Scientists have developed a new digital tool designed to help the global medical community keep pace with the ever-changing nature of COVID-19. Known as the COVID-19 Virus Genomics Ontology (VGO), this system acts as a knowledge graph that allows researchers to link complex genetic data with real-world information about patients, locations, and lab results. Created by a team from the Indian Statistical Institute, Bengaluru, and Calcutta University, Kolkata, the project aims to address a major bottleneck in pandemic research: the difficulty of searching and analysing the massive volume of genetic data generated since 2020. As of May 2025, more than 17 million genome sequences have been recorded, and the VGO provides a structured way to make sense of this Big Data to identify new threats faster than ever before.

VGO operates on the principle of ontology, a way of classifying entities and their relationships. While a standard database might simply list a mutation and a variant name, the VGO teaches the computer that a specific mutation, such as N501Y, is part of the Spike protein, which, in turn, defines the Omicron variant. Using the Web Ontology Language (OWL), a logic-based language, the researchers created a system that enables artificial intelligence to reason over data. This means if a scientist enters a new test result, the system can automatically categorise the variant and predict its characteristics based on its genetic code.

To build this tool, the team combined two established engineering methods, known as YAMO and NeOn, to ensure the framework was both sturdy and flexible. They populated the system with real-world data from the Global Initiative on Sharing All Influenza Data (GISAID), the world’s largest repository of influenza and COVID-19 sequences. By organising this data into 261 different classes and 55 unique relationships, they created a map that allows doctors to ask complex questions, such as which mutations are appearing most frequently in specific regions or which labs are seeing the most potent versions of the virus.

The VGO was benchmarked against 10 other major biomedical databases and found to be more rigorous, with fewer errors and a much better ability to handle detailed, specific data on how the virus actually functions. Earlier digital tools were often simple taxonomies that lacked the properties needed to show how different pieces of information connect. Many older systems also could not handle automated reasoning, requiring humans to do the heavy lifting of connecting the dots between a mutation and a variant. 

However, the researchers noted that the VGO will require regular updates to include new variants as they emerge. There is also the challenge of ambiguity, as different scientists around the world sometimes use different names for the same genetic features. Furthermore, for the VGO to reach its full potential, it requires technical competence to operate, meaning that not every local clinic may yet have the resources to use it.

Despite these hurdles, the VGO provides a standardised language for the disease. By eliminating confusion and making data easier to share across different countries, it helps scientists develop better vaccines and more accurate diagnostic tests. In the long run, it provides a blueprint for using information science to fight future outbreaks, potentially stopping the next pandemic before it starts.