HiTAD: detecting the structural and functional hierarchies of topologically associating domains from chromatin interactions

[An identification method of chromatin topological associated domains based on spatial density clustering]

The rapid development of high-throughput chromatin conformation capture (Hi-C) technology provides rich genomic interaction data between chromosomal loci for chromatin structure analysis. However, existing methods for identifying topologically associated domains (TADs) based on Hi-C data suffer from low accuracy and sensitivity to parameters. In this context, a TAD identification method based on spatial density clustering was designed and implemented in this paper. The method preprocessed the raw Hi-C data to obtain normalized Hi-C contact matrix data. Then, it computed the distance matrix between loci, generated a reachability graph based on the core distance and reachability distance of loci, and extracted clustering clusters. Finally, it extracted TAD boundaries based on clustering results. This method could identify TAD structures with higher coherence, and TAD boundaries were enriched with more ChIP-seq factors. Experimental results demonstrate that our method has advantages such as higher accuracy and practical significance in TAD identification.

Stratifying TAD boundaries pinpoints focal genomic regions of regulation, damage, and repair

Advances in chromatin mapping have exposed the complex chromatin hierarchical organization in mammals, including topologically associating domains (TADs) and their substructures, yet the functional implications of this hierarchy in gene regulation and disease progression are not fully elucidated. Our study delves into the phenomenon of shared TAD boundaries, which are pivotal in maintaining the hierarchical chromatin structure and regulating gene activity. By integrating high-resolution Hi-C data, chromatin accessibility, and DNA double-strand breaks (DSBs) data from various cell lines, we systematically explore the complex regulatory landscape at high-level TAD boundaries. Our findings indicate that these boundaries are not only key architectural elements but also vibrant hubs, enriched with functionally crucial genes and complex transcription factor binding site-clustered regions. Moreover, they exhibit a pronounced enrichment of DSBs, suggesting a nuanced interplay between transcriptional regulation and genomic stability. Our research provides novel insights into the intricate relationship between the 3D genome structure, gene regulation, and DNA repair mechanisms, highlighting the role of shared TAD boundaries in maintaining genomic integrity and resilience against perturbations. The implications of our findings extend to understanding the complexities of genomic diseases and open new avenues for therapeutic interventions targeting the structural and functional integrity of TAD boundaries.

A comprehensive benchmarking with interpretation and operational guidance for the hierarchy of topologically associating domains

Topologically associating domains (TADs), megabase-scale features of chromatin spatial architecture, are organized in a domain-within-domain TAD hierarchy. Within TADs, the inner and smaller subTADs not only manifest cell-to-cell variability, but also precisely regulate transcription and differentiation. Although over 20 TAD callers are able to detect TAD, their usability in biomedicine is confined by a disagreement of outputs and a limit in understanding TAD hierarchy. We compare 13 computational tools across various conditions and develop a metric to evaluate the similarity of TAD hierarchy. Although outputs of TAD hierarchy at each level vary among callers, data resolutions, sequencing depths, and matrices normalization, they are more consistent when they have a higher similarity of larger TADs. We present comprehensive benchmarking of TAD hierarchy callers and operational guidance to researchers of life science researchers. Moreover, by simulating the mixing of different types of cells, we confirm that TAD hierarchy is generated not simply from stacking Hi-C heatmaps of heterogeneous cells. Finally, we propose an air conditioner model to decipher the role of TAD hierarchy in transcription.

Drought response revealed by chromatin organization variation and transcriptional regulation in cotton

BACKGROUND

Cotton is a major world cash crop and an important source of natural fiber, oil, and protein. Drought stress is becoming a restrictive factor affecting cotton production. To facilitate the development of drought-tolerant cotton varieties, it is necessary to study the molecular mechanism of drought stress response by exploring key drought-resistant genes and related regulatory factors.

RESULTS

In this study, two cotton varieties, ZY007 (drought-sensitive) and ZY168 (drought-tolerant), showing obvious phenotypic differences under drought stress, were selected. A total of 25,898 drought-induced genes were identified, exhibiting significant enrichment in pathways related to plant stress responses. Under drought induction, At subgenome expression bias was observed at the whole-genome level, which may be due to stronger inhibition of Dt subgenome expression. A gene co-expression module that was significantly associated with drought resistance was identified. About 90% of topologically associating domain (TAD) boundaries were stable, and 6613 TAD variation events were identified between the two varieties under drought. We identified 92 genes in ZY007 and 98 in ZY168 related to chromatin 3D structural variation and induced by drought stress. These genes are closely linked to the cotton response to drought stress through canonical hormone-responsive pathways, modulation of kinase and phosphatase activities, facilitation of calcium ion transport, and other related molecular mechanisms.

CONCLUSIONS

These results lay a foundation for elucidating the molecular mechanism of the cotton drought response and provide important regulatory locus and gene resources for the future molecular breeding of drought-resistant cotton varieties.

A time-resolved multi-omics atlas of transcriptional regulation in response to high-altitude hypoxia across whole-body tissues

High-altitude hypoxia acclimatization requires whole-body physiological regulation in highland immigrants, but the underlying genetic mechanism has not been clarified. Here we use sheep as an animal model for low-to-high altitude translocation. We generate multi-omics data including whole-genome sequences, time-resolved bulk RNA-Seq, ATAC-Seq and single-cell RNA-Seq from multiple tissues as well as phenotypic data from 20 bio-indicators. We characterize transcriptional changes of all genes in each tissue, and examine multi-tissue temporal dynamics and transcriptional interactions among genes. Particularly, we identify critical functional genes regulating the short response to hypoxia in each tissue (e.g., PARG in the cerebellum and HMOX1 in the colon). We further identify TAD-constrained cis-regulatory elements, which suppress the transcriptional activity of most genes under hypoxia. Phenotypic and transcriptional evidence indicate that antenatal hypoxia could improve hypoxia tolerance in offspring. Furthermore, we provide time-series expression data of candidate genes associated with human mountain sickness (e.g., BMPR2) and high-altitude adaptation (e.g., HIF1A). Our study provides valuable resources and insights for future hypoxia-related studies in mammals.

Identifying TAD-like domains on single-cell Hi-C data by graph embedding and changepoint detection

MOTIVATION

Topologically associating domains (TADs) are fundamental building blocks of 3D genome. TAD-like domains in single cells are regarded as the underlying genesis of TADs discovered in bulk cells. Understanding the organization of TAD-like domains helps to get deeper insights into their regulatory functions. Unfortunately, it remains a challenge to identify TAD-like domains on single-cell Hi-C data due to its ultra-sparsity.

