This repository was created to upload the scripts used for the population genetics analyses of the paper: "Recombination facilitates adaptive evolution in rhizobial soil bacteria", Cavassim et al., 2021.
Primary steps (previously published in Cavassim et al., 2020)
- Orthology search: https://github.com/izabelcavassim/Rhizobium_analysis/#orthologous-identification-proteinortho
- Codon-aware alignment of orthologous genes: https://github.com/izabelcavassim/Rhizobium_analysis/#codon-aware-alignment
python script: https://github.com/izabelcavassim/Rhizobium_analysis/#snp-calling
python script: hdf5_call_variants.py
python script: ts_tv_calc.py
We used the software GRAPES to estimate the DFE across pairs of species. We first computed the folded Site frequency spectrum of synonysmous and non-synonymous sites using the customized python script:
Grapes_files_pairwise_quantile.py
To produce a text file ({species1}_{species2}_grapes.txt) that looks like this one:
gsE+gsD (548.000000 genes)
'all_genes' 11 547966.6905329467 365 257 354 107 259 149830.30946705345 2698 2220 2469 1062 1952 547966.6905329467 963 149830.30946705345 8810In which species_1 (this case gsE) is the focal group (polymorphism) and species 2 (in this case gsD) is the outgroup (divergence). A description of each entry is found here.
And then for each input file generated we ran GRAPES as follow:
/home/mica16/grapes-izabel-work/bin/grapes -in {inputs_dir}{species1}_{species2}_grapes.txt -out {results_dir}{species1}_{species2}_grapes_output.txt -nb_rand_start 20 -model allThe same procedure is done for computing alpha across recombination classes.
python script: LD_per_gene.py
For my own sake I created a python workflow that combines all the described analyses above. I used the software gwf to build and run this workflow.
Workflow pipeline (workflow.py), scripts and files connected to it are found here.
Feel free to contact me if you want to apply these scripts in your own dataset or if you have any questions.