TY - JOUR
T1 - A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology.
AU - He, Yongqun
AU - Yu, Hong
AU - Huffman, Anthony
AU - Lin, Asiyah Yu
AU - Natale, Darren A
AU - Beverley, John
AU - Zheng, Ling
AU - Perl, Yehoshua
AU - Wang, Zhigang
AU - Liu, Yingtong
AU - Ong, Edison
AU - Wang, Yang
AU - Huang, Philip
AU - Tran, Long
AU - Du, Jinyang
AU - Shah, Zalan
AU - Shah, Easheta
AU - Desai, Roshan
AU - Huang, Hsin-Hui
AU - Tian, Yujia
AU - Merrell, Eric
AU - Duncan, William D
AU - Arabandi, Sivaram
AU - Schriml, Lynn M
AU - Zheng, Jie
AU - Masci, Anna Maria
AU - Wang, Liwei
AU - Liu, Hongfang
AU - Smaili, Fatima Z.
AU - Hoehndorf, Robert
AU - Pendlington, Zoë May
AU - Roncaglia, Paola
AU - Ye, Xianwei
AU - Xie, Jiangan
AU - Tang, Yi-Wei
AU - Yang, Xiaolin
AU - Peng, Suyuan
AU - Zhang, Luxia
AU - Chen, Luonan
AU - Hur, Junguk
AU - Omenn, Gilbert S
AU - Athey, Brian
AU - Smith, Barry
N1 - KAUST Repository Item: Exported on 2022-10-24
Acknowledgements: We acknowledge Dr. Melissa A Haendel’s contribution as a source of ontological content and the N3C use case. This project is supported by NIH grants 1UH2AI132931 (to YH) and 1U24AI171008 (to YH and JH); U24CA210967 and P30ES017885 (to GSO); R01GM080646, 1UL1TR001412, 1U24CA199374, and 1T15LM012495 (to BS); the National Natural Science Foundation of China 61801067 (to JX); the Natural Science Foundation of Chongqing CSTC2018JCYJAX0243 (to JX); the non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences 2019PT320003 (to HY); and Undergraduate Research Opportunity Program (UROP) and University of Michigan Medical School Global Reach award (to YH). The work of ZMP and PR was supported by Open Targets (OTAR005). Publication costs are paid by a discretionary fund from Dr. William King, the director of the Unit for Laboratory Animal Medicine (ULAM) in the University of Michigan, Ann Arbor, MI, USA.
PY - 2022/10/21
Y1 - 2022/10/21
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/685064
UR - https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-022-00279-z
U2 - 10.1186/s13326-022-00279-z
DO - 10.1186/s13326-022-00279-z
M3 - Article
C2 - 36271389
SN - 2041-1480
VL - 13
JO - Journal of biomedical semantics
JF - Journal of biomedical semantics
IS - 1
ER -
