Ontologies have emerged to become critical to support data and knowledge representation, standardization, integration, and analysis. The SARS-CoV-2 pandemic led to the rapid proliferation of COVID-19 data, as well as the development of many COVID-19 ontologies. In the interest of supporting data interoperability, we initiated a community-based effort to harmonize COVID-19 ontologies. Our effort involves the collaborative discussion among developers of seven COVID-19 related ontologies, and the merging of four ontologies. This effort demonstrates the feasibility of harmonizing these ontologies in an interoperable framework to support integrative representation and analysis of COVID-19 related data and knowledge.
Despite the development and distribution of effective COVID-19 vaccines, COVID-19 pandemic
remains a challenge to overcome. The sheer volume of data collected by researchers, the speed at which
it is generated, range of its sources, quality, accuracy, and need for assessment of usefulness, results in
complex, multidimensional datasets [
Ontologies - interoperable, logically well-defined, controlled vocabularies representing common entities and relations across disciplines - is a well-known solution to data silo problems. Ontologies are widely used in bioinformatics and biomedical data standardization, supporting data integration, sharing, reproducibility, and automated reasoning. To meet different needs for COVID-19 studies, different groups of ontology developers have worked separately since the start of the pandemic, resulting in the development of several COVID-19 ontologies. A lack of coordination among these groups would risk the proliferation of COVID-19 ontologies using distinct, potentially non-interoperable, vocabularies.
The Workshop on COVID-19 Ontologies (WCO-2020) held on Oct. 23 and Oct. 30, 2020 brought the developers from international groups to report their efforts on building COVID-19 related ontologies. To harmonize heterogeneous knowledge and data for better COVID-19 study, the workshop attendees formed a COVID-19 Ontology Harmonization Working Group (WG) and discussed the ways to harmonize these related ontologies. This paper reports the current results of our harmonization effort.
In this study, the following seven COVID-19 related ontologies were covered in the ontology
harmonization process by the COVID-19 Ontology Harmonization Working Group:
1. Virus Infectious Disease Ontology (VIDO) [
Each of the above ontologies has their own scope and purpose. Three ontologies: Virus Infectious
Disease Ontology (VIDO), Coronavirus Infectious Disease Ontology (CIDO), and COVID-19
Infectious Disease Ontology (COVID-19-IDO) all extend the Infectious Disease Ontology (IDO) [
The mission statement of the COVID-19 Ontology Harmonization WG is to harmonize different COVID-19 related ontologies to support COVID-19 related data and knowledge interoperability. To achieve the mission, WG members held regular virtual Zoom meetings and communicated through emails. We identified overlapping domains or subdomains from different ontology groups and built consensus on ontology terms needed to characterize specific COVID-19 related entities. 2.1 VIDO 2.2 CIDO
VIDO (https://bioportal.bioontology.org/ontologies/VIDO) is an extension of the IDO designed to bridge IDO - which is composed of terms common to any scientific investigation of infectious disease - to virus-specific ontologies. As such, VIDO follows OBO Foundry guidelines closely. VIDO is composed of terms common to any investigation of viral infectious diseases, including virus classification, virus infection epidemiology, pathogenesis, and treatment. For example, VIDO defines terms such as virus, prion, viricide, virus infection incidence, and so on.
By extending IDO and other OBO ontologies including the Ontology for Biomedical Investigations (OBI), CIDO (https://github.com/cido-ontology/cido) is developed to cover coronavirus infectious diseases including their etiology, transmission, epidemiology, host-coronavirus interaction, pathogenesis, diagnosis, prevention, and treatment. CIDO covers SARS-CoV, SARS-CoV-2, and MERS-CoV, and other coronavirus strains that cause common human cold.
COVID-19-IDO (https://bioportal.bioontology.org/ontologies/IDO-COVID-19), which was created by the developers of VIDO, is a direct extension of VIDO. As such, IDO-COVID-19 covers the epidemiology, classification, pathogenesis, and treatment of terms used to represent infection by the SARS-CoV-2 virus strain and the associated COVID-19 disease.
Controlled Vocabulary for COVID-19 (COVoc) (https://github.com/EBISPOT/covoc) is an application ontology created in collaboration between the European Bioinformatics Institute (EMBLEBI) and the Swiss Institute of Bioinformatics (SIB) in March 2020. Its primary use case is to enable seamless annotation of biomedical literature to core databases and ELIXIR tools (ELIXIR is a European-wide intergovernmental organization for life sciences). The ontology covers 9 axes related to the COVID-19 pandemic (biomedical vocabulary, cell lines, chemical entities, clinical trials, conceptual entities, diseases and syndromes, geographic locations, organisms, and proteins and genomes). COVoc utilizes existing OBO ontologies where possible to augment connections to other useful resources such as the COVID-19 Data Portal (https://www.covid19dataportal.org/). 2.5 CODO
Ontology for Collection and Analysis of COviD-19 Data (CODO) (https://w3id.org/codo,
https://github.com/biswanathdutta/CODO) is a formal Ontology for collection and analysis of
COVID19 data [
Homeostasis imbalance process ontology (HOIP) (https://bioportal.bioontology.org/ontologies/HOIP) focuses on homeostatic imbalances between virus action and innate defense processes and covers the causal relationship of organelle/cellular/organ processes from early stage to clinical manifestation in COVID-19. The design patterns between CIDO and HOIP have now been aligned after shared discussion and communication.
