We have numerous scripts with specific niche functionality. Therefore, we expect that most of the scripts are not of high interest for most users. Because of this, we only rudimentarily polished most of the code and do not guaranty it's functionality.
download from
http://www.iedb.org/bcelldetails_v3.php
export to csv file
utils/download_from_iedb.py
(change local variables like line 17 (path to csv file))
utils/download_proteins_from_epitopeNumber.py
(change local variables like line 13 (path to csv file))
curate_iedb_linear_epitopes.py
(again changing input path)
cd previous_output_dir
cat * >> protein_all.fasta
cd-hit -i protein_all.fasta -c 1 -o 1_seqID.fasta
cd-hit -i protein_all.fasta -c 0.9 -o 0.9_seqID.fasta
cd-hit -i protein_all.fasta -c 0.8 -o 0.8_seqID.fasta
cd-hit -i protein_all.fasta -c 0.7 -o 0.7_seqID.fasta
cd-hit -i protein_all.fasta -n 4 -c 0.6 -o 0.6_seqID.fasta
cd-hit -i protein_all.fasta -n 3 -c 0.5 -o 0.5_seqID.fasta
select proteins with most verified regions
utils/cluster_to_proteins_with_markings.py
simple clustered
generate_binary_clustered_training_sets.py
more complex, if your data is clustered twice (like in the paper explained), to reduce bias of similar sequences in the test set.
generate_binary_double_clustered_training_sets.py
train_DL.py trains multiple neural networks on your training data
epidope.py testing suit for trained your models
make multi fasta file with only test set entries
utils/filter_test_set_fastas.py
get the ROC precision-recall und distribution of predictions:
utils/make_ROC_curves.py
get plots with only the parts marked which are part of ROC
utils/plots_test_set.py