Inferring Population Genetics Parameters of Evolving Viruses Using Time-series Data
1With the advent of deep sequencing techniques, it is now possible to track the evolution of viruses with ever-increasing detail. Here we present FITS (Flexible Inference from Time-Series) - a computational framework that allows inference of either the fitness of a 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 for rapidly recombining populations of viruses. We thoroughly explore the performance of FITS on noisy simulated data, and highlight its ability to infer meaningful information even in those circumstances. In particular FITS is able to categorize a mutation as Advantageous, Neutral or Deleterious. We next apply FITS to empirical data from an E&R experiment on poliovirus where parameters were determined experimentally and demonstrate extremely high accuracy in inference. We highlight the ease of use of FITS for step-wise or iterative inference of mutation rates, population size, and fitness values for each mutation sequenced, when deep sequencing data is available at multiple time-points.\n\nAvailabilityFITS is written in C++ and is available both with a highly user friendly graphical user interface but also as a command line program that allows parallel high throughput analyses. Source code, binaries (Windows and Mac) and complementary scripts, are available from GitHub at https://github.com/SternLabTAU/FITS.\n\nContactsternadi@tau.ac.il