In the landscape of viral genomic studies and knowledge representation solutions, there exists an emerging need for an ontology designed specifically to represent the genomic sequence data of the COVID-19 pandemic. Existing ontologies often lack the specificity and comprehensiveness required to adequately capture the various facets of COVID-19 genomics. To address this gap, we present the development of the COVID-19 Virus Genomics Ontology (VGO). VGO is designed to facilitate the utilization and publication of COVID-19 genomic sequence data, offering a comprehensive solution for researchers and healthcare professionals. By integrating data from the Global Initiative on Sharing All Influenza Data (GISAID), VGO enables efficient querying and visualization of genomic information, enhancing accessibility and usability. To construct VGO, we employed a combination of two established methodologies, the Yet Another Methodology for Ontology Development (YAMO), and Networked Ontology (NeOn) methodology. Utilizing the Web Ontology Language (OWL), VGO provides a robust representation of COVID-19 genomic sequence data, filling a critical gap in existing ontologies. To ensure reliability and usability, VGO underwent a comprehensive evaluation process, benchmarking it against ten prominent COVID-19-related biomedical ontologies using a multidimensional framework that includes structural, functional, usability, and quantitative dimensions. Structural quality was assessed through an extensive pitfall analysis using the OOPS! framework, identifying critical modeling issues across peer ontologies, with VGO exhibiting no significant pitfalls–indicating high modeling rigor and syntactic correctness. Logical consistency and structural soundness were validated through OntoDebug and the Pellet Reasoner, confirming the absence of inconsistencies and reinforcing internal coherence. Quantitative evaluation using OntoMetrics highlighted VGO’s balanced design, demonstrating moderate attribute and relation richness, with notable instance-level coverage–a feature absent in most comparator ontologies. Despite a smaller axiom base, VGO maintains robust schema expressivity and a clear TBox-ABox integration, enabling both knowledge representation and data-level reasoning. Finally, a SPARQL-based competency question assessment confirmed the ontology’s functional completeness and real-world applicability, mapping successfully to its structure.

VGO: COVID-19 Virus Genomics Ontology for Semantic Annotation and Querying of Genome Sequence Data

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发表日期：

2026年1月12日

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Debanjali Bain,
Biswanath Dutta

摘要

In the landscape of viral genomic studies and knowledge representation solutions, there exists an emerging need for an ontology designed specifically to represent the genomic sequence data of the COVID-19 pandemic. Existing ontologies often lack the specificity and comprehensiveness required to adequately capture the various facets of COVID-19 genomics. To address this gap, we present the development of the COVID-19 Virus Genomics Ontology (VGO). VGO is designed to facilitate the utilization and publication of COVID-19 genomic sequence data, offering a comprehensive solution for researchers and healthcare professionals. By integrating data from the Global Initiative on Sharing All Influenza Data (GISAID), VGO enables efficient querying and visualization of genomic information, enhancing accessibility and usability. To construct VGO, we employed a combination of two established methodologies, the Yet Another Methodology for Ontology Development (YAMO), and Networked Ontology (NeOn) methodology. Utilizing the Web Ontology Language (OWL), VGO provides a robust representation of COVID-19 genomic sequence data, filling a critical gap in existing ontologies. To ensure reliability and usability, VGO underwent a comprehensive evaluation process, benchmarking it against ten prominent COVID-19-related biomedical ontologies using a multidimensional framework that includes structural, functional, usability, and quantitative dimensions. Structural quality was assessed through an extensive pitfall analysis using the OOPS! framework, identifying critical modeling issues across peer ontologies, with VGO exhibiting no significant pitfalls–indicating high modeling rigor and syntactic correctness. Logical consistency and structural soundness were validated through OntoDebug and the Pellet Reasoner, confirming the absence of inconsistencies and reinforcing internal coherence. Quantitative evaluation using OntoMetrics highlighted VGO’s balanced design, demonstrating moderate attribute and relation richness, with notable instance-level coverage–a feature absent in most comparator ontologies. Despite a smaller axiom base, VGO maintains robust schema expressivity and a clear TBox-ABox integration, enabling both knowledge representation and data-level reasoning. Finally, a SPARQL-based competency question assessment confirmed the ontology’s functional completeness and real-world applicability, mapping successfully to its structure.