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Khan AM, Işık EB, Tan TW. A global initiative on addressing bioinformatics' grand challenges. Brief Bioinform 2024; 25:bbae278. [PMID: 38881075 PMCID: PMC11180599 DOI: 10.1093/bib/bbae278] [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: 03/29/2024] [Accepted: 06/01/2024] [Indexed: 06/18/2024] Open
Abstract
The Bioinformatics Grand Challenges Consortium (BGCC) is a collaborative effort to address the most pressing challenges in bioinformatics. Initially focusing on education and training, the consortium successfully defined seven key grand challenges and is actively developing actionable solutions for these challenges. Building on this foundation, the BGCC plans to broaden its focus to include additional grand challenges in emerging areas.
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Affiliation(s)
- Asif M Khan
- College of Computing and Information Technology, University of Doha for Science and Technology, Doha, Qatar
- APBioNET.org, Singapore, Singapore
| | - Esra Büşra Işık
- Beykoz Institute of Life Sciences and Biotechnology, Bezmialem Vakif University, Beykoz, 34820, Istanbul, Turkiye
| | - Tin Wee Tan
- Department of Biochemistry, YLL School of Medicine, National University of Singapore, 117596, Singapore
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2
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Karnatz N, Schwerter M, Liu S, Parviz A, Wilkat M, Rana M. Mixed Reality as a Digital Visualisation Solution for the Head and Neck Tumour Board: Application Creation and Implementation Study. Cancers (Basel) 2024; 16:1392. [PMID: 38611070 PMCID: PMC11011089 DOI: 10.3390/cancers16071392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 03/21/2024] [Accepted: 03/26/2024] [Indexed: 04/14/2024] Open
Abstract
The preparation and implementation of interdisciplinary oncological case reviews are time-consuming and complex. The variety of clinical and radiological information must be presented in a clear and comprehensible manner. Only if all relevant patient-specific information is demonstrated in a short time frame can well-founded treatment decisions be made on this basis. Mixed reality (MR) technology as a multimodal interactive user interface could enhance understanding in multidisciplinary collaboration by visualising radiological or clinical data. The aim of the work was to develop an MR-based software prototype for a head and neck tumour board (HNTB) to support clinical decision-making. The article describes the development phases and workflows in the planning and creation of a MR-based software prototype that were required to meet the multidisciplinary characteristics of a HNTB.
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Affiliation(s)
- Nadia Karnatz
- Department of Oral and Plastic Maxillofacial Surgery, Heinrich Heine University Hospital Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany (M.R.)
| | | | - Shufang Liu
- Brainlab AG, Olof-Palme-Str. 9, 81829 München, Germany
| | - Aida Parviz
- Department of Oral and Plastic Maxillofacial Surgery, Heinrich Heine University Hospital Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany (M.R.)
| | - Max Wilkat
- Department of Oral and Plastic Maxillofacial Surgery, Heinrich Heine University Hospital Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany (M.R.)
| | - Majeed Rana
- Department of Oral and Plastic Maxillofacial Surgery, Heinrich Heine University Hospital Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany (M.R.)
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3
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Guo B, Huuki-Myers LA, Grant-Peters M, Collado-Torres L, Hicks SC. escheR: unified multi-dimensional visualizations with Gestalt principles. BIOINFORMATICS ADVANCES 2023; 3:vbad179. [PMID: 38107654 PMCID: PMC10723033 DOI: 10.1093/bioadv/vbad179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/14/2023] [Accepted: 12/04/2023] [Indexed: 12/19/2023]
Abstract
Summary The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables. The proposed visualization can be applied to spatially-resolved transcriptomics data, but also broadly to data visualized in 2D space, such as embedding visualizations. We provide an open source R package escheR, which is built off of the state-of-the-art ggplot2 visualization framework and can be seamlessly integrated into genomics toolboxes and workflows. Availability and implementation The open source R package escheR is freely available on Bioconductor (https://bioconductor.org/packages/escheR).
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Affiliation(s)
- Boyi Guo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, United States
| | - Louise A Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, United States
| | - Melissa Grant-Peters
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, WC1N 1EH, United Kingdom
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, United States
| | - Leonardo Collado-Torres
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, United States
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, United States
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, United States
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, 21218, United States
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205, United States
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, 21218, United States
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Poinsignon T, Gallopin M, Grognet P, Malagnac F, Lelandais G, Poulain P. 3D models of fungal chromosomes to enhance visual integration of omics data. NAR Genom Bioinform 2023; 5:lqad104. [PMID: 38058589 PMCID: PMC10696920 DOI: 10.1093/nargab/lqad104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 10/11/2023] [Accepted: 11/17/2023] [Indexed: 12/08/2023] Open
Abstract
The functions of eukaryotic chromosomes and their spatial architecture in the nucleus are reciprocally dependent. Hi-C experiments are routinely used to study chromosome 3D organization by probing chromatin interactions. Standard representation of the data has relied on contact maps that show the frequency of interactions between parts of the genome. In parallel, it has become easier to build 3D models of the entire genome based on the same Hi-C data, and thus benefit from the methodology and visualization tools developed for structural biology. 3D modeling of entire genomes leverages the understanding of their spatial organization. However, this opportunity for original and insightful modeling is underexploited. In this paper, we show how seeing the spatial organization of chromosomes can bring new perspectives to omics data integration. We assembled state-of-the-art tools into a workflow that goes from Hi-C raw data to fully annotated 3D models and we re-analysed public omics datasets available for three fungal species. Besides the well-described properties of the spatial organization of their chromosomes (Rabl conformation, hypercoiling and chromosome territories), our results highlighted (i) in Saccharomyces cerevisiae, the backbones of the cohesin anchor regions, which were aligned all along the chromosomes, (ii) in Schizosaccharomyces pombe, the oscillations of the coiling of chromosome arms throughout the cell cycle and (iii) in Neurospora crassa, the massive relocalization of histone marks in mutants of heterochromatin regulators. 3D modeling of the chromosomes brings new opportunities for visual integration of omics data. This holistic perspective supports intuition and lays the foundation for building new concepts.
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Affiliation(s)
- Thibault Poinsignon
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Saclay, 91198 Gif-sur-Yvette, France
- Université Paris Cité, CNRS, Institut Jacques Monod, F-75013 Paris, France
| | - Mélina Gallopin
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Saclay, 91198 Gif-sur-Yvette, France
| | - Pierre Grognet
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Saclay, 91198 Gif-sur-Yvette, France
| | - Fabienne Malagnac
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Saclay, 91198 Gif-sur-Yvette, France
| | - Gaëlle Lelandais
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Saclay, 91198 Gif-sur-Yvette, France
| | - Pierre Poulain
- Université Paris Cité, CNRS, Institut Jacques Monod, F-75013 Paris, France
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Midlik A, Nair S, Anyango S, Deshpande M, Sehnal D, Varadi M, Velankar S. PDBImages: a command-line tool for automated macromolecular structure visualization. Bioinformatics 2023; 39:btad744. [PMID: 38085238 PMCID: PMC10746859 DOI: 10.1093/bioinformatics/btad744] [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: 08/03/2023] [Revised: 10/20/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023] Open
Abstract
SUMMARY PDBImages is an innovative, open-source Node.js package that harnesses the power of the popular macromolecule structure visualization software Mol*. Designed for use by the scientific community, PDBImages provides a means to generate high-quality images for PDB and AlphaFold DB models. Its unique ability to render and save images directly to files in a browserless mode sets it apart, offering users a streamlined, automated process for macromolecular structure visualization. Here, we detail the implementation of PDBImages, enumerating its diverse image types, and elaborating on its user-friendly setup. This powerful tool opens a new gateway for researchers to visualize, analyse, and share their work, fostering a deeper understanding of bioinformatics. AVAILABILITY AND IMPLEMENTATION PDBImages is available as an npm package from https://www.npmjs.com/package/pdb-images. The source code is available from https://github.com/PDBeurope/pdb-images.
