Real-time identification of epistatic interactions in SARS-CoV-2 from large genome collections.

ABSTRACT:

Background

The emergence of the SARS-CoV-2 virus has highlighted the importance of genomic epidemiology in understanding the evolution of pathogens and guiding public health interventions. The Omicron variant in particular has underscored the role of epistasis in the evolution of lineages with both higher infectivity and immune escape, and therefore the necessity to update surveillance pipelines to detect them early on.

Results

In this study, we apply a method based on mutual information between positions in a multiple sequence alignment, which is capable of scaling up to millions of samples. We show how it can reliably predict known experimentally validated epistatic interactions, even when using as little as 10,000 sequences, which opens the possibility of making it a near

SUBMITTER: Innocenti G 

PROVIDER: S-EPMC11342480 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature