English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratorysyndrome coronavirus 2 (SARS-CoV-2) has a worldwide devastating effect. Theunderstanding of evolution and transmission of SARS-CoV-2 is of paramountimportance for the COVID-19 control, combating, and prevention. Due to therapid growth of both the number of SARS-CoV-2 genome sequences and the numberof unique mutations, the phylogenetic analysis of SARS-CoV-2 genome isolatesfaces an emergent large-data challenge. We introduce a dimension-reduced$k$-means clustering strategy to tackle this challenge. We examine theperformance and effectiveness of three dimension-reduction algorithms:principal component analysis (PCA), t-distributed stochastic neighbor embedding(t-SNE), and uniform manifold approximation and projection (UMAP). By usingfour benchmark datasets, we found that UMAP is the best-suited technique due toits stable, reliable, and efficient performance, its ability to improveclustering accuracy, especially for large Jaccard distanced-based datasets, andits superior clustering visualization. The UMAP-assisted $k$-means clusteringenables us to shed light on increasingly large datasets from SARS-CoV-2 genomeisolates.

UMAP-assisted $K$-means clustering of large-scale SARS-CoV-2 mutation datasets