Inferring population genetics parameters of evolving viruses using time-series data. Issue 1 (8th June 2019)

• Record Type: Journal Article
• Authors: Zinger, Tal; Gelbart, Maoz; Miller, Danielle; Pennings, Pleuni S; Stern, Adi
• Is Part Of: Virus evolution. Volume 5:Issue 1(2019)
• Publication Date: 2019-06-08
• Subjects: fitness landscape -- mutation rate -- experimental evolution; Viruses -- Evolution -- Periodicals; 579.2138
• DOI: 10.1093/ve/vez011
• Languages: English
• ISSNs: 2057-1577

Abstract

Abstract: With the advent of deep sequencing techniques, it is now possible to track the evolution of viruses with ever-increasing detail. Here, we present Flexible Inference from Time-Series (FITS)—a computational tool that allows inference of one of three parameters: the fitness of a specific mutation, the mutation rate or the population size from genomic time-series sequencing data. FITS was designed first and foremost for analysis of either short-term Evolve & Resequence (E&R) experiments or rapidly recombining populations of viruses. We thoroughly explore the performance of FITS on simulated data and highlight its ability to infer the fitness/mutation rate/population size. We further show that FITS can infer meaningful information even when the input parameters are inexact. In particular, FITS is able to successfully categorize a mutation as advantageous or deleterious. We next apply FITS to empirical data from an E&R experiment on poliovirus where parameters were determined experimentally and demonstrate high accuracy in inference.