RESULTS

We propose scKTLD, an in silico tool for the identification of TAD-like domains on single-cell Hi-C data. It takes Hi-C contact matrix as the adjacency matrix for a graph, embeds the graph structures into a low-dimensional space with the help of sparse matrix factorization followed by spectral propagation, and the TAD-like domains can be identified using a kernel-based changepoint detection in the embedding space. The results tell that our scKTLD is superior to the other methods on the sparse contact matrices, including downsampled bulk Hi-C data as well as simulated and experimental single-cell Hi-C data. Besides, we demonstrated the conservation of TAD-like domain boundaries at single-cell level apart from heterogeneity within and across cell types, and found that the boundaries with higher frequency across single cells are more enriched for architectural proteins and chromatin marks, and they preferentially occur at TAD boundaries in bulk cells, especially at those with higher hierarchical levels.

AVAILABILITY AND IMPLEMENTATION

scKTLD is freely available at https://github.com/lhqxinghun/scKTLD.

BL-Hi-C reveals the 3D genome structure of Brassica crops with high sensitivity

High-throughput Chromatin Conformation Capture (Hi-C) technologies can be used to investigate the three-dimensional genomic structure of plants. However, the practical utility of these technologies is impeded by significant background noise, hindering their capability in detecting fine 3D genomic structures. In this study, we optimized the Bridge Linker Hi-C technology (BL-Hi-C) to comprehensively investigate the 3D chromatin landscape of Brassica rapa and Brassica oleracea. The Bouquet configuration of both B. rapa and B. oleracea was elucidated through the construction of a 3D genome simulation. The optimized BL-Hi-C exhibited lower background noise compared to conventional Hi-C methods. Taking this advantage, we used BL-Hi-C to identify FLC gene loops in Arabidopsis, B. rapa, and B. oleracea. We observed that gene loops of FLC2 exhibited conservation across Arabidopsis, B. rapa, and B. oleracea. While gene loops of syntenic FLCs exhibited conservation across B. rapa and B. oleracea, variations in gene loops were evident among multiple paralogs FLCs within the same species. Collectively, our findings highlight the high sensitivity of optimized BL-Hi-C as a powerful tool for investigating the fine 3D genomic organization.

Maternal dominance contributes to subgenome differentiation in allopolyploid fishes

Teleost fishes, which are the largest and most diverse group of living vertebrates, have a rich history of ancient and recent polyploidy. Previous studies of allotetraploid common carp and goldfish (cyprinids) reported a dominant subgenome, which is more expressed and exhibits biased gene retention. However, the underlying mechanisms contributing to observed 'subgenome dominance' remains poorly understood. Here we report high-quality genomes of twenty-one cyprinids to investigate the origin and subsequent subgenome evolution patterns following three independent allopolyploidy events. We identify the closest extant relatives of the diploid progenitor species, investigate genetic and epigenetic differences among subgenomes, and conclude that observed subgenome dominance patterns are likely due to a combination of maternal dominance and transposable element densities in each polyploid. These findings provide an important foundation to understanding subgenome dominance patterns observed in teleost fishes, and ultimately the role of polyploidy in contributing to evolutionary innovations.

Improving Hi-C contact matrices using genome graphs

Three-dimensional chromosome structure plays an important role in fundamental genomic functions. Hi-C, a high-throughput, sequencing-based technique, has drastically expanded our comprehension of 3D chromosome structures. The first step of Hi-C analysis pipeline involves mapping sequencing reads from Hi-C to linear reference genomes. However, the linear reference genome does not incorporate genetic variation information, which can lead to incorrect read alignments, especially when analyzing samples with substantial genomic differences from the reference such as cancer samples. Using genome graphs as the reference facilitates more accurate mapping of reads, however, new algorithms are required for inferring linear genomes from Hi-C reads mapped on genome graphs and constructing corresponding Hi-C contact matrices, which is a prerequisite for the subsequent steps of the Hi-C analysis such as identifying topologically associated domains and calling chromatin loops. We introduce the problem of genome sequence inference from Hi-C data mediated by genome graphs. We formalize this problem, show the hardness of solving this problem, and introduce a novel heuristic algorithm specifically tailored to this problem. We provide a theoretical analysis to evaluate the efficacy of our algorithm. Finally, our empirical experiments indicate that the linear genomes inferred from our method lead to the creation of improved Hi-C contact matrices. These enhanced matrices show a reduction in erroneous patterns caused by structural variations and are more effective in accurately capturing the structures of topologically associated domains.

Accessible gene borders establish a core structural unit for chromatin architecture in Arabidopsis

Three-dimensional (3D) chromatin structure is linked to transcriptional regulation in multicellular eukaryotes including plants. Taking advantage of high-resolution Hi-C (high-throughput chromatin conformation capture), we detected a small structural unit with 3D chromatin architecture in the Arabidopsis genome, which lacks topologically associating domains, and also in the genomes of tomato, maize, and Marchantia polymorpha. The 3D folding domain unit was usually established around an individual gene and was dependent on chromatin accessibility at the transcription start site (TSS) and transcription end site (TES). We also observed larger contact domains containing two or more neighboring genes, which were dependent on accessible border regions. Binding of transcription factors to accessible TSS/TES regions formed these gene domains. We successfully simulated these Hi-C contact maps via computational modeling using chromatin accessibility as input. Our results demonstrate that gene domains establish basic 3D chromatin architecture units that likely contribute to higher-order 3D genome folding in plants.

TOAST: A novel method for identifying topologically associated domains based on graph auto-encoders and clustering

Topologically associated domains (TADs) play a pivotal role in disease detection. This study introduces a novel TADs recognition approach named TOAST, leveraging graph auto-encoders and clustering techniques. TOAST conceptualizes each genomic bin as a node of a graph and employs the Hi-C contact matrix as the graph's adjacency matrix. By employing graph auto-encoders, TOAST generates informative embeddings as features. Subsequently, the unsupervised clustering algorithm HDBSCAN is utilized to assign labels to each genomic bin, facilitating the identification of contiguous regions with the same label as TADs. Our experimental analysis of several simulated Hi-C data sets shows that TOAST can quickly and accurately identify TADs from different types of simulated Hi-C contact matrices, outperforming existing algorithms. We also determined the anchoring ratio of TAD boundaries by analyzing different TAD recognition algorithms, and obtained an average ratio of anchoring CTCF, SMC3, RAD21, POLR2A, H3K36me3, H3K9me3, H3K4me3, H3K4me1, Enhancer, and Promoters of 0.66, 0.47, 0.54, 0.27, 0.24, 0.12, 0.32, 0.41, 0.26, and 0.13, respectively. In conclusion, TOAST is a method that can quickly identify TAD boundary parameters that are easy to understand and have important biological significance. The TOAST web server can be accessed via http://223.223.185.189:4005/. The code of TOAST is available online at https://github.com/ghaiyan/TOAST.

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.

Integrative Identification by Hi-C Revealed Distinct Advanced Structural Variations in Lung Adenocarcinoma Tissue

Advanced three-dimensional structure variations of chromatin in large genome fragments, such as conversion of A/B compartment, topologically associated domains (TADs) and chromatin loops are related closely to occurrence of malignant tumors. However, the structural characteristics of lung cancer still remain uncovered. In this study, we used high-throughput chromosome (Hi-C) conformation capture technology to detect the advanced structural variations in chromatin of two non-smoking lung adenocarcinoma (LUAD) tumor and paired normal tissues. The results indicate that significant chromatin variations are detected in tumor tissues compared with normal tissues. At compartment scale, the main conversion type of compartment is A → B in tumor tissues, which are concentrated mainly on chromosome 3 (Chr3) (33.6%). A total of 216 tumor-specific TADs are identified in tumor tissues, which are distributed mainly in Chr1 (19), Chr2 (15) and Chr3 (17). Forty-one distinct enhancer-promoter loops are observed in tumor tissue, which are associated closely to tumor-related pathways including mitogen-activated protein kinase (MAPK), Phosphatidylinositol-3-kinase-Protein kinase B (PI3K-AKT), Ras, Wnt and Ras1. The most important observation in this study is that we identify five important genes (SYT16, NCEH1, NXPE3, MB21D2, and DZIP1L), which are detected in both A → B compartment, TADs and chromatin loops in tumor samples, and four of these genes (NCEH1, NXPE3, MB21D2, and DZIP1L) locate on q arm of Chr3. Further gene expression and invasion experiment analysis show that NCEH1, MB21D2 and SYT16 are involved in the tumor development. Thus, we provide a comprehensive overview of advanced structures in LUAD for the first time and provide a basis for further research on the genetic variation of this tumor.

Defining the separation landscape of topological domains for decoding consensus domain organization of the 3D genome

Topologically associating domains (TADs) have emerged as basic structural and functional units of genome organization and have been determined by many computational methods from Hi-C contact maps. However, the TADs obtained by different methods vary greatly, which makes the accurate determination of TADs a challenging issue and hinders subsequent biological analyses about their organization and functions. Obvious inconsistencies among the TADs identified by different methods indeed make the statistical and biological properties of TADs overly depend on the chosen method rather than on the data. To this end, we use the consensus structural information captured by these methods to define the TAD separation landscape for decoding the consensus domain organization of the 3D genome. We show that the TAD separation landscape could be used to compare domain boundaries across multiple cell types for discovering conserved and divergent topological structures, decipher three types of boundary regions with diverse biological features, and identify consensus TADs (ConsTADs). We illustrate that these analyses could deepen our understanding of the relationships between the topological domains and chromatin states, gene expression, and DNA replication timing.

HPTAD: A computational method to identify topologically associating domains from HiChIP and PLAC-seq datasets

High-throughput chromatin conformation capture technologies, such as Hi-C and Micro-C, have enabled genome-wide view of chromatin spatial organization. Most recently, Hi-C-derived enrichment-based technologies, including HiChIP and PLAC-seq, offer attractive alternatives due to their high signal-to-noise ratio and low cost. While a series of computational tools have been developed for Hi-C data, methods tailored for HiChIP and PLAC-seq data are still under development. Here we present HPTAD, a computational method to identify topologically associating domains (TADs) from HiChIP and PLAC-seq data. We performed comprehensive benchmark analysis to demonstrate its superior performance over existing TAD callers designed for Hi-C data. HPTAD is freely available at https://github.com/yunliUNC/HPTAD.

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.

Reference panel guided topological structure annotation of Hi-C data

Accurately annotating topological structures (e.g., loops and topologically associating domains) from Hi-C data is critical for understanding the role of 3D genome organization in gene regulation. This is a challenging task, especially at high resolution, in part due to the limited sequencing coverage of Hi-C data. Current approaches focus on the analysis of individual Hi-C data sets of interest, without taking advantage of the facts that (i) several hundred Hi-C contact maps are publicly available, and (ii) the vast majority of topological structures are conserved across multiple cell types. Here, we present RefHiC, an attention-based deep learning framework that uses a reference panel of Hi-C datasets to facilitate topological structure annotation from a given study sample. We compare RefHiC against tools that do not use reference samples and find that RefHiC outperforms other programs at both topological associating domain and loop annotation across different cell types, species, and sequencing depths.

Genomic innovation and regulatory rewiring during evolution of the cotton genus Gossypium

Phenotypic diversity and evolutionary innovation ultimately trace to variation in genomic sequence and rewiring of regulatory networks. Here, we constructed a pan-genome of the Gossypium genus using ten representative diploid genomes. We document the genomic evolutionary history and the impact of lineage-specific transposon amplification on differential genome composition. The pan-3D genome reveals evolutionary connections between transposon-driven genome size variation and both higher-order chromatin structure reorganization and the rewiring of chromatin interactome. We linked changes in chromatin structures to phenotypic differences in cotton fiber and identified regulatory variations that decode the genetic basis of fiber length, the latter enabled by sequencing 1,005 transcriptomes during fiber development. We showcase how pan-genomic, pan-3D genomic and genetic regulatory data serve as a resource for delineating the evolutionary basis of spinnable cotton fiber. Our work provides insights into the evolution of genome organization and regulation and will inform cotton improvement by enabling regulome-based approaches.

Regulation of cohesin-mediated chromosome folding by PDS5 in mammals

Cohesin regulates sister chromatid cohesion but also contributes to chromosome folding by promoting the formation of chromatin loops, a process mediated by loop extrusion. Although PDS5 regulates cohesin dynamics on chromatin, the exact function of PDS5 in cohesin-mediated chromatin looping remains unclear. Two paralogs of PDS5 exist in vertebrates, PDS5A and PDS5B. Here we show that PDS5A and PDS5B co-localize with RAD21 and CTCF at loop anchors. Rapid PDS5A or PDS5B degradation in liver cancer cells using an inducible degron system reduces chromatin loops and increases loop size. RAD21 enrichment at loop anchors is decreased upon depletion of PDS5A or PDS5B. PDS5B loss also reduces CTCF signals at loop anchors and has a stronger effect on loop enlargement compared with PDS5A. Co-depletion of PDS5A and PDS5B reduces RAD21 levels at loop anchors although the amount of cohesin on chromatin is increased. Our study provides insight into how PDS5 proteins regulate cohesin-mediated chromatin looping.

