Department of Genetics, Stanford University, Stanford, California, US.
Department of Physics, Stanford University, Stanford, California, US.
BMC Genomics. 2023 May 6;24(1):246. doi: 10.1186/s12864-023-09345-x.
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.
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.
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.
遗传条码为同时追踪大量竞争和进化的微生物谱系的频率提供了一种高通量的方法。然而，要对正在发生的进化的性质做出推断仍然是一项艰巨的任务。
在这里，我们描述了一种从条码测序数据推断适应度效应和有益突变建立时间的算法，该算法通过在种群平均适应度和谱系内突变的个体效应之间强制自洽，构建在贝叶斯推断方法的基础上。通过在 40000 个条形码谱系在连续分批培养中进化的模拟中测试我们的推断方法，我们发现这种新方法优于其前身，能够识别更多的适应性突变，并更准确地推断它们的突变参数。
当读取深度较低时，我们的新算法特别适合推断突变参数。我们已经在 GitHub（https://github.com/FangfeiLi05/FitMut2）上提供了用于串行稀释进化模拟的 Python 代码，以及旧的和新的推断方法，希望它能得到微生物进化社区更广泛的应用。
