Relevance Scores

Frequency Increase During Selection Sweep

Default Value: TRUE

IAMBEE assigns a relevance score to each mutation based on the relative increase in frequency of the mutation in the evolving population during a selection sweep. Selective sweeps are defined as sudden jumps in fitness or an increase in adaptation during selection and are derived from the fitness trajectory of the evolving population.

The frequency increase of a mutation is equal to the difference in frequency of a mutation before and after the sweep (as derived from the input files). In case only one end-point is available for which sequencing information is available, the frequency increase is calculated using the variants of the parental strain as reference i.e. the observed frequency at the end point minus the frequency of the variant in the ancestral population. The user can provide the frequency increases in the input file or can ask IAMBEE to automatically derive the frequency increase from the provided mutation file.

An example of how the frequency increase can be derived from the input data is shown here:

$$ (Freq_{endPoint} - Freq_{startPoint} > -0.02) * 100 $$

Functional-Score Data

Default Value: TRUE

IAMBEE assigns also relevance score to each mutation based on the estimated functional impact of the mutation. This score reflects how likely it is a mutation causes a functional change in the resulting protein.

The user should provide the information on the functional impact scores in the mutation file. Functional impact scores can be derived from the SIFT scores which are calculated using the SIFT4G-annotator (Click Here) or by using any other functional impact annotator.

When functional impact scores are this data is not available, turn off this the "switch" and do not supply functional score data in the mutation data file.

Correction for Mutation Rate

Populations with a mutation rate that is significantly higher than the mutation rates of the other populations can bias the search and the results. Therefore IAMBEE allows reducing the impact of the population’s mutation rate on the search. Here to a correction factor is calculated for each population.

The user can choose whether or not to use this correction. If this correction is applied mutations that are derived from populations with a high mutation rate will contribute relatively less to the end results.