Data for: Gene birth in a model of non-genic adaptation

Over evolutionary timescales, genomic loci switch between functional and non-functional states through processes such as pseudogenization and de novo gene birth. Here we ask about the likelihood and rate of functionalization of non-functional loci. We simulate an evolutionary model to look at the contributions of mutations and structural variation using biologically reasonable distributions of mutational effects. We find that a wide range of mutational effects are conducive to functionalization, thus indicating the ubiquity of this process. During functionalization, loci transition from a mutation dominated ’learning’ phase to a selection-dominated adaptation phase. Interestingly, in the special case of de novo gene birth, whereby non-functional loci begin to express a functional product, we find that expression level changes lead to rare, extreme jumps in fitness, whereas sustained adaptation is driven by product functionality. Our work supports the idea that the potential for adaptati..., The data was generated in simulations written in python., The data is in Python's pickle format., # Data for: Gene birth in a model of non-genic adaptation

[https://doi.org/10.5061/dryad.fbg79cnxx](https://doi.org/10.5061/dryad.fbg79cnxx)

The below files contain codes used for data generation and analysis and processed data that were used in the associated manuscript titled 'Gene birth in a model of non-genic adaptation'.
All codes are written in Python 3.10.

#### codes (.py files):

1. 'model2\_March2022.py' can be used to generate raw data for high mutation rates
2. ‘analyse\_model2.py’ can be used to organize raw data produced by model2\_March2022.py (outputs df\_genebirth.pickle) into dataframes and to generate plots in the associated manuscript using this data.
3. 'model3\_May2023.py' can be used to generate raw data for low mutation rates
4. 'analyse\_model3.py' can be used to organize raw data generated by 'model3\_May2023.py' (outputs lowmut\_df\_genebirth.pickle).
5. 'get\_manuscript\_Fig3\_4.py' can be used to generate plots for data processed with 'analyse\_model3.py'
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