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Affiliation(s)
- Adam Midlik
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, United Kingdom
| | - Sreenath Nair
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, United Kingdom
| | - Stephen Anyango
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, United Kingdom
| | - Mandar Deshpande
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, United Kingdom
| | - David Sehnal
- Biological Data Management and Analysis Core Facility, Centre for Structural Biology, CEITEC—Central European Institute of Technology, Masaryk University, Brno 62500, Czech Republic
- National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Brno 62500, Czech Republic
| | - Mihaly Varadi
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, United Kingdom
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, United Kingdom
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Hodzhev Y, Tsafarova B, Tolchkov V, Youroukova V, Ivanova S, Kostadinov D, Yanev N, Zhelyazkova M, Tsonev S, Kalfin R, Panaiotov S. Visualization of the individual blood microbiome to study the etiology of sarcoidosis. Comput Struct Biotechnol J 2023; 22:50-57. [PMID: 37928975 PMCID: PMC10624578 DOI: 10.1016/j.csbj.2023.10.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 10/14/2023] [Accepted: 10/16/2023] [Indexed: 11/07/2023] Open
Abstract
Introduction Single microbial pathogens or host-microbiome dysbiosis are the causes of lung diseases with suspected infectious etiology. Metagenome sequencing provides an overview of the microbiome content. Due to the rarity of most granulomatous lung diseases collecting large systematic datasets is challenging. Thus, single-patient data often can only be summarized visually. Objective To increase the information gain from a single-case metagenome analysis we suggest a quantitative and qualitative approach. Results The 16S metagenomic results of 7 patients with pulmonary sarcoidosis were compared with those of 22 healthy individuals. From lysed blood, total microbial DNA was extracted and sequenced. Cleaned data reads were identified taxonomically using Kraken 2 software. Individual metagenomic data were visualized with a Sankey diagram, Krona chart, and a heat-map. We identified five genera that were exclusively present or significantly enhanced in patients with sarcoidosis - Veillonella, Prevotella, Cutibacterium, Corynebacterium, and Streptococcus. Conclusions Our approach can characterize the blood microbiome composition and diversity in rare diseases at an individual level. Investigation of the blood microbiome in patients with granulomatous lung diseases of unknown etiology, such as sarcoidosis could enhance our comprehension of their origin and pathogenesis and potentially uncover novel personalized therapeutics.
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Affiliation(s)
- Yordan Hodzhev
- Microbiology Department, National Center of Infectious and Parasitic Diseases, Yanko Sakazov 26 Blvd., Sofia 1504, Bulgaria
| | - Borislava Tsafarova
- Microbiology Department, National Center of Infectious and Parasitic Diseases, Yanko Sakazov 26 Blvd., Sofia 1504, Bulgaria
| | - Vladimir Tolchkov
- Microbiology Department, National Center of Infectious and Parasitic Diseases, Yanko Sakazov 26 Blvd., Sofia 1504, Bulgaria
| | - Vania Youroukova
- Department of Pulmonary Diseases, University Hospital for Pulmonary Diseases “St. Sofia”, Medical University of Sofia, Akad. Ivan Evstratiev Geshov 17 Blvd., Sofia 1431, Bulgaria
| | - Silvia Ivanova
- Department of Pulmonary Diseases, University Hospital for Pulmonary Diseases “St. Sofia”, Medical University of Sofia, Akad. Ivan Evstratiev Geshov 17 Blvd., Sofia 1431, Bulgaria
| | - Dimitar Kostadinov
