1
|
Beverley J, Babcock S, Carvalho G, Cowell LG, Duesing S, He Y, Hurley R, Merrell E, Scheuermann RH, Smith B. Coordinating virus research: The Virus Infectious Disease Ontology. PLoS One 2024; 19:e0285093. [PMID: 38236918 PMCID: PMC10796065 DOI: 10.1371/journal.pone.0285093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 04/12/2023] [Indexed: 01/22/2024] Open
Abstract
The COVID-19 pandemic prompted immense work on the investigation of the SARS-CoV-2 virus. Rapid, accurate, and consistent interpretation of generated data is thereby of fundamental concern. Ontologies-structured, controlled, vocabularies-are designed to support consistency of interpretation, and thereby to prevent the development of data silos. This paper describes how ontologies are serving this purpose in the COVID-19 research domain, by following principles of the Open Biological and Biomedical Ontology (OBO) Foundry and by reusing existing ontologies such as the Infectious Disease Ontology (IDO) Core, which provides terminological content common to investigations of all infectious diseases. We report here on the development of an IDO extension, the Virus Infectious Disease Ontology (VIDO), a reference ontology covering viral infectious diseases. We motivate term and definition choices, showcase reuse of terms from existing OBO ontologies, illustrate how ontological decisions were motivated by relevant life science research, and connect VIDO to the Coronavirus Infectious Disease Ontology (CIDO). We next use terms from these ontologies to annotate selections from life science research on SARS-CoV-2, highlighting how ontologies employing a common upper-level vocabulary may be seamlessly interwoven. Finally, we outline future work, including bacteria and fungus infectious disease reference ontologies currently under development, then cite uses of VIDO and CIDO in host-pathogen data analytics, electronic health record annotation, and ontology conflict-resolution projects.
Collapse
Affiliation(s)
- John Beverley
- Department of Philosophy, University at Buffalo, Buffalo, NY, United States of America
- National Center for Ontological Research, Buffalo, NY, United States of America
| | - Shane Babcock
- National Center for Ontological Research, Buffalo, NY, United States of America
- Air Force Research Laboratory, Wright Patterson Air Force Base, Riverside, OH, United States of America
| | - Gustavo Carvalho
- Department of Cognitive Science, Northwestern University, Evanston, IL, United States of America
| | - Lindsay G. Cowell
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States of America
| | - Sebastian Duesing
- Department of Philosophy, Loyola University, Chicago, IL, United States of America
| | - Yongqun He
- Computational Medicine and Bioinformatics, University of Michigan Medical School, He Group, Ann Arbor, MI, United States of America
| | - Regina Hurley
- National Center for Ontological Research, Buffalo, NY, United States of America
- Department of Philosophy, Northwestern University, Evanston, IL, United States of America
| | - Eric Merrell
- Department of Philosophy, University at Buffalo, Buffalo, NY, United States of America
- National Center for Ontological Research, Buffalo, NY, United States of America
| | - Richard H. Scheuermann
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA, United States of America
- Department of Pathology, University of California, San Diego, CA, United States of America
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, United States of America
| | - Barry Smith
- Department of Philosophy, University at Buffalo, Buffalo, NY, United States of America
- National Center for Ontological Research, Buffalo, NY, United States of America
| |
Collapse
|
2
|
Ritsch M, Cassman NA, Saghaei S, Marz M. Navigating the Landscape: A Comprehensive Review of Current Virus Databases. Viruses 2023; 15:1834. [PMID: 37766241 PMCID: PMC10537806 DOI: 10.3390/v15091834] [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: 07/04/2023] [Revised: 08/18/2023] [Accepted: 08/21/2023] [Indexed: 09/29/2023] Open
Abstract
Viruses are abundant and diverse entities that have important roles in public health, ecology, and agriculture. The identification and surveillance of viruses rely on an understanding of their genome organization, sequences, and replication strategy. Despite technological advancements in sequencing methods, our current understanding of virus diversity remains incomplete, highlighting the need to explore undiscovered viruses. Virus databases play a crucial role in providing access to sequences, annotations and other metadata, and analysis tools for studying viruses. However, there has not been a comprehensive review of virus databases in the last five years. This study aimed to fill this gap by identifying 24 active virus databases and included an extensive evaluation of their content, functionality and compliance with the FAIR principles. In this study, we thoroughly assessed the search capabilities of five database catalogs, which serve as comprehensive repositories housing a diverse array of databases and offering essential metadata. Moreover, we conducted a comprehensive review of different types of errors, encompassing taxonomy, names, missing information, sequences, sequence orientation, and chimeric sequences, with the intention of empowering users to effectively tackle these challenges. We expect this review to aid users in selecting suitable virus databases and other resources, and to help databases in error management and improve their adherence to the FAIR principles. The databases listed here represent the current knowledge of viruses and will help aid users find databases of interest based on content, functionality, and scope. The use of virus databases is integral to gaining new insights into the biology, evolution, and transmission of viruses, and developing new strategies to manage virus outbreaks and preserve global health.
Collapse
Affiliation(s)
- Muriel Ritsch
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, 07743 Jena, Germany;
- European Virus Bioinformatics Center, 07743 Jena, Germany
| | - Noriko A. Cassman
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, 07743 Jena, Germany;
- European Virus Bioinformatics Center, 07743 Jena, Germany
| | - Shahram Saghaei
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, 07743 Jena, Germany;
- European Virus Bioinformatics Center, 07743 Jena, Germany
| | - Manja Marz
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, 07743 Jena, Germany;
- European Virus Bioinformatics Center, 07743 Jena, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, 04103 Leipzig, Germany
- FLI Leibniz Institute for Age Research, 07745 Jena, Germany
| |
Collapse
|
3
|
Cheng Y, Ji C, Zhou HY, Zheng H, Wu A. Web Resources for SARS-CoV-2 Genomic Database, Annotation, Analysis and Variant Tracking. Viruses 2023; 15:v15051158. [PMID: 37243244 DOI: 10.3390/v15051158] [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: 04/04/2023] [Revised: 05/10/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
The SARS-CoV-2 genomic data continue to grow, providing valuable information for researchers and public health officials. Genomic analysis of these data sheds light on the transmission and evolution of the virus. To aid in SARS-CoV-2 genomic analysis, many web resources have been developed to store, collate, analyze, and visualize the genomic data. This review summarizes web resources used for the SARS-CoV-2 genomic epidemiology, covering data management and sharing, genomic annotation, analysis, and variant tracking. The challenges and further expectations for these web resources are also discussed. Finally, we highlight the importance and need for continued development and improvement of related web resources to effectively track the spread and understand the evolution of the virus.
Collapse
Affiliation(s)
- Yexiao Cheng
- School of Life Science and Technology, China Pharmaceutical University, Nanjing 211100, China
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China
- Suzhou Institute of Systems Medicine, Suzhou 215123, China
| | - Chengyang Ji
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China
- Suzhou Institute of Systems Medicine, Suzhou 215123, China
| | - Hang-Yu Zhou
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China
- Suzhou Institute of Systems Medicine, Suzhou 215123, China
| | - Heng Zheng
- School of Life Science and Technology, China Pharmaceutical University, Nanjing 211100, China
| | - Aiping Wu
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China
- Suzhou Institute of Systems Medicine, Suzhou 215123, China
| |
Collapse
|
4
|
Li Q, Liu C, Hou J, Wang P. Affective memories and perceived value: motivators and inhibitors of the data search-access process. JOURNAL OF DOCUMENTATION 2023. [DOI: 10.1108/jd-06-2022-0129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/15/2023]
Abstract
PurposeAs an emerging tool for data discovery, data retrieval systems fail to effectively support users' cognitive processes during data search and access. To uncover the relationship between data search and access and the cognitive mechanisms underlying this relationship, this paper examines the associations between affective memories, perceived value, search effort and the intention to access data during users' interactions with data retrieval systems.Design/methodology/approachThis study conducted a user experiment for which 48 doctoral students from different disciplines were recruited. The authors collected search logs, screen recordings, questionnaires and eye movement data during the interactive data search. Multiple linear regression was used to test the hypotheses.FindingsThe results indicate that positive affective memories positively affect perceived value, while the effects of negative affective memories on perceived value are nonsignificant. Utility value positively affects search effort, while attainment value negatively affects search effort. Moreover, search effort partially positively affects the intention to access data, and it serves a full mediating role in the effects of utility value and attainment value on the intention to access data.Originality/valueThrough the comparison between the findings of this study and relevant findings in information search studies, this paper reveals the specificity of behaviour and cognitive processes during data search and access and the special characteristics of data discovery tasks. It sheds light on the inhibiting effect of attainment value and the motivating effect of utility value on data search and the intention to access data. Moreover, this paper provides new insights into the role of memory bias in the relationships between affective memories and data searchers' perceived value.
Collapse
|
5
|
Bernasconi A, Guizzardi G, Pastor O, Storey VC. Semantic interoperability: ontological unpacking of a viral conceptual model. BMC Bioinformatics 2022; 23:491. [PMID: 36396980 PMCID: PMC9672571 DOI: 10.1186/s12859-022-05022-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 10/29/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Genomics and virology are unquestionably important, but complex, domains being investigated by a large number of scientists. The need to facilitate and support work within these domains requires sharing of databases, although it is often difficult to do so because of the different ways in which data is represented across the databases. To foster semantic interoperability, models are needed that provide a deep understanding and interpretation of the concepts in a domain, so that the data can be consistently interpreted among researchers. RESULTS In this research, we propose the use of conceptual models to support semantic interoperability among databases and assess their ontological clarity to support their effective use. This modeling effort is illustrated by its application to the Viral Conceptual Model (VCM) that captures and represents the sequencing of viruses, inspired by the need to understand the genomic aspects of the virus responsible for COVID-19. For achieving semantic clarity on the VCM, we leverage the "ontological unpacking" method, a process of ontological analysis that reveals the ontological foundation of the information that is represented in a conceptual model. This is accomplished by applying the stereotypes of the OntoUML ontology-driven conceptual modeling language.As a result, we propose a new OntoVCM, an ontologically grounded model, based on the initial VCM, but with guaranteed interoperability among the data sources that employ it. CONCLUSIONS We propose and illustrate how the unpacking of the Viral Conceptual Model resolves several issues related to semantic interoperability, the importance of which is recognized by the "I" in FAIR principles. The research addresses conceptual uncertainty within the domain of SARS-CoV-2 data and knowledge.The method employed provides the basis for further analyses of complex models currently used in life science applications, but lacking ontological grounding, subsequently hindering the interoperability needed for scientists to progress their research.
Collapse
Affiliation(s)
- Anna Bernasconi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
- PROS Research Center, VRAIN Research Institute, Universitat Politècnica de València, Valencia, Spain.
| | - Giancarlo Guizzardi
- Conceptual and Cognitive Modeling Research Group, Free University of Bozen-Bolzano, Bolzano, Italy
- Services and Cybersecurity Group, University of Twente, Enschede, The Netherlands
| | - Oscar Pastor
- PROS Research Center, VRAIN Research Institute, Universitat Politècnica de València, Valencia, Spain
| | - Veda C Storey
- J. Mack Robinson College of Business, Georgia State University, Atlanta, Georgia, USA
| |
Collapse
|
6
|
CoV2K model, a comprehensive representation of SARS-CoV-2 knowledge and data interplay. Sci Data 2022; 9:260. [PMID: 35650205 PMCID: PMC9160032 DOI: 10.1038/s41597-022-01348-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 04/26/2022] [Indexed: 11/08/2022] Open
Abstract
Since the outbreak of the COVID-19 pandemic, many research organizations have studied the genome of the SARS-CoV-2 virus; a body of public resources have been published for monitoring its evolution. While we experience an unprecedented richness of information in this domain, we also ascertained the presence of several information quality issues. We hereby propose CoV2K, an abstract model for explaining SARS-CoV-2-related concepts and interactions, focusing on viral mutations, their co-occurrence within variants, and their effects. CoV2K provides a clear and concise route map for understanding different connected types of information related to the virus; it thus drives a process of data and knowledge integration that aggregates information from several current resources, harmonizing their content and overcoming incompleteness and inconsistency issues. CoV2K is available for exploration as a graph that can be queried through a RESTful API addressing single entities or paths through their relationships. Practical use cases demonstrate its application to current knowledge inquiries.
Collapse
|
7
|
Chen C, Nadeau S, Topolsky I, Beerenwinkel N, Stadler T. Advancing genomic epidemiology by addressing the bioinformatics bottleneck: Challenges, design principles, and a Swiss example. Epidemics 2022; 39:100576. [PMID: 35605437 PMCID: PMC9107180 DOI: 10.1016/j.epidem.2022.100576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 04/05/2022] [Accepted: 05/05/2022] [Indexed: 11/28/2022] Open
Abstract
The SARS-CoV-2 pandemic led to a huge increase in global pathogen genome sequencing efforts, and the resulting data are becoming increasingly important to detect variants of concern, monitor outbreaks, and quantify transmission dynamics. However, this rapid up-scaling in data generation brought with it many IT infrastructure challenges. In this paper, we report about developing an improved system for genomic epidemiology. We (i) highlight key challenges that were exacerbated by the pandemic situation, (ii) provide data infrastructure design principles to address them, and (iii) give an implementation example developed by the Swiss SARS-CoV-2 Sequencing Consortium (S3C) in response to the COVID-19 pandemic. Finally, we discuss remaining challenges to data infrastructure for genomic epidemiology. Improving these infrastructures will help better detect, monitor, and respond to future public health threats.
Collapse
Affiliation(s)
- Chaoran Chen
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland
| | - Sarah Nadeau
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland
| | - Ivan Topolsky
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland.
| |
Collapse
|
8
|
Bernasconi A, Cascianelli S. Scenarios for the Integration of Microarray Gene Expression Profiles in COVID-19-Related Studies. Methods Mol Biol 2022; 2401:195-215. [PMID: 34902130 DOI: 10.1007/978-1-0716-1839-4_13] [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] [Indexed: 06/14/2023]
Abstract
The COVID-19 pandemic has hit heavily many aspects of our lives. At this time, genomic research is concerned with exploiting available datasets and knowledge to fuel discovery on this novel disease. Studies that can precisely characterize the gene expression profiles of human hosts infected by SARS-CoV-2 are of significant relevance. However, not many such experiments have yet been produced to date, nor made publicly available online. Thus, it is of paramount importance that data analysts explore all possibilities to integrate information coming from similar viruses and related diseases; interestingly, microarray gene profile experiments become extremely valuable for this purpose. This chapter reviews the aspects that should be considered when integrating transcriptomics data, considering mainly samples infected by different viruses and combining together various data types and also the extracted knowledge. It describes a series of scenarios from studies performed in literature and it suggests possible other directions of noteworthy integration.
Collapse
Affiliation(s)
- Anna Bernasconi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.
