This should be rather fast as it's just visualizing all computed ES scores. (conda environment setup time depends on internet connection condition; the visualization time should be very minimal)
git clone git@github.com:fuxialexander/ES.git
conda env create -f environment.yml
conda activate es
cd website
python app.pyTo plot ES score for new genes, you can use the following command:
python plot.py --transition cosmic_aa_transition.csv --gap 5 --interaction 15 --hotspot 0.1 --kernel 10 --smooth_method 'gaussian' plddt/9606.pLDDT.tdt uniprot_to_genename.txt {data_folder} genes.txt
where 9606.pLDDT.tdt can be downloaded at https://github.com/normandavey/ProcessedAlphafold/blob/main/9606.pLDDT.tdt.zip
and {data_folder} contains two files: genes.txt (which list the genes you want to predict) and mutations.txt (which list mutated residue #, gene, and mutation frequency).
Such files for COSMIC mutations and oncogenes can be found in https://github.com/fuxialexander/ES/tree/main/rank_all_cosmic.
The running result will be in a file like https://github.com/fuxialexander/ES/blob/main/rank_all_cosmic/genes.txt.scores.txt, which you can use the following command or https://github.com/fuxialexander/ES/blob/main/plot_gs_rank.py script to visualize
python plot.py --plot --transition cosmic_aa_transition.csv --gap 5 --interaction 15 --hotspot 0.1 --kernel 10 --smooth_method 'gaussian' plddt/9606.pLDDT.tdt uniprot_to_genename.txt {data_folder} genes.txt