Deciphering the Subtype Differentiation History of SARS-CoV-2 Based on a New Breadth-First Searching Optimized Alignment Method Over a Global Data Set of 24,768 Sequences - Lin, Qianyu; Huang, Yunchuanxiang; Jiang, Ziyi; Wu, Feng; Ma, Lan (Frontiers in Genetics, 2021)
The SARS-CoV-2 has caused a worldwide pandemic. Existing research on coronavirus mutations is based on small datasets, while multiple sequence alignment using a global scale dataset has yet to be conducted. Statistical analysis of integral mutations and global spread are necessary, and could help improve primer design for nucleic acid diagnosis and vaccine development. Here, we optimized multiple sequence alignment using a conserved sequence search algorithm to align 24768 sequences from the GISAID dataset. Phylogenetic tree was constructed using the Maximum Likelihood (ML) method. Coronavirus subtypes were analyzed via t-SNE clustering. We performed haplotype network analysis and t-SNE clustering to analysis the coronavirus origin and spreading. Overall, we identified 33 sense, 17 nonsense, 79 amino acid loss, and 4 amino acid insertion mutations in full length open reading frames. Phylogenetic trees were successfully constructed and samples clustered into subtypes. The COVID-19 pandemic differed among countries and continents. Samples from the USA and western Europe were more diverse, while those from China and Asia mainly contained specific subtypes. Clades G/GH/GR are more likely to be the origin clades of SARS-CoV-2 compared with clades S/L/V. Conserved sequence searches can be used to segment long sequences, making large-scale multi-sequence alignment possible, facilitating more comprehensive gene mutation analysis. Mutation analysis of the SARS-CoV-2 can inform primer design for nucleic acid diagnosis, to improve virus detection efficiency. In addition, research into the characteristics of viral spread and relationships among geographic regions can help formulate health policies and in reducing the increase of imported cases.