ViralBottleneck

ViralBottleneck integrates six established methods for estimating viral transmission bottleneck size from deep sequencing data.

Key Features:

• Multiple estimation methods: Implements six established approaches including the presence-absence method, Kullback-Leibler (KL) method, binomial method, two versions of the beta-binomial method, and the Wright-Fisher method.
• Simulated dataset validation: Demonstrates functionality using simulated datasets generated with SANTA-Sim under different scenarios with known bottleneck sizes.
• Cross-method comparison: Enables comparison of bottleneck size estimates across methods to highlight variation and the impact of methodological choice.

Scientific Applications:

• Transmission bottleneck quantification: Estimates the number of viral individuals successfully establishing infection in a new host from deep sequencing data.
• Viral evolution and disease dynamics research: Supports studies of how transmission bottlenecks influence viral evolution, immune escape, infection severity, and disease emergence.
• Control strategy development: Informs predictive models and intervention design by characterizing how viral population size and genetic diversity change during host-to-host transmission.

Methodology:

ViralBottleneck is implemented as an R package that applies six statistical and population genetic methods to deep sequencing data for bottleneck size estimation.