Your browser doesn't support javascript.
A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology.
He, Yongqun; Yu, Hong; Huffman, Anthony; Lin, Asiyah Yu; Natale, Darren A; Beverley, John; Zheng, Ling; Perl, Yehoshua; Wang, Zhigang; Liu, Yingtong; Ong, Edison; Wang, Yang; Huang, Philip; Tran, Long; Du, Jinyang; Shah, Zalan; Shah, Easheta; Desai, Roshan; Huang, Hsin-Hui; Tian, Yujia; Merrell, Eric; Duncan, William D; Arabandi, Sivaram; Schriml, Lynn M; Zheng, Jie; Masci, Anna Maria; Wang, Liwei; Liu, Hongfang; Smaili, Fatima Zohra; Hoehndorf, Robert; Pendlington, Zoë May; Roncaglia, Paola; Ye, Xianwei; Xie, Jiangan; Tang, Yi-Wei; Yang, Xiaolin; Peng, Suyuan; Zhang, Luxia; Chen, Luonan; Hur, Junguk; Omenn, Gilbert S; Athey, Brian; Smith, Barry.
  • He Y; University of Michigan Medical School, Ann Arbor, MI, USA. yongqunh@med.umich.edu.
  • Yu H; People's Hospital of Guizhou Province, Guiyang, Guizhou, China. yuhong20040416@sina.com.
  • Huffman A; University of Michigan Medical School, Ann Arbor, MI, USA.
  • Lin AY; National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
  • Natale DA; National Center for Ontological Research, Buffalo, NY, USA.
  • Beverley J; Georgetown University Medical Center, Washington, DC, USA.
  • Zheng L; National Center for Ontological Research, Buffalo, NY, USA.
  • Perl Y; The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
  • Wang Z; Computer Science and Software Engineering Department, Monmouth University, West Long Branch, NJ, USA.
  • Liu Y; Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA.
  • Ong E; Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, Beijing, China.
  • Wang Y; University of Michigan Medical School, Ann Arbor, MI, USA.
  • Huang P; University of Michigan Medical School, Ann Arbor, MI, USA.
  • Tran L; University of Michigan Medical School, Ann Arbor, MI, USA.
  • Du J; People's Hospital of Guizhou Province, Guiyang, Guizhou, China.
  • Shah Z; University of Michigan Medical School, Ann Arbor, MI, USA.
  • Shah E; University of Michigan Medical School, Ann Arbor, MI, USA.
  • Desai R; University of Michigan Medical School, Ann Arbor, MI, USA.
  • Huang HH; University of Michigan Medical School, Ann Arbor, MI, USA.
  • Tian Y; University of Michigan Medical School, Ann Arbor, MI, USA.
  • Merrell E; University of Michigan Medical School, Ann Arbor, MI, USA.
  • Duncan WD; University of Michigan Medical School, Ann Arbor, MI, USA.
  • Arabandi S; National Yang-Ming University, Taipei, Taiwan.
  • Schriml LM; Rutgers University, New Brunswick, NJ, USA.
  • Zheng J; University at Buffalo, Buffalo, NY, 14260, USA.
  • Masci AM; University of Florida, Gainesville, FL, USA.
  • Wang L; OntoPro LLC, Houston, TX, USA.
  • Liu H; University of Maryland School of Medicine, Baltimore, MD, USA.
  • Smaili FZ; Department of Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
  • Hoehndorf R; Office of Data Science, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA.
  • Pendlington ZM; Mayo Clinic, Rochester, MN, USA.
  • Roncaglia P; Mayo Clinic, Rochester, MN, USA.
  • Ye X; King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
  • Xie J; King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
  • Tang YW; European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK.
  • Yang X; European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK.
  • Peng S; People's Hospital of Guizhou Province, Guiyang, Guizhou, China.
  • Zhang L; School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China.
  • Chen L; Cepheid, Danaher Diagnostic Platform, Shanghai, China.
  • Hur J; Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, Beijing, China.
  • Omenn GS; National Institute of Health Data Science, Peking University, Beijing, China.
  • Athey B; National Institute of Health Data Science, Peking University, Beijing, China.
  • Smith B; Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China.
J Biomed Semantics ; 13(1): 25, 2022 10 21.
Article in English | MEDLINE | ID: covidwho-2089232
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.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: Vaccines / Communicable Diseases / Coronavirus / COVID-19 Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Topics: Vaccines / Variants Limits: Humans Language: English Journal: J Biomed Semantics Year: 2022 Document Type: Article Affiliation country: S13326-022-00279-z

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Vaccines / Communicable Diseases / Coronavirus / COVID-19 Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Topics: Vaccines / Variants Limits: Humans Language: English Journal: J Biomed Semantics Year: 2022 Document Type: Article Affiliation country: S13326-022-00279-z