3D chromatin remodeling potentiates transcriptional programs driving cell invasion

The contribution of deregulated chromatin architecture, including topologically associated domains (TADs), to cancer progression remains ambiguous. CCCTC-binding factor (CTCF) is a central regulator of higher-order chromatin structure that undergoes copy number loss in over half of all breast cancers, but the impact of this defect on epigenetic programming and chromatin architecture remains unclear. We find that under physiological conditions, CTCF organizes subTADs to limit the expression of oncogenic pathways, including phosphatidylinositol 3-kinase (PI3K) and cell adhesion networks. Loss of a single CTCF allele potentiates cell invasion through compromised chromatin insulation and a reorganization of chromatin architecture and histone programming that facilitates de novo promoter-enhancer contacts. However, this change in the higher-order chromatin landscape leads to a vulnerability to inhibitors of mTOR. These data support a model whereby subTAD reorganization drives both modification of histones at de novo enhancer-promoter contacts and transcriptional up-regulation of oncogenic transcriptional networks.

3D chromatin maps of the human pancreas reveal lineage-specific regulatory architecture of T2D risk

Three-dimensional (3D) chromatin organization maps help dissect cell-type-specific gene regulatory programs. Furthermore, 3D chromatin maps contribute to elucidating the pathogenesis of complex genetic diseases by connecting distal regulatory regions and genetic risk variants to their respective target genes. To understand the cell-type-specific regulatory architecture of diabetes risk, we generated transcriptomic and 3D epigenomic profiles of human pancreatic acinar, alpha, and beta cells using single-cell RNA-seq, single-cell ATAC-seq, and high-resolution Hi-C of sorted cells. Comparisons of these profiles revealed differential A/B (open/closed) chromatin compartmentalization, chromatin looping, and transcriptional factor-mediated control of cell-type-specific gene regulatory programs. We identified a total of 4,750 putative causal-variant-to-target-gene pairs at 194 type 2 diabetes GWAS signals using pancreatic 3D chromatin maps. We found that the connections between candidate causal variants and their putative target effector genes are cell-type stratified and emphasize previously underappreciated roles for alpha and acinar cells in diabetes pathogenesis.

Genome architecture plasticity underlies DNA replication timing dynamics in cell differentiation

During the S-phase of eukaryotic cell cycle, DNA is replicated in a dedicatedly regulated temporal order, with regions containing active and inactive genes replicated early and late, respectively. Recent advances in sequencing technology allow us to explore the connection between replication timing (RT), histone modifications, and three-dimensional (3D) chromatin structure in diverse cell types. To characterize the dynamics during cell differentiation, corresponding sequencing data for human embryonic stem cells and four differentiated cell types were collected. By comparing RT and its extent of conservation before and after germ layer specification, the human genome was partitioned into distinct categories. Each category is then subject to comparisons on genomic, epigenetic, and chromatin 3D structural features. As expected, while constitutive early and late replication regions showed active and inactive features, respectively, dynamic regions with switched RT showed intermediate features. Surprisingly, although early-to-late replication and late-to-early replication regions showed similar histone modification patterns in hESCs, their structural preferences were opposite. Specifically, in hESCs, early-to-late replication regions tended to appear in the B compartment and large topologically associated domains, while late-to-early replication regions showed the opposite. Our results uncover the coordinated regulation of RT and 3D genome structure that underlies the loss of pluripotency and lineage commitment and indicate the importance and potential roles of genome architecture in biological processes.

Single base-pair resolution analysis of DNA binding motif with MoMotif reveals an oncogenic function of CTCF zinc-finger 1 mutation

Defining the impact of missense mutations on the recognition of DNA motifs is highly dependent on bioinformatic tools that define DNA binding elements. However, classical motif analysis tools remain limited in their capacity to identify subtle changes in complex binding motifs between distinct conditions. To overcome this limitation, we developed a new tool, MoMotif, that facilitates a sensitive identification, at the single base-pair resolution, of complex, or subtle, alterations to core binding motifs, discerned from ChIP-seq data. We employed MoMotif to define the previously uncharacterized recognition motif of CTCF zinc-finger 1 (ZF1), and to further define the impact of CTCF ZF1 mutation on its association with chromatin. Mutations of CTCF ZF1 are exclusive to breast cancer and are associated with metastasis and therapeutic resistance, but the underlying mechanisms are unclear. Using MoMotif, we identified an extension of the CTCF core binding motif, necessitating a functional ZF1 to bind appropriately. Using a combination of ChIP-Seq and RNA-Seq, we discover that the inability to bind this extended motif drives an altered transcriptional program associated with the oncogenic phenotypes observed clinically. Our study demonstrates that MoMotif is a powerful new tool for comparative ChIP-seq analysis and characterising DNA-protein contacts.

TADfit is a multivariate linear regression model for profiling hierarchical chromatin domains on replicate Hi-C data

Topologically associating domains (TADs) are fundamental building blocks of three dimensional genome, and organized into complex hierarchies. Identifying hierarchical TADs on Hi-C data helps to understand the relationship between genome architectures and gene regulation. Herein we propose TADfit, a multivariate linear regression model for profiling hierarchical chromatin domains, which tries to fit the interaction frequencies in Hi-C contact matrix with and without replicates using all-possible hierarchical TADs, and the significant ones can be determined by the regression coefficients obtained with the help of an online learning solver called Follow-The-Regularized-Leader (FTRL). Beyond the existing methods, TADfit has an ability to handle multiple contact matrix replicates and find partially overlapping TADs on them, which helps to find the comprehensive underlying TADs across replicates from different experiments. The comparative results tell that TADfit has better accuracy and reproducibility, and the hierarchical TADs called by it exhibit a reasonable biological relevance.

TADfit is a computational method that can identify hierarchical or partially overlapping TADs from Hi-C data, in part by using information from multiple replicates to improve detection power.

