diff --git a/.gitignore b/.gitignore
deleted file mode 100644
index e39bf38496c61e9da540ed82bb786c11eb75e36e..0000000000000000000000000000000000000000
--- a/.gitignore
+++ /dev/null
@@ -1,23 +0,0 @@
-**/venv
-**/.idea
-**/.DS_Store
-*.DS_Store
-**/__pycache__/*
-**.pyc
-*._*
-**.ipynb_checkpoints
-**.gz
-**.pkl
-**.gpickle
-**.pickle
-*.log
-
-OptiVax_Data/
-download.zip
-
-**/.snakemake
-Pipeline/*/
-!Pipeline/envs/
-!Pipeline/scripts/
-
-HOGVAX/*/
diff --git a/ContextEmbedding/README.md b/ContextEmbedding/README.md
deleted file mode 100644
index 77190d848db34856d3fbaee753451d3b3da9eef0..0000000000000000000000000000000000000000
--- a/ContextEmbedding/README.md
+++ /dev/null
@@ -1,3 +0,0 @@
-### Note
-
-Here, you find the scripts used for the context embedding computations. They were designed for personal usage and may require manual work to be executed on your machine.
diff --git a/ContextEmbedding/binding_affinities_for_embedded_peptide.py b/ContextEmbedding/binding_affinities_for_embedded_peptide.py
deleted file mode 100644
index b29f5a30f8eedf4388b139dbc739c7dcf24556a3..0000000000000000000000000000000000000000
--- a/ContextEmbedding/binding_affinities_for_embedded_peptide.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import pandas as pd
-import numpy as np
-from ProcessData import read_peptides
-
-peptide_file = '../../Gifford_Data/peptides/mhcI_peptides_with_mutations_1embedding.pep'
-embedded_epitope_features = '../../Gifford_Data/peptide_context_embedding/1EmbeddedEpitopeFeatures.pkl'
-ba_file = '/home/sara/Documents/VaccinesProject/ivp/Gifford_Data/25June_mhc1_netmhc-4.1_pred_affinity_pivot.pkl.gz'
-
-embedded_peptides = read_peptides.main(peptide_file)
-# embedded_peptides = ['MYSFVSEET', 'MYSFVSEETG', 'YSFVSEETGT', 'SFVSEETGTL']
-all_embedded = pd.read_pickle(embedded_epitope_features)
-ba_affinities = pd.read_pickle(ba_file)
-
-mapped_peptides = pd.DataFrame(all_embedded.loc[embedded_peptides]['peptide']).reset_index(level=0).set_index('peptide')
-print(mapped_peptides)
-same_pep = list(set([p for p in mapped_peptides.index if p in ba_affinities.index]))
-print(same_pep)
-mapped_peptides = mapped_peptides.loc[same_pep]
-filtered = ba_affinities.reset_index()
-filtered.Peptide = filtered.Peptide.apply(lambda x: mapped_peptides.loc[x]['embedded'] if any(x == p for p in same_pep) else np.nan)
-filtered = filtered[filtered['Peptide'].notna()]
-print(filtered)
-filtered = filtered.set_index(['Peptide'])
-for embed in mapped_peptides['embedded']:
- if embed not in filtered.index:
- print(embed, ' is not in filtered index!!!')
- exit(-1)
-
-print(filtered)
-# new_df.to_pickle('../../Gifford_Data/' + str(embed) + 'embedded_25June_mhc1_netmhc-4.1_pred_affinity_pivot.pkl.gz', compression='gzip')
\ No newline at end of file
diff --git a/ContextEmbedding/create_embedded_input_peptides.py b/ContextEmbedding/create_embedded_input_peptides.py
deleted file mode 100644
index 2eb0d345679f5b73c08660e8ece1fcace9382eb7..0000000000000000000000000000000000000000
--- a/ContextEmbedding/create_embedded_input_peptides.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import pandas as pd
-from ProcessData import read_peptides
-
-max_embed = 10
-mhc = 'mhcII'
-peptide_range = [13, 26] # exclusive end value
-original_feature_file = '../../Gifford_Data/AllEpitopeFeatures.pkl'
-ba_file = '/home/sara/Documents/VaccinesProject/ivp/Gifford_Data/25June_mhc2_netmhcii-4.1_pred_affinity_pivot_v1v2.pkl.gz'
-
-for i in range(1, max_embed+1):
- embed = str(i)
- feature_file = '../../Gifford_Data/peptide_context_embedding/' + embed + 'EmbeddedEpitopeFeatures.pkl'
- out_file_ori = '../../Gifford_Data/peptides/' + mhc + '_peptides_with_mutations.pep'
- out_file = '../../Gifford_Data/peptides/' + mhc + '_peptides_with_mutations_' + embed + 'embedding.pep'
-
- ori_features = pd.read_pickle(original_feature_file)
- features = pd.read_pickle(feature_file)
-
- # filtering like Gifford
- selfp = open('../../Gifford_Data/self_pept.txt').read().split('\n')
- ori_peptides = ori_features[(ori_features['epi_len'].isin(range(*peptide_range))) &
- (ori_features['glyco_probs']<=0.0) &
- (ori_features['crosses_cleavage']==0) &
- (ori_features['Is_self_pept']==False)].index.to_list()
- print(len(ori_peptides))
-
- # drop peptides that do not occur in ba predictions
- ba = pd.read_pickle(ba_file)
- na = []
- for pep in ori_peptides:
- if pep not in ba.index:
- ori_peptides.remove(pep)
- na.append(pep)
- print(len(na), na)
- print(len(ori_peptides))
-
- peptides = features[features['peptide'].isin(ori_peptides)].index
- print(len(peptides))
-
- with open(out_file, 'w') as ori_out:
- ori_out.write(('\n').join(peptides))
-
-with open(out_file_ori, 'w') as out:
- out.write('\n'.join(ori_peptides))
\ No newline at end of file
diff --git a/ContextEmbedding/embedded_epitope_features.py b/ContextEmbedding/embedded_epitope_features.py
deleted file mode 100644
index 9b1e81effa880ded7f73a47a4db4b9761bc65920..0000000000000000000000000000000000000000
--- a/ContextEmbedding/embedded_epitope_features.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import pandas as pd
-import re
-
-
-def get_embedded_epitope_features(embed, all_epi_feat, min_len):
- with open('../../Gifford_Data/peptide_context_embedding/base_proteins_with_end_codon.fasta', 'r') as fasta:
- proteins = {}
- key = ''
- for line in fasta:
- if line.startswith('>'):
- key = re.match('> Protein: ([A-Z]+\d*[a-z]*) |', line).group(1)
- continue
- proteins[key] = line.strip('\n').strip('*')
-
- df_list = []
- for peptide in all_epi_feat.index:
- protein = all_epi_feat.loc[peptide]['protein']
- if protein == 'S1' or protein == 'S2':
- protein = 'S'
- pos = proteins[protein].index(peptide)
- start_embed = pos - embed
- end_embed = pos + len(peptide) + embed
- if start_embed < 0 or end_embed > len(proteins[protein]):
- if len(peptide) < min_len:
- embedded = peptide
- else:
- continue
- else:
- embedded = proteins[protein][start_embed:end_embed]
- # embedded = proteins[protein][max(pos-embed, 0):min(pos+len(peptide)+embed, len(proteins[protein]))]
- tmp = pd.concat([pd.Series({'embedded': embedded, 'peptide': peptide, 'embedding_length': embed}), all_epi_feat.loc[peptide]])
- df_list.append(tmp)
-
- new_df = pd.concat(df_list, axis=1).T.set_index('embedded')
- print('Writing ../../Gifford_Data/' + str(embed) + 'EmbeddedEpitopeFeatures.pkl')
- new_df.to_pickle('../../Gifford_Data/peptide_context_embedding/' + str(embed) + 'EmbeddedEpitopeFeatures.pkl')
-
-
-max_embed = 10
-min_len = 10
-# read in all epitope features of original peptides
-all_epi_feat = pd.read_pickle('../../Gifford_Data/AllEpitopeFeatures.pkl')
-
-for i in range(1, max_embed+1):
- get_embedded_epitope_features(embed=i, all_epi_feat=all_epi_feat, min_len=min_len)
\ No newline at end of file
diff --git a/ContextEmbedding/reformat_netchop_out.py b/ContextEmbedding/reformat_netchop_out.py
deleted file mode 100644
index 127ebd594357eb36ed12ccb7a397984ee0fdcf7c..0000000000000000000000000000000000000000
--- a/ContextEmbedding/reformat_netchop_out.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import re
-import os
-
-prefix = '../../Proteasomal_Cleavage_Predictions/24SeptEmbeddingPredictions/MHCI/'
-files = [f for f in os.listdir(prefix) if re.search(r'NetChop3\.1_17Aug_mhcI+(_\d+embedded)*_hogvax(_with_mutations)*\.txt$', f)]
-# files = ['NetChop3.1_17Aug_mhcI_1embedded_hogvax.txt', 'NetChop3.1_17Aug_mhcI_3embedded_hogvax.txt',
-# 'NetChop3.1_17Aug_mhcI_4embedded_hogvax.txt', 'NetChop3.1_17Aug_mhcI_5embedded_hogvax.txt',
-# 'NetChop3.1_17Aug_mhcI_8embedded_hogvax.txt']
-# prefix = '../../Proteasomal_Cleavage_Predictions/SarsCoVCleavage/'
-print(files)
-
-for file in files:
- keep = []
- indices = []
- with open(prefix + file, 'r') as f:
- for line in f:
- line = line.lstrip()
- if line.startswith('#') or line.startswith('-'):
- continue
- if line.startswith('pos'):
- indices.append(len(keep))
- line = re.sub(pattern='\s+', repl='\t', string=line)
- line = line.strip() + '\n'
- keep.append(line)
- continue
- if re.match('^\d', line):
- match = re.search(r'\s+(\D+\d*\D*)(\s*_*\t*)\n', line)
- if match:
- ident = match.group(1)
- line = line.replace(match.group(1), '"' + ident + '"')
- line = re.sub(pattern='\s+', repl='\t', string=line)
- line = line.strip() + '\n'
- match2 = re.search(r'(\t_\t\D*)\"', line)
- if match2:
- line = line.replace(match2.group(1), '')
- keep.append(line)
-
- if len(indices) == 2:
- with open('../../Proteasomal_Cleavage_Predictions/24SeptEmbeddingPredictions/MHCI/' + file.replace('.txt', '_reformat.txt'), 'w') as out:
- out.write(''.join(keep[indices[0]:indices[1]]))
-
- with open('../../Proteasomal_Cleavage_Predictions/24SeptEmbeddingPredictions/MHCI/' + file.replace('.txt', '_reformat_concat.txt'), 'w') as out:
- out.write(''.join(keep[indices[1]:-1]))
- else:
- with open('../../Proteasomal_Cleavage_Predictions/SarsCoVCleavage/' + file.replace('.txt', '_reformat.txt'), 'w') as out:
- out.write(''.join(keep[0]))
- for i in range(len(indices)):
- end = i+1
- if end >= len(indices):
- end = -1
- out.write(''.join(keep[indices[i]+1:indices[end]]))
diff --git a/ContextEmbedding/sars_netchop_to_pkl.py b/ContextEmbedding/sars_netchop_to_pkl.py
deleted file mode 100644
index 993ea4338b36a70d01a63d6b9f83dbe594a276ce..0000000000000000000000000000000000000000
--- a/ContextEmbedding/sars_netchop_to_pkl.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import pandas as pd
-import re
-
-keep = []
-indices = []
-with open('/home/sara/Documents/VaccinesProject/ivp/Proteasomal_Cleavage_Predictions/SarsCoVCleavage/NetChop3.1_sars_cov_2_predictions.txt') as file:
- for line in file:
- line = line.lstrip()
- if line.startswith('#') or line.startswith('-'):
- continue
- if line.startswith('pos'):
- indices.append(len(keep))
- line = re.sub(pattern='\s+', repl='\t', string=line)
- line = line.strip().split('\t')
- keep.append(line)
- continue
- if re.match('^\d', line):
- match = re.search(r'\s+(\D+)\n', line)
- if match:
- line = line.replace('Base', '').replace('_', '')
- line = re.sub(pattern='\s+', repl='\t', string=line)
- line = line.strip().split('\t')
- keep.append(line)
-
-print(keep)
-df = pd.DataFrame(keep)
-df = df.iloc[: , :-1]
-print(df)
-df.to_pickle('/home/sara/Documents/VaccinesProject/ivp/Proteasomal_Cleavage_Predictions/SarsCoVCleavage/NetChop3.1_sars_cov_2_predictions.pkl')
\ No newline at end of file
diff --git a/Evaluation/README.md b/Evaluation/README.md
deleted file mode 100644
index 76881f4c93c970d3cbac67ab37353b9883c59a38..0000000000000000000000000000000000000000
--- a/Evaluation/README.md
+++ /dev/null
@@ -1,3 +0,0 @@
-### Note
-
-Here, you can find the evaluation scripts used to create the plots of this thesis. However, they were designed for personal usage and may contain absolute paths and other specific adjustments. To execute the evaluations, you may need to do manual adjustments. `comparison_data.py` is the main script, where you add paths to the vaccine peptide sets you like to evaluate.
diff --git a/Evaluation/binarize_entries.py b/Evaluation/binarize_entries.py
deleted file mode 100644
index f232cdd968120314830d06ce81d425c567bb4306..0000000000000000000000000000000000000000
--- a/Evaluation/binarize_entries.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import pandas as pd
-
-
-def binarize_entries(df, th):
- for key in df.keys():
- df.loc[df[key] >= th, key] = 1.0
- df.loc[df[key] < th, key] = 0.0
- return df
-
-
-def set_small_to_zero(df, th):
- for key in df.keys():
- df.loc[df[key] < th, key] = 0.0
- return df
diff --git a/Evaluation/calculate_cleaved_peptides.py b/Evaluation/calculate_cleaved_peptides.py
deleted file mode 100644
index 529fd8180822876722cff0e1a2578e9c8d97e562..0000000000000000000000000000000000000000
--- a/Evaluation/calculate_cleaved_peptides.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import sys
-import pandas as pd
-
-
-def get_cleaved_peptides(cleavage):
- vax_seq = ''.join(cleavage['residue'])
- start = 0
- peptides_from_prediction = []
- # print(cleavage.iloc[0])
- # print(cleavage[cleavage['cleaved']])
- for outer in cleavage[cleavage['cleaved']].itertuples():
- outer = outer._asdict()
- # cut happens after cleaved = True but file count is 1-based
- next_start = outer['position']
- for inner in cleavage[(cleavage['cleaved']) & (cleavage['position'] > start)].itertuples():
- inner = inner._asdict()
- # 1-based!
- pos = inner['position']
- peptide = vax_seq[start:pos]
- if 8 <= len(peptide) <= 25:
- peptides_from_prediction.append(vax_seq[start:pos])
- start = next_start
-
- return list(set(peptides_from_prediction))
-
-
-def main(pepsickle_file, netchop_file):
- with open(pepsickle_file, 'r') as file:
- df_pep = pd.read_csv(file, sep='\t', lineterminator='\n')
-
- peptides_from_pepsickle = get_cleaved_peptides(df_pep)
-
- with open('/home/sara/Documents/VaccinesProject/ivp/Proteasomal_Cleavage_Predictions/SarsCoVCleavage/pepsickle_sars_cov_2_cleavage_peptides.txt', 'w') as out:
- out.write('\n'.join(peptides_from_pepsickle))
-
- with open(netchop_file, 'r') as file:
- df_net = pd.read_csv(file, sep='\s+', lineterminator='\n')
-
- df_net = df_net.rename(columns={'pos': 'position', 'AA': 'residue', 'C': 'cleaved'})
- df_net['cleaved'] = df_net['cleaved'].replace(['S'], True).replace(['.'], False)
-
- peptides_from_netchop = get_cleaved_peptides(df_net)
-
- with open(
- '/home/sara/Documents/VaccinesProject/ivp/Proteasomal_Cleavage_Predictions/SarsCoVCleavage/netchop_sars_cov_2_cleavage_peptides.txt',
- 'w') as out:
- out.write('\n'.join(peptides_from_netchop))
-
-
-if __name__ == '__main__':
- pepsickle = sys.argv[1]
- netchop = sys.argv[2]
- main(pepsickle, netchop)
-
diff --git a/Evaluation/calculate_protein_cleaved_peptides.py b/Evaluation/calculate_protein_cleaved_peptides.py
deleted file mode 100644
index 1fec70db488fca82d877d6d466273a7ee3e96b9a..0000000000000000000000000000000000000000
--- a/Evaluation/calculate_protein_cleaved_peptides.py
+++ /dev/null
@@ -1,110 +0,0 @@
-"""
-Compare cleavage prediction peptides of different (embedded) vaccines with cleavage predicted peptides from SARS-CoV-2
-proteome.
-"""
-import re
-import glob
-import pandas as pd
-import altair as alt
-from EvaluateVaccines import calculate_cleaved_peptides
-
-
-def read_peptides(file):
- peptides = []
- with open(file, 'r') as f:
- for line in f:
- pep = line.strip()
- peptides.append(pep)
- print(len(peptides))
- return peptides
-
-
-def transform_data(data):
- new_dict = {'embedding': [], 'tool': [], 'fraction': []}
- for key in data:
- new_dict['embedding'].extend([key, key])
- new_dict['tool'].extend(['NetChop3.1', 'Pepsickle'])
- new_dict['fraction'].extend([data[key]['NetChop3.1'], data[key]['Pepsickle']])
- return new_dict
-
-
-out_name = '23Aug_mhcI_cleaved_virus_proteins'
-sars_peptides_pepsickle = read_peptides('/home/sara/Documents/VaccinesProject/ivp/Proteasomal_Cleavage_Predictions/SarsCoVCleavage/pepsickle_sars_cov_2_cleavage_peptides.txt')
-sars_peptides_netchop = read_peptides('/home/sara/Documents/VaccinesProject/ivp/Proteasomal_Cleavage_Predictions/SarsCoVCleavage/netchop_sars_cov_2_cleavage_peptides.txt')
-
-intersection_count = {}
-files = glob.glob('/home/sara/Documents/VaccinesProject/ivp/Proteasomal_Cleavage_Predictions/23AugEmbeddingPredictions/MHCI/NetChop3.1_17Aug_mhcI_*_reformat.txt') + \
- ['/home/sara/Documents/VaccinesProject/ivp/Proteasomal_Cleavage_Predictions/23AugEmbeddingPredictions/MHCI/NetChop3.1_17Aug_mhcI_with_mutations_reformat_concat.txt']
-for file in files:
- match = re.search(r'_17Aug_(mhcI+(_\d+embedded)*(_hogvax)*)(_with_mutations)*(_reformat_concat)*', file)
- name = match.group(1).replace('_hogvax', '')
- if match.group(5):
- name += '_concat'
-
- df_net = pd.read_csv(file, sep='\s+', lineterminator='\n')
- df_net = df_net.rename(columns={'pos': 'position', 'AA': 'residue', 'C': 'cleaved'})
- df_net['cleaved'] = df_net['cleaved'].replace(['S'], True).replace(['.'], False)
-
- peptides_from_netchop = calculate_cleaved_peptides.get_cleaved_peptides(df_net)
-
- intersec = [p for p in peptides_from_netchop if p in sars_peptides_netchop]
- intersection_count[name] = {'NetChop3.1': len(intersec)/len(peptides_from_netchop)}
-
-for file in glob.glob('/home/sara/Documents/VaccinesProject/ivp/Proteasomal_Cleavage_Predictions/23AugEmbeddingPredictions/MHCI/pepsickle_17Aug_mhcI_*.txt'):
- match = re.search(r'_17Aug_(mhcI+(_\d+embedded)*(_hogvax)*)(_with_mutations)*', file)
- name = match.group(1).replace('_hogvax', '')
-
- df_pep = pd.read_csv(file, sep='\t', lineterminator='\n')
- df_pep = df_pep[df_pep['protein_id'] != 'HOGVAX']
- peptides_from_pepsickle = calculate_cleaved_peptides.get_cleaved_peptides(df_pep)
-
- intersec = [p for p in peptides_from_pepsickle if p in sars_peptides_pepsickle]
- if not name in intersection_count:
- print('ATTENTION! Is not in dict:', name)
- exit(-1)
-
- intersection_count[name]['Pepsickle'] = len(intersec) / len(peptides_from_pepsickle)
-
-# add concat data
-with open('/home/sara/Documents/VaccinesProject/ivp/Proteasomal_Cleavage_Predictions/23AugEmbeddingPredictions/MHCI/pepsickle_17Aug_mhcI_with_mutations.txt') as f:
- df_pep = pd.read_csv(file, sep='\t', lineterminator='\n')
- df_pep = df_pep[df_pep['protein_id'] != 'Concat']
- peptides_from_pepsickle = calculate_cleaved_peptides.get_cleaved_peptides(df_pep)
-
- intersec = [p for p in peptides_from_pepsickle if p in sars_peptides_pepsickle]
- if not name in intersection_count:
- print('ATTENTION! Is not in dict:', name)
- exit(-1)
-
- intersection_count['mhcI_concat']['Pepsickle'] = len(intersec) / len(peptides_from_pepsickle)
-
-
-print(intersection_count)
-df = pd.DataFrame(transform_data(intersection_count))
-print(df)
-
-# sorting = ['mhcII', 'mhcII_concat', 'mhcII_1embedded', 'mhcII_2embedded', 'mhcII_3embedded', 'mhcII_4embedded', 'mhcII_5embedded', 'mhcII_6embedded', 'mhcII_7embedded', 'mhcII_8embedded', 'mhcII_9embedded', 'mhcII_10embedded']
-sorting = ['mhcI', 'mhcI_concat', 'mhcI_1embedded', 'mhcI_2embedded', 'mhcI_3embedded', 'mhcI_4embedded', 'mhcI_5embedded', 'mhcI_6embedded', 'mhcI_7embedded', 'mhcI_8embedded', 'mhcI_9embedded', 'mhcI_10embedded']
-colors = ['#FFAD00', '#3A7A9B']
-bar = alt.Chart(df).mark_bar(size=25).encode(
- x=alt.X('tool:O', axis=alt.Axis(title=None, labels=False, ticks=False)),
- y=alt.Y('fraction:Q', title='Fraction of cleaved peptides'),
- color=alt.Color('tool:O', scale=alt.Scale(range=colors), legend=alt.Legend(title='Tool')),
- column=alt.Column('embedding:O', title='Cleaved peptides that are equally predicted from SARS-CoV-2 proteome for different embedding lengths', sort=sorting)
- ).configure_axis(
- # grid=False
- ).configure_view(
- strokeOpacity=0
- ).properties(
- width=90
- )
-
-bar.save('cleavage_plots/' + out_name + '.html')
-
-
-# all_epi_ft = pd.read_pickle('/home/sara/Documents/VaccinesProject/ivp/Gifford_Data/AllEpitopeFeatures.pkl')
-# known_peptides_pepsickle = [p for p in sars_peptides_pepsickle if p in all_epi_ft.index]
-# print(len(known_peptides_pepsickle))
-# known_peptides_netchop = [p for p in sars_peptides_netchop if p in all_epi_ft.index]
-# print(len(known_peptides_netchop))
-
diff --git a/Evaluation/compare_pep_count_vs_score.py b/Evaluation/compare_pep_count_vs_score.py
deleted file mode 100644
index 0dcdec92774718d3bb65020bb85755070ad0fce4..0000000000000000000000000000000000000000
--- a/Evaluation/compare_pep_count_vs_score.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import re
-import glob
-import json
-import pandas as pd
-import altair as alt
-from ProcessData import read_peptides
-
-
-data = {'Peptide Count': [], 'Objective Value': [], 'Embedding': []}
-files = ['/home/sara/Documents/VaccinesProject/ivp/Pipeline/17Aug_mhcI_with_mutations/ilp/lp_out/27030_vaccine_ilp_hog.json'] + \
- glob.glob('/home/sara/Documents/VaccinesProject/ivp/Pipeline/17Aug_mhcI_*embedded/ilp/lp_out/*_vaccine_ilp_hog.json')
-for file in files:
- js = json.load(open(file))
- tmp = pd.DataFrame(js['SolutionInfo'])
- obj_val = tmp['ObjVal'][0]
-
- embed_len = re.search(r'17Aug_mhcI_(\d*)(embedded)*', file).group(1)
- if not embed_len:
- embed_len = 0
- else:
- embed_len = int(embed_len)
-
- pep_file = file.replace('lp_out', 'pep_out').replace('vaccine_ilp_hog.json', 'chosen_peptides_hog_inc_substrings.txt')
- peptides = read_peptides.main(pep_file)
- pep_count = len(peptides)
-
- data['Peptide Count'].append(pep_count)
- data['Objective Value'].append(obj_val)
- data['Embedding'].append(embed_len)
-
-print(data)
-df = pd.DataFrame(data)
-
-sorting = ['mhcI', 'mhcI_1embedded', 'mhcI_2embedded', 'mhcI_3embedded', 'mhcI_4embedded', 'mhcI_5embedded', 'mhcI_6embedded', 'mhcI_7embedded', 'mhcI_8embedded', 'mhcI_9embedded', 'mhcI_10embedded']
-domain = [df.min()['Objective Value'], (df.min()['Objective Value'] + df.max()['Objective Value'])/2, df.max()['Objective Value']]
-print(domain)
-# colors = ['#9AD6E5', '#66A6D9', '#BBB465', '#7A9B3A', '#DA0034', '#3A7A9B', '#FFAD00', '#9B3A7A', '#294B5D', '#DA2300', '#9B5B3A']
-colors = ['#7A9B3A', '#DA0034', '#3A7A9B']
-chart = alt.Chart(df).mark_circle(size=90).encode(
- x = alt.X('Embedding:Q'),
- y = alt.Y('Peptide Count'),
- # y = alt.Y('Objective Value', scale=alt.Scale(domain=[2, 2.8])),
- color = alt.Color('Objective Value', scale=alt.Scale(domain=domain, range=colors))
-)
-
-chart.save('cleavage_plots/embedding_vs_pepcount_vs_score_mhc1.html')
\ No newline at end of file
diff --git a/Evaluation/compare_single_vs_combined.py b/Evaluation/compare_single_vs_combined.py
deleted file mode 100644
index ff105cf1984a8c1a2507c1ad24699376563b8c05..0000000000000000000000000000000000000000
--- a/Evaluation/compare_single_vs_combined.py
+++ /dev/null
@@ -1,64 +0,0 @@
-# used this script for debugging
-import gzip
-import pandas as pd
-from ProcessData import read_peptides, binarize_entries, find_superstrings
-
-combined = '/home/sara/Documents/VaccinesProject/ivp/Pipeline/13July_mhcIandII/ilp/pep_out/41947_chosen_peptides_hog.txt'
-combined_substr = '/home/sara/Documents/VaccinesProject/ivp/Pipeline/13July_mhcIandII/ilp/pep_out/41947_chosen_peptides_hog_inc_substrings.txt'
-combined_peptides = read_peptides.main(combined)
-combined_peptides_substr = read_peptides.main(combined_substr)
-print(len(combined_peptides))
-
-# mhcI = '/home/sara/Documents/VaccinesProject/ivp/Pipeline/11July_filt_substrmhcI/ilp/pep_out/4512_chosen_peptides_hog_inc_substrings.txt'
-# mhcI_peptides = read_peptides.main(mhcI)
-# mhcII = '/home/sara/Documents/VaccinesProject/ivp/Pipeline/12July_filt_substrmhcII/ilp/pep_out/37435_chosen_peptides_hog_inc_substrings.txt'
-# mhcII_peptides = read_peptides.main(mhcII)
-
-# mhcI_equal = set(mhcI_peptides).intersection(combined_peptides)
-# print(len(mhcI_equal), 'of', len(mhcI_peptides))
-#
-# mhcII_equal = set(mhcII_peptides).intersection(combined_peptides)
-# print(len(mhcII_equal), 'of', len(mhcII_peptides))
-
-combined_ba = pd.read_pickle(gzip.open('/home/sara/Documents/VaccinesProject/ivp/Gifford_Data/25June_combined_mhc_netmhc_pred_affinity.pkl.gz'))
-combined_ba_substr = pd.read_pickle(gzip.open('/home/sara/Documents/VaccinesProject/ivp/Gifford_Data/substr_modified_25June_combined_mhc_netmhc_pred_affinity.pkl.gz'))
-# mhcI_ba = pd.read_pickle(gzip.open('../../Gifford_Data/25June_mhc1_netmhc-4.1_pred_affinity_pivot.pkl.gz'))
-# mhcII_ba = pd.read_pickle(gzip.open('../../Gifford_Data/25June_mhc2_netmhcii-4.1_pred_affinity_pivot_v1v2.pkl.gz'))
-
-combined_af = pd.read_pickle('../../Gifford_Data/IEDB_combined_population_frequency.pkl')
-# mhcI_af = pd.read_pickle('../../Gifford_Data/IEDB_population_frequency2392_normalized.pkl')
-# mhcII_af = pd.read_pickle('../../Gifford_Data/IEDB_population_frequency_mhc2_275normalized.pkl')
-
-print(set(combined_peptides_substr).intersection(combined_peptides))
-
-bin_ba = binarize_entries.binarize_entries(combined_ba, 0.638)
-bin_ba = bin_ba.loc[combined_peptides_substr]
-hit_alleles = bin_ba[[x for x in bin_ba if sum(bin_ba[x]) > 0]].columns
-score = sum(combined_af[[al for al in hit_alleles if al in combined_af]].loc['World'])
-print(score)
-
-bin_ba_sub = binarize_entries.binarize_entries(combined_ba_substr, 0.638)
-bin_ba_sub = bin_ba_sub.loc[combined_peptides]
-hit_alleles_sub = bin_ba_sub[[x for x in bin_ba_sub if sum(bin_ba_sub[x]) > 0]].columns
-score_sub = sum(combined_af[[al for al in hit_alleles_sub if al in combined_af]].loc['World'])
-print(score_sub)
-
-all_peptides = read_peptides.main('/home/sara/Documents/VaccinesProject/ivp/Gifford_Data/peptides/combined_filtered_mhc_peptides_gifford_style.pep')
-# superstrings = find_superstrings.main(all_peptides)
-super_covered_alleles = []
-for al in hit_alleles_sub:
- if al not in hit_alleles:
- super_covered_alleles.append(al)
- # for p in bin_ba_sub.index:
- # if bin_ba_sub[al][p] > 0.0 and combined_ba[al][p] == 0.0:
- # print('Peptide {} covering allele {} in merged data, but not in original data'.format(p, al))
- # substrings = superstrings[p]
- # print(substrings)
- # if not any(combined_ba[al][x] > 0.0 for x in substrings):
- # print('None of the substrings has non-zero value in original data')
- # else:
- # for x in substrings:
- # if x not in combined_peptides_substr:
- # print('why is {} not in combined substr peptied, but {} is in and superstr'.format(x, p))
-
-print(super_covered_alleles)
\ No newline at end of file
diff --git a/Evaluation/compare_size_to_coverage.py b/Evaluation/compare_size_to_coverage.py
deleted file mode 100644
index 7c761bb7b84d3f35f1eabc2429764b46f153bf04..0000000000000000000000000000000000000000
--- a/Evaluation/compare_size_to_coverage.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import os
-import glob
-import json
-import re
-import gzip
-import pandas as pd
-import altair as alt
-from EvaluateVaccines import evaluate_vaccine as ev
-from EvaluateVaccines import comparison_data as cd
-from ProcessData import read_peptides
-
-
-mhc1_all_ba = pd.read_pickle(gzip.open('../../Gifford_Data/25June_mhc1_netmhc-4.1_pred_affinity_pivot.pkl.gz'))
-mhc1_af = pd.read_pickle('../../Gifford_Data/IEDB_population_frequency2392_normalized.pkl')
-
-mhc2_all_ba = pd.read_pickle(gzip.open('../../Gifford_Data/25June_mhc2_netmhcii-4.1_pred_affinity_pivot_v1v2.pkl.gz'))
-mhc2_af = pd.read_pickle('../../Gifford_Data/IEDB_population_frequency_mhc2_275normalized.pkl')
-
-mhc1_scores = {'Size': [], 'Score': [], 'Method': []}
-for file in glob.glob('/home/sara/Documents/VaccinesProject/ivp/Gifford_Code/optivax_1-19_vaccines_mhc1/13Sept_optivax_unlinked_peptides_*.txt'):
- size = re.search(r'13Sept_optivax_unlinked_peptides_(\d+)', file).group(1)
- mhc1_scores['Size'].append(size)
-
- peptides = read_peptides.main(file)
- mhc1_scores['Score'].append(cd.get_cmp_obj(peptides, mhc1_all_ba, mhc1_af, smaller_to_zero=True).pred_at_least_one('World'))
- mhc1_scores['Method'].append('OptiVax')
-
-print(mhc1_scores)
-
-loci_cov = {'Locus': [], 'Score': [], 'Size': []}
-for hog_file in glob.glob('/home/sara/Documents/VaccinesProject/ivp/Pipeline/15Sept_mhcI_*/'):
- size = re.search(r'15Sept_mhcI_(\d+)', hog_file).group(1)
- mhc1_scores['Size'].append(size)
-
- peptides = read_peptides.main(hog_file + 'ilp/pep_out/4512_chosen_peptides_hog_inc_substrings.txt')
- mhc1_scores['Score'].append(cd.get_cmp_obj(peptides, mhc1_all_ba, mhc1_af, smaller_to_zero=True).pred_at_least_one('World'))
- mhc1_scores['Method'].append('HOGVAX')
-
-print(mhc1_scores)
-
-df = pd.DataFrame.from_dict(mhc1_scores)
-print(df)
-
-chart = alt.Chart(df).mark_line().encode(
- x='Size:Q',
- y=alt.Y('Score:Q', scale=alt.Scale(domain=[0.4, 1.0])),
- color=alt.Color('Method:N', scale=alt.Scale(range=['#DA0034', '#FFC12C']))
-)
-chart.save('plots/evalvax_cmp_mhc1.html')
-
-
-mhc2_scores = {'Size': [], 'Score': [], 'Method': []}
-for file in glob.glob('/home/sara/Documents/VaccinesProject/ivp/Gifford_Code/optivax_1-19_vaccines_mhc2/13Sept_optivax_unlinked_peptides_*.txt'):
- size = re.search(r'13Sept_optivax_unlinked_peptides_(\d+)', file).group(1)
- mhc2_scores['Size'].append(size)
-
- peptides = read_peptides.main(file)
- mhc2_scores['Score'].append(cd.get_cmp_obj(peptides, mhc2_all_ba, mhc2_af, smaller_to_zero=True).pred_at_least_one('World'))
