期刊
VIRUS EVOLUTION
卷 5, 期 1, 页码 -出版社
OXFORD UNIV PRESS
DOI: 10.1093/ve/vez011
关键词
fitness landscape; mutation rate; experimental evolution
类别
资金
- Israeli Science Foundation [1333/16]
- NSF-US-Israel Binational Science Foundation [2016555, 1655212]
- Edmond J. Safra Center for Bioinformatics at Tel-Aviv University
- Dir for Tech, Innovation, & Partnerships
- Translational Impacts [2016555] Funding Source: National Science Foundation
- Division Of Environmental Biology
- Direct For Biological Sciences [1655212] Funding Source: National Science Foundation
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.
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