A Computer Simulation of SARS-CoV-2 Mutation Spectra for Empirical Data Characterization and Analysis

Ming Xiao, Fubo Ma, Jun Yu, Jianghang Xie, Qiaozhen Zhang, Peng Liu, Fei Yu, Yuming Jiang, Le Zhang

Research output: Contribution to journalArticleAcademicpeer-review


It is very important to compute the mutation spectra, and simulate the intra-host mutation processes by sequencing data, which is not only for the understanding of SARS-CoV-2 genetic mechanism, but also for epidemic prediction, vaccine, and drug design. However, the current intra-host mutation analysis algorithms are not only inaccurate, but also the simulation methods are unable to quickly and precisely predict new SARS-CoV-2 variants generated from the accumulation of mutations. Therefore, this study proposes a novel accurate strand-specific SARS-CoV-2 intra-host mutation spectra computation method, develops an efficient and fast SARS-CoV-2 intra-host mutation simulation method based on mutation spectra, and establishes an online analysis and visualization platform. Our main results include: (1) There is a significant variability in the SARS-CoV-2 intra-host mutation spectra across different lineages, with the major mutations from G- > A, G- > C, G- > U on the positive-sense strand and C- > U, C- > G, C- > A on the negative-sense strand; (2) our mutation simulation reveals the simulation sequence starts to deviate from the base content percentage of Alpha-CoV/Delta-CoV after approximately 620 mutation steps; (3) 2019-NCSS provides an easy-to-use and visualized online platform for SARS-Cov-2 online analysis and mutation simulation.
Original languageEnglish
Article number63
Issue number1
Publication statusPublished - 1 Jan 2023


  • SARS-CoV-2
  • bioinformatics
  • computational biology
  • mutation simulation
  • mutation spectra

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