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.
Original language | English |
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Article number | vez011 |
Journal | Virus Evolution |
Volume | 5 |
Issue number | 1 |
DOIs | |
State | Published - 1 Jan 2019 |
Keywords
- Experimental evolution
- Fitness landscape
- Mutation rate
All Science Journal Classification (ASJC) codes
- Virology
- Microbiology