EagleC: A deep-learning framework for detecting a full range of structural variations from bulk and single-cell contact maps

The Hi-C technique has been shown to be a promising method to detect structural variations (SVs) in human genomes. However, algorithms that can use Hi-C data for a full-range SV detection have been severely lacking. Current methods can only identify interchromosomal translocations and long-range intrachromosomal SVs (>1 Mb) at less-than-optimal resolution. Therefore, we develop EagleC, a framework that combines deep-learning and ensemble-learning strategies to predict a full range of SVs at high resolution. We show that EagleC can uniquely capture a set of fusion genes that are missed by whole-genome sequencing or nanopore. Furthermore, EagleC also effectively captures SVs in other chromatin interaction platforms, such as HiChIP, Chromatin interaction analysis with paired-end tag sequencing (ChIA-PET), and capture Hi-C. We apply EagleC in more than 100 cancer cell lines and primary tumors and identify a valuable set of high-quality SVs. Last, we demonstrate that EagleC can be applied to single-cell Hi-C and used to study the SV heterogeneity in primary tumors.

A comparison of topologically associating domain callers over mammals at high resolution

BACKGROUND

Topologically associating domains (TADs) are locally highly-interacting genome regions, which also play a critical role in regulating gene expression in the cell. TADs have been first identified while investigating the 3D genome structure over High-throughput Chromosome Conformation Capture (Hi-C) interaction dataset. Substantial degree of efforts have been devoted to develop techniques for inferring TADs from Hi-C interaction dataset. Many TAD-calling methods have been developed which differ in their criteria and assumptions in TAD inference. Correspondingly, TADs inferred via these callers vary in terms of both similarities and biological features they are enriched in.

RESULT

We have carried out a systematic comparison of 27 TAD-calling methods over mammals. We use Micro-C, a recent high-resolution variant of Hi-C, to compare TADs at a very high resolution, and classify the methods into 3 categories: feature-based methods, Clustering methods, Graph-partitioning methods. We have evaluated TAD boundaries, gaps between adjacent TADs, and quality of TADs across various criteria. We also found particularly CTCF and Cohesin proteins to be effective in formation of TADs with corner dots. We have also assessed the callers performance on simulated datasets since a gold standard for TADs is missing. TAD sizes and numbers change remarkably between TAD callers and dataset resolutions, indicating that TADs are hierarchically-organized domains, instead of disjoint regions. A core subset of feature-based TAD callers regularly perform the best while inferring reproducible domains, which are also enriched for TAD related biological properties.

CONCLUSION

We have analyzed the fundamental principles of TAD-calling methods, and identified the existing situation in TAD inference across high resolution Micro-C interaction datasets over mammals. We come up with a systematic, comprehensive, and concise framework to evaluate the TAD-calling methods performance across Micro-C datasets. Our research will be useful in selecting appropriate methods for TAD inference and evaluation based on available data, experimental design, and biological question of interest. We also introduce our analysis as a benchmarking tool with publicly available source code.

Mapping nucleosome and chromatin architectures: A survey of computational methods

With ever-growing genomic sequencing data, the data variabilities and the underlying biases of the sequencing technologies pose significant computational challenges ranging from the need for accurately detecting the nucleosome positioning or chromatin interaction to the need for developing normalization methods to eliminate systematic biases. This review mainly surveys the computational methods for mapping the higher-resolution nucleosome and higher-order chromatin architectures. While a detailed discussion of the underlying algorithms is beyond the scope of our survey, we have discussed the methods and tools that can detect the nucleosomes in the genome, then demonstrated the computational methods for identifying 3D chromatin domains and interactions. We further illustrated computational approaches for integrating multi-omics data with Hi-C data and the advance of single-cell (sc)Hi-C data analysis. Our survey provides a comprehensive and valuable resource for biomedical scientists interested in studying nucleosome organization and chromatin structures as well as for computational scientists who are interested in improving upon them.

3D disorganization and rearrangement of genome provide insights into pathogenesis of NAFLD by integrated Hi-C, Nanopore, and RNA sequencing

The three-dimensional (3D) conformation of chromatin is integral to the precise regulation of gene expression. The 3D genome and genomic variations in non-alcoholic fatty liver disease (NAFLD) are largely unknown, despite their key roles in cellular function and physiological processes. High-throughput chromosome conformation capture (Hi-C), Nanopore sequencing, and RNA-sequencing (RNA-seq) assays were performed on the liver of normal and NAFLD mice. A high-resolution 3D chromatin interaction map was generated to examine different 3D genome hierarchies including A/B compartments, topologically associated domains (TADs), and chromatin loops by Hi-C, and whole genome sequencing identifying structural variations (SVs) and copy number variations (CNVs) by Nanopore sequencing. We identified variations in thousands of regions across the genome with respect to 3D chromatin organization and genomic rearrangements, between normal and NAFLD mice, and revealed gene dysregulation frequently accompanied by these variations. Candidate target genes were identified in NAFLD, impacted by genetic rearrangements and spatial organization disruption. Our data provide a high-resolution 3D genome interaction resource for NAFLD investigations, revealed the relationship among genetic rearrangements, spatial organization disruption, and gene regulation, and identified candidate genes associated with these variations implicated in the pathogenesis of NAFLD. The newly findings offer insights into novel mechanisms of NAFLD pathogenesis and can provide a new conceptual framework for NAFLD therapy.

Comparative Genome Analyses Highlight Transposon-Mediated Genome Expansion and the Evolutionary Architecture of 3D Genomic Folding in Cotton

Transposable element (TE) amplification has been recognized as a driving force mediating genome size expansion and evolution, but the consequences for shaping 3D genomic architecture remains largely unknown in plants. Here, we report reference-grade genome assemblies for three species of cotton ranging 3-fold in genome size, namely Gossypium rotundifolium (K2), G. arboreum (A2), and G. raimondii (D5), using Oxford Nanopore Technologies. Comparative genome analyses document the details of lineage-specific TE amplification contributing to the large genome size differences (K2, 2.44 Gb; A2, 1.62 Gb; D5, 750.19 Mb) and indicate relatively conserved gene content and synteny relationships among genomes. We found that approximately 17% of syntenic genes exhibit chromatin status change between active ("A") and inactive ("B") compartments, and TE amplification was associated with the increase of the proportion of A compartment in gene regions (∼7,000 genes) in K2 and A2 relative to D5. Only 42% of topologically associating domain (TAD) boundaries were conserved among the three genomes. Our data implicate recent amplification of TEs following the formation of lineage-specific TAD boundaries. This study sheds light on the role of transposon-mediated genome expansion in the evolution of higher-order chromatin structure in plants.

