High-throughput Chromosome Conformation Capture Data Research Articles

Genome structural dynamics: insights from Gaussian network analysis of Hi-C data.

Characterization of the spatiotemporal properties of the chromatin is essential to gaining insights into the physical bases of gene co-expression, transcriptional regulation and epigenetic modifications. The Gaussian network model (GNM) has proven in recent work to serve as a useful tool for modeling chromatin structural dynamics, using as input high-throughput chromosome conformation capture data. We focus here on the exploration of the collective dynamics of chromosomal structures at hierarchical levels of resolution, from single gene loci to topologically associating domains or entire chromosomes. The GNM permits us to identify long-range interactions between gene loci, shedding light on the role of cross-correlations between distal regions of the chromosomes in regulating gene expression. Notably, GNM analysis performed across diverse cell lines highlights the conservation of the global/cooperative movements of the chromatin across different types of cells. Variations driven by localized couplings between genomic loci, on the other hand, underlie cell differentiation, underscoring the significance of the four-dimensional properties of the genome in defining cellular identity. Finally, we demonstrate the close relation between the cell type-dependent mobility profiles of gene loci and their gene expression patterns, providing a clear demonstration of the role of chromosomal 4D features in defining cell-specific differential expression of genes.

Multidimensional scaling methods can reconstruct genomic DNA loops using Hi-C data properties.

This paper proposes multidimensional scaling (MDS) applied to high-throughput chromosome conformation capture (Hi-C) data on genomic interactions to visualize DNA loops. Currently, the mechanisms underlying the regulation of gene expression are poorly understood, and where and when DNA loops are formed remains undetermined. Previous studies have focused on reproducing the entire three-dimensional structure of chromatin; however, identifying DNA loops using these data is time-consuming and difficult. MDS is an unsupervised method for reconstructing the original coordinates from a distance matrix. Here, MDS was applied to high-throughput chromosome conformation capture (Hi-C) data on genomic interactions to visualize DNA loops. Hi-C data were converted to distances by taking the inverse to reproduce loops via MDS, and the missing values were set to zero. Using the converted data, MDS was applied to the log-transformed genomic coordinate distances and this process successfully reproduced the DNA loops in the given structure. Consequently, the reconstructed DNA loops revealed significantly more DNA-transcription factor interactions involved in DNA loop formation than those obtained from previously applied methods. Furthermore, the reconstructed DNA loops were significantly consistent with chromatin immunoprecipitation followed by sequencing (ChIP-seq) peak positions. In conclusion, the proposed method is an improvement over previous methods for identifying DNA loops.

DeCOOC Deconvoluted Hi-C Map Characterizes the Chromatin Architecture of Cells in Physiologically Distinctive Tissues.

Deciphering variations in chromosome conformations based on bulk three-dimensional (3D) genomic data from heterogenous tissues is a key to understanding cell-type specific genome architecture and dynamics. Surprisingly, computational deconvolution methods for high-throughput chromosome conformation capture (Hi-C) data remain very rare in the literature. Here, a deep convolutional neural network (CNN), deconvolve bulk Hi-C data (deCOOC) that remarkably outperformed all the state-of-the-art tools in the deconvolution task is developed. Interestingly, it is noticed that the chromatin accessibility or the Hi-C contact frequency alone is insufficient to explain the power of deCOOC, suggesting the existence of a latent embedded layer of information pertaining to the cell type specific 3D genome architecture. By applying deCOOC to in-house-generated bulk Hi-C data from visceral and subcutaneous adipose tissues, it is found that the characteristic chromatin features of M2 cells in the two anatomical loci are distinctively bound to different physiological functionalities. Taken together, deCOOC is both a reliable Hi-C data deconvolution method and a powerful tool for functional extraction of 3D genome architecture.

Genome-Wide Association Study Points to Novel Locus for Gilles de la Tourette Syndrome

BackgroundTourette syndrome (TS) is a childhood-onset neurodevelopmental disorder of complex genetic architecture and is characterized by multiple motor tics and at least one vocal tic persisting for more than 1 year. MethodsWe performed a genome-wide meta-analysis integrating a novel TS cohort with previously published data, resulting in a sample size of 6133 individuals with TS and 13,565 ancestry-matched control participants. ResultsWe identified a genome-wide significant locus on chromosome 5q15. Integration of expression quantitative trait locus, Hi-C (high-throughput chromosome conformation capture), and genome-wide association study data implicated the NR2F1 gene and associated long noncoding RNAs within the 5q15 locus. Heritability partitioning identified statistically significant enrichment in brain tissue histone marks, while polygenic risk scoring of brain volume data identified statistically significant associations with right and left thalamus volumes and right putamen volume. ConclusionsOur work presents novel insights into the neurobiology of TS, thereby opening up new directions for future studies.

ViralCC retrieves complete viral genomes and virus-host pairs from metagenomic Hi-C data

The introduction of high-throughput chromosome conformation capture (Hi-C) into metagenomics enables reconstructing high-quality metagenome-assembled genomes (MAGs) from microbial communities. Despite recent advances in recovering eukaryotic, bacterial, and archaeal genomes using Hi-C contact maps, few of Hi-C-based methods are designed to retrieve viral genomes. Here we introduce ViralCC, a publicly available tool to recover complete viral genomes and detect virus-host pairs using Hi-C data. Compared to other Hi-C-based methods, ViralCC leverages the virus-host proximity structure as a complementary information source for the Hi-C interactions. Using mock and real metagenomic Hi-C datasets from several different microbial ecosystems, including the human gut, cow fecal, and wastewater, we demonstrate that ViralCC outperforms existing Hi-C-based binning methods as well as state-of-the-art tools specifically dedicated to metagenomic viral binning. ViralCC can also reveal the taxonomic structure of viruses and virus-host pairs in microbial communities. When applied to a real wastewater metagenomic Hi-C dataset, ViralCC constructs a phage-host network, which is further validated using CRISPR spacer analyses. ViralCC is an open-source pipeline available at https://github.com/dyxstat/ViralCC.

Genomic analyses provide insights into the evolution and salinity adaptation of halophyte Tamarix chinensis.

The woody halophyte Tamarix chinensis is a pioneer tree species in the coastal wetland ecosystem of northern China, exhibiting high resistance to salt stress. However, the genetic information underlying salt tolerance in T. chinensis remains to be seen. Here we present a genomic investigation of T. chinensis to elucidate the underlying mechanism of its high resistance to salinity. Using a combination of PacBio and high-throughput chromosome conformation capture data, a chromosome-level T. chinensis genome was assembled with a size of 1.32 Gb and scaffold N50 of 110.03 Mb. Genome evolution analyses revealed that T. chinensis significantly expanded families of HAT and LIMYB genes. Whole-genome and tandem duplications contributed to the expansion of genes associated with the salinity adaptation of T. chinensis. Transcriptome analyses were performed on root and shoot tissues during salt stress and recovery, and several hub genes responding to salt stress were identified. WRKY33/40, MPK3/4, and XBAT31 were critical in responding to salt stress during early exposure, while WRKY40, ZAT10, AHK4, IRX9, and CESA4/8 were involved in responding to salt stress during late stress and recovery. In addition, PER7/27/57/73 encoding class III peroxidase and MCM3/4/5/7 encoding DNA replication licensing factor maintained up/downregulation during salt stress and recovery stages. The results presented here reveal the genetic mechanisms underlying salt adaptation in T. chinensis, thus providing important genomic resources for evolutionary studies on tamarisk and plant salt tolerance genetic improvement.

High-resolution sequencing of nine elite upland cotton cultivars uncovers genic variations and breeding improvement targets.

Structural variations (SVs) are critical factors affecting genome evolution and important traits. However, identification results and functional analyses of SVs in upland cotton are rare. Here, based on the genetic relationships, breeding history and cumulative planting area of upland cotton in China, nine predominant cultivars from the past 60 years (1950s-2010s) were selected for long read sequencing to uncover genic variations and breeding improvement targets for this crop. Based on the ZM24 reference genome, 0.88-1.47 × 104 SVs per cultivar were identified, and an SV set was constructed. SVs affected the expression of a large number of genes during fiber elongation, and a transposable element insertion resulted in the glandless phenotype in upland cotton. Six widespread inversions were identified based on nine draft genomes and high-throughput chromosome conformation capture data. Multiple haplotype blocks that were always associated with aggregated SVs were demonstrated to play a pivotal role in the agronomic traits of upland cotton and drove its adaptation to the northern planting region. Exotic introgression was the source of these haplotype blocks and increased the genetic diversity of upland cotton. Our results enrich the genome resources of upland cotton, and the identified SVs will promote genetic and breeding research in cotton.

