An improved algorithm for inferring mutational parameters from bar-seq evolution experiments

• Published in: BMC genomics Vol. 24; no. 1; pp. 246 - 12
• Main Authors: Li, Fangfei, Mahadevan, Aditya, Sherlock, Gavin
• Format: Journal Article
• Language: English
• Published: London BioMed Central 06.05.2023; Springer Nature B.V; BMC
• Subjects: Algorithms; Animal Genetics and Genomics; Bar codes; Batch culture; Bayes Theorem; Bayesian analysis; Biomedical and Life Sciences; Cloning; Computer Simulation; Dilution; Eukaryote microbial genomics; Evolution; Experimental evolution; Experiments; Fitness; Genetic barcoding; Genomes; Genomics; Inference; Life Sciences; Microarrays; Microbial Genetics and Genomics; Microorganisms; Mutation; Parameter identification; Performance evaluation; Plant Genetics and Genomics; Population; Proteomics; Reproductive fitness; Software; Statistical inference; Genetic barcoding; Inference; Experimental evolution
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-023-09345-x

Background 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 nature of the evolution that is taking place remains a difficult task. Results Here we describe an algorithm for the inference of fitness effects and establishment times of beneficial mutations from barcode sequencing data, which builds upon a Bayesian inference 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. Conclusion Our new algorithm is particularly suited to inference of mutational parameters when read depth is low. We have made Python code for our serial dilution evolution simulations, as well as both the old and new inference methods, available on GitHub ( https://github.com/FangfeiLi05/FitMut2 ), in the hope that it can find broader use by the microbial evolution community.