10.6084/M9.FIGSHARE.C.6261481.V1
Yongqun He
Yongqun
He
University of Michigan–Ann Arbor
Hong Yu
Hong
Yu
Anthony Huffman
Anthony
Huffman
University of Michigan–Ann Arbor
Asiyah Yu Lin
Asiyah Yu
Lin
National Human Genome Research Institute
Darren A. Natale
Darren A.
Natale
Georgetown University Medical Center
John Beverley
John
Beverley
Ling Zheng
Ling
Zheng
Monmouth University
Yehoshua Perl
Yehoshua
Perl
New Jersey Institute of Technology
Zhigang Wang
Zhigang
Wang
Chinese Academy of Medical Sciences & Peking Union Medical College
Yingtong Liu
Yingtong
Liu
University of Michigan–Ann Arbor
Edison Ong
Edison
Ong
University of Michigan–Ann Arbor
Yang Wang
Yang
Wang
University of Michigan–Ann Arbor
Philip Huang
Philip
Huang
University of Michigan–Ann Arbor
Long Tran
Long
Tran
University of Michigan–Ann Arbor
Jinyang Du
Jinyang
Du
University of Michigan–Ann Arbor
Zalan Shah
Zalan
Shah
University of Michigan–Ann Arbor
Easheta Shah
Easheta
Shah
University of Michigan–Ann Arbor
Roshan Desai
Roshan
Desai
University of Michigan–Ann Arbor
Hsin-hui Huang
Hsin-hui
Huang
University of Michigan–Ann Arbor
National Yang Ming Chiao Tung University
Yujia Tian
Yujia
Tian
Rutgers, The State University of New Jersey
Eric Merrell
Eric
Merrell
University at Buffalo, State University of New York
William D. Duncan
William D.
Duncan
University of Florida
Sivaram Arabandi
Sivaram
Arabandi
Lynn M. Schriml
Lynn M.
Schriml
University of Maryland, Baltimore
Jie Zheng
Jie
Zheng
University of Pennsylvania
Anna Maria Masci
Anna Maria
Masci
National Institute of Environmental Health Sciences
Liwei Wang
Liwei
Wang
Mayo Clinic
Hongfang Liu
Hongfang
Liu
Mayo Clinic
Fatima Zohra Smaili
Fatima Zohra
Smaili
King Abdullah University of Science and Technology
Robert Hoehndorf
Robert
Hoehndorf
King Abdullah University of Science and Technology
Zoë May Pendlington
Zoë May
Pendlington
Paola Roncaglia
Paola
Roncaglia
Xianwei Ye
Xianwei
Ye
Jiangan Xie
Jiangan
Xie
Chongqing University of Posts and Telecommunications
Yi-Wei Tang
Yi-Wei
Tang
Danaher (China)
Xiaolin Yang
Xiaolin
Yang
Chinese Academy of Medical Sciences & Peking Union Medical College
Suyuan Peng
Suyuan
Peng
Peking University
Luxia Zhang
Luxia
Zhang
Peking University
Luonan Chen
Luonan
Chen
Center for Excellence in Molecular Cell Science
Junguk Hur
Junguk
Hur
University of North Dakota
Gilbert S. Omenn
Gilbert S.
Omenn
University of Michigan–Ann Arbor
Brian Athey
Brian
Athey
University of Michigan–Ann Arbor
Barry Smith
Barry
Smith
A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology
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.
Medicine
Genetics
Biotechnology
Sociology
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
Cancer
Infectious Diseases
figshare
2022
2022-10-22
2022-10-22
Collection
10.6084/m9.figshare.c.6261481
CC BY 4.0