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Liu T, Qiu QT, Hua KJ, Ma BG. Chromosome structure modeling tools and their evaluation in bacteria. Brief Bioinform 2024; 25:bbae044. [PMID: 38385874 PMCID: PMC10883143 DOI: 10.1093/bib/bbae044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/31/2023] [Accepted: 01/22/2024] [Indexed: 02/23/2024] Open
Abstract
The three-dimensional (3D) structure of bacterial chromosomes is crucial for understanding chromosome function. With the growing availability of high-throughput chromosome conformation capture (3C/Hi-C) data, the 3D structure reconstruction algorithms have become powerful tools to study bacterial chromosome structure and function. It is highly desired to have a recommendation on the chromosome structure reconstruction tools to facilitate the prokaryotic 3D genomics. In this work, we review existing chromosome 3D structure reconstruction algorithms and classify them based on their underlying computational models into two categories: constraint-based modeling and thermodynamics-based modeling. We briefly compare these algorithms utilizing 3C/Hi-C datasets and fluorescence microscopy data obtained from Escherichia coli and Caulobacter crescentus, as well as simulated datasets. We discuss current challenges in the 3D reconstruction algorithms for bacterial chromosomes, primarily focusing on software usability. Finally, we briefly prospect future research directions for bacterial chromosome structure reconstruction algorithms.
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Affiliation(s)
- Tong Liu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Qin-Tian Qiu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Kang-Jian Hua
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Bin-Guang Ma
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
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2
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Tuszynska I, Bednarz P, Wilczynski B. Effective modeling of the chromatin structure by coarse-grained methods. J Biomol Struct Dyn 2024:1-9. [PMID: 38165232 DOI: 10.1080/07391102.2023.2291176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 11/25/2023] [Indexed: 01/03/2024]
Abstract
The interphase chromatin structure is extremely complex, precise and dynamic. Experimental methods can only show the frequency of interaction of the various parts of the chromatin. Therefore, it is extremely important to develop theoretical methods to predict the chromatin structure. In this publication, we implemented an extended version of the SBS model described by Barbieri et al. and created the ChroMC program that is easy to use and freely available (https://github.com/regulomics/chroMC) to other users. We also describe the necessary factors for the effective modeling of the chromatin structure in Drosophila melanogaster. We compared results of chromatin structure predictions using two methods: Monte Carlo and Molecular Dynamic. Our simulations suggest that incorporating black, non-reactive chromatin is necessary for successful prediction of chromatin structure, while the loop extrusion model with a long range attraction potential or Lennard-Jones (with local attraction force) as well as using Hi-C data as input are not essential for the basic structure reconstruction. We also proposed a new way to calculate the similarity of the properties of contact maps including the calculation of local similarity.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Irina Tuszynska
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
| | - Paweł Bednarz
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
| | - Bartek Wilczynski
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
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Wang X, Gu WC, Li J, Ma BG. EVRC: reconstruction of chromosome 3D structure models using error-vector resultant algorithm with clustering coefficient. Bioinformatics 2023; 39:btad638. [PMID: 37847746 PMCID: PMC11318666 DOI: 10.1093/bioinformatics/btad638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 09/28/2023] [Accepted: 10/16/2023] [Indexed: 10/19/2023] Open
Abstract
MOTIVATION Reconstruction of 3D structure models is of great importance for the study of chromosome function. Software tools for this task are highly needed. RESULTS We present a novel reconstruction algorithm, called EVRC, which utilizes co-clustering coefficients and error-vector resultant for chromosome 3D structure reconstruction. As an update of our previous EVR algorithm, EVRC now can deal with both single and multiple chromosomes in structure modeling. To evaluate the effectiveness and accuracy of the EVRC algorithm, we applied it to simulation datasets and real Hi-C datasets. The results show that the reconstructed structures have high similarity to the original/real structures, indicating the effectiveness and robustness of the EVRC algorithm. Furthermore, we applied the algorithm to the 3D conformation reconstruction of the wild-type and mutant Arabidopsis thaliana chromosomes and demonstrated the differences in structural characteristics between different chromosomes. We also accurately showed the conformational change in the centromere region of the mutant compared with the wild-type of Arabidopsis chromosome 1. Our EVRC algorithm is a valuable software tool for the field of chromatin structure reconstruction, and holds great promise for advancing our understanding on the chromosome functions. AVAILABILITY AND IMPLEMENTATION The software is available at https://github.com/mbglab/EVRC.
