Author:
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
Abstract
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.
Funder
National Institute of Allergy and Infectious Diseases
University of Michigan Medical School Global Reach award
Undergraduate Research Opportunity Program of the University of Michigan
Chinese Academy of Medical Sciences
Open Targets
National Natural Science Foundation of China
National Cancer Institute
National Institute of Environmental Health Sciences
National Institute of General Medical Sciences
National Center for Advancing Translational Sciences
U.S. National Library of Medicine
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Health Informatics,Computer Science Applications,Information Systems
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