- mhc2_scores['Method'].append('OptiVax')
-
-print(mhc2_scores)
-
-for hog_file in glob.glob('/home/sara/Documents/VaccinesProject/ivp/Pipeline/22Sept_mhc2_*/'):
- size = re.search(r'22Sept_mhc2_(\d+)', hog_file).group(1)
- mhc2_scores['Size'].append(size)
-
- pepfile = hog_file + 'ilp/pep_out/37435_chosen_peptides_hog_incl_substrings.txt'
- if not os.path.isfile(pepfile):
- pepfile = hog_file + 'ilp/pep_out/37435_chosen_peptides_hogvax_incl_substrings.txt'
- peptides = read_peptides.main(pepfile)
- mhc2_scores['Score'].append(cd.get_cmp_obj(peptides, mhc2_all_ba, mhc2_af, smaller_to_zero=True).pred_at_least_one('World'))
- mhc2_scores['Method'].append('HOGVAX')
-
-print(mhc2_scores)
-
-df = pd.DataFrame.from_dict(mhc2_scores)
-print(df)
-
-chart = alt.Chart(df).mark_line().encode(
- x='Size:Q',
- y=alt.Y('Score:Q', scale=alt.Scale(domain=[0.6, 1.0])),
- color=alt.Color('Method:N', scale=alt.Scale(range=['#DA0034', '#FFC12C']))
-)
-chart.save('plots/evalvax_cmp_mhc2.html')
\ No newline at end of file
diff --git a/Evaluation/comparison_data.py b/Evaluation/comparison_data.py
deleted file mode 100644
index 187138466296f881be6ef6406d01b7e564992602..0000000000000000000000000000000000000000
--- a/Evaluation/comparison_data.py
+++ /dev/null
@@ -1,302 +0,0 @@
-import gzip
-import pickle
-import os
-import pandas as pd
-import numpy as np
-import evaluate_vaccine as ev
-import plot_evaluation_results as plot
-import read_peptides, binarize_entries, create_haplotype_ba_predictions
-from collections import Counter
-
-
-def get_cmp_obj(peptides, all_ba, af, smaller_to_zero=False, binary=False, filter=True):
- # only keep peptide columns corresponding to chosen peptides that also exist in prediction file
- good_keys = all_ba.index.intersection(peptides)
- pep_ba = all_ba.loc[good_keys]
-
- if smaller_to_zero:
- pep_ba = binarize_entries.set_small_to_zero(pep_ba, 0.638)
-
- if binary:
- pep_ba = binarize_entries.binarize_entries(pep_ba, 0.638)
-
- if filter:
- if len(pep_ba.columns) != len(af.columns):
- columns_to_keep = [key for key in pep_ba if key in af]
- pep_ba = pep_ba[columns_to_keep]
- af = af[columns_to_keep]
-
- return ev.EvaluationMetrices(peptides, pep_ba, af)
-
-
-def collect_data_for_bar_plot(method_A, method_A_mhc1, method_A_mhc2, method_B, method_B_mhc1, method_B_mhc2,
- mhc1_all_ba, mhc1_af, mhc2_all_ba, mhc2_af, outdir):
-
- results = {'Method': [], 'Population Coverage': [], 'Number of Peptides': []}
-
- # mhc1
- peptides = read_peptides.main(method_A_mhc1)
- results['Method'].append('MHCI ' + method_A)
- results['Population Coverage'].append(get_cmp_obj(peptides, mhc1_all_ba, mhc1_af, smaller_to_zero=True).pred_at_least_one('World'))
- results['Number of Peptides'].append(len(peptides))
-
- peptides = read_peptides.main(method_B_mhc1)
- results['Method'].append('MHCI ' + method_B)
- results['Population Coverage'].append(get_cmp_obj(peptides, mhc1_all_ba, mhc1_af, smaller_to_zero=True).pred_at_least_one('World'))
- results['Number of Peptides'].append(len(peptides))
-
- # mhc2
- peptides = read_peptides.main(method_A_mhc2)
- results['Method'].append('MHCII ' + method_A)
- results['Population Coverage'].append(get_cmp_obj(peptides, mhc2_all_ba, mhc2_af, smaller_to_zero=True).pred_at_least_one('World'))
- results['Number of Peptides'].append(len(peptides))
-
- peptides = read_peptides.main(method_B_mhc2)
- results['Method'].append('MHCII ' + method_B)
- results['Population Coverage'].append(get_cmp_obj(peptides, mhc2_all_ba, mhc2_af, smaller_to_zero=True).pred_at_least_one('World'))
- results['Number of Peptides'].append(len(peptides))
-
- df = pd.DataFrame.from_dict(results)
- plot.EvaluationPlots(df, outdir).ilp_opitvax_bar_plot()
- df.to_csv(outdir + 'evalvax-unlinked.csv')
-
-
-def collect_data_for_locus_cov_pie(peptide_file, mhc, mhc_ba, af, loci, population, method, outdir):
- cov_loci = {}
- hit_loci = {}
- hit_loci_freq = {}
- peptides = read_peptides.main(peptide_file)
- for locus in loci:
- covered, unpred_alleles, unpred_frac, total, allele_hit_counts, hit_allele_freq = get_cmp_obj(peptides,
- mhc_ba,
- af,
- binary=True,
- filter=False
- ).number_covered_alleles(population, locus)
- uncovered = abs(total - covered - unpred_frac)
- values = [round(v, 4) for v in [covered, unpred_frac, uncovered]]
- cov_loci[locus] = {'allele_category': ['Covered', 'No-BA-prediction', 'Uncovered'], 'values': values}
- hit_loci[locus] = allele_hit_counts
-
- categories = []
- fractions = []
- for key in sorted(hit_allele_freq.keys(), key=float):
- categories.append(str(key))
- fractions.append(hit_allele_freq[key])
- hit_loci_freq[locus] = {'allele_category': categories + ['No-BA-prediction', 'Uncovered'],
- 'values': fractions + [unpred_frac, uncovered],
- 'method': [locus + method] * (len(categories) + 2)}
- print(hit_loci_freq)
-
- plot.EvaluationPlots(cov_loci, outdir).covered_locus_pie(loci, mhc, method)
- plot.EvaluationPlots(hit_loci, outdir).hit_pies(loci, mhc, method)
- plot.EvaluationPlots(hit_loci_freq, outdir).hit_covered_locus_pie(loci, mhc, method)
- return hit_loci_freq
-
-
-def collect_data_for_haplotype_eval(peptide_file, single_ba, hap_ba, af, max_int, mhc, method, outdir):
- peptides = read_peptides.main(peptide_file)
- single_ba = binarize_entries.binarize_entries(single_ba, 0.638).loc[[p for p in peptides if p in single_ba.index]]
- hit_dict = {}
- hit_n_dict = []
- for population in af.index:
- # hits is number of hits per genotype, hits_n is probability for n>=N hits per person in population
- hits, hits_n, exp, no_predictions = get_cmp_obj(peptides, hap_ba.T, af, binary=True).pred_genotype_hits(population, single_ba, max_int, mhc)
- hit_dict[population] = [hits, {'expected': [exp]}]
- tmp = pd.DataFrame(hits)
- tmp.to_csv(outdir + 'hit_hist_' + mhc + '_' + method + '.csv')
- # preparation for grouped bar plot
- hit_n_dict.append(hits_n | {'population': [population]*max_int})
- # plot histogram of peptide-hla hits
- plot.EvaluationPlots(hit_dict, outdir).hit_histogram(mhc, method)
-
- cur_probs = [np.array(dic['prob']) for dic in hit_n_dict]
- mean_probs = list(np.mean(cur_probs, axis=0))
- hit_n_dict.append({'prob': mean_probs, 'n': [i for i in range(1, max_int+1)], 'population': ['Average']*max_int})
-
- # reformat dict for grouped bar plots
- df = pd.concat(pd.DataFrame(entry) for entry in hit_n_dict)
- plot.EvaluationPlots(df, outdir).population_hit_bar_plot(mhc, method)
- df.to_csv(outdir + 'min_hit_dict' + mhc + '_' + method + '.csv')
-
-
-def compute_hap_freq_no_ld(population, allele_freq, loci):
- hap_data_values = []
- hap_data_labels = []
- for locus in loci:
- tmp = allele_freq[locus].loc[population]
- tmp = tmp[tmp > 0]
- if 'unknown' in tmp:
- tmp = tmp.drop('unknown')
- hap_data_labels.append(tmp.index)
- hap_data_values.append(tmp.values)
-
- hap_freq = []
- for i in range(len(hap_data_labels[0])):
- print(i)
- for j in range(len(hap_data_labels[1])):
- for k in range(len(hap_data_labels[2])):
- f = hap_data_values[0][i] * hap_data_values[1][j] * hap_data_values[2][k]
- hap_freq.append(((hap_data_labels[0][i], hap_data_labels[1][j], hap_data_labels[2][k]), f))
-
- unknown = 1 - sum(i for _, i in hap_freq)
- hap_freq.append((('unknown', 'unknown', 'unknown'), unknown))
- df = pd.DataFrame(hap_freq, columns=['Haplotype', population])
- df = df.set_index('Haplotype').T
-
- return df
-
-
-def collect_data_for_geographic_eval(threads, peptide_file, populations, allele_freq, single_ba, loci, mhc_type):
- peptides = read_peptides.main(peptide_file)
- hit_n_dict = []
- for population in populations:
- print(population)
- haplotype_data = compute_hap_freq_no_ld(population, allele_freq, loci)
- print('Calculated haplotype frequencies for ' + population)
- hap_ba = create_haplotype_ba_predictions.main(threads, single_ba, haplotype_data, loci, population, mhc_type)
- print('Calculated haplotype binding affinities')
- single_ba = binarize_entries.binarize_entries(single_ba, 0.638).loc[peptides]
- hits, hits_n, exp, no_predictions = get_cmp_obj(peptides, hap_ba.T, haplotype_data, binary=True).pred_genotype_hits(population, single_ba, 1, mhc_type)
- print('Calculated evaluation results')
- hit_n_dict.append(hits_n | {'population': population})
-
- with open('data/hit_predictions_no_ld.pkl', 'wb') as file:
- pickle.dump(hit_n_dict, file)
-
-
-def collect_data_for_composition_eval(peptide_file, feature_table, method, mhc_type, outdir):
- peptides = read_peptides.main(peptide_file)
- # composition = dict(Counter(feature_table.loc[peptides].protein))
- composition = dict(Counter(feature_table.loc[peptides].protein))
- df = pd.DataFrame(composition.items(), columns=['Protein', 'Count'])
- print(df)
- plot.EvaluationPlots(df, outdir).composition_plot(mhc_type, method)
-
-
-def call_single_evaluations(peptides, mhc_type, feature_table, allele_ba_df, haplotype_ba_df, allele_af_df, haplotype_af_df, loci,
- population, name, outdir, max_hit=10):
- # print('Create pie charts')
- # hit_dict = collect_data_for_locus_cov_pie(peptides, mhc_type, allele_ba_df, allele_af_df, loci, population, name, outdir)
- print('Create histograms for haplotype comparison')
- collect_data_for_haplotype_eval(peptides, allele_ba_df, haplotype_ba_df, haplotype_af_df, max_hit, mhc_type, name, outdir)
- # print('Plot vaccine composition')
- # collect_data_for_composition_eval(peptides, feature_table, mhc_type, name, outdir)
- # return hit_dict
-
-
-def new_df(dict_list):
- new_dict = {'allele_category': [], 'values': [], 'method': []}
- for dict in dict_list:
- for key in dict:
- new_dict['allele_category'].extend(dict[key]['allele_category'])
- new_dict['values'].extend(dict[key]['values'])
- new_dict['method'].extend(dict[key]['method'])
- df = pd.DataFrame(new_dict)
- return df
-
-
-def main():
- # optivax_mhc1 = '/home/sara/Documents/VaccinesProject/ivp/Gifford_Code/mhc1_optivax_unlinked_World_June28/28June_optivax_unlinked_mhc1_World_beam_peptides.txt'
- # optivax_mhc2 = '/home/sara/Documents/VaccinesProject/ivp/Gifford_Code/mhc2_optivax_unlinked_World_June28/28June_optivax_unlinked_mhc2_World_beam_peptides.txt'
- #
- # hogvax_mhc1 = '/home/sara/Documents/VaccinesProject/ivp/Pipeline/18Sept_mhcI_122peptides/ilp/pep_out/4512_chosen_peptides_hog_inc_substrings.txt'
- # hogvax_mhc2 = '/home/sara/Documents/VaccinesProject/ivp/Pipeline/18Sept_mhcII_1112/ilp/pep_out/37435_chosen_peptides_hog_inc_substrings.txt'
-
- # haplotypes
- optivax_mhc1 = '/home/sara/Documents/VaccinesProject/ivp/Gifford_Code/mhc1_optivax_robust_haplotype_modifications_July27/27July_optivax_robust_mhc1_beam_peptides.txt'
- optivax_mhc2 = '/home/sara/Documents/VaccinesProject/ivp/Gifford_Code/mhc2_optivax_robust_haplotype_modifications_July27/27July_optivax_robust_mhc2_beam_peptides.txt'
-
- hogvax_mhc1 = '/home/sara/Documents/VaccinesProject/ivp/Pipeline/28July_mhcI_haplotypes/ilp/pep_out/4519_chosen_peptides_hog_inc_substrings.txt'
- hogvax_mhc2 = '/home/sara/Documents/VaccinesProject/ivp/Pipeline/28July_mhcII_haplotypes/ilp/pep_out/37435_chosen_peptides_hog_inc_substrings.txt'
-
- # hogvax_mhc1_gen = '/home/sara/Documents/VaccinesProject/ivp/Pipeline/29Aug_mhcI_100k_genotypes_average/ilp/pep_out/4519_chosen_peptides_hog_inc_substrings.txt'
- # hogvax_mhc2_gen = '/home/sara/Documents/VaccinesProject/ivp/Pipeline/29Aug_mhcII_100k_genotypes_average/ilp/pep_out/37435_chosen_peptides_hog_inc_substrings.txt'
- #
- # optivax_mhc1_gen = '/home/sara/Documents/VaccinesProject/ivp/Gifford_Code/mhc1_optivax_robust_genotypes_July31/31July_optivax_robust_mhc1_genotype_peptides.txt'
- # optivax_mhc2_gen = '/home/sara/Documents/VaccinesProject/ivp/Gifford_Code/mhc2_optivax_robust_genotypes_July31/31July_optivax_robust_mhc2_genotype_peptides.txt'
-
- # optivax_mhc1_spike = '/home/sara/Documents/VaccinesProject/ivp/Gifford_Code/mhc1_optivax_unlinked_spike_protein/5Sep_optivax_unlinked_spike_peptides.txt'
- # optivax_mhc2_spike = '/home/sara/Documents/VaccinesProject/ivp/Gifford_Code/mhc2_optivax_unlinked_spike_protein/5Sep_optivax_unlinked_spike_peptides.txt'
- #
- # hogvax_mhc1_spike = '/home/sara/Documents/VaccinesProject/ivp/Code/HOGVAX/spike_new_mhc1/pep_out/639_chosen_peptides_hogvax_inc_substrings.txt'
- # hogvax_mhc2_spike = '/home/sara/Documents/VaccinesProject/ivp/Code/HOGVAX/spike_new_mhc2/pep_out/5064_chosen_peptides_hogvax_inc_substrings.txt'
-
- # concat_mhcI = '/home/sara/Documents/VaccinesProject/ivp/Pipeline/28July_mhcI/new_concat(not using mod ba predictions)/naively_chosen_peptides.txt'
- # concat_mhcII = '/home/sara/Documents/VaccinesProject/ivp/Pipeline/28July_mhcII/new_concat(no mod ba data)/naively_chosen_peptides.txt'
-
- # evaluation for combined mhcI and II vaccine
- # comb_peptide_file = '/home/sara/Documents/VaccinesProject/ivp/Pipeline/25Sept_mhcIandII/ilp/pep_out/41947_chosen_peptides_hog_inc_substrings.txt'
-
- all_epitope_features = pd.read_pickle('../../Gifford_Data/AllEpitopeFeatures.pkl')
-
- mhc1_all_ba = pd.read_pickle(gzip.open('../../Gifford_Data/25June_mhc1_netmhc-4.1_pred_affinity_pivot.pkl.gz'))
- mhc1_af = pd.read_pickle('../../Gifford_Data/IEDB_population_frequency2392_normalized.pkl')
-
- mhc2_all_ba = pd.read_pickle(gzip.open('../../Gifford_Data/25June_mhc2_netmhcii-4.1_pred_affinity_pivot_v1v2.pkl.gz'))
- mhc2_af = pd.read_pickle('../../Gifford_Data/IEDB_population_frequency_mhc2_275normalized.pkl')
-
- mhc1_haplotype_ba = pd.read_pickle(gzip.open('../../Gifford_Data/all_mhc1_predictions_for_haplotypes.pkl.gz')).T
- mhc1_haplotype_freq = pd.read_pickle('../../Gifford_Data/haplotype_frequency_marry.pkl')
-
- mhc2_haplotype_ba = pd.read_pickle(gzip.open('../../Gifford_Data/all_mhc2_predictions_for_haplotypes.pkl.gz')).T
- mhc2_haplotype_freq = pd.read_pickle('../../Gifford_Data/haplotype_frequency_marry2.pkl')
-
- outdir = 'plots_haplotypes/'
- if not os.path.exists(outdir):
- os.mkdir(outdir)
-
- # collect data and create bar plot
- print('Create simple bar plot')
- collect_data_for_bar_plot('OptiVax-Unlinked', optivax_mhc1, optivax_mhc2, 'HOGVAX', hogvax_mhc1, hogvax_mhc2,
- mhc1_all_ba, mhc1_af, mhc2_all_ba, mhc2_af, outdir)
- # collect_data_for_bar_plot('Concat', concat_mhcI, concat_mhcII, 'HOGVAX', hogvax_mhc1, hogvax_mhc2,
- # mhc1_all_ba, mhc1_af, mhc2_all_ba, mhc2_af, outdir)
- # collect_data_for_bar_plot('Combined', comb_peptide_file, comb_peptide_file, 'HOGVAX', hogvax_mhc1, hogvax_mhc2,
- # mhc1_all_ba, mhc1_af, mhc2_all_ba, mhc2_af, outdir)
-
- print('Call eval for MHC1 HOGVAX')
- hits_alleles_hogvax_mhc1 = call_single_evaluations(hogvax_mhc1, 'mhc1', all_epitope_features,
- mhc1_all_ba, mhc1_haplotype_ba, mhc1_af, mhc1_haplotype_freq,
- ['HLA-A', 'HLA-B', 'HLA-C'], 'World', 'HOGVAX', outdir)
- print('Call eval for MHC1 OptiVax')
- hits_alleles_optivax_mhc1 = call_single_evaluations(optivax_mhc1, 'mhc1', all_epitope_features,
- mhc1_all_ba, mhc1_haplotype_ba, mhc1_af, mhc1_haplotype_freq,
- ['HLA-A', 'HLA-B', 'HLA-C'], 'World', 'OptiVax', outdir)
- print('Call eval for MHC2 HOGVAX')
- hits_alleles_hogvax_mhc2 = call_single_evaluations(hogvax_mhc2, 'mhc2', all_epitope_features,
- mhc2_all_ba, mhc2_haplotype_ba, mhc2_af, mhc2_haplotype_freq,
- ['DRB1', 'HLA-DP', 'HLA-DQ'], 'World', 'HOGVAX', outdir)
- print('Call eval for MHC2 OptiVax')
- hits_alleles_optivax_mhc2 = call_single_evaluations(optivax_mhc2, 'mhc2', all_epitope_features,
- mhc2_all_ba, mhc2_haplotype_ba, mhc2_af, mhc2_haplotype_freq,
- ['DRB1', 'HLA-DP', 'HLA-DQ'], 'World', 'OptiVax', outdir)
-
- # print('Plot Allele hit frequencies hogvax + optivax mhc2')
- # df = new_df([hits_alleles_hogvax_mhc2, hits_alleles_optivax_mhc2])
- # plot.EvaluationPlots(df, outdir).stacked_hit_covered_alleles('mhc2', 'hogvax_optivax_unlinked')
-
- # call_single_evaluations(concat_mhcI, 'mhc1', all_epitope_features, mhc1_all_ba, mhc1_haplotype_ba, mhc1_af, mhc1_haplotype_freq,
- # ['HLA-A', 'HLA-B', 'HLA-C'], 'World', 'Concat')
- # call_single_evaluations(concat_mhcII, 'mhc2', all_epitope_features, mhc2_all_ba, mhc2_haplotype_ba, mhc2_af, mhc2_haplotype_freq,
- # ['DRB1', 'HLA-DP', 'HLA-DQ'], 'World', 'Concat')
- #
- # call_single_evaluations(comb_peptide_file, 'mhc1', all_epitope_features, mhc1_all_ba, mhc1_haplotype_ba, mhc1_af,
- # mhc1_haplotype_freq, ['HLA-A', 'HLA-B', 'HLA-C'], 'World', 'Combined')
- # call_single_evaluations(comb_peptide_file, 'mhc2', all_epitope_features, mhc2_all_ba, mhc2_haplotype_ba, mhc2_af,
- # mhc2_haplotype_freq, ['DRB1', 'HLA-DP', 'HLA-DQ'], 'World', 'Combined')
- # countries = ['Europe',
- # 'Northeast Asia',
- # 'Oceania', 'East Africa',
- # 'East Asia', 'South Africa',
- # 'Southeast Asia',
- # 'Central Africa', 'North America',
- # 'South America', 'West Africa',
- # 'West Indies', 'South Asia',
- # 'North Africa', 'Southwest Asia']
- #
- # threads = 1
- # collect_data_for_geographic_eval(threads, ilp_mhc1, countries, mhc1_af, mhc1_all_ba, ['HLA-A', 'HLA-B', 'HLA-C'], 'mhc1')
-
-if __name__ == '__main__':
- main()
diff --git a/Evaluation/create_haplotype_ba_predictions.py b/Evaluation/create_haplotype_ba_predictions.py
deleted file mode 100644
index e2362ba91211ba76aa7fa7e2f2c87f305356d118..0000000000000000000000000000000000000000
--- a/Evaluation/create_haplotype_ba_predictions.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import argparse
-import sys
-import pandas as pd
-from ProcessData import read_peptides
-from multiprocessing import Pool
-from datetime import date
-
-
-def ba_for_haplotype(tup):
- k, keys, i = tup
- max_col = allele_ba_predictions[keys].max(axis=1)
- df = pd.DataFrame(max_col, columns=[k])
- return df
-
-
-def get_parser():
- """Get parser object for calculating haplotype / genotype ba predictions."""
-
- parser = argparse.ArgumentParser()
- parser.add_argument('--population', '-pop', dest='population', default='World', type=str,
- help='Target population. Default "World"')
- parser.add_argument('--peptides', '-pep', dest='peptides', required=True, type=str,
- help='Preprocessed peptide file with every peptide in a new line.')
- parser.add_argument('--frequencies', '-f', dest='f_data', required=True, type=str,
- help='(Normalized) haplotype / genotype frequency file.')
- parser.add_argument('--allele_binding_affinities', '-ba', dest='predictions', required=True, type=str,
- help='Binding affinity file for peptides and single alleles.')
- parser.add_argument('--type', '-t', dest='loci', required=True, type=str,
- help='Type of data that is processed, e.g. mhc1 for haplotype mhc1; mhc2 for haplotype mhc2;'
- 'mhc1_genotype for mhc1 genotype; mhc2_genotype for mhc2 genotype')
- parser.add_argument('--nworkers', '-n', dest='nworkers', default=1, type=int,
- help='Number of cores to use in parallel.')
-
- return parser
-
-
-def main():
- args = get_parser().parse_args()
-
- filtered_peptides = read_peptides.main(args.peptides)
- print('Read number of peptides', len(filtered_peptides))
-
- global allele_ba_predictions
- allele_ba_predictions = pd.read_pickle(args.predictions).loc[filtered_peptides]
- print('Loaded ba predictions', allele_ba_predictions.shape)
-
- haplotypes = pd.read_pickle(args.f_data).keys()
-
- if args.loci == 'mhc1':
- loci = ['HLA-A', 'HLA-B', 'HLA-C']
- elif args.loci == 'mhc2':
- loci = ['DRB1', 'HLA-DP', 'HLA-DQ']
- elif args.loci == 'mhc1_genotype':
- loci = ['HLA-A', 'HLA-A', 'HLA-B', 'HLA-B', 'HLA-C', 'HLA-C']
- elif args.loci == 'mhc2_genotype':
- loci = ['DRB1', 'DRB1', 'HLA-DP', 'HLA-DP', 'HLA-DQ', 'HLA-DQ']
- else:
- print('Type unknown! Choose from [mhc1, mhc2, mhc1_genotype, mhc2_genotype].')
- exit(-1)
-
- print('Start collecting keys')
- hap_list = []
- for i, key in enumerate(haplotypes):
- if i % 10000 == 0:
- print(i, 'of', len(haplotypes))
- key = ['unknown' if x=='nope' else x for x in key]
- keys = list(zip(loci, key))
- if all(k in allele_ba_predictions for k in keys):
- hap_list.append((tuple(key), keys, i))
- print('Processed all keys in genotypes / haplotypes')
-
- print('Create threads')
- pool = Pool(processes=args.nworkers)
- outputs = pool.map_async(ba_for_haplotype, hap_list)
-
- pool.close()
- pool.join()
-
- print('Finished all tasks')
- new_df = pd.concat([out for out in outputs.get()], axis=1)
- print('Finished finding maximum for each genotype / haplotype')
-
- today = date.today().strftime('%d-%b')
- new_df.to_pickle('../../Gifford_Data/' + today + '_' + args.population + '_predictions_for_' + args.loci +
- '_filtered_peptides.pkl.gz', compression='gzip')
-
- return new_df
-
-
-if __name__ == '__main__':
- main()
-
- countries = ['Europe',
- 'Northeast Asia',
- 'Oceania', 'East Africa',
- 'East Asia', 'South Africa',
- 'Southeast Asia',
- 'Central Africa', 'North America',
- 'South America', 'West Africa',
- 'West Indies', 'South Asia',
- 'North Africa', 'Southwest Asia']
-
- # for country in countries:
- # print(country)
- # haplotypes = pd.read_pickle('../EvaluateVaccines/data/haplotype_freq_no_ld_' + country + '.pkl')
- # main(threads, mhc1_ba_predictions, haplotypes, ['HLA-A', 'HLA-B', 'HLA-C'], country, 'mhc1')
-
- # haplotypes = pd.read_pickle('../EvaluateVaccines/data/haplotype_freq_no_ld_' + countries[8] + '.pkl')
- # main(threads, mhc1_ba_predictions, haplotypes, ['HLA-A', 'HLA-B', 'HLA-C'], countries[8], 'mhc1')
diff --git a/Evaluation/evaluate_cleavage_predictions.py b/Evaluation/evaluate_cleavage_predictions.py
deleted file mode 100644
index af1cf3414b48a3d12b0041783aefd39ac0da8124..0000000000000000000000000000000000000000
--- a/Evaluation/evaluate_cleavage_predictions.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import sys
-import pandas as pd
-from ProcessData import read_peptides
-from EvaluateVaccines import calculate_cleaved_peptides
-
-
-def count_pred_peptides(chosen_peptides, peptides_from_prediction):
- counter = 0
- peptides = []
- for pep in chosen_peptides:
- if pep in peptides_from_prediction:
- counter += 1
- peptides.append(pep)
-
- print(len(chosen_peptides), counter, sorted(peptides))
- return counter, peptides
-
-
-# read peptides chosen by ILP including all substrings
-peptide_file = sys.argv[1]
-chosen_peptides = read_peptides.main(peptide_file)
-
-# Note that netchop requires manually deleting header and footer lines + last column modification
-pepsickle_file = sys.argv[2]
-netchop_file = sys.argv[3]
-
-# csv file to write output
-out_file = sys.argv[4]
-
-concat_eval = False
-if len(sys.argv) > 5:
- concat_eval = True
-
-with open(pepsickle_file, 'r') as file:
- df_pep = pd.read_csv(file, sep='\t', lineterminator='\n')
- if concat_eval:
- df_pep = df_pep[df_pep['protein_id'] != 'HOGVAX']
- else:
- df_pep = df_pep[df_pep['protein_id'] != 'Concat']
-
-peptides_from_pepsickle = calculate_cleaved_peptides.get_cleaved_peptides(df_pep)
-count_pepsickle, pred_peptides_pepsickle = count_pred_peptides(chosen_peptides, peptides_from_pepsickle)
-
-
-with open(netchop_file, 'r') as file:
- df_net = pd.read_csv(file, sep='\s+', lineterminator='\n')
-
-df_net = df_net.rename(columns={'pos': 'position', 'AA': 'residue', 'C': 'cleaved'})
-df_net['cleaved'] = df_net['cleaved'].replace(['S'], True).replace(['.'], False)
-
-peptides_from_netchop = calculate_cleaved_peptides.get_cleaved_peptides(df_net)
-count_netchop, pred_peptides_netchop = count_pred_peptides(chosen_peptides, peptides_from_netchop)
-
-d = {'Chosen Peptides': chosen_peptides,
- 'Pepsickle Predicted Peptides': pred_peptides_pepsickle,
- 'NetChop Predicted Peptides': pred_peptides_netchop}
-print(d)
-df = pd.DataFrame.from_dict(d, orient='index')
-df = df.T
-df.to_csv(out_file)
-
-
-
diff --git a/Evaluation/evaluate_vaccine.py b/Evaluation/evaluate_vaccine.py
deleted file mode 100644
index 2f7711b63e6add9a3d9cf6d917b0d89027c95c34..0000000000000000000000000000000000000000
--- a/Evaluation/evaluate_vaccine.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import gzip
-import re
-import numpy as np
-import pandas as pd
-import read_peptides, binarize_entries
-
-
-class EvaluationMetrices(object):
- def __init__(self, peptides, binding_affinities, allele_freq):
- self.peptides = peptides
- self.binding_affinities = binding_affinities
- self.allele_freq = allele_freq
-
- def pred_at_least_one(self, population):
- """
- Calculate the probability that any person of the given population presents at least one peptide
- (based on Gifford EvalVax-Unlinked)
- """
- self.population = population
- loci_prob = 1
- for locus in self.binding_affinities.columns.levels[0]:
- loci_prob *= (1 - self.locus_prob(locus))
- loci_prob = 1 - loci_prob
- # print('Total prob', loci_prob)
- return loci_prob
-
- def locus_prob(self, locus):
- """ based on Gifford """
- # Equation 5
- alleles_ba = self.binding_affinities[locus]
- alleles_prob = 1 - np.prod(1-alleles_ba, axis=0)
- # Equation 6
- diploid_ba = 1 - np.outer((1-alleles_prob), (1-alleles_prob))
- np.fill_diagonal(diploid_ba, alleles_prob)
- diploid_ba = pd.DataFrame(diploid_ba, index=alleles_prob.index, columns=alleles_prob.index)
- # Equation 7
- diploid_af = np.outer(self.allele_freq[locus].loc[self.population], self.allele_freq[locus].loc[self.population])
- diploid_af = pd.DataFrame(diploid_af, index=self.allele_freq[locus].columns, columns=self.allele_freq[locus].columns)
- # sum up the multiplied values for each allele pair (done by np.sum automatically)
- prob = np.sum(np.sum(diploid_af * diploid_ba))
- # print(prob)
- return prob
-
- def get_all_genotypes(self, genotypes):
- # generate each genotype only once
- indices = genotypes.index
- for i in range(len(indices)):
- hap1 = genotypes.index[i]
- for j in range(0, len(indices)):#range(i, len(indices)):
- hap2 = genotypes.index[j]
- yield hap1, hap2
-
- def pred_genotype_hits(self, population, single_allele_ba, max_interest, mhc_type):
- """
- Calculate the probability that an individual of a specific population has exactly n peptide hla hits
- (based on Gifford EvalVax-Robust)
- """
- # Equation 1
- genotype_af = np.outer(self.allele_freq.loc[population], self.allele_freq.loc[population])
- genotype_af = pd.DataFrame(genotype_af, index=self.allele_freq.columns, columns=self.allele_freq.columns)
- # compute number of peptides binding to each allele e(a)
- hits_per_allele = np.sum(single_allele_ba)
- # Equation 2
- hits_per_genotype = {}
- for hap1, hap2 in self.get_all_genotypes(genotype_af):
- unique_alleles = set(hap1).union(set(hap2))
- count, no_predictions = self.get_genotype_counts(unique_alleles, hits_per_allele, mhc_type)
- if count in hits_per_genotype:
- hits_per_genotype[count].append((hap1, hap2))
- else:
- hits_per_genotype[count] = [(hap1, hap2)]
- # Equation3 count exact hits
- m = int(max(hits_per_genotype))
- probs = {'count': [], 'prob': []}
- for i in range(m + 1):
- if i in hits_per_genotype:
- frq = np.sum(genotype_af[h1][h2] for h1, h2 in hits_per_genotype[i])
- probs['count'].append(i)
- probs['prob'].append(frq)
- else:
- probs['count'].append(i)
- probs['prob'].append(0)
- # calculate expected value
- expected_value = np.sum(np.array(probs['count']) * np.array(probs['prob']))
- # Equation 4 for k in [1, max_interest] filter data frame for all counts >= k
- cur_df = pd.DataFrame.from_dict(probs)
- prob_n = {'n': [], 'prob': []}
- for n in range(1, max_interest + 1):
- prob_n['n'].append(n)
- # have to use converse probability
- prob_n['prob'].append(1-sum(cur_df[cur_df['count'] < n]['prob']))
- return probs, prob_n, expected_value, no_predictions
-
- def get_genotype_counts(self, alleles, hits_per_allele, mhc_type):
- count = 0
- no_ba_prediction = []
- for allele in alleles:
- if mhc_type == 'mhc1':
- reg = re.search(r'(HLA-[A-Z]+)(\d)', allele)
- locus = reg.group(1)
- else:
- reg = re.search(r'HLA-DP|HLA-DQ|DRB1', allele)
- locus = reg.group(0)
- if (locus, allele) in hits_per_allele:
- count += hits_per_allele[locus, allele]
- else:
- no_ba_prediction.append((locus, allele))
- return int(count), no_ba_prediction
-
- def number_covered_alleles(self, population, locus):
- """
- Calculate the number of covered alleles of the population and with this metric the population coverage;
- Track information about alleles for which we don't have prediction data from netmhc.
- """
- # binding affinities only contain peptides from vaccine
- alleles_ba = self.binding_affinities[locus]
- alleles_fa = self.allele_freq[locus]
-
- # count hits per allele
- tmp = self.binding_affinities[locus].sum().to_frame().reset_index()
- tmp = tmp.rename(columns={'genotype':'count', 0:'hits'})
- allele_hit_counts = tmp.groupby('hits').count().reset_index()
- fractions = allele_hit_counts['count'] / sum(allele_hit_counts['count'])
- allele_hit_counts = allele_hit_counts.assign(frac=fractions)
-
- # check for all alleles with (entry >= 0.638) / non-zero entry -> sum up corresponding frequencies
- hit_allele_freq = {}
- filter = (alleles_ba != 0).any()
- covered_alleles = filter.index[filter]
- covered_fraction = 0
- for allele in covered_alleles:
- if allele in alleles_fa:
- covered_fraction += alleles_fa[allele][population]
- hit = tmp[tmp['count'] == allele]['hits'].values[0]
- if hit in hit_allele_freq:
- hit_allele_freq[hit] += alleles_fa[allele][population]
- else:
- hit_allele_freq[hit] = alleles_fa[allele][population]
-
- no_predictions = []
- fraction_no_predictions = 0
- for allele in alleles_fa:
- if allele == 'unknown':
- no_predictions.append(allele)
- fraction_no_predictions += alleles_fa[allele][population]
- if allele not in alleles_ba:
- no_predictions.append(allele)
- fraction_no_predictions += alleles_fa[allele][population]
-
- total = sum(alleles_fa.loc[population])
- return covered_fraction, no_predictions, fraction_no_predictions, total, allele_hit_counts, hit_allele_freq
diff --git a/Evaluation/get_gifford_vax_length.py b/Evaluation/get_gifford_vax_length.py
deleted file mode 100644
index af12e5a0e621936252e65b272997290c5ca74359..0000000000000000000000000000000000000000
--- a/Evaluation/get_gifford_vax_length.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import glob
-import re
-
-def get_vax_len(in_file, out_file):
- out = open(out_file, 'w')
- peptide_list = []
- with open(in_file) as file:
- for line in file:
- peptides_, score = line.split(' ')
- peptide_list = peptides_.split('_')
- for p in peptide_list:
- out.write(p + '\n')
- break
-
- concat_seq = ''.join(peptide_list)
- seq_len = len(concat_seq)
- out.write('# Total length ' + str(seq_len))
-
- print(concat_seq)
- print(seq_len)
- return seq_len
-
-
-dict_k = {}
-for path in glob.glob('/Users/sara/Documents/VaccinesProject/ivp/Gifford_Code/result_mhc2_maxround*'):
- print(path)
- size = re.search(r'maxround(\d+)', path).group(1)
- file = path + '/best_result.txt'
- out_file = path + '/13Sept_optivax_unlinked_peptides.txt'
- dict_k[size] = get_vax_len(file, out_file)
-
-print(dict_k)
-
-with open('../../Pipeline/snakemake_mhc2_call_file.sh', 'w') as f:
- for key in dict_k:
- f.write('snakemake --use-conda --cores 32 --config mhc=\'14Sept_mhcII_' + key + '\' k=' + str(dict_k[key]) + '\n')
diff --git a/Evaluation/plot_cleavage_predictions.py b/Evaluation/plot_cleavage_predictions.py
deleted file mode 100644
index f04c91772660467c6203cd89a574f16735f469b2..0000000000000000000000000000000000000000
--- a/Evaluation/plot_cleavage_predictions.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import glob
-import re
-import pandas as pd
-import altair as alt
-
-out_name = '24Sept_mhcI_cleavage_prediction_for_embedding_methods'
-
-data = {'embedding': [], 'tool': [], 'fraction': []}
-for file in glob.glob('/home/sara/Documents/VaccinesProject/ivp/Proteasomal_Cleavage_Predictions/24SeptEmbeddingPredictions/MHCI/CleavagePredictionsForEvaluation/*.csv'):
- match = re.search(r'_17Aug_(mhcI+(_\d+embedded)*_+hogvax)(_with_mutations)*(_concat)*', file)
- name = match.group(1).replace('_hogvax', '')
- if match.group(4):
- name += '_concat'
-
- tmp = pd.read_csv(file)
- tmp = tmp.drop(columns=tmp.columns[0])
- chosen_pep = len(tmp['Chosen Peptides'].dropna())
- pepsickle_pep = len(tmp['Pepsickle Predicted Peptides'].dropna())
- netchop_pep = len(tmp['NetChop Predicted Peptides'].dropna())
-
- check_a = [p for p in tmp['Pepsickle Predicted Peptides'] if p in list(tmp['Chosen Peptides'])]
- if len(check_a) != pepsickle_pep:
- print('Attention!')
- exit(-1)
-
- check_b = [p for p in tmp['NetChop Predicted Peptides'] if p in list(tmp['Chosen Peptides'])]
- if len(check_b) != netchop_pep:
- print('Attention!')
- exit(-1)
-
- data['embedding'].extend([name, name])
- data['tool'].extend(['NetChop3.1', 'Pepsickle'])
- data['fraction'].extend([netchop_pep/chosen_pep, pepsickle_pep/chosen_pep])
-
-data = pd.DataFrame.from_dict(data)
-print(data)
-
-# sorting = ['mhcII', 'mhcII_concat', 'mhcII_1embedded', 'mhcII_2embedded', 'mhcII_3embedded', 'mhcII_4embedded', 'mhcII_5embedded', 'mhcII_6embedded', 'mhcII_7embedded', 'mhcII_8embedded', 'mhcII_9embedded', 'mhcII_10embedded']
-sorting = ['mhcI', 'mhcI_concat', 'mhcI_1embedded', 'mhcI_2embedded', 'mhcI_3embedded', 'mhcI_4embedded', 'mhcI_5embedded', 'mhcI_6embedded', 'mhcI_7embedded', 'mhcI_8embedded', 'mhcI_9embedded', 'mhcI_10embedded']
-colors = ['#DA0034', '#3A7A9B']
-bar = alt.Chart(data).mark_bar(size=25).encode(
- x=alt.X('tool:O', axis=alt.Axis(title=None, labels=False, ticks=False)),
- y=alt.Y('fraction:Q', title='Cleaved peptides'),
- color=alt.Color('tool:O', scale=alt.Scale(range=colors), legend=alt.Legend(title='Tool')),
- column=alt.Column('embedding:O', title='Cleaved peptides for different context embedding lengths', sort=sorting)
- ).configure_axis(
- # grid=False
- ).configure_view(
- strokeOpacity=0
- ).properties(
- width=90
- )
-
-bar.save('cleavage_plots/' + out_name + '.html')
\ No newline at end of file
diff --git a/Evaluation/plot_evaluation_results.py b/Evaluation/plot_evaluation_results.py
deleted file mode 100644
index 1c904583a7b0d12ac902e53e453d46cf61070f59..0000000000000000000000000000000000000000
--- a/Evaluation/plot_evaluation_results.py
+++ /dev/null
@@ -1,199 +0,0 @@
-import altair_saver as saver
-import altair as alt
-import pandas as pd
-
-
-class EvaluationPlots(object):
- def __init__(self, data, outdir):
- self.colors = ['#9AD6E5', '#66A6D9', '#BBB465', '#FFC12C', '#DA0034', '#3A7A9B', '#FFAD00']
- self.data = data
- self.outdir = outdir
-
- def ilp_opitvax_bar_plot(self):
- min_ = round(min(self.data['Population Coverage']), 2) - 0.01
- bar = alt.Chart(self.data).mark_bar().encode(
- x='Method',
- y=alt.Y('Population Coverage:Q', scale=alt.Scale(domain=[min_,1.0]), axis=alt.Axis(titleColor='#3a7a9b')),
- color=alt.Color('Method',
- scale=alt.Scale(
- domain=self.data['Method'].tolist(),
- range=['#9ad6e5', '#3a7a9b', '#9ad6e5', '#3a7a9b']),
- legend=None)
- )
-
- crosses = alt.Chart(self.data).mark_circle(size=60, color='#FFAD00').encode(
- x='Method',
- y=alt.Y('Number of Peptides:Q', axis=alt.Axis(titleColor='#FFAD00'))
- )
-
- layered = alt.layer(bar, crosses).resolve_scale(
- y='independent'
- ).configure_axis(
- labelFontSize=20,
- titleFontSize=20
- )
-
- layered.save(self.outdir + 'unlinked_pop_coverage_bar.html')
-
- def covered_locus_pie(self, loci, mhc_type, method):
- charts = []
- for locus in loci:
- df = pd.DataFrame.from_dict(self.data[locus])
- pie = alt.Chart(df).encode(
- theta=alt.Theta('values:Q', stack=True),
- color=alt.Color('allele_category:N', scale=alt.Scale(range=self.colors))
- ).properties(
- title='Coverage of alleles at ' + locus
- )
-
- c1 = pie.mark_arc(stroke='#fff')
- c2 = pie.mark_text(outerRadius=168).encode(
- text='values:Q'
- )
-
- chart = (c1 + c2)
- charts.append(chart)
-
- layered = alt.hconcat(*[chart for chart in charts])
- layered.configure_view(
- strokeWidth=0
- ).configure_axis(
- labelFontSize=20,
- titleFontSize=20
- ).save(self.outdir + 'pies_covered_alleles_' + mhc_type + '_' + method + '.html')
-
- def hit_covered_locus_pie(self, loci, mhc_type, method):
- charts = []
- for locus in loci:
- df = pd.DataFrame.from_dict(self.data[locus])
- order = sorted(df.allele_category[:-2], key=float) + ['No-BA-prediction', 'Uncovered']
- pie = alt.Chart(df).transform_calculate(
- order=f"-indexof({order}, datum.Origin)"
- ).encode(
- theta=alt.Theta('values:Q', stack=True),
- color=alt.Color('allele_category:N', scale=alt.Scale(scheme='category20b')),
- order='order:Q'
- ).properties(
- title='Coverage of alleles at ' + locus
- )
-
- chart = pie.mark_arc(stroke='#fff')
- charts.append(chart)
-
- layered = alt.hconcat(*[chart for chart in charts]).resolve_scale(color='independent')
- layered.configure_view(
- strokeWidth=0
- ).configure_axis(
- labelFontSize=20,
- titleFontSize=20
- ).save(self.outdir + 'pies_hit_covered_alleles_' + mhc_type + '_' + method + '.html')
-
- def stacked_hit_covered_alleles(self, mhc_key, method_key):
- order = ['No-BA-prediction', 'Uncovered'] + sorted([x for x in self.data['allele_category'] if x!='No-BA-prediction' and x!='Uncovered'], key=float, reverse=True)
- chart = alt.Chart(self.data).mark_bar().encode(
- x='method',
- y='sum(values)',
- color=alt.Color('allele_category',
- scale=alt.Scale(scheme='viridis'),
- sort=order,
- legend=alt.Legend(columns=3, symbolLimit=0)),
- order='order:Q'
- )
- chart.show()
- chart.save(self.outdir + 'stacked_bar_hit_alleles_' + mhc_key + '_' + method_key + '.html')
-
- def hit_pies(self, loci, mhc_type, method):
- if mhc_type == 'mhc1':
- bins = int(max([self.data[loc]['hits'].max() for loc in loci])) + 1
- else:
- bins = 10
- for locus in loci:
- df = self.data[locus]
- pie = alt.Chart(df).encode(
- theta=alt.Theta('count', stack=True),
- color=alt.Color('hits', scale=alt.Scale(range=self.colors), bin=alt.Bin(maxbins=bins))
- ).properties(
- title='Coverage of alleles at ' + locus
- )
-
- # c1 = pie.mark_arc(stroke='#fff')
- c2 = pie.mark_text(outerRadius=168).encode(
- text='hits'
- )
-
- # chart = (c1 + c2)
- chart = pie.mark_arc() + c2
- chart.configure_view(
- strokeWidth=0
- ).configure_axis(
- labelFontSize=20,
- titleFontSize=20
- ).save(self.outdir + 'allele_hit_pie_' + locus + '_' + mhc_type + '_' + method + '.html')
-
- def hit_histogram(self, mhc, method):
- charts = []
- for population, data in self.data.items():
- df = pd.DataFrame.from_dict(data[0])
- hist = alt.Chart(df).mark_bar(color='#9AD6E5').encode(
- x=alt.X('count:Q',
- title='Number of peptide-HLA hits',
- bin=alt.Bin(maxbins=30)),
- y=alt.Y('prob:Q', title='Frequency')
- ).properties(
- title=population
- )
-
- exp = pd.DataFrame(data[1])
- line = alt.Chart(exp).mark_rule(size=4, color='#FFAD00').encode(
- x='expected:Q'
- )
-
- text = line.mark_text(
- align='left',
- baseline='middle',
- dx=7,
- color='#FFAD00'
- ).encode(
- text=alt.Text('expected', format=',.3f')
- )
-
- chart = (hist + line + text)
- charts.append(chart)
-
- layered = alt.hconcat(*[chart for chart in charts])
- layered.configure_view(
- strokeWidth=0
- ).configure_axis(
- labelFontSize=20,
- titleFontSize=20
- ).save(self.outdir + 'hit_histogram_' + mhc + '_' + method + '.html')
-
- def population_hit_bar_plot(self, mhc, method):
- sorting = sorted(list(set([p for p in self.data['population'] if not p == 'Average']))) + ['Average']
- bar = alt.Chart(self.data).mark_bar().encode(
- x=alt.X('population', axis=alt.Axis(title=None, labels=False, ticks=False), sort=sorting),
- y=alt.Y('prob:Q', title='Population coverage'),
- color=alt.Color('population', scale=alt.Scale(range=self.colors)),
- column=alt.Column('n', title='Minimum number of peptide-Hla hits')
- ).configure_axis(
- grid=False,
- labelFontSize=20,
- titleFontSize=20
- ).configure_view(
- strokeOpacity=0
- )
-
- bar.save(self.outdir + 'min_hit_number_bar_' + mhc + '_' + method + '.html')
-
- def composition_plot(self, mhc, method):
- bar = alt.Chart(self.data).mark_bar().encode(
- x=alt.X('Protein:N', axis=alt.Axis(labelAngle=0)),
- y='Count',
- color=alt.Color('Protein', scale=alt.Scale(range=self.colors))
- ).properties(
- width=230
- ).configure_axis(
- labelFontSize=20,
- titleFontSize=20
- )
- bar.save(self.outdir + 'composition_bar_' + mhc + '_' + method + '.html')
\ No newline at end of file
diff --git a/Evaluation/read_peptides.py b/Evaluation/read_peptides.py
deleted file mode 100644
index 58eb33be566a7381d2b55e6c4f9a058965552dc8..0000000000000000000000000000000000000000
--- a/Evaluation/read_peptides.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import sys
-
-
-def main(file):
- peptides = []
- for line in open(file, 'r'):
- if line.startswith('#'):
- continue
- pep = line.strip()
- peptides.append(pep)
- return peptides
-
-if __name__ == '__main__':
- f = sys.argv[1]
- main(f)
\ No newline at end of file
diff --git a/HOGVAX/__init__.py b/HOGVAX/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/HOGVAX/aho_corasick_trie.py b/HOGVAX/aho_corasick_trie.py
deleted file mode 100644
index 40e59c00b826d1e695dbaabadfa4642feff500e1..0000000000000000000000000000000000000000
--- a/HOGVAX/aho_corasick_trie.py
+++ /dev/null
@@ -1,139 +0,0 @@
-import os
-import pickle
-import pydot
-import networkx as nx
-
-
-def log(string):
- if logging_enabled:
- print(string)
-
-
-def get_next():
- global counter
- counter += 1
- return str(counter)
-
-
-def get_slink(c, node, graph):
- cur_node = node
- # loop until slink found and we return
- while True:
- for i in graph.successors(cur_node):
- if graph.nodes[i]['char'] == c:
- return i
- # if we are at root node and root has no child labeled with c, we return root node as slink
- if cur_node == '0':
- return cur_node
- # follow the suffix links further
- else:
- cur_node = graph.nodes[cur_node]['slink']
-
-
-def add_slinks(graph):
- queue = ['0']
- while queue:
- cur = queue.pop(0)
- for i in graph.successors(cur):
- queue.append(i)
- # children of root node have suffix link back to root
- if cur == '0':
- graph.nodes[i]['slink'] = '0'
- # for all other nodes, follow suffix link of parent and check for child with same label as node i
- else:
- pred_slink = graph.nodes[cur]['slink']
- char = graph.nodes[i]['char']
- graph.nodes[i]['slink'] = get_slink(char, pred_slink, graph)
-
-
-def get_or_add_node(c, prefix, node, graph):
- for i in graph.successors(node):
- if graph.nodes[i]['char'] == c:
- return i
- next_node = get_next()
- graph.add_node(next_node, char=c, string=prefix)
- graph.add_edge(node, next_node, weight=1)
- return next_node
-
-
-def calc_lps(word):
- lps_table = [0] * len(word)
- lps_len = 0 # longest prefix suffix length is 0 at beginning
- i = 1 # first entry is always 0 so we start at index 1
- while i < len(word):
- if word[i] == word[lps_len]:
- lps_len += 1
- lps_table[i] = lps_len
- i += 1
- else:
- if lps_len > 0:
- lps_len = lps_table[lps_len-1]
- else:
- i += 1
- return lps_table
-
-
-def insert(word, graph):
- cur_node = '0'
- word_nodes = [cur_node]
- lps = calc_lps(word)
- prefix = ''
- for c, l in zip(word, lps):
- prefix += c
- cur_node = get_or_add_node(c, prefix, cur_node, graph)
- word_nodes.append(cur_node)
- graph.nodes[cur_node]['ancestor'] = word_nodes[l]
- return cur_node
-
-
-def main(strings, out_dir, logging=True, dot_bool=False):
- global logging_enabled
- logging_enabled = logging
-
- global counter
- counter = -1
-
- DG = nx.DiGraph()
- root = get_next()
-
- # we calculate ancestor here already for linear time hog construction
- DG.add_node(root, string='', ancestor='')
-
- log('Build trie')
- leaves = {}
- for i, entry in enumerate(strings):
- word = entry
- log('Insert word ' + word)
- leaves[word] = insert(word, DG)
- log('Built trie')
-
- log('Add slinks')
- add_slinks(DG)
-
- if not os.path.exists(out_dir + 'pickle_files/'):
- os.mkdir(out_dir + 'pickle_files/')
- nx.write_gpickle(DG, out_dir + 'pickle_files/' + str(len(strings)) + '_aho_corasick.gpickle')
- with open(out_dir + 'pickle_files/' + str(len(strings)) + '_ac_leaves.pickle', 'wb') as handle:
- pickle.dump(leaves, handle, protocol=pickle.HIGHEST_PROTOCOL)
-
- # draw graph
- if dot_bool:
- if not os.path.exists(out_dir + 'vis/'):
- os.mkdir(out_dir + 'vis/')
-
- log('Write dot')
- dot = nx.drawing.nx_pydot.to_pydot(DG)
-
- for i, node in enumerate(dot.get_nodes()):
- attr = node.get_attributes()
- # use int here cause root is first node in list and it is easier to check for index here
- if i == 0:
- node.set_label('0 root')
- else:
- node.set_label(node.get_name() + ', ' + attr['string'])
- edge = pydot.Edge(node.get_name(), attr['slink'], color='blue', style='dashed')
- dot.add_edge(edge, )
-
- dot.write_png(out_dir + 'vis/aho_corasick_trie.png')
-
- return leaves, DG
\ No newline at end of file
diff --git a/HOGVAX/ba_for_embedded_peptides.py b/HOGVAX/ba_for_embedded_peptides.py
deleted file mode 100644
index 160625b2588dbb6fbe465257f797b036fb562d83..0000000000000000000000000000000000000000
--- a/HOGVAX/ba_for_embedded_peptides.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import os
-import pandas as pd
-import numpy as np
-
-
-def embedding_ba_predictions(embedded_epitope_features, ba_affinities, embedded_peptides, path):
- all_embedded = pd.read_pickle(embedded_epitope_features)
-
- mapped_peptides = pd.DataFrame(all_embedded.loc[embedded_peptides]['peptide']).reset_index(level=0).set_index('peptide')
- same_pep = list(set([p for p in mapped_peptides.index if p in ba_affinities.index]))
- mapped_peptides = mapped_peptides.loc[same_pep]
- filtered = ba_affinities.reset_index()
- filtered.Peptide = filtered.Peptide.apply(lambda x: mapped_peptides.loc[x]['embedded'] if x in same_pep else np.nan)
- filtered = filtered[filtered['Peptide'].notna()]
- filtered = filtered.set_index(['Peptide'])
- for embed in mapped_peptides['embedded']:
- if embed not in filtered.index:
- print(embed, ' is not in filtered index!!!')
- exit(-1)
-
- if not os.path.exists(path + 'data/'):
- os.mkdir(path + 'data/')
- filtered.to_pickle(path + 'data/' + str(len(embedded_peptides)) + '_embedding_ba_predictions.pkl.gz', compression='gzip')
- return filtered
diff --git a/HOGVAX/draw_hog.py b/HOGVAX/draw_hog.py
deleted file mode 100644
index 23b238e93b81a7c0308ed33b64fe8ac792e736db..0000000000000000000000000000000000000000
--- a/HOGVAX/draw_hog.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import networkx as nx
-
-
-def draw_hog(hog, leaves, vals, key, draw_all=False):
- if draw_all:
- SG = hog
- else:
- SG = hog.edge_subgraph([(node_a, node_b) for node_a, node_b in vals if round(vals[node_a, node_b], 0) == 1.0])
-
- dot = nx.drawing.nx_pydot.to_pydot(SG)
- for i, e in enumerate(dot.get_edges()):
- attr = e.get_attributes()
- e.set_label(attr['weight'])
- if attr['is_slink'] == 'yes':
- e.set_color('blue')
- e.set_style('dashed')
-
- node_a, node_b = e.obj_dict['points']
- if round(vals[node_a, node_b], 0) == 1.0:
- e.set_color('red')
- if node_a in leaves.values():
- dot.get_node(node_a)[0].set_style('filled')
- if node_b in leaves.values():
- dot.get_node(node_b)[0].set_style('filled')
- dot.get_node(node_a)[0].set_color('red')
- dot.get_node(node_b)[0].set_color('red')
-
- for i, node in enumerate(dot.get_nodes()):
- attr = node.get_attributes()
- node.set_label(node.get_name() + ', ' + attr['string'] + ', ' + attr['interval_size'])
-
- dot.write_png(key)
\ No newline at end of file
diff --git a/HOGVAX/example_arguments.txt b/HOGVAX/example_arguments.txt
deleted file mode 100644
index 6d73afd13b63bf5b75b59b701a15b6bb7c0e5a96..0000000000000000000000000000000000000000
--- a/HOGVAX/example_arguments.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-# MHC I single allele frequencies
-python hogvax.py --k 170 --outdir mhcI_unlinked --population World --peptides ../OptiVax_Data/Peptides/optivax_unlinked_mhc1_4512_filtered_peptides.pep --allele-frequencies ../OptiVax_Data/Frequencies/IEDB_population_frequency2392_normalized.pkl --ba-threshold 0.638 --binding-affinities ../OptiVax_Data/BindingAffinities/25June_mhc1_netmhc-4.1_pred_affinity_pivot.pkl.gz --min-hits 1 --verbose 1
-
-# MHC I haplotypye frequencies
-python hogvax.py --k 174 --outdir mhc1_robust --population White Black Asians --peptides ../OptiVax_Data/Peptides/optivax_unlinked_mhc1_4512_filtered_peptides.pep -af ../OptiVax_Data/Frequencies/haplotype_frequency_marry.pkl -t 0.638 -ba ../OptiVax_Data/BindingAffinities/all_mhc1_predictions_for_haplotypes.pkl.gz --min-hits 1
-
-# MHC II single allele frequencies
-python hogvax.py --k 322 --outdir mhc2_unlinked --population World --peptides ../OptiVax_Data/Peptides/optivax_unlinked_mhc2_37435_filtered_peptides.pep -af ../OptiVax_Data/Frequencies/IEDB_population_frequency_mhc2_275normalized.pkl -t 0.638 -ba ../OptiVax_Data/BindingAffinities/25June_mhc2_netmhcii-4.1_pred_affinity_pivot_v1v2.pkl.gz --min-hits 1
-
-# MHC II haplotype frequencies
-python hogvax.py --k 324 --outdir mhc2_robust --population White Black Asians --peptides ../OptiVax_Data/Peptides/optivax_unlinked_mhc2_haplotype_37435_filtered_peptides.pep -af .../OptiVax_Data/Frequencies/haplotype_frequency_marry2.pkl -t 0.638 -ba ../OptiVax_Data/BindingAffinities/all_mhc2_predictions_for_haplotypes.pkl.gz --min-hits 1
diff --git a/HOGVAX/filter_substrings_in_ba_predictions.py b/HOGVAX/filter_substrings_in_ba_predictions.py
deleted file mode 100644
index 31bbac8d0bfcb84532e548c50023acd581e84830..0000000000000000000000000000000000000000
--- a/HOGVAX/filter_substrings_in_ba_predictions.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import os
-import numpy as np
-import pandas as pd
-import find_superstrings
-
-
-def compare_row_entries(df, row_super):
- data = np.vstack([df.values, row_super.values])
- max_row = np.max(data, axis=0).reshape(row_super.shape)
- new_row = pd.DataFrame(max_row, index=row_super.index, columns=row_super.columns)
- return new_row
-
-
-def modify_ba_predictions(peptides, ba_df, path):
- # first read in all peptides and identify superstrings
- superstrings = find_superstrings.find_superstrings(peptides)
- print(len(superstrings))
-
- ba_df.replace([np.inf, -np.inf], np.nan, inplace=True)
- if ba_df.isnull().values.any():
- ba_df.fillna(0, inplace=True)
- for super in superstrings:
- new_row = compare_row_entries(ba_df.loc[superstrings[super]], ba_df.loc[[super]])
- ba_df.loc[[super]] = new_row
-
- # print(superstrings)
- if not os.path.exists(path + 'data/'):
- os.mkdir(path + 'data/')
- ba_df.to_pickle(path + 'data/' + str(len(peptides)) + '_substr_modified_ba_predictions.pkl.gz', compression='gzip')
-
- return ba_df
diff --git a/HOGVAX/find_superstrings.py b/HOGVAX/find_superstrings.py
deleted file mode 100644
index 0834a6ebe9a15c167aeddd77501b3726dc7a7941..0000000000000000000000000000000000000000
--- a/HOGVAX/find_superstrings.py
+++ /dev/null
@@ -1,16 +0,0 @@
-def find_superstrings(peptides):
- # sort peptides in reversed order
- sorted_peptides = sorted(peptides, key=len, reverse=True)
- # first read in all peptides and identify superstrings
- superstrings = {}
- for i, peptide in enumerate(sorted_peptides):
- if i % 1000 == 0:
- print(i, 'of', len(sorted_peptides))
- for j in range(i + 1, len(sorted_peptides)):
- sec_peptide = sorted_peptides[j]
- if sec_peptide in peptide:
- if peptide in superstrings:
- superstrings[peptide].append(sec_peptide)
- else:
- superstrings[peptide] = [sec_peptide]
- return superstrings
\ No newline at end of file
diff --git a/HOGVAX/get_all_peptides_incl_substrings.py b/HOGVAX/get_all_peptides_incl_substrings.py
deleted file mode 100644
index 1a460c42279d7c76d6c3bf9fd9b289905a1819f9..0000000000000000000000000000000000000000
--- a/HOGVAX/get_all_peptides_incl_substrings.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import sys
-import re
-import find_superstrings
-
-
-def get_all_peptides(all_peptides, vax_peptides):
- superstrings_mhc = find_superstrings.find_superstrings(all_peptides)
- pep_incl_substr = vax_peptides
- for pep in vax_peptides:
- if pep in superstrings_mhc:
- pep_incl_substr = pep_incl_substr + superstrings_mhc[pep]
-
- print(len(set(pep_incl_substr)))
- return set(pep_incl_substr)
-
-
-def remove_embedding(peptides, strip):
- stripped = [p[strip:-strip] for p in peptides]
- return stripped
-
-
-def recreate_unembedded_peptides(input_peptides, hogvax_peptides, embedding_length, path):
- # optional for embedding use embedding length as third argument
- if embedding_length > 0:
- print('################### Get all peptides inc substrings: embedded will be executed! ##################')
- hogvax_peptides = remove_embedding(hogvax_peptides, embedding_length)
-
- # use input of all peptides from which ilp may choose
- unembedded_substr_incl_peptides = get_all_peptides(input_peptides, hogvax_peptides)
-
- with open(path + 'pep_out/' + str(len(input_peptides)) + '_chosen_peptides_hogvax_inc_substrings.txt', 'w') as file:
- file.write('\n'.join(unembedded_substr_incl_peptides))
diff --git a/HOGVAX/hogvax.py b/HOGVAX/hogvax.py
deleted file mode 100644
index 3e59e4a8dd389f20cd7a218fbf9f49c82db3d60b..0000000000000000000000000000000000000000
--- a/HOGVAX/hogvax.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import argparse
-import os
-import gzip
-import pickle
-import networkx as nx
-import pandas as pd
-import gurobi as gp
-import aho_corasick_trie
-import linear_time_hog
-import filter_substrings_in_ba_predictions
-import ba_for_embedded_peptides
-import hogvax_ilp
-import draw_hog
-import get_all_peptides_incl_substrings
-
-
-def get_parser():
- """Get parser object for combinatorial vaccine design."""