| | - Silvia Cascianelli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| |
Collapse
|
9
|
Pinoli P, Bernasconi A, Sandionigi A, Ceri S. VirusLab: A Tool for Customized SARS-CoV-2 Data Analysis. BIOTECH 2021; 10:biotech10040027. [PMID: 35822801 PMCID: PMC9245481 DOI: 10.3390/biotech10040027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/26/2021] [Accepted: 11/02/2021] [Indexed: 12/14/2022] Open
Abstract
Since the beginning of 2020, the COVID-19 pandemic has posed unprecedented challenges to viral data analysis and connected host disease diagnostic methods. We propose VirusLab, a flexible system for analysing SARS-CoV-2 viral sequences and relating them to metadata or clinical information about the host. VirusLab capitalizes on two existing resources: ViruSurf, a database of public SARS-CoV-2 sequences supporting metadata-driven search, and VirusViz, a tool for visual analysis of search results. VirusLab is designed for taking advantage of these resources within a server-side architecture that: (i) covers pipelines based on approaches already in use (ARTIC, Galaxy) but entirely cutomizable upon user request; (ii) predigests analysis of raw sequencing data from different platforms (Oxford Nanopore and Illumina); (iii) gives access to public archives datasets; (iv) supplies user-friendly reporting – making it a tool that can also be integrated into a business environment. VirusLab can be installed and hosted within the premises of any organization where information about SARS-CoV-2 sequences can be safely integrated with information about hosts (e.g., clinical metadata). A system such as VirusLab is not currently available in the landscape of similar providers: our results show that VirusLab is a powerful tool to generate tabular/graphical and machine readable reports that can be integrated in more complex pipelines. We foresee that the proposed system can support many research-oriented and therapeutic scenarios within hospitals or the tracing of viral sequences and their mutational processes within organizations for viral surveillance.
Collapse
Affiliation(s)
- Pietro Pinoli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy; (P.P.); (S.C.)
| | - Anna Bernasconi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy; (P.P.); (S.C.)
- Correspondence: ; Tel.: +39-02-2399-3655
| | - Anna Sandionigi
- Quantia Consulting S.r.l., Mariano Comense, 22066 Como, Italy;
| | - Stefano Ceri
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy; (P.P.); (S.C.)
| |
Collapse
|
10
|
Masoudi-Sobhanzadeh Y, Salemi A, Pourseif MM, Jafari B, Omidi Y, Masoudi-Nejad A. Structure-based drug repurposing against COVID-19 and emerging infectious diseases: methods, resources and discoveries. Brief Bioinform 2021; 22:bbab113. [PMID: 33993214 PMCID: PMC8194848 DOI: 10.1093/bib/bbab113] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 02/15/2021] [Accepted: 03/13/2021] [Indexed: 01/09/2023] Open
Abstract
To attain promising pharmacotherapies, researchers have applied drug repurposing (DR) techniques to discover the candidate medicines to combat the coronavirus disease 2019 (COVID-19) outbreak. Although many DR approaches have been introduced for treating different diseases, only structure-based DR (SBDR) methods can be employed as the first therapeutic option against the COVID-19 pandemic because they rely on the rudimentary information about the diseases such as the sequence of the severe acute respiratory syndrome coronavirus 2 genome. Hence, to try out new treatments for the disease, the first attempts have been made based on the SBDR methods which seem to be among the proper choices for discovering the potential medications against the emerging and re-emerging infectious diseases. Given the importance of SBDR approaches, in the present review, well-known SBDR methods are summarized, and their merits are investigated. Then, the databases and software applications, utilized for repurposing the drugs against COVID-19, are introduced. Besides, the identified drugs are categorized based on their targets. Finally, a comparison is made between the SBDR approaches and other DR methods, and some possible future directions are proposed.
Collapse
Affiliation(s)
- Yosef Masoudi-Sobhanzadeh
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Aysan Salemi
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mohammad M Pourseif
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Behzad Jafari
- Department of Medicinal Chemistry, Faculty of Pharmacy, Urmia University of Medical Sciences, Urmia, Iran
| | - Yadollah Omidi
- Nova Southeastern University College of Pharmacy, Florida, USA
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| |
Collapse
|
11
|
Zocchi E, Terrazzano G. COVID-19: why not learn from the past? Front Med 2021; 15:776-781. [PMID: 34463906 PMCID: PMC8407128 DOI: 10.1007/s11684-021-0883-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 07/23/2021] [Indexed: 11/26/2022]
Affiliation(s)
- Elena Zocchi
- Department of Experimental Medicine (DIMES), University of Genova, Genova, Italy.