- Department of Pulmonary Diseases, University Hospital for Pulmonary Diseases “St. Sofia”, Medical University of Sofia, Akad. Ivan Evstratiev Geshov 17 Blvd., Sofia 1431, Bulgaria
| | - Nikolay Yanev
- Department of Pulmonary Diseases, University Hospital for Pulmonary Diseases “St. Sofia”, Medical University of Sofia, Akad. Ivan Evstratiev Geshov 17 Blvd., Sofia 1431, Bulgaria
| | - Maya Zhelyazkova
- Faculti of Mathematics and Informatics, Sofia University St. Kliment Ohridski, 5 James Bourchier Blvd., 1164 Sofia, Bulgaria
| | - Stefan Tsonev
- Agrobioinstitute (ABI), 8 Dragan Tsankov, Blvd, Sofia 1164, Bulgaria
| | - Reni Kalfin
- Institute of Neurobiology, Bulgarian Academy of Sciences, Sofia 1113, Bulgaria
- Department of Health Care, South-West University “Neofit Rilski”, Blagoevgrad 2700, Bulgaria
| | - Stefan Panaiotov
- Microbiology Department, National Center of Infectious and Parasitic Diseases, Yanko Sakazov 26 Blvd., Sofia 1504, Bulgaria
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Stabile AM, Pistilli A, Mariangela R, Rende M, Bartolini D, Di Sante G. New Challenges for Anatomists in the Era of Omics. Diagnostics (Basel) 2023; 13:2963. [PMID: 37761332 PMCID: PMC10529314 DOI: 10.3390/diagnostics13182963] [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: 07/31/2023] [Revised: 09/08/2023] [Accepted: 09/10/2023] [Indexed: 09/29/2023] Open
Abstract
Anatomic studies have traditionally relied on macroscopic, microscopic, and histological techniques to investigate the structure of tissues and organs. Anatomic studies are essential in many fields, including medicine, biology, and veterinary science. Advances in technology, such as imaging techniques and molecular biology, continue to provide new insights into the anatomy of living organisms. Therefore, anatomy remains an active and important area in the scientific field. The consolidation in recent years of some omics technologies such as genomics, transcriptomics, proteomics, and metabolomics allows for a more complete and detailed understanding of the structure and function of cells, tissues, and organs. These have been joined more recently by "omics" such as radiomics, pathomics, and connectomics, supported by computer-assisted technologies such as neural networks, 3D bioprinting, and artificial intelligence. All these new tools, although some are still in the early stages of development, have the potential to strongly contribute to the macroscopic and microscopic characterization in medicine. For anatomists, it is time to hitch a ride and get on board omics technologies to sail to new frontiers and to explore novel scenarios in anatomy.
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Affiliation(s)
- Anna Maria Stabile
- Department of Medicine and Surgery, Section of Human, Clinical and Forensic Anatomy, University of Perugia, 60132 Perugia, Italy; (A.M.S.); (A.P.); (R.M.); (M.R.)
| | - Alessandra Pistilli
- Department of Medicine and Surgery, Section of Human, Clinical and Forensic Anatomy, University of Perugia, 60132 Perugia, Italy; (A.M.S.); (A.P.); (R.M.); (M.R.)
| | - Ruggirello Mariangela
- Department of Medicine and Surgery, Section of Human, Clinical and Forensic Anatomy, University of Perugia, 60132 Perugia, Italy; (A.M.S.); (A.P.); (R.M.); (M.R.)
| | - Mario Rende
- Department of Medicine and Surgery, Section of Human, Clinical and Forensic Anatomy, University of Perugia, 60132 Perugia, Italy; (A.M.S.); (A.P.); (R.M.); (M.R.)
| | - Desirée Bartolini
- Department of Medicine and Surgery, Section of Human, Clinical and Forensic Anatomy, University of Perugia, 60132 Perugia, Italy; (A.M.S.); (A.P.); (R.M.); (M.R.)
- Department of Pharmaceutical Sciences, University of Perugia, 06126 Perugia, Italy
| | - Gabriele Di Sante
- Department of Medicine and Surgery, Section of Human, Clinical and Forensic Anatomy, University of Perugia, 60132 Perugia, Italy; (A.M.S.); (A.P.); (R.M.); (M.R.)
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Pavia MJ, Chede A, Wu Z, Cadillo-Quiroz H, Zhu Q. BinaRena: a dedicated interactive platform for human-guided exploration and binning of metagenomes. MICROBIOME 2023; 11:186. [PMID: 37596696 PMCID: PMC10439608 DOI: 10.1186/s40168-023-01625-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 07/16/2023] [Indexed: 08/20/2023]
Abstract
BACKGROUND Exploring metagenomic contigs and "binning" them into metagenome-assembled genomes (MAGs) are essential for the delineation of functional and evolutionary guilds within microbial communities. Despite the advances in automated binning algorithms, their capabilities in recovering MAGs with accuracy and biological relevance are so far limited. Researchers often find that human involvement is necessary to achieve representative binning results. This manual process however is expertise demanding and labor intensive, and it deserves to be supported by software infrastructure. RESULTS We present BinaRena, a comprehensive and versatile graphic interface dedicated to aiding human operators to explore metagenome assemblies via customizable visualization and to associate contigs with bins. Contigs are rendered as an interactive scatter plot based on various data types, including sequence metrics, coverage profiles, taxonomic assignments, and functional annotations. Various contig-level operations are permitted, such as selection, masking, highlighting, focusing, and searching. Binning plans can be conveniently edited, inspected, and compared visually or using metrics including silhouette coefficient and adjusted Rand index. Completeness and contamination of user-selected contigs can be calculated in real time. In demonstration of BinaRena's usability, we show that it facilitated biological pattern discovery, hypothesis generation, and bin refinement in a complex tropical peatland metagenome. It enabled isolation of pathogenic genomes within closely related populations from the gut microbiota of diarrheal human subjects. It significantly improved overall binning quality after curating results of automated binners using a simulated marine dataset. CONCLUSIONS BinaRena is an installation-free, dependency-free, client-end web application that operates directly in any modern web browser, facilitating ease of deployment and accessibility for researchers of all skill levels. The program is hosted at https://github.com/qiyunlab/binarena , together with documentation, tutorials, example data, and a live demo. It effectively supports human researchers in intuitive interpretation and fine tuning of metagenomic data. Video Abstract.
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Affiliation(s)
- Michael J Pavia
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
- Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ, USA
- Biodesign Swette Center for Environmental Biotechnology, Arizona State University, Tempe, AZ, USA
| | - Abhinav Chede
- Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ, USA
| | - Zijun Wu
- Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ, USA
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Hinsby Cadillo-Quiroz
- School of Life Sciences, Arizona State University, Tempe, AZ, USA.
- Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ, USA.
- Biodesign Swette Center for Environmental Biotechnology, Arizona State University, Tempe, AZ, USA.
| | - Qiyun Zhu
- School of Life Sciences, Arizona State University, Tempe, AZ, USA.
- Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ, USA.
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Guo B, Huuki-Myers LA, Grant-Peters M, Collado-Torres L, Hicks SC. escheR: Unified multi-dimensional visualizations with Gestalt principles. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.18.533302. [PMID: 36993732 PMCID: PMC10055209 DOI: 10.1101/2023.03.18.533302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables. The proposed visualization can be applied to spatially-resolved transcriptomics data, but also broadly to data visualized in 2D space, such as embedding visualizations. We provide an open source R package escheR, which is built off of the state-of-the-art ggplot2 visualization framework and can be seamlessly integrated into genomics toolboxes and workflows.
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Affiliation(s)
- Boyi Guo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, MD, USA
| | - Louise A. Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Melissa Grant-Peters
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | | | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, MD, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, MD, USA
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10
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Kumar R, Yadav G, Kuddus M, Ashraf GM, Singh R. Unlocking the microbial studies through computational approaches: how far have we reached? ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:48929-48947. [PMID: 36920617 PMCID: PMC10016191 DOI: 10.1007/s11356-023-26220-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 02/24/2023] [Indexed: 04/16/2023]
Abstract
The metagenomics approach accelerated the study of genetic information from uncultured microbes and complex microbial communities. In silico research also facilitated an understanding of protein-DNA interactions, protein-protein interactions, docking between proteins and phyto/biochemicals for drug design, and modeling of the 3D structure of proteins. These in silico approaches provided insight into analyzing pathogenic and nonpathogenic strains that helped in the identification of probable genes for vaccines and antimicrobial agents and comparing whole-genome sequences to microbial evolution. Artificial intelligence, more precisely machine learning (ML) and deep learning (DL), has proven to be a promising approach in the field of microbiology to handle, analyze, and utilize large data that are generated through nucleic acid sequencing and proteomics. This enabled the understanding of the functional and taxonomic diversity of microorganisms. ML and DL have been used in the prediction and forecasting of diseases and applied to trace environmental contaminants and environmental quality. This review presents an in-depth analysis of the recent application of silico approaches in microbial genomics, proteomics, functional diversity, vaccine development, and drug design.
