An Empirical Mode Decomposition-Based Method to Identify Topologically Associated Domains from Chromatin Interactions

BackgroundHi-C data have been widely used to reconstruct chromosomal three-dimensional (3D) structures. One of the key limitations of Hi-C is the unclear relationship between spatial distance and the number of Hi-C contacts. Many methods used a fixed parameter when converting the number of Hi-C contacts to wish distances. However, a single parameter cannot properly explain the relationship between wish distances and genomic distances or the locations of topologically associating domains (TADs).ResultsWe have addressed one of the key issues of using Hi-C data, that is, the unclear relationship between spatial distances and the number of Hi-C contacts, which is crucial to understand significant biological functions, such as the enhancer-promoter interactions. Specifically, we developed a new method to infer this converting parameter and pairwise Euclidean distances based on the topology of the Hi-C complex network (HiCNet). The inferred distances were modeled by clustering coefficient and multiple other types of constraints. We found that our inferred distances between bead-pairs within the same TAD were apparently smaller than those distances between bead-pairs from different TADs. Our inferred distances had a higher correlation with fluorescence in situ hybridization (FISH) data, fitted the localization patterns of Xist transcripts on DNA, and better matched 156 pairs of protein-enabled long-range chromatin interactions detected by ChIA-PET. Using the inferred distances and another round of optimization, we further reconstructed 40 kb high-resolution 3D chromosomal structures of mouse male ES cells. The high-resolution structures successfully illustrate TADs and DNA loops (peaks in Hi-C contact heatmaps) that usually indicate enhancer-promoter interactions.ConclusionsWe developed a novel method to infer the wish distances between DNA bead-pairs from Hi-C contacts. High-resolution 3D structures of chromosomes were built based on the newly-inferred wish distances. This whole process has been implemented as a tool named HiCNet, which is publicly available at http://dna.cs.miami.edu/HiCNet/.

Topologically associating domains (TADs) are fundamental units of 3D genomes and play essential roles in gene regulation. Hi-C data suggests a hierarchical organization of TADs. Accurately annotating nested TADs from Hi-C data remains challenging, both in terms of the precise identification of boundaries and the correct inference of hierarchies. While domain boundary is relatively well conserved across cells, few approaches have taken advantage of this fact. Here, we present RobusTAD to annotate TAD hierarchies. It incorporates additional Hi-C data to refine boundaries annotated from the study sample. RobusTAD outperforms existing tools at boundary and domain annotation across several benchmarking tasks.

TADs and Their Borders: Free Movement or Building a Wall?

Invariant TAD Boundaries Constrain Cell-Type-Specific Looping Interactions between Promoters and Distal Elements around the CFTR Locus

BackgroundGene expression has long been known to be influenced by the relative proximity of DNA regulatory elements. Topologically associating domains (TADs) are self-interacting genomic regions involved in regulating gene expression by controlling the proximity of these elements. Prior studies of TADs and their biological roles have revealed correlations between TAD changes and cellular differentiation. Here, we used Hi-C and RNA-seq data to correlate TCR-induced changes in TAD structure and gene expression in human CD4+ T cells.ResultsWe developed a pipeline, Differentially Expressed Gene Enrichment Finder (DEGEF), that identifies regions of differentially expressed gene enrichment. Using DEGEF, we found that TCR-regulated genes cluster non-uniformly across the genome and that these clusters preferentially localized in regions of TAD rearrangement. Interestingly, clusters of upregulated genes preferentially formed new Hi-C contacts compared to downregulated clusters, suggesting that TCR-activated CD4+ T cells may regulate genes by changing stimulatory contacts rather than inhibitory contacts.ConclusionsOur observations support a significant relationship between TAD rearrangements and changes in local gene expression. These findings indicate potentially important roles for TAD rearrangements in shaping their local regulatory environments and thus driving differential expression of nearby genes during CD4+ T cell activation. Moreover, they provide new insights into global mechanisms that regulate gene expression.

Topologically associating domains (TADs) are the most fundamental elements and significant structures of the eukaryotic genome. Currently, algorithms have been developed to find the TADs. But few algorithms are reported to compare the similarity of TADs between genomes. In this study, mice Hi-C sequencing data of four contrasts were enrolled. Seventeen algorithms, including BPscore, Jaccard index (JI) distance, VI distance, image hash, image subtraction, image variance, and so on, were used to quantify the genomic similarity of TADs. Image subtraction, Euclidean distance, and Manhattan distance were significantly better for TAD difference detection than the others. Deferent Hash (dHash) with the best zoom size ranked the second, followed by improved Hamming distance algorithm and JI distance. Advantages and disadvantages of various algorithms for quantifying the similarity of TADs were compared. Our work could provide the fundament for TADs comparison.

The complete DNA sequencing of the entire human genome, or better known as the Human Genome Project, concluded in 2003. Ever since then, many headways have been made to better understand the organization of the human genome. There are many levels of organizations of the genome, its most basic unit, the nucleosome, which consists of DNA wrapped around histone proteins. Some of the higher levels of organization include tetranucleosomes, which consists of several nucleosomes, and topological associating domains (TADs), which are regions in the genome that self-interact more frequently with themselves compared to outside the TAD. In particular, TADs have been relatively new to the scene; there are currently no experimentally validated TADs. Furthermore, disruption of TAD boundaries are associated with several diseases like cancer. Nevertheless, there are currently many methods to call TADs, all of which are not based on a rigorous topological mathematical model. The HodgeRank algorithm, based on the Hodge decomposition theorem, gives us an avenue to quantify these self-interactions. The HodgeRank algorithm was previously used to rank imcomplete or imbalanced data from several e-commerce sites and movies from Netflix. We first show that HodgeRank can also be used to successfully quantify the ``curvedness'' of different biomolecules, such as modelling the protein folding process and comparing biomolecules of different scales and complexities. We then turn our attention back to Hi-C data, which encompasses TADs/TAD regions. We show that under a suitable metric, HodgeRank can be used to quantify the self-interactions within each TAD region of Chromosome 10, each of these regions generated by an existing TAD calling method. Solar power, a renewable source of energy, plays a significant role in allowing us to reduce our dependence on fossil fuels. Solar cells allow us to harness solar power by converting light energy from the Sun to electrical power through the photovoltaic effect, where light energy excites an electron, allowing it to reach a higher energy state. One of the types of materials that are used to make these solar cells are hybrid organic-inorganic perovskites (HOIPs). Not only have HOIPs been projected to be one of the most cost-effective options for future solar cells, its efficiency levels have risen from 5% to 25% in the last decade. Current machine learning-based perovskite designs rely heavily on the prediction of the bandgap of HOIPs. We show that the PerSpect-ML model, which is based on the generation of machine learning features using the eigenvalues of the Hodge Laplacian matrices, and previously applied to protein-ligand binding affinity prediction to great success, can be applied to the prediction of the bandgap of a comprehensive data set of hybrid organic-inorganic perovskites. We show that the resulting machine learning model not only significantly reduces the computational costs of current models, but also is superior in terms of overall predictive ability.

BackgroundThe human genome is highly organized in the three-dimensional nucleus. Chromosomes fold locally into topologically associating domains (TADs) defined by increased intra-domain chromatin contacts. TADs contribute to gene regulation by restricting chromatin interactions of regulatory sequences, such as enhancers, with their target genes. Disruption of TADs can result in altered gene expression and is associated to genetic diseases and cancers. However, it is not clear to which extent TAD regions are conserved in evolution and whether disruption of TADs by evolutionary rearrangements can alter gene expression.ResultsHere, we hypothesize that TADs represent essential functional units of genomes, which are stable against rearrangements during evolution. We investigate this using whole-genome alignments to identify evolutionary rearrangement breakpoints of different vertebrate species. Rearrangement breakpoints are strongly enriched at TAD boundaries and depleted within TADs across species. Furthermore, using gene expression data across many tissues in mouse and human, we show that genes within TADs have more conserved expression patterns. Disruption of TADs by evolutionary rearrangements is associated with changes in gene expression profiles, consistent with a functional role of TADs in gene expression regulation.ConclusionsTogether, these results indicate that TADs are conserved building blocks of genomes with regulatory functions that are often reshuffled as a whole instead of being disrupted by rearrangements.

Topologically associating domains (TADs) are critical structural units in three-dimensional genome organization of mammalian genome. Dynamic reorganizations of TADs between health and disease states are associated with essential genome functions. However, computational methods for identifying reorganized TADs are still in the early stages of development. Here, we present DiffDomain, an algorithm leveraging high-dimensional random matrix theory to identify structurally reorganized TADs using high-throughput chromosome conformation capture (Hi–C) contact maps. Method comparison using multiple real Hi–C datasets reveals that DiffDomain outperforms alternative methods for false positive rates, true positive rates, and identifying a new subtype of reorganized TADs. Applying DiffDomain to Hi–C data from different cell types and disease states demonstrates its biological relevance. Identified reorganized TADs are associated with structural variations and epigenomic changes such as changes in CTCF binding sites. By applying to a single-cell Hi–C data from mouse neuronal development, DiffDomain can identify reorganized TADs between cell types with reasonable reproducibility using pseudo-bulk Hi–C data from as few as 100 cells per condition. Moreover, DiffDomain reveals differential cell-to-population variability and heterogeneous cell-to-cell variability in TADs. Therefore, DiffDomain is a statistically sound method for better comparative analysis of TADs using both Hi–C and single-cell Hi–C data.

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.

