Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur)

The fluctuation assay remains an important tool for analyzing the levels of mutagenesis in microbial populations. The mutant counts originating from some average number of mutations are usually assumed to obey the Luria–Delbrück distribution. While several tools for estimating mutation rates are available, they sometimes lack accuracy or versatility under non-standard conditions. In this work, extensions to the Luria–Delbrück protocol to account for phenotypic lag and cellular death with either perfect or partial plating were developed. Hence, the novel MLE MUtation Rate calculator, or mlemur, is the first tool that provides a user-friendly graphical interface allowing the researchers to model their data with consideration for partial plating, differential growth of mutants and non-mutants, phenotypic lag, cellular death, variability of the final number of cells, post-exponential-phase mutations, and the size of the inoculum. Additionally, mlemur allows the users to incorporate most of these special conditions at the same time to obtain highly accurate estimates of mutation rates and P values, confidence intervals for an arbitrary function of data (such as fold), and perform power analysis and sample size determination for the likelihood ratio test. The accuracy of point and interval estimates produced by mlemur against historical and simulated fluctuation experiments are assessed. Both mlemur and the analyses in this work might be of great help when evaluating fluctuation experiments and increase the awareness of the limitations of the widely-used Lea–Coulson formulation of the Luria–Delbrück distribution in the more realistic biological contexts. • A new tool for fluctuation data analysis, mlemur, is introduced. • mlemur combines relaxations of different assumptions of the fluctuation protocol. • Extensions for phenotypic lag and cell death with partial plating were developed. • The new extensions perform well against more biologically realistic scenarios. • Tools for calculating P values, statistical power, and sample size are available.