wavess

wavess simulates adaptive within-host virus sequence evolution using a discrete-time individual-based model with recombination, latent infected cell reservoirs, and multiple forms of selection.

Key Features:

• Recombination modeling: The underlying model simulates recombination during virus sequence evolution.
• Latent infected cell reservoir: The model incorporates a latent infected cell reservoir as part of the within-host evolutionary dynamics.
• Conserved sites fitness: The model implements selection based on conserved sites fitness in comparison to a reference sequence.
• Replicative fitness: The model implements selection based on replicative fitness in comparison to a reference sequence.
• Immune fitness with cross-reactivity: The model implements immune fitness including cross-reactivity imposed by a co-evolving immune response.
• Empirical data input generation: The R package provides functions to generate model inputs from empirical data.
• Simulation output analysis: The R package provides functions to analyze the simulation outputs.
• Longitudinal population tracking: At user-defined time points, the software returns various counts related to the virus population(s) and a set of sampled virus sequences.

Scientific Applications:

• Recombination in virus diversification: Simulating within-host virus sequence evolution allows investigation of the role of recombination in virus diversification.
• Selective pressure impact: Simulating within-host virus sequence evolution allows investigation of the impact of selective pressures on virus evolution.
• HIV-1 env sequence evolution: The model has been applied to investigate selection pressures on HIV-1 env sequences longitudinally collected from 11 individuals.
• Immune response heterogeneity: The model enables investigation of heterogeneous immune responses among human hosts through estimation of best-fitting immune cost parameters.
• Phylogenetic validation: Simulated sequences can be used to reconstruct phylogenies for comparison with real sequence phylogenies using summary statistics.

Methodology:

wavess uses a discrete-time individual-based model. The model simulates 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 imposed by a co-evolving immune response. Functions are provided to generate model inputs from empirical data and to analyze simulation outputs. At user-defined time points, the software returns various counts related to the virus population(s) and a set of sampled virus sequences.