Disruption of topologically associating domains by structural variations in tetraploid cottons

Structural variations (SVs) are recognized to have an important role in transcriptional regulation, especially in the light of resolved 3D genome structure using high-throughput chromosome conformation capture (Hi-C) technology in mammals. However, the effect of SVs on 3D genome organization in plants remains rarely understood. In this study, we identified 295,496 SVs and 5251 topologically associating domains (TADs) in two diploid and two tetraploid cottons. We observed that approximately 16% of SVs occurred in TAD boundary regions that were called boundary affecting-structural variations (BA-SVs), and had a large effect on disrupting TAD organization. Nevertheless, SVs preferred occurring in TAD interior instead of TAD boundary, probably associated with the relaxed evolutionary selection pressure. We noticed the biased evolution of the At and Dt subgenomes of tetraploid cottons, in terms of SV-mediated disruption of 3D genome structure relative to diploids. In addition, we provide evidence showing that both SVs and TAD disruption could lead to expression difference of orthologous genes. This study advances our understanding of the effect of SVs on 3D genome organization and gene expression regulation in plants.

The comparative integrated multi-omics analysis identifies CA2 as a novel target for chordoma

BACKGROUND

Chordoma is a rare mesenchymal malignancy, with a high recurrence rate and unclear tumorigenic mechanism. Genetic alterations, epigenetic regulators, and chromatin spatial organization play crucial roles in the initiation and progression of chordoma. In the current study, we aim to uncover the novel therapeutical targets for chordoma via using integrated multi-omics analysis.

METHODS

The RNA-sequencing (RNA-seq), assay for transposable accessible chromatin by high throughput sequencing (ATAC-seq) and Hi-C were performed between chordoma and human nucleus pulposus (HNP), along with imageological examination and clinical information. The expressions of identified targets were validated by clinical samples and their function were further evaluated by cell and animal experiments via gene knockdown and inhibitors.

RESULTS

The integrated multi-omics analysis revealed the important roles of bone microenvironment in chordoma tumorigenesis. By comparing the hierarchical structures, CA2 and THNSL2 were identified in the switched compartments, cell-specific boundaries and loops. Additionally, CA2 was highly expressed in chordoma, but barely found in HNP. The cell growth and migration of chordoma cells were dramatically suppressed via inhibition of CA2 either with genetic deletion or pharmaceutical treatment with Dorzolamide HCl. Furthermore, Dorzolamide HCl also regulated the bone microenvironment by blocking the osteoclast differentiation of bone marrow monocytes.

CONCLUSION

This study uncovers the roles of bone microenvironment in the chordoma tumorigenesis and identifies CA2 as a novel therapeutic target for chordoma. Besides, our findings suggest Dorzolamide HCl as a promising therapeutic option for chordoma.

Genome-wide detection of enhancer-hijacking events from chromatin interaction data in rearranged genomes

Recent efforts have shown that structural variations (SVs) can disrupt three-dimensional genome organization and induce enhancer hijacking, yet no computational tools exist to identify such events from chromatin interaction data. Here, we develop NeoLoopFinder, a computational framework to identify the chromatin interactions induced by SVs, including interchromosomal translocations, large deletions and inversions. Our framework can automatically resolve complex SVs, reconstruct local Hi-C maps surrounding the breakpoints, normalize copy number variation and allele effects and predict chromatin loops induced by SVs. We applied NeoLoopFinder in Hi-C data from 50 cancer cell lines and primary tumors and identified tens of recurrent genes associated with enhancer hijacking. To experimentally validate NeoLoopFinder, we deleted the hijacked enhancers in prostate adenocarcinoma cells using CRISPR-Cas9, which significantly reduced expression of the target oncogene. In summary, NeoLoopFinder enables identification of critical oncogenic regulatory elements that can potentially reveal therapeutic targets.

Comparative genome analyses highlight transposon-mediated genome expansion and the evolutionary architecture of 3D genomic folding in cotton

Transposable element (TE) amplification has been recognized as a driving force mediating genome size expansion and evolution, but the consequences for shaping 3D genomic architecture remains largely unknown in plants. Here, we report reference-grade genome assemblies for three species of cotton ranging 3-fold in genome size, namely Gossypium rotundifolium (K₂), G. arboreum (A₂), and G. raimondii (D₅), using Oxford Nanopore Technologies. Comparative genome analyses document the details of lineage-specific TE amplification contributing to the large genome size differences (K₂, 2.44 Gb; A₂, 1.62 Gb; D₅, 750.19 Mb) and indicate relatively conserved gene content and synteny relationships among genomes. We found that approximately 17% of syntenic genes exhibit chromatin status change between active (“A”) and inactive (“B”) compartments, and TE amplification was associated with the increase of the proportion of A compartment in gene regions (∼7,000 genes) in K₂ and A₂ relative to D₅. Only 42% of topologically associating domain (TAD) boundaries were conserved among the three genomes. Our data implicate recent amplification of TEs following the formation of lineage-specific TAD boundaries. This study sheds light on the role of transposon-mediated genome expansion in the evolution of higher-order chromatin structure in plants.

TAD boundary and strength prediction by integrating sequence and epigenetic profile information

Topologically associated domains (TADs) are one of the important higher order chromatin structures with various sizes in the eukaryotic genomes. TAD boundaries, as the flanking regions between adjacent domains, can restrict the interactions of regulatory elements, including enhancers and promoters, and are generally dynamic and variable in different cells. However, the influence of sequence and epigenetic profile-based features in the identification of TAD boundaries is largely unknown. In this work, we proposed a method called pTADS (prediction of TAD boundary and strength), to predict TAD boundaries and boundary strength across multiple cell lines with DNA sequence and epigenetic profile information. The performance was assessed in seven cell lines and three TAD calling methods. The results demonstrate that the TAD boundary can be well predicted by the selected shared features across multiple cell lines. Especially, the model can be transferable to predict the TAD boundary from one cell line to other cell lines. The boundary strength can be characterized by boundary score with good performance. The predicted TAD boundary and TAD boundary strength are further confirmed by three Hi-C contact matrix-based methods across multiple cell lines. The codes and datasets are available at https://github.com/chrom3DEpi/pTADS.

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.

Chromatin loop anchors contain core structural components of the gene expression machinery in maize

BACKGROUND

Three-dimensional chromatin loop structures connect regulatory elements to their target genes in regions known as anchors. In complex plant genomes, such as maize, it has been proposed that loops span heterochromatic regions marked by higher repeat content, but little is known on their spatial organization and genome-wide occurrence in relation to transcriptional activity.

