The Infectious Disease Ontology in the age of COVID-19

Background Effective response to public health emergencies, such as we are now experiencing with COVID-19, requires data sharing across multiple disciplines and data systems. Ontologies offer a powerful data sharing tool, and this holds especially for those ontologies built on the design principles of the Open Biomedical Ontologies Foundry. These principles are exemplified by the Infectious Disease Ontology (IDO), a suite of interoperable ontology modules aiming to provide coverage of all aspects of the infectious disease domain. At its center is IDO Core, a disease- and pathogen-neutral ontology covering just those types of entities and relations that are relevant to infectious diseases generally. IDO Core is extended by disease and pathogen-specific ontology modules. Results To assist the integration and analysis of COVID-19 data, and viral infectious disease data more generally, we have recently developed three new IDO extensions: IDO Virus (VIDO); the Coronavirus Infectious Disease Ontology (CIDO); and an extension of CIDO focusing on COVID-19 (IDO-COVID-19). Reflecting the fact that viruses lack cellular parts, we have introduced into IDO Core the term acellular structure to cover viruses and other acellular entities studied by virologists. We now distinguish between infectious agents – organisms with an infectious disposition – and infectious structures – acellular structures with an infectious disposition. This in turn has led to various updates and refinements of IDO Core’s content. We believe that our work on VIDO, CIDO, and IDO-COVID-19 can serve as a model for yielding greater conformance with ontology building best practices. Conclusions IDO provides a simple recipe for building new pathogen-specific ontologies in a way that allows data about novel diseases to be easily compared, along multiple dimensions, with data represented by existing disease ontologies. The IDO strategy, moreover, supports ontology coordination, providing a powerful method of data integration and sharing that allows physicians, researchers, and public health organizations to respond rapidly and efficiently to current and future public health crises.

[1]  D. Baltimore Expression of animal virus genomes. , 1971, Bacteriological reviews.

[2]  D. Baltimore Expression of animal virus genomes , 1971 .

[3]  T. Hodge,et al.  Host genes and HIV: the role of the chemokine receptor gene CCR5 and its allele. , 1997, Emerging infectious diseases.

[4]  T. Hodge,et al.  Host genes and HIV: the role of the chemokine receptor gene CCR5 and its allele. , 1997, Emerging infectious diseases.

[5]  T. Clemmer,et al.  A computer-assisted management program for antibiotics and other antiinfective agents. , 1998, The New England journal of medicine.

[6]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[7]  Werner Ceusters,et al.  Ontology-Based Error Detection in SNOMED-CT® , 2004, MedInfo.

[8]  Werner Ceusters,et al.  Mistakes in medical ontologies: where do they come from and how can they be detected? , 2004, Studies in health technology and informatics.

[9]  A. Rector,et al.  Relations in biomedical ontologies , 2005, Genome Biology.

[10]  R. Durbin,et al.  The Sequence Ontology: a tool for the unification of genome annotations , 2005, Genome Biology.

[11]  H. Becher,et al.  Malaria in a holoendemic area of Burkina Faso: a cross-sectional study , 2006, Parasitology Research.

[12]  David L Paterson,et al.  The role of antimicrobial management programs in optimizing antibiotic prescribing within hospitals. , 2006, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[13]  Yongqun He,et al.  BBP: Brucella genome annotation with literature mining and curation , 2006, BMC Bioinformatics.

[14]  Karin A. Thursky,et al.  User-centered design techniques for a computerised antibiotic decision support system in an intensive care unit , 2007, Int. J. Medical Informatics.

[15]  M. Ashburner,et al.  The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration , 2007, Nature Biotechnology.

[16]  Olivier Bodenreider,et al.  Investigating subsumption in SNOMED CT: An exploration into large description logic-based biomedical terminologies , 2007, Artif. Intell. Medicine.

[17]  Chris F. Taylor,et al.  Survey-based naming conventions for use in OBO Foundry ontology development , 2009, BMC Bioinformatics.

[18]  Michael Darsow,et al.  ChEBI: a database and ontology for chemical entities of biological interest , 2007, Nucleic Acids Res..

[19]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[20]  Yu Qian,et al.  Ontology-based knowledge representation of experiment metadata in biological data mining , 2009 .

