Clustering analysis for the evolutionary relationships of SARS-CoV-2 strains Article Swipe
Xiangzhong Chen
,
Mingzhao Wang
,
Xinglin Liu
,
Wenjie Zhang
,
Huan Yan
,
Xiang Lan
,
Yandi Xu
,
Sanyi Tang
,
Juanying Xie
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-024-57001-5
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-024-57001-5
To explore the differences and relationships between the available SARS-CoV-2 strains and predict the potential evolutionary direction of these strains, we employ the hierarchical clustering analysis to investigate the evolutionary relationships between the SARS-CoV-2 strains utilizing the genomic sequences collected in China till January 7, 2023. We encode the sequences of the existing SARS-CoV-2 strains into numerical data through k -mer algorithm, then propose four methods to select the representative sample from each type of strains to comprise the dataset for clustering analysis. Three hierarchical clustering algorithms named Ward-Euclidean, Ward-Jaccard, and Average-Euclidean are introduced through combing the Euclidean and Jaccard distance with the Ward and Average linkage clustering algorithms embedded in the OriginPro software. Experimental results reveal that BF.28, BE.1.1.1, BA.5.3, and BA.5.6.4 strains exhibit distinct characteristics which are not observed in other types of SARS-CoV-2 strains, suggesting their being the majority potential sources which the future SARS-CoV-2 strains’ evolution from. Moreover, BA.2.75, CH.1.1, BA.2, BA.5.1.3, BF.7, and B.1.1.214 strains demonstrate enhanced abilities in terms of immune evasion, transmissibility, and pathogenicity. Hence, closely monitoring the evolutionary trends of these strains is crucial to mitigate their impact on public health and society as far as possible.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-024-57001-5
- https://www.nature.com/articles/s41598-024-57001-5.pdf
- OA Status
- gold
- Cited By
- 1
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392922351
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392922351Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41598-024-57001-5Digital Object Identifier
- Title
-
Clustering analysis for the evolutionary relationships of SARS-CoV-2 strainsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-18Full publication date if available
- Authors
-
Xiangzhong Chen, Mingzhao Wang, Xinglin Liu, Wenjie Zhang, Huan Yan, Xiang Lan, Yandi Xu, Sanyi Tang, Juanying XieList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-024-57001-5Publisher landing page
- PDF URL
-
https://www.nature.com/articles/s41598-024-57001-5.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.nature.com/articles/s41598-024-57001-5.pdfDirect OA link when available
- Concepts
-
Jaccard index, Cluster analysis, Hierarchical clustering, Euclidean distance, Biology, Dendrogram, Computational biology, Genome, Data mining, Computer science, Genetics, Artificial intelligence, Gene, Medicine, Genetic diversity, Environmental health, PopulationTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
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31Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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