An Improved Algorithm for Inferring Mutational Parameters from Bar-Seq Evolution Experiments

Abstract

Genetic barcoding provides a high-throughput way to simultaneously track the frequencies of large numbers of competing and evolving microbial lineages. However making inferences about the character of the evolution that is taking place remains a difficult task. Here we describe a step toward more accurate inference of fitness effects and establishment times of beneficial mutations, which builds upon a prior method by enforcing self-consistency between the population mean fitness and the individual effects of mutations within lineages. By testing our inference method on a simulation of 40,000 barcoded lineages evolving in serial batch culture, we find that this new method outperforms its predecessor, identifying more adaptive mutations and more accurately inferring their mutational parameters. We have made available our code for the joint Bayesian inference of population mean fitness and lineage-specific mutational parameters, in the hope that it can find broader use by the microbial evolution community.