[21]  Gregory R. Madey,et al.  VectorBase: a data resource for invertebrate vector genomics , 2008, Nucleic Acids Res..

[22]  Werner Ceusters,et al.  Toward an Ontological Treatment of Disease and Diagnosis , 2009, Summit on translational bioinformatics.

[23]  Bjoern Peters,et al.  VO: Vaccine Ontology , 2009 .

[24]  Mark H. Wilcox,et al.  Clostridium difficile infection: new developments in epidemiology and pathogenesis , 2009, Nature Reviews Microbiology.

[25]  Pantelis Topalis,et al.  MIRO and IRbase: IT Tools for the Epidemiological Monitoring of Insecticide Resistance in Mosquito Disease Vectors , 2009, PLoS neglected tropical diseases.

[26]  Werner Ceusters,et al.  Foundations for a realist ontology of mental disease , 2010, J. Biomed. Semant..

[27]  Ryan R Brinkman,et al.  OntoFox: web-based support for ontology reuse , 2010, BMC Research Notes.

[28]  Barry Smith,et al.  Dispositions and the Infectious Disease Ontology , 2010, FOIS.

[29]  Barry Smith,et al.  Infectious Disease Ontology , 2010 .

[30]  Vitali Sintchenko,et al.  Infectious disease informatics , 2010 .

[31]  I. Siden-Kiamos,et al.  IDOMAL: an ontology for malaria , 2010, Malaria Journal.

[32]  Lars Eisen,et al.  Emerging Information Technologies to Provide Improved Decision Support for Surveillance, Prevention, and Control of Vector-Borne Diseases , 2011 .

[33]  Barry Smith,et al.  Vital Sign Ontology , 2011 .

[34]  Alberto Maria Segre,et al.  The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic , 2011, PloS one.

[35]  Yu Lin,et al.  Brucellosis Ontology (IDOBRU) as an extension of the Infectious Disease Ontology , 2011, J. Biomed. Semant..

[36]  Barry Smith,et al.  Towards an ontological representation of resistance: The case of MRSA , 2009, J. Biomed. Informatics.

[37]  Lars Eisen,et al.  Multi-Disease Data Management System Platform for Vector-Borne Diseases , 2011, PLoS neglected tropical diseases.

[38]  Jay Vyas,et al.  HIVToolbox, an Integrated Web Application for Investigating HIV , 2011, PloS one.

[39]  Scott Federhen,et al.  The NCBI Taxonomy database , 2011, Nucleic Acids Res..

[40]  Son Doan,et al.  Enhancing Twitter Data Analysis with Simple Semantic Filtering: Example in Tracking Influenza-Like Illnesses , 2012, 2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology.

[41]  Werner Ceusters,et al.  Representing Mental Functioning: Ontologies for Mental Health and Disease , 2012 .

[42]  Gang Feng,et al.  Disease Ontology: a backbone for disease semantic integration , 2011, Nucleic Acids Res..

[43]  Richard H Scheuermann,et al.  Influenza Research Database: an integrated bioinformatics resource for influenza research and surveillance , 2012, Influenza and other respiratory viruses.

[44]  Barry Smith,et al.  Constructing a Lattice of Infectious Disease Ontologies from a Staphylococcus aureus Isolate Repository , 2012, ICBO.

[45]  Barry Smith,et al.  A plant disease extension of the Infectious Disease Ontology , 2012, ICBO.

[46]  Mathias Brochhausen,et al.  Towards a Consistent and Scientifically Accurate Drug Ontology , 2013, ICBO.

[47]  Christian J. Stoeckert,et al.  Development of an Application Ontology for Beta Cell Genomics Based On the Ontology for Biomedical Investigations , 2013, ICBO.

[48]  Alexander D. Diehl,et al.  The Ocular Disease Ontology , 2013, ICBO.

[49]  L. Cowell,et al.  Ontology for Vector Surveillance and Management , 2013, Journal of medical entomology.

[50]  Sudipto Ghosh,et al.  Cell Phone-Based System (Chaak) for Surveillance of Immatures of Dengue Virus Mosquito Vectors , 2013, Journal of medical entomology.

[51]  Chunhua Weng,et al.  Design and Evaluation of a Bacterial Clinical Infectious Diseases Ontology , 2013, AMIA.

