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The Evolving Faces of the SARS-CoV-2 Genome
Maria Schmidt1, Mamoona Arshad1, Stephan H Bernhart1
1IZBI, Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany.
Viruses
|September 28, 2021
Summary
This study introduces Self-Organizing Maps (SOM) portrayal for SARS-CoV-2 genomic surveillance. This machine learning approach visualizes viral evolution and mutations, aiding pandemic control efforts.
Area of Science:
- Virology
- Genomics
- Bioinformatics
Background:
- The COVID-19 pandemic necessitates continuous genomic surveillance of the SARS-CoV-2 virus.
- Effective control requires integrating genomic data with epidemiological monitoring and vaccination strategies.
- Current bioinformatics methods for analyzing viral evolution need enhancement for intuitive visualization.
Purpose of the Study:
- To develop and apply a novel machine learning approach for characterizing SARS-CoV-2 genome diversity and evolution.
- To visualize the relationships between viral lineages and track the emergence of variants of concern.
- To provide an intuitive tool for bioinformatics surveillance of viral mutations.
Main Methods:
- Application of Self-Organizing Maps (SOM) machine learning for molecular portrayal of SARS-CoV-2 genomes.
- Analysis of viral genome sequences to map genetic diversity and relatedness.
- Visualization of mutation patterns and evolutionary trajectories in a genetic state space.
Main Results:
- The SOM portrayal effectively characterized the diversity and relatedness of SARS-CoV-2 genomes over time.
- The genetic landscape visualized lineage-specific mutations and evolutionary paths from early strains to variants like Alpha, Beta, Gamma, and Delta.
- Distinct viral genes showed specific patterns in the landscape, reflecting their biological significance.
Conclusions:
- SOM portrayal offers a powerful new method for bioinformatics surveillance of viral pandemics.
- This approach enhances visualization and intuitive understanding of complex viral genomic data.
- It provides a personalized view of mutational patterns, aiding in tracking and understanding viral evolution.
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