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Affiliation(s)
- Xiao Wang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of
Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Wei-Cheng Gu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of
Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Jie Li
- Hubei Key Laboratory of Agricultural Bioinformatics, College of
Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Bin-Guang Ma
- Hubei Key Laboratory of Agricultural Bioinformatics, College of
Informatics, Huazhong Agricultural University, Wuhan 430070, China
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Li Z, Portillo-Ledesma S, Schlick T. Techniques for and challenges in reconstructing 3D genome structures from 2D chromosome conformation capture data. Curr Opin Cell Biol 2023; 83:102209. [PMID: 37506571 PMCID: PMC10529954 DOI: 10.1016/j.ceb.2023.102209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 06/07/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023]
Abstract
Chromosome conformation capture technologies that provide frequency information for contacts between genomic regions have been crucial for increasing our understanding of genome folding and regulation. However, such data do not provide direct evidence of the spatial 3D organization of chromatin. In this opinion article, we discuss the development and application of computational methods to reconstruct chromatin 3D structures from experimental 2D contact data, highlighting how such modeling provides biological insights and can suggest mechanisms anchored to experimental data. By applying different reconstruction methods to the same contact data, we illustrate some state-of-the-art of these techniques and discuss our gene resolution approach based on Brownian dynamics and Monte Carlo sampling.
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Affiliation(s)
- Zilong Li
- Department of Chemistry, New York University, 100 Washington Square East, Silver Building, New York, 10003, NY, USA; Simons Center for Computational Physical Chemistry, New York University, 24 Waverly Place, Silver Building, New York, NY, 10003, USA
| | - Stephanie Portillo-Ledesma
- Department of Chemistry, New York University, 100 Washington Square East, Silver Building, New York, 10003, NY, USA; Simons Center for Computational Physical Chemistry, New York University, 24 Waverly Place, Silver Building, New York, NY, 10003, USA
| | - Tamar Schlick
- Department of Chemistry, New York University, 100 Washington Square East, Silver Building, New York, 10003, NY, USA; Courant Institute of Mathematical Sciences, New York University, 251 Mercer St., New York, 10012, NY, USA; New York University-East China Normal University Center for Computational Chemistry, New York University Shanghai, Room 340, Geography Building, 3663 North Zhongshan Road, Shanghai, 200122, China; Simons Center for Computational Physical Chemistry, New York University, 24 Waverly Place, Silver Building, New York, NY, 10003, USA.
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Li FZ, Zhang XF, Cai HY, Ran LQ, Zhou HY, Liu ZE. Chromosome Three-Dimensional Structure Reconstruction: An Iterative ShRec3D Algorithm. J Comput Biol 2023; 30:575-587. [PMID: 36847350 DOI: 10.1089/cmb.2022.0179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023] Open
Abstract
The three-dimensional (3D) structure of chromosomes is of great significance to ensure that the genome performs various functions (e.g., gene expression) correctly and replicates and separates correctly in mitosis. Since the emergence of Hi-C in 2009, a new experimental technique in molecular biology, researchers have been paying more and more attention to the reconstruction of chromosome 3D structure. To reconstruct the 3D structure of chromosomes based on Hi-C experimental data, many algorithms have been proposed, among which ShRec3D is one of the most outstanding. In this article, an iterative ShRec3D algorithm is presented to greatly improve the native ShRec3D algorithm. Experimental results show that our algorithm can significantly promote the performance of ShRec3D, and this improvement is applicable to almost all data noise range and signal coverage range, so it is universal.
