A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology
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 disea...
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Published in | Journal of biomedical semantics Vol. 13; no. 1; pp. 25 - 21 |
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Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
London
BioMed Central
21.10.2022
BMC |
Subjects | |
Online Access | Get full text |
ISSN | 2041-1480 2041-1480 |
DOI | 10.1186/s13326-022-00279-z |
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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. |
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AbstractList | 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. 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. 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. 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. 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. 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. 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.BACKGROUNDThe 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.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.RESULTSAs 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.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.CONCLUSIONCIDO 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. |
ArticleNumber | 25 |
Author | Zheng, Jie Zhang, Luxia Zheng, Ling Wang, Zhigang Liu, Yingtong Smaili, Fatima Zohra Yang, Xiaolin Hoehndorf, Robert Chen, Luonan Lin, Asiyah Yu Huang, Philip Shah, Zalan Huang, Hsin-hui Peng, Suyuan Hur, Junguk Omenn, Gilbert S. Ong, Edison Du, Jinyang Smith, Barry Tran, Long Huffman, Anthony Tang, Yi-Wei Schriml, Lynn M. Xie, Jiangan Yu, Hong Beverley, John Merrell, Eric He, Yongqun Desai, Roshan Athey, Brian Liu, Hongfang Pendlington, Zoë May Masci, Anna Maria Wang, Yang Arabandi, Sivaram Tian, Yujia Duncan, William D. Shah, Easheta Roncaglia, Paola Perl, Yehoshua Natale, Darren A. Wang, Liwei Ye, Xianwei |
Author_xml | – sequence: 1 givenname: Yongqun surname: He fullname: He, Yongqun email: yongqunh@med.umich.edu organization: University of Michigan Medical School – sequence: 2 givenname: Hong surname: Yu fullname: Yu, Hong email: yuhong20040416@sina.com organization: People’s Hospital of Guizhou Province – sequence: 3 givenname: Anthony surname: Huffman fullname: Huffman, Anthony organization: University of Michigan Medical School – sequence: 4 givenname: Asiyah Yu surname: Lin fullname: Lin, Asiyah Yu organization: National Human Genome Research Institute, National Institutes of Health, National Center for Ontological Research – sequence: 5 givenname: Darren A. surname: Natale fullname: Natale, Darren A. organization: Georgetown University Medical Center – sequence: 6 givenname: John surname: Beverley fullname: Beverley, John organization: National Center for Ontological Research, The Johns Hopkins University Applied Physics Laboratory – sequence: 7 givenname: Ling surname: Zheng fullname: Zheng, Ling organization: Computer Science and Software Engineering Department, Monmouth University – sequence: 8 givenname: Yehoshua surname: Perl fullname: Perl, Yehoshua organization: Department of Computer Science, New Jersey Institute of Technology – sequence: 9 givenname: Zhigang surname: Wang fullname: Wang, Zhigang organization: Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College – sequence: 10 givenname: Yingtong surname: Liu fullname: Liu, Yingtong organization: University of Michigan Medical School – sequence: 11 givenname: Edison surname: Ong fullname: Ong, Edison organization: University of Michigan Medical School – sequence: 12 givenname: Yang surname: Wang fullname: Wang, Yang organization: University of Michigan Medical School, People’s Hospital of Guizhou Province – sequence: 13 givenname: Philip surname: Huang fullname: Huang, Philip organization: University of Michigan Medical School – sequence: 14 givenname: Long surname: Tran fullname: Tran, Long organization: University of Michigan Medical School – sequence: 15 givenname: Jinyang surname: Du fullname: Du, Jinyang organization: University of Michigan Medical School – sequence: 16 givenname: Zalan surname: Shah fullname: Shah, Zalan organization: University of Michigan Medical School – sequence: 17 givenname: Easheta surname: Shah fullname: Shah, Easheta organization: University of Michigan Medical School – sequence: 18 givenname: Roshan surname: Desai fullname: Desai, Roshan organization: University of Michigan Medical School – sequence: 19 givenname: Hsin-hui surname: Huang fullname: Huang, Hsin-hui organization: University of Michigan Medical School, National Yang-Ming University – sequence: 20 givenname: Yujia surname: Tian fullname: Tian, Yujia organization: Rutgers University – sequence: 21 givenname: Eric surname: Merrell fullname: Merrell, Eric organization: University at Buffalo – sequence: 22 givenname: William D. surname: Duncan fullname: Duncan, William D. organization: University of Florida – sequence: 23 givenname: Sivaram surname: Arabandi fullname: Arabandi, Sivaram organization: OntoPro LLC – sequence: 24 givenname: Lynn M. surname: Schriml fullname: Schriml, Lynn M. organization: University of Maryland School of Medicine – sequence: 25 givenname: Jie surname: Zheng fullname: Zheng, Jie organization: Department of Biology, University of Pennsylvania Perelman School of Medicine – sequence: 26 givenname: Anna Maria surname: Masci fullname: Masci, Anna Maria organization: Office of Data Science, National Institute of Environmental Health Sciences – sequence: 27 givenname: Liwei surname: Wang fullname: Wang, Liwei organization: Mayo Clinic – sequence: 28 givenname: Hongfang surname: Liu fullname: Liu, Hongfang organization: Mayo Clinic – sequence: 29 givenname: Fatima Zohra surname: Smaili fullname: Smaili, Fatima Zohra organization: King Abdullah University of Science and Technology – sequence: 30 givenname: Robert surname: Hoehndorf fullname: Hoehndorf, Robert organization: King Abdullah University of Science and Technology – sequence: 31 givenname: Zoë May surname: Pendlington fullname: Pendlington, Zoë May organization: European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus – sequence: 32 givenname: Paola surname: Roncaglia fullname: Roncaglia, Paola organization: European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus – sequence: 33 givenname: Xianwei surname: Ye fullname: Ye, Xianwei organization: People’s Hospital of Guizhou Province – sequence: 34 givenname: Jiangan surname: Xie fullname: Xie, Jiangan organization: School of Bioinformatics, Chongqing University of Posts and Telecommunications – sequence: 35 givenname: Yi-Wei surname: Tang fullname: Tang, Yi-Wei organization: Cepheid, Danaher Diagnostic Platform – sequence: 36 givenname: Xiaolin surname: Yang fullname: Yang, Xiaolin organization: Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College – sequence: 37 givenname: Suyuan surname: Peng fullname: Peng, Suyuan organization: National Institute of Health Data Science, Peking University – sequence: 38 givenname: Luxia surname: Zhang fullname: Zhang, Luxia organization: National Institute of Health Data Science, Peking University – sequence: 39 givenname: Luonan surname: Chen fullname: Chen, Luonan organization: Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences – sequence: 40 givenname: Junguk surname: Hur fullname: Hur, Junguk organization: University of North Dakota School of Medicine and Health Sciences – sequence: 41 givenname: Gilbert S. surname: Omenn fullname: Omenn, Gilbert S. organization: University of Michigan Medical School – sequence: 42 givenname: Brian surname: Athey fullname: Athey, Brian organization: University of Michigan Medical School – sequence: 43 givenname: Barry surname: Smith fullname: Smith, Barry organization: National Center for Ontological Research, University at Buffalo |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36271389$$D View this record in MEDLINE/PubMed |
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CitedBy_id | crossref_primary_10_1093_bioinformatics_btac800 crossref_primary_10_1371_journal_pone_0285093 crossref_primary_10_17821_srels_2024_v61i5_171582 crossref_primary_10_3390_v15020505 crossref_primary_10_3389_fimmu_2025_1502484 crossref_primary_10_3390_info15110669 crossref_primary_10_1371_journal_pone_0295541 crossref_primary_10_1186_s12911_023_02184_6 crossref_primary_10_1186_s13326_024_00307_0 crossref_primary_10_3389_fimmu_2022_1066733 |
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Keywords | COVID-19 SARS-CoV-2 Phenotype Coronavirus Ontology Drug repurposing Vaccine Diagnosis |
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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.... 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... 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.... 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... |
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SubjectTerms | Algorithms Amino Acids Bioinformatics Collaboration Combinatorial Libraries Communicable Diseases Computational Biology/Bioinformatics Computer Appl. in Life Sciences Coronavirus Coronaviruses COVID-19 COVID-19 Drug Treatment Data integration Data Mining and Knowledge Discovery Diagnosis Drugs Epidemiology Humans Infectious diseases Integration International Conference on Biomedical Ontologies Direct to Journal Track Knowledge representation Mathematics Mathematics and Statistics Medical research Natural language processing Ontology Pandemics Phenotype Phenotypes Protein interaction Proteins Public health SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 Standardization Vaccines Viral diseases |
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Title | A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology |
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