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Affiliation(s)
- Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Lucknow, Uttar Pradesh, India
- Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, University of Missouri, Columbia, MO, USA
| | - Garima Yadav
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Lucknow, Uttar Pradesh, India
| | - Mohammed Kuddus
- Department of Biochemistry, College of Medicine, University of Hail, Hail, Saudi Arabia
| | - Ghulam Md Ashraf
- Department of Medical Laboratory Sciences, College of Health Sciences, and Sharjah Institute for Medical Research, University of Sharjah, Sharjah , 27272, United Arab Emirates
| | - Rachana Singh
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Lucknow, Uttar Pradesh, India.
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Dudaš D, Wittig U, Rey M, Weidemann A, Müller W. Improved insights into the SABIO-RK database via visualization. Database (Oxford) 2023; 2023:7098623. [PMID: 37002681 PMCID: PMC10066030 DOI: 10.1093/database/baad011] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/11/2023] [Accepted: 03/06/2023] [Indexed: 04/03/2023]
Abstract
SABIO-RK is a database for biochemical reactions and their kinetics. Data in SABIO-RK are inherently multidimensional and complex. The complex relationships between the data are often difficult to follow or even not represented when using standard tabular views. With an increasing number of data points the mismatch between tables and insights becomes more obvious, and getting an overview of the data becomes harder. Such complex data benefit from being presented using specially adapted visual tools. Visualization is a natural and user-friendly way to quickly get an overview of the data and to detect clusters and outliers. Here, we describe the implementation of a variety of visualization concepts into a common interface within the SABIO-RK biochemical reaction kinetics database. For that purpose, we use a heat map, parallel coordinates and scatter plots to allow the interactive visual exploration of general entry-based information of biochemical reactions and specific kinetic parameter values. Database URL https://sabiork.h-its.org/.
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Affiliation(s)
| | - Ulrike Wittig
- Scientific Databases and Visualization, Heidelberg Institute for Theoretical Studies (HITS gGmbH), Schloss-Wolfsbrunnenweg 35, Heidelberg 69118, Germany
| | - Maja Rey
- Scientific Databases and Visualization, Heidelberg Institute for Theoretical Studies (HITS gGmbH), Schloss-Wolfsbrunnenweg 35, Heidelberg 69118, Germany
| | - Andreas Weidemann
- Scientific Databases and Visualization, Heidelberg Institute for Theoretical Studies (HITS gGmbH), Schloss-Wolfsbrunnenweg 35, Heidelberg 69118, Germany
| | - Wolfgang Müller
- Scientific Databases and Visualization, Heidelberg Institute for Theoretical Studies (HITS gGmbH), Schloss-Wolfsbrunnenweg 35, Heidelberg 69118, Germany
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Chen T, Liu Y, Huang L. ImageGP: An easy-to-use data visualization web server for scientific researchers. IMETA 2022; 1:e5. [PMID: 38867732 PMCID: PMC10989750 DOI: 10.1002/imt2.5] [Citation(s) in RCA: 178] [Impact Index Per Article: 89.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 01/18/2022] [Accepted: 01/22/2022] [Indexed: 06/14/2024]
Abstract
Data visualization plays a crucial role in illustrating results and sharing knowledge among researchers. Though many types of visualization tools are widely used, most of them require enough coding experience or are designed for specialized usages, or are not free. Here, we present ImageGP, a specialized visualization platform designed for biology and chemistry data illustration. ImageGP could generate generalized plots like lines, bars, scatters, boxes, sets, heatmaps, and histograms with the most common input content in a user-friendly interface. Normally plotting using ImageGP only needs a few mouse clicks. For some plots, one only needs to just paste data and click submit to get the visualization results. Additionally, ImageGP supplies up to 26 parameters to meet customizable requirements. ImageGP also contains specialized plots like volcano plot, functional enrichment plot for most omics-data analysis, and other four specialized functions for microbiome analysis. Since 2017, ImageGP has been running for nearly 5 years and serving 336,951 visits from all over the world. Together, ImageGP (http://www.ehbio.com/ImageGP/) is an effective and efficient tool for experimental researchers to comprehensively visualize and interpret data generated from wet-lab and dry-lab.
