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

Tal Zinger, Maoz Gelbart, Danielle Miller, Pleuni S. Pennings, Adi Stern*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article numbervez011
JournalVirus Evolution
Issue number1
StatePublished - 1 Jan 2019


  • Experimental evolution
  • Fitness landscape
  • Mutation rate


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