[52]  Mathias Brochhausen,et al.  Developing a semantically rich ontology for the biobank-administration domain , 2013, Journal of Biomedical Semantics.

[53]  William D. Duncan,et al.  The neurological disease ontology , 2013, J. Biomed. Semant..

[54]  Christos Louis,et al.  IDOMAL: the malaria ontology revisited , 2013, J. Biomed. Semant..

[55]  Alina Deshpande,et al.  Global Disease Monitoring and Forecasting with Wikipedia , 2014, PLoS Comput. Biol..

[56]  Moussa Lo,et al.  Building a Schistosomiasis Process Ontology for an Epidemiological Monitoring System , 2014, Innovations in Intelligent Machines.

[57]  Moussa Lo,et al.  IDOSCHISTO : une extension de l'ontologie noyau des maladies infectieuses (IDO-Core) pour la schistosomiase , 2014, IC.

[58]  Cui Tao,et al.  OAE: The Ontology of Adverse Events , 2014, J. Biomed. Semant..

[59]  Anita Burgun-Parenthoine,et al.  The Cardiovascular Disease Ontology , 2014, FOIS.

[60]  Christy L. Strong,et al.  The HIVToolbox 2 Web System Integrates Sequence, Structure, Function and Mutation Analysis , 2014, PloS one.

[61]  Mário J. Silva,et al.  The epidemiology ontology: an ontology for the semantic annotation of epidemiological resources , 2014, Journal of Biomedical Semantics.

[62]  Judith A. Blake,et al.  Unification of multi-species vertebrate anatomy ontologies for comparative biology in Uberon , 2014, Journal of Biomedical Semantics.

[63]  Yu Lin,et al.  Ontology-based representation and analysis of host-Brucella interactions , 2015, Journal of Biomedical Semantics.

[64]  Prudence Mutowo-Meullenet,et al.  The GOA database: Gene Ontology annotation updates for 2015 , 2014, Nucleic Acids Res..

[65]  Christos Louis,et al.  Describing the Breakbone Fever: IDODEN, an Ontology for Dengue Fever , 2015, PLoS neglected tropical diseases.

[66]  Werner Ceusters,et al.  Aboutness: towards foundations for the Information Artifact Ontology , 2015, ICBO.

[67]  Kensaku Kawamoto,et al.  Design, Development, and Initial Evaluation of a Terminology for Clinical Decision Support and Electronic Clinical Quality Measurement , 2015, AMIA.

[68]  Gang Fu,et al.  Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data , 2014, Nucleic Acids Res..

[69]  J. Bruchfeld,et al.  Tuberculosis and HIV Coinfection. , 2015, Cold Spring Harbor perspectives in medicine.

[70]  Robert Arp,et al.  Building Ontologies with Basic Formal Ontology , 2015 .

[71]  Mathias Brochhausen,et al.  The Apollo Structured Vocabulary: an OWL2 ontology of phenomena in infectious disease epidemiology and population biology for use in epidemic simulation , 2016, J. Biomed. Semant..

[72]  Mathias Brochhausen,et al.  OBIB-a novel ontology for biobanking , 2016, Journal of Biomedical Semantics.

[73]  Jessica A. Turner,et al.  The Ontology for Biomedical Investigations , 2016, PloS one.

[74]  Francesco Amenta,et al.  An ontology-based consultation system to support medical care on board seagoing vessels. , 2016, International maritime health.

[75]  Amanda Hicks,et al.  The ontology of medically related social entities: recent developments , 2016, Journal of Biomedical Semantics.

[76]  P. Elkin,et al.  Ontoneo: the obstetric and neonatal ontology , 2016 .

[77]  Steven Sullivan,et al.  Malaria Study Data Integration and Information Retrieval Based on OBO Foundry Ontologies , 2016, ICBO/BioCreative.

[78]  Fang Li,et al.  Structure, Function, and Evolution of Coronavirus Spike Proteins. , 2016, Annual review of virology.

[79]  Emma Griffiths,et al.  Context Is Everything: Harmonization of Critical Food Microbiology Descriptors and Metadata for Improved Food Safety and Surveillance , 2017, Front. Microbiol..

[80]  J Charlet,et al.  Towards ontology-based decision support systems for complex ultrasound diagnosis in obstetrics and gynecology. , 2017, Journal of gynecology obstetrics and human reproduction.

