Speaker
Description
Simulating within-host viral sequence evolution allows for the investigation of factors such as the role of recombination in viral diversification and the impact of selective pressures on virus evolution. Here, we add another model to the toolbox of within-host sequence simulators: wavess (within-host agent-based viral evolution sequence simulator), a discrete-time agent-based model and a corresponding user-friendly R package. The model optionally includes recombination, a latent infected cell reservoir, and three forms of selection: conserved sites fitness and replicative fitness in comparison to a reference sequence, and immune fitness including cross-reactivity. In the R package, we provide functions to straightforwardly generate model inputs from empirical data, as well as functions to analyze the model outputs. The model returns, at user-defined timepoints, various metrics related to the virus population, and a set of sampled sequences. We apply this model to investigate the selection pressures on HIV-1 env sequences longitudinally collected from 13 individuals. We find that the best fitting immune cost differed across individuals, mirroring the intuitive expectation of heterogeneous immune responses among people. We also find that the phylogenies reconstructed from these simulated sequences were similar to the phylogenies reconstructed from the real sequences for all summary statistics tested. To our knowledge, compared to other similar models, wavess has been more rigorously validated against real within-host viral sequences, and is the first to be implemented as an R package. The wavess R package can be downloaded from https://github.com/MolEvolEpid/wavess.
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