Most publicly accessible single-cell Hi-C data are sparse and cannot reach a higher resolution. Therefore, learning latent representations (bin-specific embeddings) of sparse single-cell Hi-C matrices would provide us with a novel way of mining valuable information hidden in the limited number of single-cell Hi-C contacts. We present scHiCEmbed, an unsupervised computational method for learning bin-specific embeddings of single-cell Hi-C data, and the computational system is applied to the tasks of 3D structure reconstruction of whole genomes and detection of topologically associating domains (TAD). The only input of scHiCEmbed is a raw or scHiCluster-imputed single-cell Hi-C matrix. The main process of scHiCEmbed is to embed each node/bin in a higher dimensional space using graph auto-encoders. The learned n-by-3 bin-specific embedding/latent matrix is considered the final reconstructed 3D genome structure. For TAD detection, we use constrained hierarchical clustering on the latent matrix to classify bins: S_Dbw is used to determine the optimal number of clusters, and each cluster is considered as one potential TAD. Our reconstructed 3D structures for individual chromatins at different cell stages reveal the expanding process of chromatins during the cell cycle. We observe that the TADs called from single-cell Hi-C data are not shared across individual cells and that the TAD boundaries called from raw or imputed single-cell Hi-C are significantly different from those called from bulk Hi-C, confirming the cell-to-cell variability in terms of TAD definitions. The source code for scHiCEmbed is publicly available, and the URL can be found in the conclusion section.

Insulators are cis-regulatory elements that block enhancer activity and prevent heterochromatin spreading. The binding of CCCTC-binding factor (CTCF) protein is essential for insulators to play the roles in a chromatin context. The β-globin locus, consisting of multiple genes and enhancers, is flanked by two insulators 3'HS1 and HS5. However, it has been reported that the absence of these insulators did not affect the β-globin transcription. To explain the unexpected finding, we have deleted a CTCF motif at 3'HS1 or HS5 in the human β-globin locus and analyzed chromatin interactions around the locus. It was found that a topologically associating domain (TAD) containing the β-globin locus is maintained by neighboring CTCF sites in the CTCF motif-deleted loci. The additional deletions of neighboring CTCF motifs disrupted the β-globin TAD, resulting in decrease of the β-globin transcription. Chromatin interactions of the β-globin enhancers with gene promoter were weakened in the multiple CTCF motifs-deleted loci, even though the enhancers have still active chromatin features such as histone H3K27ac and histone H3 depletion. Genome-wide analysis using public CTCF ChIA-PET and ChIP-seq data showed that chromatin domains possessing multiple CTCF binding sites tend to contain super-enhancers like the β-globin enhancers. Taken together, our results show that multiple CTCF sites surrounding the β-globin locus cooperate with each other to maintain a TAD. The β-globin TAD appears to provide a compact spatial environment that enables enhancers to interact with promoter.

Hox gene clusters encode transcription factors that drive regional specialization during animal development: for example the Hox factor Ubx is expressed in the insect metathoracic (T3) wing appendages and differentiates them from T2 mesothoracic identities. Hox transcriptional regulation requires silencing activities that prevent spurious activation and regulatory crosstalks in the wrong tissues, but this has seldom been studied in insects other than Drosophila, which shows a derived Hox dislocation into two genomic clusters that disjoined Antennapedia (Antp) and Ultrabithorax (Ubx). Here, we investigated how Ubx is restricted to the hindwing in butterflies, amidst a contiguous Hox cluster. By analysing Hi-C and ATAC-seq data in the butterfly Junonia coenia, we show that a Topologically Associated Domain (TAD) maintains a hindwing-enriched profile of chromatin opening around Ubx. This TAD is bordered by a Boundary Element (BE) that separates it from a region of joined wing activity around the Antp locus. CRISPR mutational perturbation of this BE releases ectopic Ubx expression in forewings, inducing homeotic clones with hindwing identities. Further mutational interrogation of two non-coding RNA encoding regions and one putative cis-regulatory module within the Ubx TAD cause rare homeotic transformations in both directions, indicating the presence of both activating and repressing chromatin features. We also describe a series of spontaneous forewing homeotic phenotypes obtained in Heliconius butterflies, and discuss their possible mutational basis. By leveraging the extensive wing specialization found in butterflies, our initial exploration of Ubx regulation demonstrates the existence of silencing and insulating sequences that prevent its spurious expression in forewings.

Hi-C technology measures genome-wide interaction frequencies, providing a powerful tool for studying the 3D genomic structure within the nucleus. However, high sequencing costs and technical challenges often result in Hi-C data with limited coverage, leading to imprecise estimates of chromatin interaction frequencies. To address this issue, we present a novel deep learning-based method HiCMamba to enhance the resolution of Hi-C contact maps using a state space model. We adopt the UNet-based auto-encoder architecture to stack the proposed holistic scan block, enabling the perception of both global and local receptive fields at multiple scales. Experimental results demonstrate that HiCMamba outperforms state-of-the-art methods while significantly reducing computational resources. Furthermore, the 3D genome structures, including topologically associating domains (TADs) and loops, identified in the contact maps recovered by HiCMamba are validated through associated epigenomic features. Our work demonstrates the potential of a state space model as foundational frameworks in the field of Hi-C resolution enhancement. The data and source code used in this work are available at GitHub: https://github.com/myang998/HiCMamba.

Aberrant Expression of the SOX11 Oncogene in Mantle Cell Lymphoma Is Associated with Activation and De Novo 3D Looping of a Distant Enhancer Element