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SARSMutOnto: An Ontology for SARS-CoV-2 Lineages and Mutations
Jamal Bakkas1, Mohamed Hanine2, Abderrahman Chekry1
1LAPSSII Laboratory, Graduate School of Technology, Cadi Ayyad University, Safi 46000, Morocco.
Viruses
|February 28, 2023
Summary
This study introduces SARSMutOnto, an ontology modeling SARS-CoV-2 mutations to aid in understanding viral evolution and controlling the COVID-19 pandemic. The ontology helps predict future mutations for enhanced pandemic response.
Area of Science:
- Virology
- Bioinformatics
- Computational Biology
Background:
- Viruses, including SARS-CoV-2, continuously evolve through mutations, complicating efforts to control pandemics like COVID-19.
- Understanding SARS-CoV-2 mutation mechanisms is crucial for developing effective control strategies and anticipating viral spread.
- The emergence of new variants with enhanced transmissibility poses a significant challenge to global health initiatives.
Purpose of the Study:
- To develop an ontology, SARSMutOnto, for modeling SARS-CoV-2 mutations.
- To provide a structured representation of viral mutations, including genes, genomic structure, sub-lineages, and recombinant sub-lineages.
- To facilitate the analysis of viral evolution and potentially predict future mutations.
Main Methods:
- Developed SARSMutOnto ontology to model SARS-CoV-2 mutations.
- Utilized Pango research data for mutation information.
- Created a Python-based tool for automated ontology generation from Pango source files.
- Included detailed descriptions of mutations, affected genes, and genomic structure.
Main Results:
- Successfully modeled SARS-CoV-2 mutations, their hierarchical relationships (sub-lineages, recombinants), and associated genomic information.
- Generated SARSMutOnto using a Python tool for automated processing of Pango data.
- Provided examples of SPARQL queries for ontology utilization.
Conclusions:
- SARSMutOnto offers a comprehensive model for SARS-CoV-2 mutations, aiding research and pandemic control.
- The ontology can support 'wet bench' machine learning for predicting future viral mutations.
- This work facilitates a deeper understanding of viral evolution and adaptive mechanisms.
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