A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology

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Abstract

Background

The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the development of the community-based Coronavirus Infectious Disease Ontology (CIDO) in early 2020.

Results

As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment.

Conclusion

CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13326-022-00279-z.

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Most cited references 91

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The FAIR Guiding Principles for scientific data management and stewardship

Mark D Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg … (2016)

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

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UniProt: the universal protein knowledgebase in 2021

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Abstract The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.

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DrugBank 5.0: a major update to the DrugBank database for 2018

David S Wishart, Yannick D Feunang, An C Guo … (2017)

Abstract DrugBank (www.drugbank.ca) is a web-enabled database containing comprehensive molecular information about drugs, their mechanisms, their interactions and their targets. First described in 2006, DrugBank has continued to evolve over the past 12 years in response to marked improvements to web standards and changing needs for drug research and development. This year’s update, DrugBank 5.0, represents the most significant upgrade to the database in more than 10 years. In many cases, existing data content has grown by 100% or more over the last update. For instance, the total number of investigational drugs in the database has grown by almost 300%, the number of drug-drug interactions has grown by nearly 600% and the number of SNP-associated drug effects has grown more than 3000%. Significant improvements have been made to the quantity, quality and consistency of drug indications, drug binding data as well as drug-drug and drug-food interactions. A great deal of brand new data have also been added to DrugBank 5.0. This includes information on the influence of hundreds of drugs on metabolite levels (pharmacometabolomics), gene expression levels (pharmacotranscriptomics) and protein expression levels (pharmacoprotoemics). New data have also been added on the status of hundreds of new drug clinical trials and existing drug repurposing trials. Many other important improvements in the content, interface and performance of the DrugBank website have been made and these should greatly enhance its ease of use, utility and potential applications in many areas of pharmacological research, pharmaceutical science and drug education.

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Author and article information

Contributors

Yongqun He: yongqunh@med.umich.edu

Hong Yu: yuhong20040416@sina.com

Anthony Huffman: huffmaar@umich.edu

Asiyah Yu Lin: asiyah.lin@nih.gov

Darren A. Natale: dan5@georgetown.edu

John Beverley: johnbeverley2021@u.northwestern.edu

Ling Zheng: zdzhengling@gmail.com

Yehoshua Perl: yehoshua.perl@gmail.com

Zhigang Wang: wangzg@pumc.edu.cn

Yingtong Liu: yingtliu@umich.edu

Edison Ong: e4ong1031@gmail.com

Yang Wang: 15519009601@163.com

Philip Huang: huangpc@umich.edu

Long Tran: longtr@umich.edu

Jinyang Du: jinyangd@umich.edu

Zalan Shah: zalans@umich.edu

Easheta Shah: shaheash@umich.edu

Roshan Desai: roshand@umich.edu

Hsin-hui Huang: toe3273917@outlook.com

Yujia Tian: yt346@scarletmail.rutgers.edu

Eric Merrell: ericmerr@buffalo.edu

William D. Duncan: wdduncan@gmail.com

Sivaram Arabandi: sivaram.arabandi@gmail.com

Lynn M. Schriml: lschriml@som.umaryland.edu

Jie Zheng: jiezheng@pennmedicine.upenn.edu

Anna Maria Masci: mascia2@nih.gov

Liwei Wang: wang.liwei@mayo.edu

Hongfang Liu: liu.hongfang@mayo.edu

Fatima Zohra Smaili: fzohrasmaili@gmail.com

Robert Hoehndorf: robert.hoehndorf@kaust.edu.sa

Zoë May Pendlington: zmp@ebi.ac.uk

Paola Roncaglia: paola.roncaglia.phd@gmail.com

Xianwei Ye: yxw1205@163.com

Jiangan Xie: xjahardy@hotmail.com

Yi-Wei Tang: yi-wei.tang@cepheid.com

Xiaolin Yang: yangxl74@gmail.com

Suyuan Peng: peng.suyuan@bjmu.edu.cn

Luxia Zhang: zhanglx@bjmu.edu.cn

Luonan Chen: lnchen@sibcb.ac.cn

Junguk Hur: junguk.hur@med.und.edu

Gilbert S. Omenn: gomenn@med.umich.edu

Brian Athey: bleu@med.umich.edu

Barry Smith: phismith@buffalo.edu

Journal

Journal ID (nlm-ta): J Biomed Semantics

Journal ID (iso-abbrev): J Biomed Semantics

Title: Journal of Biomedical Semantics

Publisher: BioMed Central (London )

ISSN (Electronic): 2041-1480

Publication date (Electronic): 21 October 2022

Publication date PMC-release: 21 October 2022

Publication date Collection: 2022

Volume: 13

Electronic Location Identifier: 25

Affiliations

[1 ]GRID grid.214458.e, ISNI 0000000086837370, University of Michigan Medical School, ; Ann Arbor, MI USA

[2 ]People’s Hospital of Guizhou Province, Guiyang, Guizhou China

[3 ]GRID grid.280128.1, ISNI 0000 0001 2233 9230, National Human Genome Research Institute, National Institutes of Health, ; Bethesda, MD USA