[81]  Samina Abidi,et al.  A Knowledge-Modeling Approach to Integrate Multiple Clinical Practice Guidelines to Provide Evidence-Based Clinical Decision Support for Managing Comorbid Conditions , 2017, Journal of Medical Systems.

[82]  Tudor Groza,et al.  The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species , 2016, bioRxiv.

[83]  Jing Zheng,et al.  An ontology-based approach to patient follow-up assessment for continuous and personalized chronic disease management , 2017, J. Biomed. Informatics.

[84]  Jian Zhang,et al.  Protein Ontology (PRO): enhancing and scaling up the representation of protein entities , 2016, Nucleic Acids Res..

[85]  Xiaomei Li,et al.  Influenza Research Database: An integrated bioinformatics resource for influenza virus research , 2016, Nucleic Acids Res..

[86]  Adrien Barton,et al.  An ontological analysis of drug prescriptions , 2018, Appl. Ontology.

[87]  Yaliang Li,et al.  An ontology-driven clinical decision support system (IDDAP) for infectious disease diagnosis and antibiotic prescription , 2018, Artif. Intell. Medicine.

[88]  Barry Smith,et al.  ImmPort, toward repurposing of open access immunological assay data for translational and clinical research , 2018, Scientific Data.

[89]  Eugene Zhang,et al.  The Planteome database: an integrated resource for reference ontologies, plant genomics and phenomics , 2017, Nucleic Acids Res..

[90]  Ernest Mwebaze,et al.  Ontology boosted deep learning for disease name extraction from Twitter messages , 2018, Journal of Big Data.

[91]  B. Fielding,et al.  Coronavirus envelope protein: current knowledge , 2019, Virology Journal.

[92]  The Gene Ontology Consortium,et al.  The Gene Ontology Resource: 20 years and still GOing strong , 2018, Nucleic Acids Res..

[93]  Xing-Ming Zhao,et al.  Victors: a web-based knowledge base of virulence factors in human and animal pathogens , 2018, Nucleic Acids Res..

[94]  Moussa Lo,et al.  IDOMEN: An Extension of Infectious Disease Ontology for MENingitis , 2019, MedInfo.

[95]  James C. Hu,et al.  The Gene Ontology Resource: 20 years and still GOing strong , 2019 .

[96]  F. Cheng,et al.  Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2 , 2020, Cell Discovery.

[97]  J. Kuhn Virus Taxonomy , 2020, Reference Module in Life Sciences.

[98]  Qianyun Liu,et al.  Emerging coronaviruses: Genome structure, replication, and pathogenesis , 2020, Journal of medical virology.

[99]  Mei U Wong,et al.  COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning , 2020, bioRxiv.

[100]  Yang Wang,et al.  CIDO, a community-based ontology for coronavirus disease knowledge and data integration, sharing, and analysis , 2020, Scientific Data.

[101]  Xin Gao,et al.  Machine learning with biomedical ontologies , 2020, bioRxiv.

[102]  L. Cowell,et al.  Coordinating Coronavirus Research: The COVID-19 Infectious Disease Ontology , 2020 .

[103]  J. Luban SARS-CoV-2 , 2020 .

[104]  J. Tegnér,et al.  DeepViral: infectious disease phenotypes improve prediction of novel virus–host interactions , 2020 .

[105]  Andrea Marzi,et al.  Functional assessment of cell entry and receptor usage for SARS-CoV-2 and other lineage B betacoronaviruses , 2020, Nature Microbiology.

[106]  Florian Thiery Linked COVID-19 Data: Ontology , 2020 .

[107]  Robert D. Finn,et al.  COVID-19 pandemic reveals the peril of ignoring metadata standards , 2020, Scientific Data.

[108]  Marcia Lei Zeng,et al.  Implications of Knowledge Organization Systems for Health Information Exchange and Communication during the COVID-19 Pandemic , 2020, Data and Information Management.

[109]  G. Herrler,et al.  SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor , 2020, Cell.

[110]  J. Tegnér,et al.  Prediction of novel virus-host interactions by integrating clinical symptoms and protein sequences , 2020 .

[111]  Hong Yu,et al.  Ontological and Bioinformatic Analysis of Anti-Coronavirus Drugs and Their Implication for Drug Repurposing against COVID-19 , 2020 .