| | | |
Collapse
|
12
|
Bernasconi A, Gulino A, Alfonsi T, Canakoglu A, Pinoli P, Sandionigi A, Ceri S. VirusViz: comparative analysis and effective visualization of viral nucleotide and amino acid variants. Nucleic Acids Res 2021; 49:e90. [PMID: 34107016 PMCID: PMC8344854 DOI: 10.1093/nar/gkab478] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 05/11/2021] [Accepted: 05/24/2021] [Indexed: 12/27/2022] Open
Abstract
Variant visualization plays an important role in supporting the viral evolution analysis, extremely valuable during the COVID-19 pandemic. VirusViz is a web-based application for comparing variants of selected viral populations and their sub-populations; it is primarily focused on SARS-CoV-2 variants, although the tool also supports other viral species (SARS-CoV, MERS-CoV, Dengue, Ebola). As input, VirusViz imports results of queries extracting variants and metadata from the large database ViruSurf, which integrates information about most SARS-CoV-2 sequences publicly deposited worldwide. Moreover, VirusViz accepts sequences of new viral populations as multi-FASTA files plus corresponding metadata in CSV format; a bioinformatic pipeline builds a suitable input for VirusViz by extracting the nucleotide and amino acid variants. Pages of VirusViz provide metadata summarization, variant descriptions, and variant visualization with rich options for zooming, highlighting variants or regions of interest, and switching from nucleotides to amino acids; sequences can be grouped, groups can be comparatively analyzed. For SARS-CoV-2, we manually collect mutations with known or predicted levels of severity/virulence, as indicated in linked research articles; such critical mutations are reported when observed in sequences. The system includes light-weight project management for downloading, resuming, and merging data analysis sessions. VirusViz is freely available at http://gmql.eu/virusviz/.
Collapse
Affiliation(s)
- Anna Bernasconi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy
| | - Andrea Gulino
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy
| | - Tommaso Alfonsi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy
| | - Arif Canakoglu
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy
| | - Pietro Pinoli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy
| | - Anna Sandionigi
- Quantia Consulting S.r.l., Via Petrarca 20, 22066, Mariano Comense, Como, Italy
| | - Stefano Ceri
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy
| |
Collapse
|
13
|
A Conceptual Model for Geo-Online Exploratory Data Visualization: The Case of the COVID-19 Pandemic. INFORMATION 2021. [DOI: 10.3390/info12020069] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Responding to the recent COVID-19 outbreak, several organizations and private citizens considered the opportunity to design and publish online explanatory data visualization tools for the communication of disease data supported by a spatial dimension. They responded to the need of receiving instant information arising from the broad research community, the public health authorities, and the general public. In addition, the growing maturity of information and mapping technologies, as well as of social networks, has greatly supported the diffusion of web-based dashboards and infographics, blending geographical, graphical, and statistical representation approaches. We propose a broad conceptualization of Web visualization tools for geo-spatial information, exceptionally employed to communicate the current pandemic; to this end, we study a significant number of publicly available platforms that track, visualize, and communicate indicators related to COVID-19. Our methodology is based on (i) a preliminary systematization of actors, data types, providers, and visualization tools, and on (ii) the creation of a rich collection of relevant sites clustered according to significant parameters. Ultimately, the contribution of this work includes a critical analysis of collected evidence and an extensive modeling effort of Geo-Online Exploratory Data Visualization (Geo-OEDV) tools, synthesized in terms of an Entity-Relationship schema. The COVID-19 pandemic outbreak has offered a significant case to study how and how much modern public communication needs spatially related data and effective implementation of tools whose inspection can impact decision-making at different levels. Our resulting model will allow several stakeholders (general users, policy-makers, and researchers/analysts) to gain awareness on the assets of structured online communication and resource owners to direct future development of these important tools.
Collapse
|