[4 ]National Center for Ontological Research, Buffalo, NY USA

[5 ]GRID grid.411667.3, ISNI 0000 0001 2186 0438, Georgetown University Medical Center, ; Washington, DC USA

[6 ]GRID grid.474430.0, ISNI 0000 0004 0630 1170, The Johns Hopkins University Applied Physics Laboratory, ; Laurel, MD USA

[7 ]GRID grid.260185.8, ISNI 0000 0004 0484 1579, Computer Science and Software Engineering Department, , Monmouth University, ; West Long Branch, NJ USA

[8 ]GRID grid.260896.3, ISNI 0000 0001 2166 4955, Department of Computer Science, , New Jersey Institute of Technology, ; Newark, NJ USA

[9 ]GRID grid.506261.6, ISNI 0000 0001 0706 7839, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, ; Beijing, China

[10 ]GRID grid.260539.b, ISNI 0000 0001 2059 7017, National Yang-Ming University, ; Taipei, Taiwan

[11 ]GRID grid.430387.b, ISNI 0000 0004 1936 8796, Rutgers University, ; New Brunswick, NJ USA

[12 ]GRID grid.273335.3, ISNI 0000 0004 1936 9887, University at Buffalo, ; Buffalo, NY 14260 USA

[13 ]GRID grid.15276.37, ISNI 0000 0004 1936 8091, University of Florida, ; Gainesville, FL USA

[14 ]OntoPro LLC, Houston, TX USA

[15 ]GRID grid.411024.2, ISNI 0000 0001 2175 4264, University of Maryland School of Medicine, ; Baltimore, MD USA

[16 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, Department of Biology, , University of Pennsylvania Perelman School of Medicine, ; Philadelphia, PA USA

[17 ]GRID grid.280664.e, ISNI 0000 0001 2110 5790, Office of Data Science, , National Institute of Environmental Health Sciences, ; Research Triangle Park, NC USA

[18 ]GRID grid.66875.3a, ISNI 0000 0004 0459 167X, Mayo Clinic, ; Rochester, MN USA

[19 ]GRID grid.45672.32, ISNI 0000 0001 1926 5090, King Abdullah University of Science and Technology, ; Thuwal, Saudi Arabia

[20 ]European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK

[21 ]GRID grid.411587.e, ISNI 0000 0001 0381 4112, School of Bioinformatics, , Chongqing University of Posts and Telecommunications, ; Chongqing, China

[22 ]GRID grid.474503.1, Cepheid, Danaher Diagnostic Platform, ; Shanghai, China

[23 ]GRID grid.11135.37, ISNI 0000 0001 2256 9319, National Institute of Health Data Science, Peking University, ; Beijing, China

[24 ]GRID grid.507739.f, ISNI 0000 0001 0061 254X, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, ; Shanghai, China

[25 ]GRID grid.266862.e, ISNI 0000 0004 1936 8163, University of North Dakota School of Medicine and Health Sciences, ; Grand Forks, ND USA

Article

Publisher ID: 279

DOI: 10.1186/s13326-022-00279-z

PMC ID: 9585694

PubMed ID: 36271389

SO-VID: 33c8beb5-e407-47dd-ace8-f366e6d0cfb4

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 17 May 2022

Date accepted : 13 September 2022

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;

Award ID: 1UH2AI132931

Funded by: University of Michigan Medical School Global Reach award

Funded by: Undergraduate Research Opportunity Program of the University of Michigan

Funded by: FundRef http://dx.doi.org/10.13039/501100005150, Chinese Academy of Medical Sciences;

Award ID: 2019PT320003

Funded by: Open Targets

Award ID: OTAR005

Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;

Award ID: 61801067

Funded by: FundRef http://dx.doi.org/10.13039/100000054, National Cancer Institute;

Award ID: U24CA210967

Award ID: 1U24CA199374

Funded by: FundRef http://dx.doi.org/10.13039/100000066, National Institute of Environmental Health Sciences;

Award ID: P30ES017885

Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;

Award ID: R01GM080646

Funded by: FundRef http://dx.doi.org/10.13039/100006108, National Center for Advancing Translational Sciences;

Award ID: 1UL1TR001412

Funded by: FundRef http://dx.doi.org/10.13039/100000092, U.S. National Library of Medicine;

Award ID: 1T15LM012495

Custom metadata

ScienceOpen disciplines: Bioinformatics & Computational biology

Keywords: coronavirus,covid-19,sars-cov-2,ontology,phenotype,diagnosis,vaccine,drug repurposing

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ScienceOpen disciplines: Bioinformatics & Computational biology

Keywords: coronavirus, covid-19, sars-cov-2, ontology, phenotype, diagnosis, vaccine, drug repurposing

A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology

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Abstract

Background

Results

Conclusion

Supplementary Information

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Drug Repurposing Research Collection

Most cited references 91

The FAIR Guiding Principles for scientific data management and stewardship

UniProt: the universal protein knowledgebase in 2021

DrugBank 5.0: a major update to the DrugBank database for 2018

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