RESULTS

Here, ultra-deep Hi-C sequencing of maize B73 leaf tissue was combined with gene expression and open chromatin sequencing for chromatin loop discovery and correlation with hierarchical topologically-associating domains (TADs) and transcriptional activity. A majority of all anchors are shared between multiple loops from previous public maize high-resolution interactome datasets, suggesting a highly dynamic environment, with a conserved set of anchors involved in multiple interaction networks. Chromatin loop interiors are marked by higher repeat contents than the anchors flanking them. A small fraction of high-resolution interaction anchors, fully embedded in larger chromatin loops, co-locate with active genes and putative protein-binding sites. Combinatorial analyses indicate that all anchors studied here co-locate with at least 81.5% of expressed genes and 74% of open chromatin regions. Approximately 38% of all Hi-C chromatin loops are fully embedded within hierarchical TAD-like domains, while the remaining ones share anchors with domain boundaries or with distinct domains. Those various loop types exhibit specific patterns of overlap for open chromatin regions and expressed genes, but no apparent pattern of gene expression. In addition, up to 63% of all unique variants derived from a prior public maize eQTL dataset overlap with Hi-C loop anchors. Anchor annotation suggests that < 7% of all loops detected here are potentially devoid of any genes or regulatory elements. The overall organization of chromatin loop anchors in the maize genome suggest a loop modeling system hypothesized to resemble phase separation of repeat-rich regions.

CONCLUSIONS

Sets of conserved chromatin loop anchors mapping to hierarchical domains contains core structural components of the gene expression machinery in maize. The data presented here will be a useful reference to further investigate their function in regard to the formation of transcriptional complexes and the regulation of transcriptional activity in the maize genome.

Order and stochasticity in the folding of individual Drosophila genomes

Mammalian and Drosophila genomes are partitioned into topologically associating domains (TADs). Although this partitioning has been reported to be functionally relevant, it is unclear whether TADs represent true physical units located at the same genomic positions in each cell nucleus or emerge as an average of numerous alternative chromatin folding patterns in a cell population. Here, we use a single-nucleus Hi-C technique to construct high-resolution Hi-C maps in individual Drosophila genomes. These maps demonstrate chromatin compartmentalization at the megabase scale and partitioning of the genome into non-hierarchical TADs at the scale of 100 kb, which closely resembles the TAD profile in the bulk in situ Hi-C data. Over 40% of TAD boundaries are conserved between individual nuclei and possess a high level of active epigenetic marks. Polymer simulations demonstrate that chromatin folding is best described by the random walk model within TADs and is most suitably approximated by a crumpled globule build of Gaussian blobs at longer distances. We observe prominent cell-to-cell variability in the long-range contacts between either active genome loci or between Polycomb-bound regions, suggesting an important contribution of stochastic processes to the formation of the Drosophila 3D genome.

Genomes are partitioned into topologically associating domains (TADs). Here the authors present single-nucleus Hi-C maps in Drosophila at 10 kb resolution, demonstrating the presence of chromatin compartments in individual nuclei, and partitioning of the genome into non-hierarchical TADs at the scale of 100 kb, which resembles population TAD profiles.

HiCHap: a package to correct and analyze the diploid Hi-C data

Background

In diploid cells, it is important to construct maternal and paternal Hi-C contact maps respectively since the two homologous chromosomes can differ in chromatin three-dimensional (3D) organization. Though previous softwares could construct diploid (maternal and paternal) Hi-C contact maps by using phased genetic variants, they all neglected the systematic biases in diploid Hi-C contact maps caused by variable genetic variant density in the genome. In addition, few of softwares provided quantitative analyses on allele-specific chromatin 3D organization, including compartment, topological domain and chromatin loop.

Results

In this work, we revealed the feature of allele-assignment bias caused by the variable genetic variant density, and then proposed a novel strategy to correct the systematic biases in diploid Hi-C contact maps. Based on the bias correction, we developed an integrated tool, called HiCHap, to perform read mapping, contact map construction, whole-genome identification of compartments, topological domains and chromatin loops, and allele-specific testing for diploid Hi-C data. Our results show that the correction on allele-assignment bias in HiCHap does significantly improve the quality of diploid Hi-C contact maps, which subsequently facilitates the whole-genome identification of diploid chromatin 3D organization, including compartments, topological domains and chromatin loops. Finally, HiCHap also supports the data analysis for haploid Hi-C maps without distinguishing two homologous chromosomes.

Conclusions

We provided an integrated package HiCHap to perform the data processing, bias correction and structural analysis for diploid Hi-C data. The source code and tutorial of software HiCHap are freely available at https://pypi.org/project/HiCHap/.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-020-07165-x.

Highly interconnected enhancer communities control lineage-determining genes in human mesenchymal stem cells

Adipocyte differentiation is driven by waves of transcriptional regulators that reprogram the enhancer landscape and change the wiring of the promoter interactome. Here, we use high-throughput chromosome conformation enhancer capture to interrogate the role of enhancer-to-enhancer interactions during differentiation of human mesenchymal stem cells. We find that enhancers form an elaborate network that is dynamic during differentiation and coupled with changes in enhancer activity. Transcription factors (TFs) at baited enhancers amplify TF binding at target enhancers, a phenomenon we term cross-interaction stabilization of TFs. Moreover, highly interconnected enhancers (HICE) act as integration hubs orchestrating differentiation by the formation of three-dimensional enhancer communities, inside which, HICE, and other enhancers, converge on phenotypically important gene promoters. Collectively, these results indicate that enhancer interactions play a key role in the regulation of enhancer function, and that HICE are important for both signal integration and compartmentalization of the genome.

TADBD: a sensitive and fast method for detection of typologically associated domain boundaries

A topologically associated domain (TAD) is a self-interacting genomic block. Detection of TAD boundaries on Hi-C contact matrix is one of the most important issues in the analysis of 3D genome architecture at TAD level. Here, we present TAD boundary detection (TADBD), a sensitive and fast computational method for detection of TAD boundaries on Hi-C contact matrix. This method implements a Haar-based algorithm by considering Haar diagonal template, acceleration via a compact integrogram, multi-scale aggregation at template size and statistical filtering. In most cases, comparison results from simulated and experimental data show that TADBD outperforms the other five methods. In addition, a new R package for TADBD is freely available online.

