DOI: 10.5061/dryad.fbg79cnxx
title: Data for: Gene birth in a model of non-genic adaptation

description: 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 adaptation is spread widely across the genome, and our results offer
 mechanistic insights into the process of de novo gene birth.
description: The data was generated in simulations written in python.
description: The data is in Python's pickle format.
description: # 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' 6. 'pop\_dyn\_btw\_mutations.py' can be used to estimate mutant fixation probabilities between model time-steps #### parameter value lists (.pickle files): 1. 'model3\_param\_all.pickle': each row corresponds to parameter values for 'model3\_May2023.py' 2. 'estimate\_pfix\_paramlist.pickle': each row corresponds to parameter values for 'pop\_dyn\_btw\_mutations.py' #### Data (.pickle files): 1. 'fitness\_trajectories\_0.001\_0.01\_0.75\_0.3\_0\_1000\_1000.pickle': raw data (python dictionary) for Chlamy parameters run with high mutation rate 2. 'lomut\_fitness\_trajectories\_0.001\_0.01\_0.75\_0.3\_0\_1000\_1000.pickle': raw data (python dictionary) for Chlamy parameters run with low mutation rate 3. 'exp\_adapt\_powerlaw\_.001\_0.01\_0.75\_0.3\_0\_1000\_1000.pickle’: python dictionary that contains expression level and adaptive value trajectories for all individuals in simulated populations with Chlamy parameters and high mutation rate. 4. 'df\_concat.pickle': concatenated processed raw data which is an input for 'analyse\_model2.py' 5. 'df\_genebirth.pickle': data from analysis of processed data generated by 'analyse\_model2.py'. 6. 'lomut\_df\_concat.pickle': concatenated processed raw data which is an input for 'analyse\_model3.py' 7. 'lomut\_df\_genebirth.pickle': data from analysis of processed data generated by 'analyse\_model3.py'.