Unsupervised clustering analysis of SARS-Cov-2 population structure reveals six major subtypes at early stage across the world.

ABSTRACT: Identifying 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 16,873 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 lea

SUBMITTER: Li Y 

PROVIDER: S-EPMC8629198 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature