experimental techniques to interrogate both the higher-order structure of chromatin and the interactions between regulatory elements has recently lead to important insights on how gene expression is controlled. The ability to gain these and future insights is critically dependent on computational tools for the analysis and visualisation of data produced by these techniques.

Results and conclusion

We have developed GenomicInteractions, a freely available R/Bioconductor package designed for processing, analysis and visualisation of data generated from various types of chromosome conformation capture experiments. The package allows the easy annotation and summarisation of large genome-wide datasets at both the level of individual interactions and sets of genomic features, and provides several different methods for interrogating and visualising this type of data. We demonstrate this package’s utility by showing example analyses performed on interaction datasets generated using Hi-C and ChIA-PET.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2140-x) contains supplementary material, which is available to authorized users.