Stochastic mutation as a mechanism for the emergence of SARS-CoV-2 new variants.

ABSTRACT: Predicting the future evolutionary trajectory of SARS-CoV-2 remains a critical challenge, particularly due to the pivotal role of spike protein mutations. It is therefore essential to develop evolutionary models capable of continuously integrating new experimental data. In this study, we employ a cladogram algorithm that incorporates established assumptions for mutant representation - using both four-letter and two-letter formats - along with an n-mer distance algorithm to construct a cladogenetic tree of SARS-CoV-2 mutations. This tree accurately captures the observed changes across macro-lineages. We introduce a stochastic method for generating new strains on this tree based on spike protein mutations. For a given set A of existing mutation sites, we define a set X comprising x randomly

SUBMITTER: Luo L 

PROVIDER: S-EPMC12789824 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature