Inferring population genetics parameters of evolving viruses using time-series data.

Area of Science:

• Virology
• Computational Biology
• Genomics

Background:

• Deep sequencing enables detailed tracking of viral evolution.
• Analyzing genomic time-series data presents computational challenges.

Purpose of the Study:

• To introduce Flexible Inference from Time-Series (FITS), a computational tool for inferring key evolutionary parameters.
• To assess FITS's performance on simulated and empirical viral genomic data.

Main Methods:

• Development of FITS, a tool for inferring mutation fitness, mutation rate, or population size.
• Validation using simulated genomic time-series data.
• Application to empirical poliovirus Evolve & Resequence (E&R) experiment data.

Main Results:

• FITS accurately infers fitness, mutation rate, and population size from time-series genomic data.
• The tool can categorize mutations as advantageous or deleterious, even with inexact input parameters.
• High accuracy was demonstrated when applying FITS to experimental poliovirus data.

Conclusions:

• FITS is a robust computational tool for analyzing viral evolution from genomic time-series data.
• The tool is particularly suited for short-term Evolve & Resequence (E&R) experiments and rapidly recombining viruses.
• FITS provides accurate inference of crucial evolutionary parameters, enhancing our understanding of viral dynamics.