ArticleVirus evolution2019
Inferring population genetics parameters of evolving viruses using time-series data.
Article in Virus evolution, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Direct estimation of genotype fitness from time series.bioRxiv : the preprint server for biology · 2026Article
- Linkage-aware inference of fitness from short-read time-series genomic data.bioRxiv : the preprint server for biology · 2026Article
- Linkage-aware inference of fitness from short-read time-series genomic data.Virus evolution · 2026Article
- Selection Estimation from Genetic Time-Series Data: Effects of Limited Sampling and Genetic Drift.Molecular biology and evolution · 2025Article
- Extinction scenarios in evolutionary processes: a multinomial Wright-Fisher approach.Journal of mathematical biology · 2023Article
- Mutation rate, selection, and epistasis inferred from RNA virus haplotypes via neural posterior estimation.Virus evolution · 2023Article
- Inferring Epistasis from Genetic Time-series Data.Molecular biology and evolution · 2022Article
- Inference of population genetic parameters from an irregular time series of seasonal influenza virus sequences.Genetics · 2021Article
- Competition between social cheater viruses is driven by mechanistically different cheating strategies.Science advances · 2020Article
- A Bayesian Framework for Inferring the Influence of Sequence Context on Point Mutations.Molecular biology and evolution · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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