Unsupervised clustering analysis of SARS-Cov-2 population structure reveals six major subtypes at early stage across the world
2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
; : 58-63, 2021.
Article
in English
| Scopus | ID: covidwho-1722876
ABSTRACT
the population structure of the newly emerged coronavirus SARS-CoV-2 has significant potential to inform public health management and diagnosis. As SARS-CoV-2 sequencing data accrued, grouping them into clusters is important for organizing the landscape of the population structure of the virus. Due to the limited prior information on the newly emerged coronavirus, we utilized four different clustering algorithms to group 16, S73 SARS-CoV-2 strains, which automatically enables the identification of spatial structure for SARS-CoV-2. A total of six distinct genomic clusters were identified using mutation profiles as input features. Comparison of the clustering results reveals that the four algorithms produced highly consistent results, but the state-of-the-art unsupervised deep learning clustering algorithm performed best and produced the smallest intra-cluster pairwise genetic distances. The varied proportions of the six clusters within different continents revealed specific geographical distributions. In particular, our analysis found that Oceania was the only continent on which the strains were dispersively distributed into six clusters. In summary, this study provides a concrete framework for the use of clustering methods to study the global population structure of SARS-CoV-2. In addition, clustering methods can be used for future studies of variant population structures in specific regions of these fast-growing viruses. © 2021 IEEE.
Deep learning clustering; evolution; population structure; SARS-CoV-2; SNP; Cluster analysis; Clustering algorithms; Deep learning; Diseases; Geographical distribution; Population statistics; Clustering analysis; Clustering methods; Health management; Learning clustering; Population structures; Unsupervised clustering; SARS
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Year:
2021
Document Type:
Article
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