An improved algorithm for inferring mutational parameters from bar-seq evolution experiments.
An improved algorithm for inferring mutational parameters from bar-seq evolution experiments.
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DOI：
10.1186/s12864-023-09345-x
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发表时间：
2023-05-06
期刊：
影响因子：
4.4
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中科院分区：
文献类型：
--
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遗传条形码提供了一种高通量的方式来同时跟踪大量竞争和进化的微生物谱系的频率。然而，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形码测序数据中推断有益突变的适应性效应和建立时间的算法，该算法通过在群体平均适应性和谱系内突变的个体效应之间强制执行自一致性来建立贝叶斯推断方法。通过在连续分批培养中进化的40，000个条形码谱系的模拟上测试我们的推断方法，我们发现这种新方法优于其前任，识别出更多的适应性突变，并更准确地推断出它们的突变参数。我们的新算法特别适合于阅读深度低时突变参数的推断。我们已经为我们的系列稀释进化模拟以及新旧推理方法编写了Python代码，可在GitHub（https：//github.com/FangfeiLi05/FitMut2）上获得，希望它可以在微生物进化社区中得到更广泛的使用。在线版本包含补充材料，可通过10.1186/s12864-023-09345-x获得。
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. The online version contains supplementary material available at 10.1186/s12864-023-09345-x.
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