- parser = argparse.ArgumentParser()
- parser.add_argument('--k', '-k', required=True, dest='k', type=int, help='Maximal length of vaccine sequence')
- parser.add_argument('--populations', '-pop', dest='populations', default=['World'], nargs='+',
- help='Target population(s). Default "World"')
- parser.add_argument('--peptides', '-pep', dest='peptides', required=True, type=str,
- help='Preprocessed peptide file with every peptide in a new line.')
- parser.add_argument('--allele-frequencies', '-af', dest='f_data', required=True, type=str,
- help='(Normalized) allele frequency file.')
- parser.add_argument('--ba-threshold', '-t', dest='ba_threshold', default=0.638, type=float,
- help='If provided, binding affinities are converted to binary data.')
- parser.add_argument('--binding-affinities', '-ba', dest='ba_matrix', required=True, type=str,
- help='Binding affinity file for input peptides and alleles.')
- parser.add_argument('--required_epitopes', '-epi', dest='required_epitopes',
- help='File of peptides you want to be present in vaccine')
- parser.add_argument('--min-hits', '-mh', dest='min_hits', default=1, type=int,
- help='Minimum number of hits for an allele to be covered')
- parser.add_argument('--maximize-peptides', dest='maximize_peptides', action='store_const', const=True,
- default=False, help='Maximize number of peptides in the vaccine in a second optimization')
- parser.add_argument('--embedding-length', default=0, type=int, help='Set length of embedding if used')
- parser.add_argument('--embedded-peptides', type=str, help='File containing embedded peptides')
- parser.add_argument('--embedded-epitope_features', type=str, help='Path to embedded epitope features')
- parser.add_argument('--outdir', '-o', default='', type=str, help='Output directory')
- parser.add_argument('--verbose', '-v', dest='logging_enabled', nargs='?')
-
- return parser
-
-
-def binarize_entries(df, th):
- for key in df.keys():
- df.loc[df[key] >= th, key] = 1.0
- df.loc[df[key] < th, key] = 0.0
- return df
-
-
-def read_peptides(file):
- peptides = []
- for line in open(file, 'r'):
- if line.startswith('#'):
- continue
- pep = line.strip()
- peptides.append(pep)
- return peptides
-
-
-def main():
- args = get_parser().parse_args()
-
- if args.outdir:
- if not args.outdir.endswith('/'):
- args.outdir = args.outdir + '/'
- if not os.path.exists(args.outdir):
- os.mkdir(args.outdir)
-
- # normal peptides
- print('Reading peptides')
- peptides = read_peptides(args.peptides)
- pep_count = len(peptides)
-
- required_peptides = []
- if args.required_epitopes:
- print('Reading required epitopes')
- required_peptides = read_peptides(args.required_epitopes)
-
- drawing_enabled = False
- if pep_count < 30:
- print('Number of peptides below 30 -> drawing enabled')
- drawing_enabled = True
-
- print('Read frequencies')
- f_data = pd.read_pickle(args.f_data)
- print('Read ba predictions')
- ba_matrix = pd.read_pickle(gzip.open(args.ba_matrix))
- print('Modify ba predictions for substrings')
- # use unembedded peptides for substr modifications
- mod_ba_df = filter_substrings_in_ba_predictions.modify_ba_predictions(peptides, ba_matrix, args.outdir)
- # if using embedded peptides
- if args.embedding_length > 0:
- print('Modify ba predictions for embedding')
- # use substr modified ba data and reset index to embedded peptides
- embedded_peptides = read_peptides(args.embedded_peptides)
- embed_count = len(embedded_peptides)
- mod_ba_df = ba_for_embedded_peptides.embedding_ba_predictions(args.embedded_epitope_features,
- mod_ba_df,
- embedded_peptides,
- args.outdir)
- print('Create HOG with embedded peptides')
- leaves_dict, hog = linear_time_hog.compute_hog(str(embed_count), embedded_peptides, args.outdir,
- drawing_enabled)
- else:
- print('Create HOG')
- leaves_dict, hog = linear_time_hog.compute_hog(str(pep_count), peptides, args.outdir, drawing_enabled)
-
- print('Binarize ba predictions')
- bin_matrix = binarize_entries(mod_ba_df, args.ba_threshold)
-
- full_known_alleles = gp.tuplelist(key for key in f_data.keys() if key in bin_matrix.keys())
-
- print('Start HOG ILP')
- hogvax_peptides = hogvax_ilp.hogvax(k=args.k,
- alleles=full_known_alleles,
- freq_vector=f_data,
- B_matrix=bin_matrix,
- leaves=leaves_dict,
- pep_count=str(pep_count),
- graph=hog,
- min_hits=args.min_hits,
- populations=args.populations,
- path=args.outdir,
- optional_peptides=required_peptides,
- maximize_peptides=args.maximize_peptides,
- logging=args.logging_enabled,
- coloring=drawing_enabled)
-
- print('Get all chosen peptides and their substrings')
- # recreate (unembedded) input peptides and get all their substrings
- get_all_peptides_incl_substrings.recreate_unembedded_peptides(peptides,
- hogvax_peptides,
- args.embedding_length,
- args.outdir)
-
-
-if __name__ == '__main__':
- main()
diff --git a/HOGVAX/hogvax_env.yaml b/HOGVAX/hogvax_env.yaml
deleted file mode 100644
index 82c6c49425555a5cba31e677befac041dfd37155..0000000000000000000000000000000000000000
--- a/HOGVAX/hogvax_env.yaml
+++ /dev/null
@@ -1,14 +0,0 @@
-name: hogvax_env
-channels:
- - conda-forge
- - gurobi
-dependencies:
- - contextlib2=21.6.0
- - pandas = 1.4.2
- - networkx = 2.8
- - pydot = 1.4.2
- - gurobi = 10.0.0
- - python = 3.9
- - pip
- - pip:
- - gurobipy==10.0.0
diff --git a/HOGVAX/hogvax_ilp.py b/HOGVAX/hogvax_ilp.py
deleted file mode 100644
index 9c79ddd95ea3657c12deb3417f07e73e48687cf2..0000000000000000000000000000000000000000
--- a/HOGVAX/hogvax_ilp.py
+++ /dev/null
@@ -1,221 +0,0 @@
-import os
-import numpy
-import random
-import draw_hog
-import gurobipy as gp
-import networkx as nx
-from gurobipy import GRB
-
-
-def log(string):
- if logging_enabled:
- print(string)
-
-
-# subtour elimination version 2
-def subtour_elim_boekler_paper(model, where):
- global save_for_later
- if where == GRB.Callback.MIPSOL:
- vals = model.cbGetSolution(model._x_edges)
- graph = model._graph
- # find cycle
- subtours = find_subtour(vals, graph)
- # sum over all incoming edges of node v in W <= sum of all incoming edges to node set W
- for subtour in sorted(subtours):
- subtour = sorted(list(subtour), key=int)
- log('## Subtour ' + ', '.join(subtour))
- save_for_later.append(subtour)
- model.cbLazy(1 <= gp.quicksum(model._x_edges[node_outer, node_inner] for node_outer, node_inner in sorted(model._x_edges)
- if node_inner in subtour and node_outer not in subtour),
- 'At least one incoming edge to subtour ' + '_'.join(subtour) + ' from the outside')
-
-
-def find_subtour(vals, graph):
- # chosen edges
- edges = gp.tuplelist((i, j) for i, j in vals.keys() if vals[i, j] >= 0.5)
- log('Subtours edges')
- log(edges)
- subgraph = graph.edge_subgraph(edges).copy()
- # finding strongly connected components with networkx contains a random factor: algorithm starts from random node
- # generate sorted list of strongly connected components to remove random factor of scc algorithm
- scc = sorted(nx.strongly_connected_components(subgraph), key=len, reverse=True)
- subtours = []
- for c in scc:
- if '0' not in c:
- subtours.append(c)
- return subtours
-
-
-def hogvax(k, alleles, freq_vector, B_matrix, leaves, pep_count, graph, path, min_hits=1, populations=['World'], optional_peptides=[], maximize_peptides=False, logging=False, coloring=False):
- global logging_enabled
- logging_enabled = logging
-
- # create new model
- m = gp.Model('ivp_on_hog')
- # set time limit to 48 hours
- m.setParam('TimeLimit', 172800)
- m.setParam('Seed', 42)
- random.seed(42)
- numpy.random.seed(42)
- # only for debugging, lp files becomes very large
- # if not os.path.exists(path + 'lp_out/'):
- # os.mkdir(path + 'lp_out/')
-
- # create hit variables
- log('Create hit variables')
- hit = gp.tupledict()
- for allele in alleles:
- hit[allele] = m.addVar(vtype=GRB.BINARY, name='_'.join(allele).replace(':', '-'))
-
- # create node variables
- log('Create node variables for peptide nodes')
- x_nodes = gp.tupledict()
- for node in leaves.values():
- x_nodes[node] = m.addVar(vtype=GRB.BINARY, name=graph.nodes[node]['string'])
-
- # create edge variables
- log('Create edge variables')
- x_edges = gp.tupledict()
- for node_a, node_b in sorted(graph.edges):
- x_edges[node_a, node_b] = m.addVar(vtype=GRB.INTEGER,
- name=graph.nodes[node_a]['string'] + '_' + graph.nodes[node_b]['string'])
-
- # the objective function is to maximize population coverage
- log('Set objective function')
- m.setObjective(gp.quicksum(hit[allele] * freq_vector[allele][pop] for pop in populations for allele in alleles),
- GRB.MAXIMIZE)
-
- # subject to the following constraints
- log('Add >>At least one outgoing edge from root<< constraint')
- m.addConstr(x_edges.sum('0', '*') >= 1, 'At least one outgoing edge from root')
-
- # optionally force include peptides in vaccine
- if optional_peptides:
- for p in optional_peptides:
- n = leaves[p]
- m.addConstr(x_nodes[n] >= 1, 'Must include peptide ' + p)
-
- log('Add >>Sum of edge weights below threshold k<< constraint')
- m.addConstr(gp.quicksum(x_edges[node_a, node_b] * graph.edges[node_a, node_b]['weight']
- for node_a, node_b in sorted(graph.edges)) <= k, 'Sum of edge weights below threshold k')
-
- log('Add flow conservation for nodes')
- for node in sorted(graph.nodes):
- m.addConstr(x_edges.sum('*', node) == x_edges.sum(node, '*'),
- 'Flow conservation at node ' + str(node))
-
- log('Add peptide node visiting constraint')
- for pep, node in leaves.items():
- m.addConstr(x_nodes[node] <= x_edges.sum('*', node), 'Node visiting constraint for ' + pep)
-
- '''
- without this update function, the data will be cached in some intermediate storage that might require more memory
- than the final storage to which the ilp will be transferred by update() and optimize().
