Quantitative Systems Biology, University of California at Merced, Merced, CA.
School of Natural Sciences, University of California at Merced, Merced, CA.
Mol Biol Evol. 2017 Dec 1;34(12):3303-3309. doi: 10.1093/molbev/msx255.
Growth rates are an important tool in microbiology because they provide high throughput fitness measurements. The release of GrowthRates, a program that uses the output of plate reader files to automatically calculate growth rates, has facilitated experimental procedures in many areas. However, many sources of variation within replicate growth rate data exist and can decrease data reliability. We have developed a new statistical package, CompareGrowthRates (CGR), to enhance the program GrowthRates and accurately measure variation in growth rate data sets. We define a metric, Variability-score (V-score), that can help determine if variation within a data set might result in false interpretations. CGR also uses the bootstrap method to determine the fraction of bootstrap replicates in which a strain will grow the fastest. We illustrate the usage of CGR with growth rate data sets similar to those in Mira, Meza, et al. (Adaptive landscapes of resistance genes change as antibiotic concentrations change. Mol Biol Evol. 32(10): 2707-2715). These statistical methods are compatible with the analytic methods described in Growth Rates Made Easy and can be used with any set of growth rate output from GrowthRates.
增长率是微生物学中的一个重要工具,因为它们提供了高通量的适应性测量。GrowthRates 的发布使得使用平板读数文件自动计算增长率的实验程序在许多领域变得更加便捷。然而,在重复增长率数据中存在许多变异来源,这可能会降低数据的可靠性。我们开发了一个新的统计软件包,即 CompareGrowthRates(CGR),以增强 GrowthRates 程序并准确测量增长率数据集的变异。我们定义了一个指标,即变异性评分(V-score),它可以帮助确定数据集中的变异是否会导致错误的解释。CGR 还使用自举法来确定在自举复制中菌株最快生长的比例。我们使用类似于 Mira、Meza 等人的增长率数据集来说明 CGR 的用法(耐药基因的适应景观随抗生素浓度的变化而变化。 Mol Biol Evol. 32(10): 2707-2715)。这些统计方法与 GrowthRates Made Easy 中描述的分析方法兼容,可以与 GrowthRates 输出的任何一组增长率数据一起使用。