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Affiliation(s)
- Tong Chen
- State Key Laboratory Breeding Base of Dao‐di HerbsNational Resource Center for Chinese Materia Medica, China Academy of Chinese Medical SciencesBeijingChina
| | - Yong‐Xin Liu
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed DesignChinese Academy of SciencesBeijingChina
- CAS Center for Excellence in Biotic InteractionsUniversity of Chinese Academy of SciencesBeijingChina
- CAS‐JIC Centre of Excellence for Plant and Microbial Science, Institute of Genetics and Developmental BiologyChinese Academy of SciencesBeijingChina
| | - Luqi Huang
- State Key Laboratory Breeding Base of Dao‐di HerbsNational Resource Center for Chinese Materia Medica, China Academy of Chinese Medical SciencesBeijingChina
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Afonso MQL, da Fonseca Júnior NJ, Miranda TG, Bleicher L. Naview: A d3.js Based JavaScript Library for Drawing and Annotating Voltage-Gated Sodium Channels Membrane Diagrams. FRONTIERS IN BIOINFORMATICS 2022; 2:774417. [PMID: 36304314 PMCID: PMC9580940 DOI: 10.3389/fbinf.2022.774417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 01/05/2022] [Indexed: 11/13/2022] Open
Abstract
Voltage-gated sodium channels (Nav) are membrane proteins essential to initiating and propagating action potential in neurons and other excitable cells. For a given organism there are often multiple, specialized sodium channels found in different tissues, whose mutations can cause deleterious effects observed in numerous diseases. Consequently, there is high medical and pharmacological interest in these proteins. Scientific literature often uses membrane diagrams to depict important patterns in these channels including the six transmembrane segments (S1–S6) present in four different homologous domains (D1–D4), the S4 voltage sensors, the pore-lining residue segments and the ion selectivity filter residues, glycosylation and phosphorylation residues, toxin binding sites and the inactivation loop, among others. Most of these diagrams are illustrated either digitally or by hand and programs specifically dedicated to the interactive and data-friendly generation of such visualizations are scarce or non-existing. This paper describes Naview, an open-source javascript visualization compatible with modern web browsers for the dynamic drawing and annotation of voltage-gated sodium channels membrane diagrams based on the D3.js library. By using a graphical user interface and combining user-defined annotations with optional UniProt code as inputs, Naview allows the creation and customization of membrane diagrams. In this interface, a user can also map and display important sodium channel properties, residues, regions and their relationships through symbols, colors, and edge connections. Such features can facilitate data exploration and provide fast, high-quality publication-ready graphics for this highly active area of research.
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Affiliation(s)
- Marcelo Querino Lima Afonso
- Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Néli José da Fonseca Júnior
- Cellular Structure and 3D Bioimaging, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, United Kingdom
| | - Thainá Godinho Miranda
- Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Lucas Bleicher
- Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
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O’Donoghue SI, Schafferhans A, Sikta N, Stolte C, Kaur S, Ho BK, Anderson S, Procter JB, Dallago C, Bordin N, Adcock M, Rost B. SARS-CoV-2 structural coverage map reveals viral protein assembly, mimicry, and hijacking mechanisms. Mol Syst Biol 2021; 17:e10079. [PMID: 34519429 PMCID: PMC8438690 DOI: 10.15252/msb.202010079] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 08/05/2021] [Accepted: 08/06/2021] [Indexed: 01/18/2023] Open
Abstract
We modeled 3D structures of all SARS-CoV-2 proteins, generating 2,060 models that span 69% of the viral proteome and provide details not available elsewhere. We found that ˜6% of the proteome mimicked human proteins, while ˜7% was implicated in hijacking mechanisms that reverse post-translational modifications, block host translation, and disable host defenses; a further ˜29% self-assembled into heteromeric states that provided insight into how the viral replication and translation complex forms. To make these 3D models more accessible, we devised a structural coverage map, a novel visualization method to show what is-and is not-known about the 3D structure of the viral proteome. We integrated the coverage map into an accompanying online resource (https://aquaria.ws/covid) that can be used to find and explore models corresponding to the 79 structural states identified in this work. The resulting Aquaria-COVID resource helps scientists use emerging structural data to understand the mechanisms underlying coronavirus infection and draws attention to the 31% of the viral proteome that remains structurally unknown or dark.