Medical Action Ontology (MAxO), launched in the spring of 2020, is a broad ontology that provides a structured vocabulary to medical procedures, interventions, therapies, treatments, or clinical recommendations. MAxO was designed to provide a thorough resource for annotating medical actions to diseases, particularly rare diseases. Given the broad nature of MAxO and the timing of the ontology development, much of the hierarchy was added with a keen awareness of the diagnostics and treatment of SARS-CoV-2. While there are no COVID-19-specific terms, terms like ‘ventilation with proning’ (MAXO:0000619) and ‘clinical RNA detection testing’ (MAXO:0000592) were added to annotate COVID-19 clinical data sets. To capture the relationship between treatments and diseases, a new tool, Phenotypic Observation Explication Tool (POET), was developed to establish a relationship between MAxO, Human Phenotype Ontology (HPO), and Mondo Disease Ontology (Mondo) terms. This tool will allow researchers to actively participate in annotating COVID-19 data sets or other diseases in their expertise. MAxO annotations and the POET tool will be available on the HPO website (hpo.jax.org) by 2022.
The ontology harmonization is started by identifying the scopes and development methods by
different ontologies covered in this work. We found that instead of reinventing the wheel, each ontology
has imported and reused many terms from other ontologies where possible (Table 1). The top 1 reused
ontologies (reused in six out of the seven ontologies) are: OBI, UBERON, CL, GO Biological
process,ChEBI, PRO, and RO. The top 2 reused ontologies (reused in five out of the seven ontologies)
are BFO, NCBI taxon, symptom ontology and Vaccine Ontology. Many of these reused ontologies are
Open Biomedical and Biological Ontologies (OBO) Foundry [
Ontology term reuse by COVID-19 related ontologies
CIDO
COVID-19
IDO Ontology
Domain
HoIP
CODO
MAxO
COVoc
Given that most of the 7 ontologies follow the OBO Foundry ontology development principles, such as reusing terms defined in OBO foundry ontologies, Our harmonization exercise found that these ontologies can be aligned under the Basic Formal Ontology (BFO) upper level ontology (Figure 1). Figure 1 below shows how VIDO, CIDO, IDO-COVID-19, MAxO and HoIP can fit into BFO’s structure.
The relationship between CIDO and IDO-COVID-19 provides an example of precisely the sort of distinct overlapping ontology development efforts our working group was designed to address. Via this alignment exercise and observing the scope of CIDO appears broad enough to include IDO-COVID19, our working group has decided to incorporate the latter ontology into CIDO. Incorporation of terms from IDO-COVID-19 into CIDO will, moreover, strengthen the logical relationship between CIDO and VIDO, given how closely related VIDO and IDO-COVID-19 are.
The HoIP developers are working on mapping and aligning with all GO process terms. Concerning harmonization, HoIP ontology has started to compare their processual entities to those in CIDO. For example, although the labels of 'SARS-CoV-2 entry to cell' (CIDO:0000088) and 'viral entry into host cell [COVID-19]' (HoIP:0037063) are different, as the HoIP entity is described using object property restriction ('has agent' some SARS-CoV2), it can be mapped to correspondent CIDO term. As an application ontology, the COVoc developers rely on CIDO developers to create new terms, and COVoc imports and reuses CIDO for their application purpose. At the time of writing, CODO developers started to align the current build to BFO as its upper ontology, which increases the future possibilities of better alignment.
While ontology creates a common language and reduces the work of mapping, the emergence of multiple ontologies may form individual silos by themselves. Given the report of many COVID-19 related ontologies, our COVID-19 Ontology Harmonization WG provided a timely effort to collaboratively identify the overlapping between different ontologies and achieve the harmonization of seven ontologies. Currently, seven ontologies have very different perspectives due to their use cases. Entities within these seven ontologies are defined heterogeneously and described in various ways with various granularities. We should align not only the same URIs but also the meaning (semantics) of the entities. Therefore, it is necessary to investigate and compare entities among ontologies carefully, such as definition, superclass, logical restrictions, and related entities. Towards the formal alignment of these ontologies, we plan to clarify and make explicit the relationships such as equivalent class among the ontologies.
Members of the COVID-19 Ontology Harmonization WG made substantial efforts to characterize SARS-CoV-2 and COVID-19 data in a collaborative, computationally tractable, responsible manner. These ontologies are also being used in different use case studies, supporting productive and interoperable COVID-19 research.
Our working group has also recognized many future challenges such as funding, resource and time commitment, and challenging infrastructure development. We are pleased to find that the willingness to join the harmonization work is high, and more interested parties are joining the effort. We aim to continue this collaborative effort to further support our active COVID-19, leading to enhanced public health.
We acknowledge the organizers and attendees of the 2020 Workshop on COVID-19 Ontologies (WCO-2020), which initiated our get-together and collaboration on the ontology harmonization effort.
The Office of Data Science Strategy, NIH, provided funding for AYL as a Data and Technology Advancement (DATA) National Service Scholar. JB was supported by NIH / NLM T5 Biomedical Informatics and Data Science Research Training Programs (5T15LM012495–03) during development of VIDO and IDO-COVID-19. CODO work has been supported by Indian Statistical Institute through internal project grant. PR’s work of COVoc has been made possible in part by a grant from Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation. YY was supported by the RIKEN Open Life Science Platform Project during knowledge systematization of COVID-19 infectious processes and development of HoIP ontology.