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3D Reconstruction of Genome Structures
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3D Reconstruction of Genome Structures

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Molecular Genetics and Genomics".

Deadline for manuscript submissions: closed (15 August 2022) | Viewed by 5977

Special Issue Editor

Dr. Zheng Wang

E-Mail Website
Guest Editor
Department of Computer Science, University of Miami, 1365 Memorial Drive, Coral Gables, FL 33124-4245, USA
Interests: 3D genome structure reconstruction; protein structure prediction; protein function prediction; biological network analysis;

Special Issue Information

Dear Colleagues,

The Hi-C experiment allows scientists to obtain the proximity relationships between genomic regions in the 3D space, which provides opportunities for computational scientists to infer and reconstruct high-resolution 3D genome structures. These 3D genome structures provide a new perspective to study how genomes function. For example, chromatin loops may indicate promoter–enhancer interactions, and topologically associating domains (TADs) are detected and considered the functional and structural units of the genomes. Conducting Hi-C experiments at different time points further allows scientists to reconstruct and analyze 4D genome structures. 

Compared to the bulk Hi-C experiment that is based on a pool of cells, the single-cell Hi-C experiment focuses on each individual cell, which can reveal cell-to-cell variability. A single-cell Hi-C contact matrix (2D) is usually sparse and contains lots of zeros, which creates challenges for reconstructing 3D genome structures. However, the sparseness also creates new research opportunities for developing novel methodologies to impute single-cell Hi-C contacts and infer single-cell 3D genome structures. 

The goal of this Special Issue is to collect and publish research for reconstructing and analyzing genome structures.  

Dr. Zheng Wang
Guest Editor

Manuscript Submission Information

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Keywords

  • 3D and 4D genome structure reconstruction and analysis
  • Single-cell 3D genome structures reconstruction and analysis
  • Topologically associating domain (TAD) detection and clustering
  • Single-cell Hi-C contact imputation and classification
  • Noise and bias removal for bulk Hi-C, single-cell Hi-C, and promoter capture Hi-C data
  • Resolution enhancement of Hi-C data
  • Chromatin loop detection
  • A/B compartment detection
  • Cell type clustering based on single-cell Hi-C data

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Published Papers (2 papers)

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19 pages, 4705 KiB  
Article
scHiCEmbed: Bin-Specific Embeddings of Single-Cell Hi-C Data Using Graph Auto-Encoders
by Tong Liu and Zheng Wang
Genes 2022, 13(6), 1048; https://doi.org/10.3390/genes13061048 - 11 Jun 2022
Cited by 5 | Viewed by 2690
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue 3D Reconstruction of Genome Structures)
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25 pages, 3377 KiB  
Article
Functional Similarities of Protein-Coding Genes in Topologically Associating Domains and Spatially-Proximate Genomic Regions
by Chenguang Zhao, Tong Liu and Zheng Wang
Genes 2022, 13(3), 480; https://doi.org/10.3390/genes13030480 - 8 Mar 2022
Viewed by 2604
Abstract
Topologically associating domains (TADs) are the structural and functional units of the genome. However, the functions of protein-coding genes existing in the same or different TADs have not been fully investigated. We compared the functional similarities of protein-coding genes existing in the same [...] Read more.
Topologically associating domains (TADs) are the structural and functional units of the genome. However, the functions of protein-coding genes existing in the same or different TADs have not been fully investigated. We compared the functional similarities of protein-coding genes existing in the same TAD and between different TADs, and also in the same gap region (the region between two consecutive TADs) and between different gap regions. We found that the protein-coding genes from the same TAD or gap region are more likely to share similar protein functions, and this trend is more obvious with TADs than the gap regions. We further created two types of gene–gene spatial interaction networks: the first type is based on Hi-C contacts, whereas the second type is based on both Hi-C contacts and the relationship of being in the same TAD. A graph auto-encoder was applied to learn the network topology, reconstruct the two types of networks, and predict the functions of the central genes/nodes based on the functions of the neighboring genes/nodes. It was found that better performance was achieved with the second type of network. Furthermore, we detected long-range spatially-interactive regions based on Hi-C contacts and calculated the functional similarities of the gene pairs from these regions. Full article
(This article belongs to the Special Issue 3D Reconstruction of Genome Structures)
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