Chromatin architectural alterations due to null mutation of a major CG methylase in rice.

Associations between 3D chromatin architectures and epigenetic modifications have been characterized in animals. However, any impact of DNA methylation on chromatin architecture in plants is understudied, which is confined to Arabidopsis thaliana. Because plant species differ in genome size, composition, and overall chromatin packing, it is unclear to what extent findings from A. thaliana hold in other species. Moreover, the incomplete chromatin architectural profiles and the low-resolution high-throughput chromosome conformation capture (Hi-C) data from A. thaliana have hampered characterizing its subtle chromatin structures and their associations with DNA methylation. We constructed a high-resolution Hi-C interaction map for the null OsMET1-2 (the major CG methyltransferase in rice) mutant (osmet1-2) and isogenic wild-type rice (WT). Chromatin structural changes occurred in osmet1-2, including intra-/inter-chromosomal interactions, compartment transition, and topologically associated domains (TAD) variations. Our findings provide novel insights into the potential function of DNA methylation in TAD formation in rice and confirmed DNA methylation plays similar essential roles in chromatin packing in A. thaliana and rice.

The Manchurian Walnut Genome: Insights into Juglone and Lipid Biosynthesis.

BackgroundManchurian walnut (Juglans mandshurica Maxim.) is a tree with multiple industrial uses and medicinal properties in the Juglandaceae family (walnuts and hickories). J. mandshurica produces juglone, which is a toxic allelopathic agent and has potential utilization value. Furthermore, the seed of J. mandshurica is rich in various unsaturated fatty acids and has high nutritive value.FindingsHere, we present a high-quality chromosome-scale reference genome assembly and annotation for J. mandshurica (n = 16) with a contig N50 of 21.4 Mb by combining PacBio high-fidelity reads with high-throughput chromosome conformation capture data. The assembled genome has an estimated sequence size of 548.7 Mb and consists of 657 contigs, 623 scaffolds, and 40,453 protein-coding genes. In total, 60.99% of the assembled genome consists of repetitive sequences. Sixteen super-scaffolds corresponding to the 16 chromosomes were assembled, with a scaffold N50 length of 33.7 Mb and a BUSCO complete gene percentage of 98.3%. J. mandshurica displays a close sequence relationship with Juglans cathayensis, with a divergence time of 13.8 million years ago. Combining the high-quality genome, transcriptome, and metabolomics data, we constructed a gene-to-metabolite network and identified 566 core and conserved differentially expressed genes, which may be involved in juglone biosynthesis. Five CYP450 genes were found that may contribute to juglone accumulation. NAC, bZip, NF-YA, and NF-YC are positively correlated with the juglone content. Some candidate regulators (e.g., FUS3, ABI3, LEC2, and WRI1 transcription factors) involved in the regulation of lipid biosynthesis were also identified.ConclusionsOur genomic data provide new insights into the evolution of the walnut genome and create a new platform for accelerating molecular breeding and improving the comprehensive utilization of these economically important tree species.

A systematic evaluation of Hi-C data enhancement methods for enhancing PLAC-seq and HiChIP data.