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MESH Headings
- Amino Acid Transport Systems, Neutral/chemistry
- Amino Acid Transport Systems, Neutral/genetics
- Amino Acid Transport Systems, Neutral/metabolism
- Angiotensin-Converting Enzyme 2/chemistry
- Angiotensin-Converting Enzyme 2/genetics
- Angiotensin-Converting Enzyme 2/metabolism
- Binding Sites
- COVID-19/genetics
- COVID-19/metabolism
- COVID-19/virology
- Computational Biology/methods
- Coronavirus Envelope Proteins/chemistry
- Coronavirus Envelope Proteins/genetics
- Coronavirus Envelope Proteins/metabolism
- Coronavirus Nucleocapsid Proteins/chemistry
- Coronavirus Nucleocapsid Proteins/genetics
- Coronavirus Nucleocapsid Proteins/metabolism
- Host-Pathogen Interactions/genetics
- Humans
- Mitochondrial Membrane Transport Proteins/chemistry
- Mitochondrial Membrane Transport Proteins/genetics
- Mitochondrial Membrane Transport Proteins/metabolism
- Mitochondrial Precursor Protein Import Complex Proteins
- Models, Molecular
- Molecular Mimicry
- Neuropilin-1/chemistry
- Neuropilin-1/genetics
- Neuropilin-1/metabolism
- Phosphoproteins/chemistry
- Phosphoproteins/genetics
- Phosphoproteins/metabolism
- Protein Binding
- Protein Conformation, alpha-Helical
- Protein Conformation, beta-Strand
- Protein Interaction Domains and Motifs
- Protein Interaction Mapping/methods
- Protein Multimerization
- Protein Processing, Post-Translational
- SARS-CoV-2/chemistry
- SARS-CoV-2/genetics
- SARS-CoV-2/metabolism
- Spike Glycoprotein, Coronavirus/chemistry
- Spike Glycoprotein, Coronavirus/genetics
- Spike Glycoprotein, Coronavirus/metabolism
- Viral Matrix Proteins/chemistry
- Viral Matrix Proteins/genetics
- Viral Matrix Proteins/metabolism
- Viroporin Proteins/chemistry
- Viroporin Proteins/genetics
- Viroporin Proteins/metabolism
- Virus Replication
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Affiliation(s)
- Seán I O’Donoghue
- Garvan Institute of Medical ResearchDarlinghurstNSWAustralia
- CSIRO Data61CanberraACTAustralia
- School of Biotechnology and Biomolecular Sciences (UNSW)KensingtonNSWAustralia
| | - Andrea Schafferhans
- Garvan Institute of Medical ResearchDarlinghurstNSWAustralia
- Department of Bioengineering SciencesWeihenstephan‐Tr. University of Applied SciencesFreisingGermany
- Department of InformaticsBioinformatics & Computational BiologyTechnical University of MunichMunichGermany
| | - Neblina Sikta
- Garvan Institute of Medical ResearchDarlinghurstNSWAustralia
| | | | - Sandeep Kaur
- Garvan Institute of Medical ResearchDarlinghurstNSWAustralia
- School of Biotechnology and Biomolecular Sciences (UNSW)KensingtonNSWAustralia
| | - Bosco K Ho
- Garvan Institute of Medical ResearchDarlinghurstNSWAustralia
| | | | | | - Christian Dallago
- Department of InformaticsBioinformatics & Computational BiologyTechnical University of MunichMunichGermany
| | - Nicola Bordin
- Institute of Structural and Molecular BiologyUniversity College LondonLondonUK
| | | | - Burkhard Rost
- Department of InformaticsBioinformatics & Computational BiologyTechnical University of MunichMunichGermany
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