Mutation rate calculator 
\(bz\)\(rates\) is a webtool that allows to compute mutation rates from fluctuation assays. \(bz\)\(rates\) estimator is the Generating Function from Hamon & Ycart 2012. This estimator calculates the parameters m (mean number of mutations) and b (mutant relative fitness), for a given fluctuation assay dataset and therfore allows to calculate a mutation rate taking into account differential growth rate.
2015/05/06: several updates in the input form. Generating Function kept as the only estimator
2015/03/10: usage of scientific notation in the 'N0' field is now possible
2015/02/28: increased the authorized maximum number of plated cells
Mutation rate calculator 
Results 

A classical approach to calculate mutation rates (\(\mu\)) in microorganisms consists in performing fluctuation analyses through multiple cultures grown in parallel under identical conditions (Luria & Delbrück 1943). Each individual culture is started with a small inoculum (\(N_{0}\)) and mutational events occur independently in each culture. At the end of the experiment, a \(\mu\) can be estimated from the proportions of mutant cells in the different cultures. Since the seminal work of Lea & Coulson in 1949, numerous methods were proposed to calculate \(\mu\) (for review see Foster 2006). They all rely on the estimation of \(m\), the mean number of mutations per culture.
The estimation of \(m\) can be affected by the fraction of cells that is plated on selective media for each culture. This criteria is defined as the plating efficiency (\(z\)) which accounts for the fact that if the entire culture is not plated, then not all the mutants will be experimentally detected. A correction proposed by Stewart and colleagues to account for this potential bias (Stewart & al. 1990) is also implemented in \(bz\)\(rates\).
Foster (2006). Methods for determining spontaneous mutation rates. Methods in Enzymology, 409(05), 195–213. doi:10.1016/S00766879(05)090129
Hamon & Ycart (2012). Statistics for the LuriaDelbrück distribution. Electronic Journal of Statistics, 6, 1251–1272. doi:10.1214/12EJS711
Jaeger & Sarkar (1995). On the distribution of bacterial mutants: the effects of differential fitness of mutants and nonmutants. Genetica, 217–223.
Luria & Delbrück (1943). Mutations of bacteria from virus sensitivity to virus resistance. Genetics, 28(6), 491.
Sarkar & Sandri (1992). On fluctuation analysis: a new, simple and efficient method for computing the expected number of mutants. Genetica, 173–179.
Stewart & al. (1990). Fluctuation Analysis: The Probability Distribution of the Number Mutants Under Different Conditions. Genetics, 124(1), 175–185.
Although bzrates is built as a simplified tool to compute mutation rates, users should have in mind the goods practices for fluctuation tests analysis: Determining mutation rates in bacterial populations, Rosche & Foster, 2000, DOI:10.1006/meth.1999.0901
© 2015 UPMC  CNRS Need help? Please contact alexandre(dot)gillet(dash)markowska(at)upmc(dot)fr if you have any questions, comments or concerns. Developed at the LCQBUPMC in the Fischer Lab. 