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Most databases contain information on the dominant genome sequence. Since the emergence of SARS-CoV-2 in late 2019, variants have been selected that are more transmissible and capable of partial immune escape. Currently, models for projecting the evolution of SARS-CoV-2 are based on using dominant genome sequences to forecast whether\u00a0a known mutation will be prevalent in the future. However, novel variants of SARS-CoV-2 (and other viruses) are driven by evolutionary pressure acting on minor variant genomes, which then become dominant and form a potential next wave of infection. In this study, sequencing data from 96\u00a0209 patients, sampled over a 3-year period, were\u00a0used to analyse patterns of minor variant genomes. These\u00a0data were\u00a0used to develop unsupervised machine learning clusters to identify amino acids that had a greater potential for mutation than others in the Spike protein. Being able to identify amino acids that may be present in future variants would better inform the design of longer-lived medical countermeasures and allow a risk-based evaluation of viral properties, including assessment of transmissibility and immune escape,\u00a0thus\u00a0providing candidates with early warning signals for when a new variant of SARS-CoV-2 emerges.<\/jats:p>","DOI":"10.1093\/nar\/gkaf077","type":"journal-article","created":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T15:24:11Z","timestamp":1738769051000},"source":"Crossref","is-referenced-by-count":3,"title":["Using minor variant genomes and machine learning to study the genome biology of SARS-CoV-2 over time"],"prefix":"10.1093","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1438-4079","authenticated-orcid":false,"given":"Xiaofeng","family":"Dong","sequence":"first","affiliation":[{"name":"Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, 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