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Affiliation(s)
- Fang-Zhen Li
- School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China
| | - Xue-Fen Zhang
- College of Smart City, Beijing Union University, Beijing, China
| | - Hui-Ying Cai
- School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China
| | - Ling-Qiang Ran
- School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China
| | - Hai-Yan Zhou
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Zhi-E Liu
- College of Physics and Electronic Engineering, Qilu Normal University, Jinan, China
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Varoquaux N, Noble WS, Vert JP. Inference of 3D genome architecture by modeling overdispersion of Hi-C data. Bioinformatics 2023; 39:btac838. [PMID: 36594573 PMCID: PMC9857972 DOI: 10.1093/bioinformatics/btac838] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 11/16/2022] [Indexed: 01/04/2023] Open
Abstract
MOTIVATION We address the challenge of inferring a consensus 3D model of genome architecture from Hi-C data. Existing approaches most often rely on a two-step algorithm: first, convert the contact counts into distances, then optimize an objective function akin to multidimensional scaling (MDS) to infer a 3D model. Other approaches use a maximum likelihood approach, modeling the contact counts between two loci as a Poisson random variable whose intensity is a decreasing function of the distance between them. However, a Poisson model of contact counts implies that the variance of the data is equal to the mean, a relationship that is often too restrictive to properly model count data. RESULTS We first confirm the presence of overdispersion in several real Hi-C datasets, and we show that the overdispersion arises even in simulated datasets. We then propose a new model, called Pastis-NB, where we replace the Poisson model of contact counts by a negative binomial one, which is parametrized by a mean and a separate dispersion parameter. The dispersion parameter allows the variance to be adjusted independently from the mean, thus better modeling overdispersed data. We compare the results of Pastis-NB to those of several previously published algorithms, both MDS-based and statistical methods. We show that the negative binomial inference yields more accurate structures on simulated data, and more robust structures than other models across real Hi-C replicates and across different resolutions. AVAILABILITY AND IMPLEMENTATION A Python implementation of Pastis-NB is available at https://github.com/hiclib/pastis under the BSD license. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Nelle Varoquaux
- TIMC, Université Grenoble Alpes, CNRS, Grenoble INP, Grenoble 38000, France
| | - William S Noble
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
| | - Jean-Philippe Vert
- Brain Team, Google Research, Paris 75009, France
- Centre for Computational Biology , MINES ParisTech, PSL University, Paris 75006, France
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Hovenga V, Kalita J, Oluwadare O. HiC-GNN: A generalizable model for 3D chromosome reconstruction using graph convolutional neural networks. Comput Struct Biotechnol J 2022; 21:812-836. [PMID: 36698967 PMCID: PMC9842867 DOI: 10.1016/j.csbj.2022.12.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 12/08/2022] [Accepted: 12/30/2022] [Indexed: 01/02/2023] Open
Abstract
Chromosome conformation capture (3 C) is a method of measuring chromosome topology in terms of loci interaction. The Hi-C method is a derivative of 3 C that allows for genome-wide quantification of chromosome interaction. From such interaction data, it is possible to infer the three-dimensional (3D) structure of the underlying chromosome. In this paper, we developed a novel method, HiC-GNN, for predicting the 3D structures of chromosomes from Hi-C data. HiC-GNN is unique from other methods for chromosome structure prediction in that the models learned by HiC-GNN can be generalized to data that is distinct from the training data. This aspect of HiC-GNN allows models that were trained on one Hi-C contact map to be used for inference on entirely different maps. To the authors' knowledge, this generalizing capability is not present in any existing methods. HiC-GNN uses a node embedding algorithm and a graph neural network to predict the 3D coordinates of each genomic loci from the corresponding Hi-C contact data. Unlike other methods, our algorithm allows for the storage of pre-trained parameters, thus enabling prediction on data that is entirely different from the training data. We show that our method can accurately generalize a single model across Hi-C resolutions, multiple restriction enzymes, and multiple cell populations while maintaining reconstruction accuracy across three Hi-C datasets. Our algorithm outperforms the state-of-the-art methods in accuracy of prediction and runtime and introduces a novel method for 3D structure prediction from Hi-C data. All our source codes and data are available at https://github.com/OluwadareLab/HiC-GNN.
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Affiliation(s)
- Van Hovenga
- Department of Mathematics, University of Colorado, Colorado Springs, CO, United States
| | - Jugal Kalita
- Department of Computer Science, University of Colorado, Colorado Springs, CO, United States
| | - Oluwatosin Oluwadare
- Department of Computer Science, University of Colorado, Colorado Springs, CO, United States,Corresponding author.
