Your browser doesn't support javascript.
loading
Real-time monitoring epidemic trends and key mutations in SARS-CoV-2 evolution by an automated tool
Binbin Xi; Dawei Jiang; Shuhua Li; Jerome R Lon; Yunmeng Bai; Shudai Lin; Meiling Hu; Yuhuan Meng; Yimo Qu; Yuting Huang; Wei Liu; Hongli Du.
Afiliación
  • Binbin Xi; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
  • Dawei Jiang; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
  • Shuhua Li; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
  • Jerome R Lon; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
  • Yunmeng Bai; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
  • Shudai Lin; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
  • Meiling Hu; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
  • Yuhuan Meng; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
  • Yimo Qu; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
  • Yuting Huang; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
  • Wei Liu; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
  • Hongli Du; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006
Preprint en En | PREPRINT-BIORXIV | ID: ppbiorxiv-424271
ABSTRACT
With the global epidemic of SARS-CoV-2, it is important to monitor the variation, haplotype subgroup epidemic trends and key mutations of SARS-CoV-2 over time effectively, which is of great significance to the development of new vaccines, the update of therapeutic drugs, and the improvement of detection reagents. The AutoVEM tool developed in the present study could complete all mutations detections, haplotypes classification, haplotype subgroup epidemic trends and key mutations analysis for 131,576 SARS-CoV-2 genome sequences in 18 hours on a 1 core CPU and 2G internal storage computer. Through haplotype subgroup epidemic trends analysis of 131,576 genome sequences, the great significance of the previous 4 specific sites (C241T, C3037T, C14408T and A23403G) was further revealed, and 6 new mutation sites of highly linked (T445C, C6286T, C22227T, G25563T, C26801G and G29645T) were discovered for the first time that might be related to the infectivity, pathogenicity or host adaptability of SARS-CoV-2. In brief, we proposed an integrative method and developed an efficient automated tool to monitor haplotype subgroup epidemic trends and screen out the key mutations in the evolution of SARS-CoV-2 over time for the first time, and all data could be updated quickly to track the prevalence of previous key mutations and new key mutations because of high efficiency of the tool. In addition, the idea of combinatorial analysis in the present study can also provide a reference for the mutation monitoring of other viruses.
Licencia
cc_by_nc_nd
Texto completo
Añadir a Mi BVS
Imprimir
XML
Buscar en Google
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-BIORXIV Tipo de estudio: Observational_studies Idioma: En Año: 2020 Tipo del documento: Preprint
Texto completo
Añadir a Mi BVS
Imprimir
XML
Buscar en Google
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-BIORXIV Tipo de estudio: Observational_studies Idioma: En Año: 2020 Tipo del documento: Preprint