The three-dimensional organization of chromatin plays a critical role in gene regulation. Recently developed technologies, such as HiChIP and proximity ligation-assisted ChIP-Seq (PLAC-seq) (hereafter referred to as HP for brevity), can measure chromosome spatial organization by interrogating chromatin interactions mediated by a protein of interest. While offering cost-efficiency over genome-wide unbiased high-throughput chromosome conformation capture (Hi-C) data, HP data remain sparse at kilobase (Kb) resolution with the current sequencing depth in the order of 108 reads per sample. Deep learning models, including HiCPlus, HiCNN, HiCNN2, DeepHiC and Variationally Encoded Hi-C Loss Enhancer (VEHiCLE), have been developed to enhance the sequencing depth of Hi-C data, but their performance on HP data has not been benchmarked. Here, we performed a comprehensive evaluation of HP data sequencing depth enhancement using models developed for Hi-C data. Specifically, we analyzed various HP data, including Smc1a HiChIP data of the human lymphoblastoid cell line GM12878, H3K4me3 PLAC-seq data of four human neural cell types as well as of mouse embryonic stem cells (mESC), and mESC CCCTC-binding factor (CTCF) PLAC-seq data. Our evaluations lead to the following three findings: (i) most models developed for Hi-C data achieve reasonable performance when applied to HP data (e.g. with Pearson correlation ranging 0.76-0.95 for pairs of loci within 300Kb), and the enhanced datasets lead to improved statistical power for detecting long-range chromatin interactions, (ii) models trained on HP data outperform those trained on Hi-C data and (iii) most models are transferable across cell types. Our results provide a general guideline for HP data enhancement using existing methods designed for Hi-C data.

Normalizing Metagenomic Hi-C Data and Detecting Spurious Contacts Using Zero-Inflated Negative Binomial Regression.

High-throughput chromosome conformation capture (Hi-C) has recently been applied to natural microbial communities and revealed great potential to study multiple genomes simultaneously. Several extraneous factors may influence chromosomal contacts rendering the normalization of Hi-C contact maps essential for downstream analyses. However, the current paucity of metagenomic Hi-C normalization methods and the ignorance for spurious interspecies contacts weaken the interpretability of the data. Here, we report on two types of biases in metagenomic Hi-C experiments: explicit biases and implicit biases, and introduce HiCzin, a parametric model to correct both types of biases and remove spurious interspecies contacts. We demonstrate that the normalized metagenomic Hi-C contact maps by HiCzin result in lower biases, higher capability to detect spurious contacts, and better performance in metagenomic contig clustering.

A Comparison of Topologically Associating Domain Callers Based on Hi-C Data.

Topologically associating domains (TADs) are local chromatin interaction domains, which have been shown to play an important role in gene expression regulation. TADs were originally discovered in the investigation of 3D genome organization based on High-throughput Chromosome Conformation Capture (Hi-C) data. Continuous considerable efforts have been dedicated to developing methods for detecting TADs from Hi-C data. Different computational methods for TADs identification vary in their assumptions and criteria in calling TADs. As a consequence, the TADs called by these methods differ in their similarities and biological features they are enriched in. In this work, we performed a systematic comparison of twenty-six TAD callers. We first compared the TADs and gaps between adjacent TADs across different methods, resolutions, and sequencing depths. We then assessed the quality of TADs and TAD boundaries according to three criteria: the decay of contact frequencies over the genomic distance, enrichment and depletion of regulatory elements around TAD boundaries, and reproducibility of TADs and TAD boundaries in replicate samples. Last, due to the lack of a gold standard of TADs, we also evaluated the performance of the methods on synthetic datasets. We discussed the key principles of TAD callers, and pinpointed current situation in the detection of TADs. We provide a concise, comprehensive, and systematic framework for evaluating the performance of TAD callers, and expect our work will provide useful guidance in choosing suitable approaches for the detection and evaluation of TADs.

Identification of Enhancers and Promoters in the Genome by Multidimensional Scaling.

The positions of enhancers and promoters on genomic DNA remain poorly understood. Chromosomes cannot be observed during the cell division cycle because the genome forms a chromatin structure and spreads within the nucleus. However, high-throughput chromosome conformation capture (Hi-C) measures the physical interactions of genomes. In previous studies, DNA extrusion loops were directly derived from Hi-C heat maps. Multidimensional Scaling (MDS) is used in this assessment to more precisely locate enhancers and promoters. MDS is a multivariate analysis method that reproduces the original coordinates from the distance matrix between elements. We used Hi-C data of cultured osteosarcoma cells and applied MDS as the distance matrix of the genome. In addition, we selected columns 2 and 3 of the orthogonal matrix U as the desired structure. Overall, the DNA loops from the reconstructed genome structure contained bioprocesses involved in transcription, such as the pre-transcriptional initiation complex and RNA polymerase II initiation complex, and transcription factors involved in cancer, such as Foxm1 and CREB3. Therefore, our results are consistent with the biological findings. Our method is suitable for identifying enhancers and promoters in the genome.

Methods for the Analysis of Topologically Associating Domains (TADs).

Chromatin folding in the 3D space of the nucleus can be explored through high-throughput chromosome conformation capture (Hi-C) approaches. These experiments quantify the number of interactions between any pair of genomic loci in the genome and, thus, allow building genome-scale maps of intra- and inter-chromosomal contacts (contact maps). Statistical and algorithmic analyses of Hi-C data consist in extracting information from these contact maps. One of the most striking patterns observed in intra-chromosomal Hi-C contact maps emerged from genomic regions that exhibit dense intra-region but sparse inter-region contacts. These have been termed topologically associating domains (TADs). The identification of TADs from Hi-C contact maps is of great interest as they have been shown to act as unit of chromosome organization and, potentially, functional activity. Several approaches have been developed to identify TADs (TAD callers). However, results from these methods are often dependent on data resolution and poorly concordant. In this chapter, we present four TAD callers and we provide detailed protocols for their use. In addition, we show how to compare TADs identified by different callers and how to assess the enrichment for TAD-associated biological features. TAD calling has become a key step in the study of chromatin 3D organization in different cellular contexts. Here we provide guidelines to improve the robustness and quality of these analyses.

Structural Variations of the 3D Genome Architecture in Cervical Cancer Development.

Human papillomavirus (HPV) integration is the major contributor to cervical cancer (CC) development by inducing structural variations (SVs) in the human genome. SVs are directly associated with the three-dimensional (3D) genome structure leading to cancer development. The detection of SVs is not a trivial task, and several genome-wide techniques have greatly helped in the identification of SVs in the cancerous genome. However, in cervical cancer, precise prediction of SVs mainly translocations and their effects on 3D-genome and gene expression still need to be explored. Here, we have used high-throughput chromosome conformation capture (Hi-C) data of cervical cancer to detect the SVs, especially the translocations, and validated it through whole-genome sequencing (WGS) data. We found that the cervical cancer 3D-genome architecture rearranges itself as compared to that in the normal tissue, and 24% of the total genome switches their A/B compartments. Moreover, translocation detection from Hi-C data showed the presence of high-resolution t(4;7) (q13.1; q31.32) and t(1;16) (q21.2; q22.1) translocations, which disrupted the expression of the genes located at and nearby positions. Enrichment analysis suggested that the disrupted genes were mainly involved in controlling cervical cancer-related pathways. In summary, we detect the novel SVs through Hi-C data and unfold the association among genome-reorganization, translocations, and gene expression regulation. The results help understand the underlying pathogenicity mechanism of SVs in cervical cancer development and identify the targeted therapeutics against cervical cancer.

EnHiC: learning fine-resolution Hi-C contact maps using a generative adversarial framework

MotivationThe high-throughput chromosome conformation capture (Hi-C) technique has enabled genome-wide mapping of chromatin interactions. However, high-resolution Hi-C data requires costly, deep sequencing; therefore, it has only been achieved for a limited number of cell types. Machine learning models based on neural networks have been developed as a remedy to this problem.ResultsIn this work, we propose a novel method, EnHiC, for predicting high-resolution Hi-C matrices from low-resolution input data based on a generative adversarial network (GAN) framework. Inspired by non-negative matrix factorization, our model fully exploits the unique properties of Hi-C matrices and extracts rank-1 features from multi-scale low-resolution matrices to enhance the resolution. Using three human Hi-C datasets, we demonstrated that EnHiC accurately and reliably enhanced the resolution of Hi-C matrices and outperformed other GAN-based models. Moreover, EnHiC-predicted high-resolution matrices facilitated the accurate detection of topologically associated domains and fine-scale chromatin interactions.Availability and implementationEnHiC is publicly available at https://github.com/wmalab/EnHiC.Supplementary informationSupplementary data are available at Bioinformatics online.

A unified framework for inferring the multi-scale organization of chromatin domains from Hi-C.

