Mutation rate, selection, and epistasis inferred from RNA virus haplotypes via neural posterior estimation.

ABSTRACT: RNA viruses are particularly notorious for their high levels of genetic diversity, which is generated through the forces of mutation and natural selection. However, disentangling these two forces is a considerable challenge, and this may lead to widely divergent estimates of viral mutation rates, as well as difficulties in inferring the fitness effects of mutations. Here, we develop, test, and apply an approach aimed at inferring the mutation rate and key parameters that govern natural selection, from haplotype sequences covering full-length genomes of an evolving virus population. Our approach employs neural posterior estimation, a computational technique that applies simulation-based inference with neural networks to jointly infer multiple model parameters. We first tested our app

SUBMITTER: Caspi I 

PROVIDER: S-EPMC10256221 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature