Variant-driven early warning via unsupervised machine learning analysis of spike protein mutations for COVID-19.

ABSTRACT: Never before such a vast amount of data, including genome sequencing, has been collected for any viral pandemic than for the current case of COVID-19. This offers the possibility to trace the virus evolution and to assess the role mutations play in its spread within the population, in real time. To this end, we focused on the Spike protein for its central role in mediating viral outbreak and replication in host cells. Employing the Levenshtein distance on the Spike protein sequences, we designed a machine learning algorithm yielding a temporal clustering of the available dataset. From this, we were able to identify and define emerging persistent variants that are in agreement with known evidences. Our novel algorithm allowed us to define persistent variants as chains that remain stable ove

SUBMITTER: de Hoffer A 

PROVIDER: S-EPMC9166699 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature