Skip to content

wrshoemaker/ParEvol

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

DOI

ParEvol

Repository for code associated with the preprint:

Predicting Parallelism and Quantifying Divergence in Experimental Evolution

https://www.biorxiv.org/content/10.1101/2020.05.13.070953v2

This project uses a number of publicly available datasets. Processed data is included in the Zenodo repository. All raw data has been previously published and will have to be accessed as described below.

Dependencies

An environment.yml file is in this repository and can be used to make the conda environment used to perform all analyses. To summarize, the analyses are primarily performed in Python 3.6 and require the following packages: numpy, pandas, matplotlib, scipy, Scikit-learn, Biopython, and networkx.

This repository also requires asa159.f90 source file which can be obtained from the FisherExact repository. The file asa159.f90 is written on fortran, so make sure you have a fortran compiler (gfortran) installed. See here ==> https://gcc.gnu.org/wiki/GFortranBinaries.

Once asa159.f90 is in ~/GitHub/ParEvol/Python, run the command:

f2py -c -m asa159 asa159.f90

Getting the data

All the processed data is available on zenodo https://doi.org/10.5281/zenodo.3779341.

Below are instructions for obtaining the original dataset, though it is not necessary to redo the analyses.

Tenaillon et al.

Data from Tenaillon et al. (2012) can be obtained from their publication URL. For the mutation data you will need to download Table S2 as an excel file (1212986tableS2.xls) and convert it to CSV format. Move the CSV to ~/GitHub/ParEvol/data/Tenaillon_et_al. Table S1 in Tenaillon et al. (2019) contains the fitness estimates. It is in the supplement.

Turner et al.

Data from Turner et al. (2018) can be obtained from the publication's Dryad repository. Move the data in the repository to ~/GitHub/ParEvol/data/Turner_et_al

Cleaning the data

Run the following command:

python clean_data.py

Running the simulations

python run_simulations.py

Making the figures

python make_figs.py

References

Olivier Tenaillon, Alejandra Rodraguez-Verdugo, Rebecca L. Gaut, Pamela McDonald, Albert F. Bennett, Anthony D. Long, and Brandon S. Gaut. The Molecular Diversity of Adaptive Convergence. Science, 416335(6067): 457–461, 2012.

Caroline B. Turner, Christopher W. Marshall, and Vaughn S. Cooper. Parallel genetic adaptation across environments differing in mode of growth or resource availability. Evolution Letters, 2(4):355–367, August 2018.

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 91.7%
  • Fortran 6.7%
  • R 1.5%
  • Shell 0.1%