Fig. 2.

Calculation of the relevance scores for each mutation by IAMBEE. In the data acquisition step, a selective sweep of interest is chosen from an evolution experiment involving multiple parallel evolved populations. Samples taken at time points just before (blue arrows) and just after (orange arrows) this selective sweep are sequenced and mutations are called. For every mutation, a functional impact score and frequency change in the population are determined by the IAMBEE software. The frequency change is derived from the degree to which the mutation changes in frequency before (blue) and after (orange) the selective sweep. Genes with mutations that rise in frequency have higher frequency increase scores (green square) while a low frequency increase score is assigned to genes with mutations that decrease in frequency in the population (red squares). Next, a functional impact score is assigned to each mutation by using SIFT4G (Vaser etal. 2016). Genes with mutations having a high functional impact score are depicted with green triangles and vice versa. In addition, populations with a mutation rate that is significantly higher than the mutation rates of the other populations are detected. The relevance of mutated genes in populations with a significantly higher mutation rate are corrected (red star) to avoid overrepresentation of mutations from these populations. Finally, combining a gene’s frequency score, functional impact score and the correction for mutation rates allows calculating a relevance score for every mutated gene in every population. Mutated genes with a high relevance score (green circles) are more likely to harbor mutations that increase in frequency during the selective sweep, have high functional impact scores and are not involved in a population with significantly higher mutation rate than the rest of the populations.