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Drogaris C, Zhang Y, Zhang E, Nazarova E, Sarrazin-Gendron R, Wilhelm-Landry S, Cyr Y, Majewski J, Blanchette M, Waldispühl J. ARGV: 3D genome structure exploration using augmented reality. BMC Bioinformatics 2024; 25:277. [PMID: 39192184 DOI: 10.1186/s12859-024-05882-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 07/25/2024] [Indexed: 08/29/2024] Open
Abstract
Over the past two decades, scientists have increasingly realized the importance of the three-dimensional (3D) genome organization in regulating cellular activity. Hi-C and related experiments yield 2D contact matrices that can be used to infer 3D models of chromosome structure. Visualizing and analyzing genomes in 3D space remains challenging. Here, we present ARGV, an augmented reality 3D Genome Viewer. ARGV contains more than 350 pre-computed and annotated genome structures inferred from Hi-C and imaging data. It offers interactive and collaborative visualization of genomes in 3D space, using standard mobile phones or tablets. A user study comparing ARGV to existing tools demonstrates its benefits.
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Affiliation(s)
| | - Yanlin Zhang
- School of Computer Science, McGill University, Montréal, QC, H3A 0E9, Canada
| | - Eric Zhang
- School of Computer Science, McGill University, Montréal, QC, H3A 0E9, Canada
| | - Elena Nazarova
- School of Computer Science, McGill University, Montréal, QC, H3A 0E9, Canada
| | | | | | - Yan Cyr
- Beam Me Up Inc., 5925 Monkland Ave, Suite, 100, Montréal, H4A 1G7, Canada
| | - Jacek Majewski
- Department of Human Genetics, McGill University, Montréal, QC, H3A 1B1, Canada
| | - Mathieu Blanchette
- School of Computer Science, McGill University, Montréal, QC, H3A 0E9, Canada
| | - Jérôme Waldispühl
- School of Computer Science, McGill University, Montréal, QC, H3A 0E9, Canada.
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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: 7] [Impact Index Per Article: 2.3] [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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Kunz T, Rieber L, Mahony S. Assessing relationships between chromatin interactions and regulatory genomic activities using the self-organizing map. Methods 2020; 189:12-21. [PMID: 32652235 DOI: 10.1016/j.ymeth.2020.07.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Revised: 06/09/2020] [Accepted: 07/03/2020] [Indexed: 11/24/2022] Open
Abstract
Few existing methods enable the visualization of relationships between regulatory genomic activities and genome organization as captured by Hi-C experimental data. Genome-wide Hi-C datasets are often displayed using "heatmap" matrices, but it is difficult to intuit from these heatmaps which biochemical activities are compartmentalized together. High-dimensional Hi-C data vectors can alternatively be projected onto three-dimensional space using dimensionality reduction techniques. The resulting three-dimensional structures can serve as scaffolds for projecting other forms of genomic information, thereby enabling the exploration of relationships between genome organization and various genome annotations. However, while three-dimensional models are contextually appropriate for chromatin interaction data, some analyses and visualizations may be more intuitively and conveniently performed in two-dimensional space. We present a novel approach to the visualization and analysis of chromatin organization based on the Self-Organizing Map (SOM). The SOM algorithm provides a two-dimensional manifold which adapts to represent the high dimensional chromatin interaction space. The resulting data structure can then be used to assess relationships between regulatory genomic activities and chromatin interactions. For example, given a set of genomic coordinates corresponding to a given biochemical activity, the degree to which this activity is segregated or compartmentalized in chromatin interaction space can be intuitively visualized on the 2D SOM grid and quantified using Lorenz curve analysis. We demonstrate our approach for exploratory analysis of genome compartmentalization in a high-resolution Hi-C dataset from the human GM12878 cell line. Our SOM-based approach provides an intuitive visualization of the large-scale structure of Hi-C data and serves as a platform for integrative analyses of the relationships between various genomic activities and genome organization.
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Affiliation(s)
- Timothy Kunz
- Biochemistry & Molecular Biology Department, Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, PA, USA
| | - Lila Rieber
- Biochemistry & Molecular Biology Department, Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, PA, USA
| | - Shaun Mahony
- Biochemistry & Molecular Biology Department, Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, PA, USA.
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Nusrat S, Harbig T, Gehlenborg N. Tasks, Techniques, and Tools for Genomic Data Visualization. COMPUTER GRAPHICS FORUM : JOURNAL OF THE EUROPEAN ASSOCIATION FOR COMPUTER GRAPHICS 2019; 38:781-805. [PMID: 31768085 PMCID: PMC6876635 DOI: 10.1111/cgf.13727] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Genomic data visualization is essential for interpretation and hypothesis generation as well as a valuable aid in communicating discoveries. Visual tools bridge the gap between algorithmic approaches and the cognitive skills of investigators. Addressing this need has become crucial in genomics, as biomedical research is increasingly data-driven and many studies lack well-defined hypotheses. A key challenge in data-driven research is to discover unexpected patterns and to formulate hypotheses in an unbiased manner in vast amounts of genomic and other associated data. Over the past two decades, this has driven the development of numerous data visualization techniques and tools for visualizing genomic data. Based on a comprehensive literature survey, we propose taxonomies for data, visualization, and tasks involved in genomic data visualization. Furthermore, we provide a comprehensive review of published genomic visualization tools in the context of the proposed taxonomies.
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Affiliation(s)
- S Nusrat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - T Harbig
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - N Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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Goodstadt MN, Marti-Renom MA. Communicating Genome Architecture: Biovisualization of the Genome, from Data Analysis and Hypothesis Generation to Communication and Learning. J Mol Biol 2018; 431:1071-1087. [PMID: 30419242 DOI: 10.1016/j.jmb.2018.11.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 10/29/2018] [Accepted: 11/01/2018] [Indexed: 01/07/2023]
Abstract
Genome discoveries at the core of biology are made by visual description and exploration of the cell, from microscopic sketches and biochemical mapping to computational analysis and spatial modeling. We outline the experimental and visualization techniques that have been developed recently which capture the three-dimensional interactions regulating how genes are expressed. We detail the challenges faced in integration of the data to portray the components and organization and their dynamic landscape. The goal is more than a single data-driven representation as interactive visualization for de novo research is paramount to decipher insights on genome organization in space.
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Affiliation(s)
- Mike N Goodstadt
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Baldiri Reixac 4, Barcelona 08028, Spain; Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain.
| | - Marc A Marti-Renom
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Baldiri Reixac 4, Barcelona 08028, Spain; Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluis Companys 23, Barcelona 08010, Spain.
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Waldispühl J, Zhang E, Butyaev A, Nazarova E, Cyr Y. Storage, visualization, and navigation of 3D genomics data. Methods 2018; 142:74-80. [PMID: 29792917 DOI: 10.1016/j.ymeth.2018.05.008] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Revised: 05/07/2018] [Accepted: 05/09/2018] [Indexed: 01/27/2023] Open
Abstract
The field of 3D genomics grew at increasing rates in the last decade. The volume and complexity of 2D and 3D data produced is progressively outpacing the capacities of the technology previously used for distributing genome sequences. The emergence of new technologies provides also novel opportunities for the development of innovative approaches. In this paper, we review the state-of-the-art computing technology, as well as the solutions adopted by the platforms currently available.
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Affiliation(s)
| | - Eric Zhang
- School of Computer Science, McGill University, Montréal, Canada
| | | | - Elena Nazarova
- School of Computer Science, McGill University, Montréal, Canada
| | - Yan Cyr
- Beam Me Up Labs, Montréal, Canada
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