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Article

The Evolving Faces of the SARS-CoV-2 Genome

1
IZBI, Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16–18, 04107 Leipzig, Germany
2
Armenian Bioinformatics Institute (ABI), 7 Hasratyan Str., Yerevan 0014, Armenia
3
Research Group of Bioinformatics, Institute of Molecular Biology of the National Academy of Sciences of the Republic of Armenia, 7 Hasratyan Str., Yerevan 0014, Armenia
*
Authors to whom correspondence should be addressed.
Academic Editor: Roger Frutos
Viruses 2021, 13(9), 1764; https://doi.org/10.3390/v13091764
Received: 3 August 2021 / Revised: 2 September 2021 / Accepted: 2 September 2021 / Published: 3 September 2021
(This article belongs to the Special Issue Global Dynamic of Viral Diseases)
Surveillance of the evolving SARS-CoV-2 genome combined with epidemiological monitoring and emerging vaccination became paramount tasks to control the pandemic which is rapidly changing in time and space. Genomic surveillance must combine generation and sharing sequence data with appropriate bioinformatics monitoring and analysis methods. We applied molecular portrayal using self-organizing maps machine learning (SOM portrayal) to characterize the diversity of the virus genomes, their mutual relatedness and development since the beginning of the pandemic. The genetic landscape obtained visualizes the relevant mutations in a lineage-specific fashion and provides developmental paths in genetic state space from early lineages towards the variants of concern alpha, beta, gamma and delta. The different genes of the virus have specific footprints in the landscape reflecting their biological impact. SOM portrayal provides a novel option for ‘bioinformatics surveillance’ of the pandemic, with strong odds regarding visualization, intuitive perception and ‘personalization’ of the mutational patterns of the virus genomes. View Full-Text
Keywords: COVID-19; virus sequencing; single nucleotide variants; SARS-CoV-2 lineages genomic surveillance; self-organizing maps portrayal; machine learning COVID-19; virus sequencing; single nucleotide variants; SARS-CoV-2 lineages genomic surveillance; self-organizing maps portrayal; machine learning
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MDPI and ACS Style

Schmidt, M.; Arshad, M.; Bernhart, S.H.; Hakobyan, S.; Arakelyan, A.; Loeffler-Wirth, H.; Binder, H. The Evolving Faces of the SARS-CoV-2 Genome. Viruses 2021, 13, 1764. https://doi.org/10.3390/v13091764

AMA Style

Schmidt M, Arshad M, Bernhart SH, Hakobyan S, Arakelyan A, Loeffler-Wirth H, Binder H. The Evolving Faces of the SARS-CoV-2 Genome. Viruses. 2021; 13(9):1764. https://doi.org/10.3390/v13091764

Chicago/Turabian Style

Schmidt, Maria, Mamoona Arshad, Stephan H. Bernhart, Siras Hakobyan, Arsen Arakelyan, Henry Loeffler-Wirth, and Hans Binder. 2021. "The Evolving Faces of the SARS-CoV-2 Genome" Viruses 13, no. 9: 1764. https://doi.org/10.3390/v13091764

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