Advances in technologies for 3D genomics research

The spatial structure of the orderly organized chromatin in the nucleus has important roles in maintaining normal cell function and in regulation of gene expression, and the high-throughput Hi-C and ChIA-PET methods have been widely used in various biological studies for determining potential spatial genome structures and their functions. However, there are still great difficulties and challenges in three-dimensional (3D) genomics research. More efficient, economical, and unbiased approaches to studying 3D genomics need to be developed for more widespread and easier applications. Here, we review the most recent studies on new 3D genomics research technologies, such as improvements of the traditional Hi-C and ChIA-PET methods, new approaches based on non-proximal-ligation strategies, and imaging-based methods improved in recent years. Especially, we review the CRISPR-based methods for functional validations in 3D genomics, which could be the forthcoming directions. We hope this review can show some insights into the potential improvements for future 3D genomics.

TADsplimer reveals splits and mergers of topologically associating domains for epigenetic regulation of transcription

We present TADsplimer, the first computational tool to systematically detect topologically associating domain (TAD) splits and mergers across the genome between Hi-C samples. TADsplimer recaptures splits and mergers of TADs with high accuracy in simulation analyses and defines hundreds of TAD splits and mergers between pairs of different cell types, such as endothelial cells and fibroblasts. Our work reveals a key role for TAD remodeling in epigenetic regulation of transcription and delivers the first tool for the community to perform dynamic analysis of TAD splits and mergers in numerous biological and disease models.

Tissue-specific Hi-C analyses of rice, foxtail millet and maize suggest non-canonical function of plant chromatin domains

Chromatins are not randomly packaged in the nucleus and their organization plays important roles in transcription regulation, which is best studied in the mammalian models. Using in situ Hi-C, we have compared the 3D chromatin architectures of rice mesophyll and endosperm, foxtail millet bundle sheath and mesophyll, and maize bundle sheath, mesophyll and endosperm tissues. We found that their global A/B compartment partitions are stable across tissues, while local A/B compartment has tissue-specific dynamic associated with differential gene expression. Plant domains are largely stable across tissues, while new domain border formations are often associated with transcriptional activation in the region. Genes inside plant domains are not conserved across species, and lack significant co-expression behavior unlike those in mammalian TADs. Although we only observed chromatin loops between gene islands in the large genomes, the maize loop gene pairs' syntenic orthologs have shorter physical distances in small genome monocots, suggesting that loops instead of domains might have conserved biological function. Our study showed that plants' chromatin features might not have conserved biological functions as the mammalian ones.

Modeling Down syndrome in animals from the early stage to the 4.0 models and next

The genotype-phenotype relationship and the physiopathology of Down Syndrome (DS) have been explored in the last 20 years with more and more relevant mouse models. From the early age of transgenesis to the new CRISPR/CAS9-derived chromosomal engineering and the transchromosomic technologies, mouse models have been key to identify homologous genes or entire regions homologous to the human chromosome 21 that are necessary or sufficient to induce DS features, to investigate the complexity of the genetic interactions that are involved in DS and to explore therapeutic strategies. In this review we report the new developments made, how genomic data and new genetic tools have deeply changed our way of making models, extended our panel of animal models, and increased our understanding of the neurobiology of the disease. But even if we have made an incredible progress which promises to make DS a curable condition, we are facing new research challenges to nurture our knowledge of DS pathophysiology as a neurodevelopmental disorder with many comorbidities during ageing.

Rich Chromatin Structure Prediction from Hi-C Data

Recent studies involving the 3-dimensional conformation of chromatin have revealed the important role it has to play in different processes within the cell. These studies have also led to the discovery of densely interacting segments of the chromosome, called topologically associating domains. The accurate identification of these domains from Hi-C interaction data is an interesting and important computational problem for which numerous methods have been proposed. Unfortunately, most existing algorithms designed to identify these domains assume that they are non-overlapping whereas there is substantial evidence to believe a nested structure exists. We present a methodology to predict hierarchical chromatin domains using chromatin conformation capture data. Our method predicts domains at different resolutions, calculated using intrinsic properties of the chromatin data, and effectively clusters these to construct the hierarchy. At each individual level, the domains are non-overlapping in such a way that the intra-domain interaction frequencies are maximized. We show that our predicted structure is highly enriched for actively transcribing housekeeping genes and various chromatin markers, including CTCF, around the domain boundaries. We also show that large-scale domains, at multiple resolutions within our hierarchy, are conserved across cell types and species. We also provide comparisons against existing tools for extracting hierarchical domains. Our software, Matryoshka, is written in C++11 and licensed under GPL v3; it is available at https://github.com/COMBINE-lab/matryoshka.

Biased gene retention during diploidization in Brassica linked to three-dimensional genome organization

The non-random three-dimensional (3D) organization of the genome in the nucleus is critical to gene regulation and genome function. Using high-throughput chromatin conformation capture, we generated chromatin interaction maps for Brassica rapa and Brassica oleracea at a high resolution and characterized the conservation and divergence of chromatin organization in these two species. Large-scale chromatin structures, including A/B compartments and topologically associating domains, are notably conserved between B. rapa and B. oleracea, yet their KNOT structures are highly divergent. We found that genes retained in less fractionated subgenomes exhibited stronger interaction strengths, and diploidization-resistant duplicates retained in pairs or triplets are more likely to be colocalized in both B. rapa and B. oleracea. These observations suggest that spatial constraint in duplicated genes is correlated to their biased retention in the diploidization process. In addition, we found strong similarities in the epigenetic modification and Gene Ontology terms of colocalized paralogues, which were largely conserved across B. rapa and B. oleracea, indicating functional constraints on their 3D positioning in the nucleus. This study presents an investigation of the spatial organization of genomes in Brassica and provides insights on the role of 3D organization in the genome evolution of this genus.

HiCDB: a sensitive and robust method for detecting contact domain boundaries

Contact domains are closely linked to gene regulation and lineage commitment, while current understanding of contact domains and their boundaries is still limited. Here, we present a novel method HiCDB, which is constructively based on local relative insulation metric and multi-scale aggregation approach to detect contact domain boundaries (CDBs) on Hi-C maps. Compared with other ‘state-of-art’ methods, HiCDB shows improved sensitivity and specificity in determining CDBs at various Hi-C resolutions. The superiority of HiCDB enabled us to study the epigenetic features of detected CDBs and showed enrichment of architectural proteins and cell-type-specific transcription factor binding sites at CDBs. The further comparison of GM12878 and IMR90 Hi-C datasets suggested that cell-type-specific CDBs are marked by active regulatory signals and correlate with activation of nearby cell identity genes.

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.