The determination of microbial mutation rates in the laboratory is a routine yet com- putationally challenging task in biological research. The experimentalist conducts exper- iments in accord with the classic Luria-Delbrück protocol [1] (aka the fluctuation assay protocol). But the resulting fluctuation assay data can pose a formidable challenge, not only to bench biologists, but also to bioinformaticians unfamiliar with the biological and statistical subtleties inherent in the fluctuation assay protocol. Due to the increasing popularity of the fluctuation experiment in recent biological research, more and more bench scientists are eager to analyze their fluctuation assay data by themselves. Some understandably expect the analyses to be as simple as calculating the sample mean us- ing a pocket calculator. The popular web tool FALCOR [2] almost fulfilled this dream. Unknown to most practitioners, this web tool has important limitations, which can lead an unwary user to faulty conclusions [3]. For example, the comparison of microbial mutation rates is beyond the capabilities of this web tool. The R package rSalvador (available at http://eeeeeric.github.io/rSalvador) makes accessible to bench scientists a myriad of latest computational methods that are necessary for proper analysis of fluctu- ation assay data. rSalvador allows the user to properly account for relative fitness and plating efficiency. In particular, rSalvador is the only tool that affords strictly likelihood- based methods for the comparison of microbial mutation rates. This presentation will discuss the role of rSalvador in biological education, in mutation research, and in the development of new algorithms for fluctuation assay data.