Chromosomes are giant chain molecules organized into an ensemble of three-dimensional structures characterized with its genomic state and the corresponding biological functions. Despite the strong cell-to-cell heterogeneity, the cell-type specific pattern demonstrated in high-throughput chromosome conformation capture (Hi-C) data hints at a valuable link between structure and function, which makes inference of chromatin domains (CDs) from the pattern of Hi-C a central problem in genome research. Here we present a unified method for analyzing Hi-C data to determine spatial organization of CDs over multiple genomic scales. By applying statistical physics-based clustering analysis to a polymer physics model of the chromosome, our method identifies the CDs that best represent the global pattern of correlation manifested in Hi-C. The multi-scale intra-chromosomal structures compared across different cell types uncover the principles underlying the multi-scale organization of chromatin chain: (i) Sub-TADs, TADs, and meta-TADs constitute a robust hierarchical structure. (ii) The assemblies of compartments and TAD-based domains are governed by different organizational principles. (iii) Sub-TADs are the common building blocks of chromosome architecture. Our physically principled interpretation and analysis of Hi-C not only offer an accurate and quantitative view of multi-scale chromatin organization but also help decipher its connections with genome function.

ReHiC: Enhancing Hi-C data resolution via residual convolutional network.

High-throughput chromosome conformation capture (Hi-C) is one of the most popular methods for studying the three-dimensional organization of genomes. However, Hi-C protocols can be expensive since they require large amounts of sample material and may be time-consuming. Most commonly used Hi-C data are low-resolution. Such data can only be used to identify large-scale genomic interactions and are not sufficient to identify the small-scale patterns. We propose a novel deep learning-based computational approach (named ReHiC) that enhances the resolution of Hi-C data and allows us to achieve high-resolution Hi-C data at a relatively low cost. Our model only requires 1/16 down-sampling ratio of the original sequence reading to predict higher resolution Hi-C data. This is very close to high-resolution data in terms of numerical distribution and interaction distribution. More importantly, our framework stacks deeper and converges faster due to residual blocks in the core of the network. Extensive experiments show that ReHiC performs better than HiCPlus and HiCNN, two recently developed and frequently used methods to look at the spatial organization of chromatin structure in the cell. Moreover, the portability of our framework verified by extensive experiments shows that the trained model can also enhance the Hi-C matrix of other cell types efficiently. In conclusion, ReHiC offers more accurate high-resolution image reconstruction in a broad field.

Spatial Architecture of Chromatin at Loci of Tumor Suppressors and Oncogene

The spatial organization of chromatin plays an important role in regulation of gene expression and in nuclear function. Methods such as high-throughput chromosome conformation capture (Hi-C) can quantify pairwise interaction frequencies among genomic loci and has provided a wealth of information on nuclear organization. Recent work based on the CHROMATIX method and deep sampling has allowed generation of large ensembles of 3D single-cell chromatin conformations. Though only considering physical excluded volume from cell nucleus confinement and sparse driver interactions inferred from Hi-C measurements, these 3D ensembles of single-cell chromatin conformations allow us to examine details of spatial organization of genomic loci. Here, we study how chromosomal architecture of loci containing tumor suppressor genes (TSG) and oncogenes differ between cancer cells and normal cells. We use publicly available Hi-C data from myelogenous leukemia cancer cells K562 and normal lymphoblastoid cells GM12878, and determine how 3D specific interactions, A/B compartment status, as well as linear epigenetic information differ between cancer and normal cells. We show results from our analysis can help to gain understanding on the differential 3D chromatin architecture in loci containing oncogenes and tumor suppressor genes.

Application of Hi-C and other omics data analysis in human cancer and cell differentiation research.

With the development of 3C (chromosome conformation capture) and its derivative technology Hi-C (High-throughput chromosome conformation capture) research, the study of the spatial structure of the genomic sequence in the nucleus helps researchers understand the functions of biological processes such as gene transcription, replication, repair, and regulation. In this paper, we first introduce the research background and purpose of Hi-C data visualization analysis. After that, we discuss the Hi-C data analysis methods from genome 3D structure, A/B compartment, TADs (topologically associated domain), and loop detection. We also discuss how to apply genome visualization technologies to the identification of chromosome feature structures. We continue with a review of correlation analysis differences among multi-omics data, and how to apply Hi-C and other omics data analysis into cancer and cell differentiation research. Finally, we summarize the various problems in joint analyses based on Hi-C and other multi-omics data. We believe this review can help researchers better understand the progress and applications of 3D genome technology.