Mathematica Notebook Fitness landscapes statistics and adaptive walkThis notebook allows to replicate the data analyses corresponding to the section of the manuscript called "The shape of the codon fitness landscape with and without synonymous effects". Please note that the data set necessary to run the analyses is obtained through the R notebooks also available within this repository.Mathematica_notebook_finalVersionJune2018.nbR notebooks for data formatting, data normalisation and statistical analysesThese R notebooks were done using R studio. They allow 1) to format the data sets after running empiricIST; 2) to do data normalisation for all the analyses performed in the manuscript; 3) to perform all statistical analyses and create the plots for the manuscript. Please note that these notebooks assume that you have run the empiricIST in the data set (the empiricIST is available at https://github.com/Matu2083/ empiricIST). The data analysed here, as indicated in the manuscript, is available from the publication: Bank C, Hietpas RT, Wong A, Bolon DN, Jensen JD (2014). A Bayesian MCMC approach to assess the complete distribution of fitness effects of new mutations: Uncovering the potential for adaptive walks in challenging environments. Genetics 196: 841–852.R_notebooks.zip

Fitness landscapes map the relationship between genotypes and fitness. However, most fitness landscape studies ignore the genetic architecture imposed by the codon table and thereby neglect the potential role of synonymous mutations. To quantify the fitness effects of synonymous mutations and their potential impact on adaptation on a fitness landscape, we use a new software based on Bayesian Monte Carlo Markov Chain methods and re-estimate selection coefficients of all possible codon mutations across 9 amino-acid positions in Saccharomyces cerevisiae Hsp90 across 6 environments. We quantify the distribution of fitness effects of synonymous mutations and show that it is dominated by many mutations of small or no effect and few mutations of larger effect. We then compare the shape of the codon fitness landscape across amino-acid positions and environments, and quantify how the consideration of synonymous fitness effects changes the evolutionary dynamics on these fitness landscapes. Together these results highlight a possible role of synonymous mutations in adaptation and indicate the potential mis-inference when they are neglected in fitness landscape studies.