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Wang H, Yang J, Zhang Y, Qian J, Wang J. Reconstruct high-resolution 3D genome structures for diverse cell-types using FLAMINGO. Nat Commun 2022; 13:2645. [PMID: 35551182 PMCID: PMC9098643 DOI: 10.1038/s41467-022-30270-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 04/22/2022] [Indexed: 11/30/2022] Open
Abstract
High-resolution reconstruction of spatial chromosome organizations from chromatin contact maps is highly demanded, but is hindered by extensive pairwise constraints, substantial missing data, and limited resolution and cell-type availabilities. Here, we present FLAMINGO, a computational method that addresses these challenges by compressing inter-dependent Hi-C interactions to delineate the underlying low-rank structures in 3D space, based on the low-rank matrix completion technique. FLAMINGO successfully generates 5 kb- and 1 kb-resolution spatial conformations for all chromosomes in the human genome across multiple cell-types, the largest resources to date. Compared to other methods using various experimental metrics, FLAMINGO consistently demonstrates superior accuracy in recapitulating observed structures with raises in scalability by orders of magnitude. The reconstructed 3D structures efficiently facilitate discoveries of higher-order multi-way interactions, imply biological interpretations of long-range QTLs, reveal geometrical properties of chromatin, and provide high-resolution references to understand structural variabilities. Importantly, FLAMINGO achieves robust predictions against high rates of missing data and significantly boosts 3D structure resolutions. Moreover, FLAMINGO shows vigorous cross cell-type structure predictions that capture cell-type specific spatial configurations via integration of 1D epigenomic signals. FLAMINGO can be widely applied to large-scale chromatin contact maps and expand high-resolution spatial genome conformations for diverse cell-types.
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Affiliation(s)
- Hao Wang
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Jiaxin Yang
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Yu Zhang
- Center for Immunobiology, Department of Investigative Medicine, Western Michigan University Homer Stryker M.D. School of Medicine, Kalamazoo, MI, 49007, USA
| | - Jianliang Qian
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Mathematics, Michigan State University, East Lansing, MI, 48824, USA.
| | - Jianrong Wang
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA.
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Collins B, Oluwadare O, Brown P. ChromeBat: A Bio-Inspired Approach to 3D Genome Reconstruction. Genes (Basel) 2021; 12:1757. [PMID: 34828363 PMCID: PMC8617892 DOI: 10.3390/genes12111757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 10/28/2021] [Accepted: 11/01/2021] [Indexed: 11/20/2022] Open
Abstract
With the advent of Next Generation Sequencing and the Hi-C experiment, high quality genome-wide contact data are becoming increasingly available. These data represents an empirical measure of how a genome interacts inside the nucleus. Genome conformation is of particular interest as it has been experimentally shown to be a driving force for many genomic functions from regulation to transcription. Thus, the Three Dimensional-Genome Reconstruction Problem (3D-GRP) seeks to take Hi-C data and produces a complete physical genome structure as it appears in the nucleus for genomic analysis. We propose and develop a novel method to solve the Chromosome and Genome Reconstruction problem based on the Bat Algorithm (BA) which we called ChromeBat. We demonstrate on real Hi-C data that ChromeBat is capable of state-of-the-art performance. Additionally, the domain of Genome Reconstruction has been criticized for lacking algorithmic diversity, and the bio-inspired nature of ChromeBat contributes algorithmic diversity to the problem domain. ChromeBat is an effective approach for solving the Genome Reconstruction Problem.
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Affiliation(s)
| | - Oluwatosin Oluwadare
- Department of Computer Science, University of Colorado, Colorado Springs, CO 80918, USA; (B.C.); (P.B.)
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Gong H, Yang Y, Zhang S, Li M, Zhang X. Application of Hi-C and other omics data analysis in human cancer and cell differentiation research. Comput Struct Biotechnol J 2021; 19:2070-2083. [PMID: 33995903 PMCID: PMC8086027 DOI: 10.1016/j.csbj.2021.04.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 04/04/2021] [Accepted: 04/04/2021] [Indexed: 02/07/2023] Open
Abstract
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.
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Affiliation(s)
- Haiyan Gong
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
- Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China
- Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China
- Shunde Graduate School of University of Science and Technology Beijing, Foshan 528000, China
| | - Yi Yang
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Sichen Zhang
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Minghong Li
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Xiaotong Zhang
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
- Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China
- Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China
- Shunde Graduate School of University of Science and Technology Beijing, Foshan 528000, China
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