- '''
- m.update()
- log('Add allele hit constraint')
- for allele in alleles:
- # check for peptides (leaf nodes) with non-zero entry in B matrix for current allele
- hit_pep = list(filter(lambda x: 1.0 == B_matrix[allele][x], leaves.keys()))
- # only sum over such peptides instead of summing over all peptides -> only beneficial for ba threshold > 0.xx
- m.addConstr(hit[allele] * min_hits <= gp.quicksum(x_nodes[leaves[pep]] for pep in hit_pep),
- 'Peptide-hit for allele ' + '_'.join(allele))
- m.update()
-
- # sub tour elimination with callbacks
- m._x_edges = x_edges
- m._graph = graph
- m.Params.LazyConstraints = 1
- # keep subtour to add them after solving to lp file: lazy constraints are not added to lp file otherwise
- global save_for_later
- save_for_later = []
- chosen_pep = []
- # Solve
- log('Now optimize:')
- m.optimize(subtour_elim_boekler_paper)
-
- # 2nd optimization: maximize number of peptides
- if maximize_peptides:
- # require optimal solution for first objective function
- assert m.Status == GRB.Status.OPTIMAL
- opt_coverage = m.getObjective()
- # set new objective function
- # maximize number of selected peptides
- m.setObjective(gp.quicksum(x_nodes), GRB.MAXIMIZE)
-
- # add previous objective function with objective value as constraint
- m.addConstr(gp.quicksum(hit[allele] * freq_vector[allele][pop] for pop in populations for allele in alleles) >= opt_coverage.getValue(),
- 'Add maximal population coverage as constraint')
- m.optimize(subtour_elim_boekler_paper)
-
- solution_edges = []
- if m.Status == GRB.OPTIMAL:
- sol = m.getAttr('X', x_edges)
- for edge in x_edges:
- node_a, node_b = edge
- # print('%s -> %s: %g' % (node_a, node_b, sol[node_a, node_b]))
- # variable tolerance of gurobi is 1e-5
- traversals = round(sol[node_a, node_b])
- for i in range(traversals):
- solution_edges.append(edge)
- if node_a in leaves.values():
- chosen_pep.append(graph.nodes[node_a]['string'])
- if node_b in leaves.values():
- chosen_pep.append(graph.nodes[node_b]['string'])
-
- # also only for more details and debugging
- # if not os.path.exists(path + 'lp_out/'):
- # os.mkdir(path + 'lp_out/')
- # m.write(path + 'lp_out/' + pep_count + '_vaccine_ilp_hog.sol')
- # m.write(path + 'lp_out/' + pep_count + '_vaccine_ilp_hog.json')
-
- if not os.path.exists(path + 'pep_out/'):
- os.mkdir(path + 'pep_out/')
-
- if coloring:
- draw_hog.draw_hog(graph, leaves, sol, path + 'pep_out/' + pep_count + '_colored_hog.png', True)
-
- # write chosen peptides to output file, remove empty string from root node
- if '' in chosen_pep:
- chosen_pep.remove('')
- chosen_pep = list(set(chosen_pep))
- with open(path + 'pep_out/' + pep_count + '_chosen_peptides_hog.txt', 'w') as file:
- file.write('\n'.join(chosen_pep))
-
- # create subgraph from edges chosen by ILP in order to find eulerian path to construct vaccine sequence
- # use multi graph here to add edges that are traversed more than once multiple times to the graph
- log('Create vaccine sequence')
- sub_hog = nx.MultiDiGraph()
- sub_hog.add_edges_from(solution_edges)
- log('sub graph is eulerian' + str(nx.is_eulerian(sub_hog)))
- if coloring:
- dot = nx.drawing.nx_pydot.to_pydot(sub_hog)
- dot.write_png(path + 'pep_out/' + pep_count + '_sub_hog.png')
- draw_hog.draw_hog(graph, leaves, sol, path + 'pep_out/' + pep_count + '_colored_sub_hog.png')
-
- hogvaxine = ''
- for e in nx.eulerian_circuit(sub_hog, source='0'):
- if graph.edges[e]['is_slink'] == 'yes':
- continue
- else:
- node_a, node_b = e
- length = graph.edges[e]['weight']
- node_seq = graph.nodes[node_b]['string']
- hogvaxine += node_seq[len(node_seq) - length:]
-
- with open(path + 'pep_out/' + pep_count + '_hogvaxine.txt', 'w') as out:
- out.write('> MHC optimized combined peptide vaccine sequence with overlaps\n')
- out.write(hogvaxine + '\n')
- out.write('> MHC optimized combined peptide vaccine sequence concatenated\n')
- out.write(''.join(chosen_pep))
-
- # after solving add lazy constraints to lp file
- for i, subtour in enumerate(save_for_later):
- m.addConstr(1 <= gp.quicksum(x_edges[node_outer, node_inner] for node_outer, node_inner in sorted(x_edges)
- if node_inner in subtour and node_outer not in subtour),
- 'At least one incoming edge to subtour ' + str(i) + ' from the outside')
-
- # only for debugging, lp file becomes quite large
- # m.write(path + 'lp_out/' + pep_count + '_vaccine_ilp_hog.lp')
-
- return chosen_pep
diff --git a/HOGVAX/linear_time_hog.py b/HOGVAX/linear_time_hog.py
deleted file mode 100644
index 5374100acf07ddfad5c9d178f7f04ead2cada1cf..0000000000000000000000000000000000000000
--- a/HOGVAX/linear_time_hog.py
+++ /dev/null
@@ -1,137 +0,0 @@
-import os
-import pydot
-import networkx as nx
-import aho_corasick_trie
-from collections import defaultdict
-
-
-def log(string):
- if logging_enabled:
- print(string)
-
-
-def contract_ac(unmarked_nodes, slinks, graph):
- for node in unmarked_nodes:
- log('Removing node ' + node)
- parent = list(graph.pred[node])[0]
- for succ in graph.successors(node):
- w = len(graph.nodes[succ]['string']) - len(graph.nodes[parent]['string'])
- graph.add_edge(parent, succ, weight=w)
- graph.nodes[succ]['char'] = graph.nodes[node]['char']
-
- if node in slinks:
- for link_node in slinks[node]:
- if link_node in graph.nodes():
- graph.nodes[link_node]['slink'] = graph.nodes[node]['slink']
- graph.remove_node(node)
- return graph
-
-
-def get_incoming_slinks(unmarked_nodes, graph):
- incoming_slinks = {}
- for node in graph.nodes():
- # skip root node
- if node == '0':
- continue
- # slink is incoming edge to slink_suc
- slink_suc = graph.nodes[node]['slink']
- # skip nodes that will not be removed
- if slink_suc not in unmarked_nodes:
- continue
-
- if slink_suc in incoming_slinks:
- incoming_slinks[slink_suc].append(node)
- else:
- incoming_slinks[slink_suc] = [node]
- return incoming_slinks
-
-
-def mark_nodes(leaves, graph):
- # root node is initially marked
- marked_nodes = {'0'}
- # for each node initialize child nodes as empty set
- children = defaultdict(lambda: set())
- for leaf in leaves.values():
- log('Current leaf ' + leaf)
- cur_node = leaf
- marked_nodes.add(cur_node)
- # while current node is not root node
- while cur_node != '0':
- interval = graph.nodes[cur_node]['interval_size']
- for child in children[cur_node]:
- interval -= graph.nodes[child]['interval_size']
- if interval > 0:
- marked_nodes.add(cur_node)
- children[cur_node] = set()
- children[graph.nodes[cur_node]['ancestor']].add(cur_node)
- cur_node = graph.nodes[cur_node]['slink']
-
- return marked_nodes
-
-
-def compute_interval_size(node, graph):
- log('Compute interval size for ' + node)
- if graph.out_degree(node) == 0:
- log('Is leaf ' + node)
- graph.nodes[node]['interval_size'] = 0
- return 1
- else:
- num_leaves = sum(compute_interval_size(suc, graph) for suc in graph.successors(node))
- graph.nodes[node]['interval_size'] = num_leaves
- return num_leaves
-
-
-def compute_hog(pep_count, peptides, path, logging=False, dot_bool=False):
- global logging_enabled
- logging_enabled = logging
-
- log('Call aho corasick construction')
- leaves_dict, aho_corasick = aho_corasick_trie.main(peptides, path, logging, dot_bool)
-
- # compute |R(u)| for each node u starting with root = 0
- log('Compute interval sizes')
- compute_interval_size('0', aho_corasick)
- # mark nodes
- log('Execute linear time algorithm')
- md_nodes = mark_nodes(leaves_dict, aho_corasick)
- log('Marked nodes' + ', '.join(md_nodes))
-
- # get nodes that we have to remove from aho corasick to get hog
- rm_nodes = set(aho_corasick.nodes) - md_nodes
- log('Nodes to be removed' + ', '.join(rm_nodes))
-
- # need to temporary store suffix link nodes for correct contraction
- incoming_slinks = get_incoming_slinks(rm_nodes, aho_corasick)
-
- # remove nodes and contract edges
- log('Contract aho corasick')
- hog = contract_ac(rm_nodes, incoming_slinks, aho_corasick)
-
- # set suffix links as explicit edges -> need this for ILP subtour elimination constraints
- nx.set_edge_attributes(hog, 'no', name='is_slink')
- slinks = [(i, hog.nodes[i]['slink'], {'weight': 0, 'is_slink': 'yes'}) for i in hog.nodes if i != '0']
- hog.add_edges_from(slinks)
-
- # write hog
- if not os.path.exists(path + 'pickle_files/'):
- os.mkdir(path + 'pickle_files/')
- nx.write_gpickle(hog, path + 'pickle_files/' + pep_count + '_hog.gpickle')
-
- if dot_bool:
- dot = nx.drawing.nx_pydot.to_pydot(hog)
- for i, e in enumerate(dot.get_edges()):
- attr = e.get_attributes()
- e.set_label(attr['weight'])
- if attr['is_slink'] == 'yes':
- e.set_color('blue')
- e.set_style('dashed')
-
- for i, node in enumerate(dot.get_nodes()):
- attr = node.get_attributes()
- node.set_label(node.get_name() + ', ' + attr['string'] + ', ' + attr['interval_size'])
-
- if not os.path.exists(path + 'vis/'):
- os.mkdir(path + 'vis/')
- dot.write_png(path + 'vis/' + pep_count + '_hog.png')
-
- return leaves_dict, hog
diff --git a/HOGVAX_logo.png b/HOGVAX_logo.png
deleted file mode 100644
index a5b5405b1f8969dd26c2f8746dbc4c3773819494..0000000000000000000000000000000000000000
Binary files a/HOGVAX_logo.png and /dev/null differ
diff --git a/LICENSE b/LICENSE
deleted file mode 100644
index 8eaa9973fa395635f48f2358b30e96ef52edd2a1..0000000000000000000000000000000000000000
--- a/LICENSE
+++ /dev/null
@@ -1,21 +0,0 @@
-The MIT License (MIT)
-
-Copyright (c) 2023 Sara Schulte, Alexander Dilthey, Gunnar Klau
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
diff --git a/Pipeline/Snakefile b/Pipeline/Snakefile
deleted file mode 100644
index 2079908c686fe11fa23a9a3dec82054d488d653d..0000000000000000000000000000000000000000
--- a/Pipeline/Snakefile
+++ /dev/null
@@ -1,250 +0,0 @@
-configfile: 'envs/config.yaml'
-
-# either embedded or original peptides
-peptides = []
-for line in open(config['peptides'], 'r'):
- pep = line.strip()
- peptides.append(pep)
-
-num_pep = sum(1 for line in open(config['peptides']))
-if config['instance_start_size'] == 'all':
- instances = [num_pep]
-else:
- instances = config['instance_start_size']
- while instances[-1] < num_pep:
- instances.append(instances[-1] * 2)
- instances[-1] = num_pep
-
-
-rule all:
- input:
- expand(config['output_dir'] + '/ilp/lp_out/{pep_count}_vaccine_ilp_{approach}.{ext}', pep_count=instances, approach=config['approach'], ext=['sol', 'lp', 'json']),
- expand(config['output_dir'] + '/ilp/pep_out/{pep_count}_vaccine_sequence_' + config['approach'] + '.txt', pep_count=instances),
- expand(config['output_dir'] + '/ilp/pep_out/{pep_count}_chosen_peptides_' + config['approach'] + '_inc_substrings.txt', pep_count=instances),
- expand(config['output_dir'] + '/ilp/lp_out/{pep_count}_vaccine_ilp_concat.{ext}', pep_count=instances, ext=['sol', 'lp', 'json']),
- expand(config['output_dir'] + '/ilp/pep_out/{pep_count}_concat_vaccine_sequence.txt', pep_count=instances)
-
-
-rule create_overlap_graph:
- input:
- peptides = config['peptides']
- output:
- config['output_dir'] + '/graph/{pep_count}_og.gpickle'
- params:
- pep = peptides
- conda:
- 'envs/graph.yaml'
- script:
- 'scripts/networkx_basic_graph.py'
-
-
-rule create_aho_corasick:
- input:
- peptides = config['peptides']
- output:
- config['output_dir'] + '/graph/{pep_count}_aho_corasick.gpickle',
- config['output_dir'] + '/graph/{pep_count}_ac_leaves.gpickle'
- params:
- pep = peptides,
- path = config['output_dir'] + '/graph/',
- log = config['logging']
- conda:
- 'envs/graph.yaml'
- script:
- 'scripts/aho_corasick_trie.py'
-
-
-rule create_hog:
- input:
- ac = config['output_dir'] + '/graph/{pep_count}_aho_corasick.gpickle',
- leaves = config['output_dir'] + '/graph/{pep_count}_ac_leaves.gpickle'
- output:
- config['output_dir'] + '/graph/{pep_count}_hog.gpickle'
- params:
- pep = peptides,
- path = config['output_dir'] + '/graph/',
- log = config['logging']
- conda:
- 'envs/graph.yaml'
- script:
- 'scripts/linear_time_hog.py'
-
-# input parameters for rule modify_ba_predictions_for_substrings
-def define_param():
- if config['unembedded']:
- pep = []
- for line in open(config['unembedded'], 'r'):
- p = line.strip()
- pep.append(p)
- return pep
- else:
- return peptides
-
-
-rule modify_ba_predictions_for_substrings:
- input:
- ba_predictions = config['binding_affinities']
- output:
- config['output_dir'] + '/data/substr_modified_ba_prediction_data.pkl.gz'
- params:
- pep = define_param()
- conda:
- 'envs/ilp.yaml'
- script:
- 'scripts/filter_substrings_in_ba_predictions.py'
-
-
-rule set_index_in_ba_predictions:
- input:
- embedded_epitope_features = config['embedded_epitope_features'],
- ba_predictions = config['output_dir'] + '/data/substr_modified_ba_prediction_data.pkl.gz'
- output:
- config['output_dir'] + '/data/embedded_substr_modified_ba_prediction_data.pkl.gz'
- params:
- pep = peptides
- conda:
- 'envs/ilp.yaml'
- script:
- 'scripts/binding_affinities_for_embedded_peptide.py'
-
-
-def define_input_ba(wildcards):
- if config['unembedded']:
- return config['output_dir'] + '/data/embedded_substr_modified_ba_prediction_data.pkl.gz'
- else:
- return config['output_dir'] + '/data/substr_modified_ba_prediction_data.pkl.gz'
-
-
-rule solve_hog_ilp:
- input:
- hog = config['output_dir'] + '/graph/{pep_count}_hog.gpickle',
- leaves = config['output_dir'] + '/graph/{pep_count}_ac_leaves.gpickle',
- frequency_data = config['frequency_data'],
- binding_affinity_data = define_input_ba
- output:
- expand(config['output_dir'] + '/ilp/lp_out/{{pep_count}}_vaccine_ilp_hog.{ext}', ext=['sol', 'lp', 'json']),
- config['output_dir'] + '/ilp/pep_out/{pep_count}_chosen_peptides_hog.txt'
- params:
- k = config['k'],
- min_hits = config['min_hits'],
- type = config['output_dir'],
- approach = config['approach'],
- binding_affinity_threshold = config['ba_threshold'],
- population = config['population'],
- pep = peptides,
- path = config['output_dir'] + '/ilp/'
- conda:
- 'envs/ilp.yaml'
- script:
- 'scripts/vaccine_data.py'
-
-
-rule solve_og_ilp:
- input:
- overlap_graph = config['output_dir'] + '/graph/{pep_count}_og.gpickle',
- frequency_data = config['frequency_data'],
- binding_affinity_data = define_input_ba
- output:
- expand(config['output_dir'] + '/ilp/lp_out/{{pep_count}}_vaccine_ilp_og.{ext}', ext=['sol', 'lp', 'json']),
- config['output_dir'] + '/ilp/pep_out/{pep_count}_chosen_peptides_og.txt'
- params:
- k = config['k'],
- min_hits = config['min_hits'],
- type = config['output_dir'],
- approach = config['approach'],
- binding_affinity_threshold = config['ba_threshold'],
- population = config['population'],
- pep = peptides,
- path = config['output_dir'] + '/ilp/'
- conda:
- 'envs/ilp.yaml'
- script:
- 'scripts/vaccine_data.py'
-
-
-# uses unembedded peptides
-rule solve_concat_ilp:
- input:
- frequency_data = config['frequency_data'],
- binding_affinity_data = config['binding_affinities']
- output:
- expand(config['output_dir'] + '/ilp/lp_out/{{pep_count}}_vaccine_ilp_concat.{ext}', ext=['sol', 'lp', 'json']),
- config['output_dir'] + '/ilp/pep_out/{pep_count}_chosen_peptides_concat.txt'
- params:
- k = config['k'],
- min_hits = config['min_hits'],
- type = config['output_dir'],
- approach = 'concat',
- binding_affinity_threshold = config['ba_threshold'],
- population = config['population'],
- pep = define_param(),
- path = config['output_dir'] + '/ilp/'
- conda:
- 'envs/ilp.yaml'
- script:
- 'scripts/vaccine_data.py'
-
-
-rule create_vaccine:
- input:
- sol = config['output_dir'] + '/ilp/lp_out/{pep_count}_vaccine_ilp_hog.sol',
- hog = config['output_dir'] + '/graph/{pep_count}_hog.gpickle',
- peptides = config['output_dir'] + '/ilp/pep_out/{pep_count}_chosen_peptides_hog.txt'
- output:
- config['output_dir'] + '/ilp/pep_out/{pep_count}_vaccine_sequence_hog.txt'
- conda:
- 'envs/graph.yaml'
- script:
- 'scripts/create_vac_seq_from_sol.py'
-
-
-rule create_vaccine_og:
- input:
- sol = config['output_dir'] + '/ilp/lp_out/{pep_count}_vaccine_ilp_og.sol',
- og = config['output_dir'] + '/graph/{pep_count}_og.gpickle',
- peptides = config['output_dir'] + '/ilp/pep_out/{pep_count}_chosen_peptides_og.txt'
- output:
- config['output_dir'] + '/ilp/pep_out/{pep_count}_vaccine_sequence_og.txt'
- conda:
- 'envs/graph.yaml'
- script:
- 'scripts/create_vac_from_og_sol.py'
-
-
-def define_input_params():
- if config['embedding_length']:
- pep = define_param()
- return {'pep': pep, 'embed_len': config['embedding_length']}
- else:
- return {'pep': peptides, 'embed_len': None}
-
-# need to get unembedded peptides back again
-rule get_all_sub_peptides:
- input:
- chosen_peptides = config['output_dir'] + '/ilp/pep_out/{pep_count}_chosen_peptides_{approach}.txt'
- output:
- config['output_dir'] + '/ilp/pep_out/{pep_count}_chosen_peptides_{approach}_inc_substrings.txt'
- params:
- **define_input_params()
- script:
- 'scripts/get_all_peptides_incl_substrings.py'
-
-
-rule create_concat_vaccine:
- input:
- config['output_dir'] + '/ilp/pep_out/{pep_count}_chosen_peptides_concat.txt'
- output:
- config['output_dir'] + '/ilp/pep_out/{pep_count}_concat_vaccine_sequence.txt'
- params:
- name = config['output_dir']
- run:
- peptides = []
- for line in open(input[0], 'r'):
- if line.startswith('#'):
- continue
- pep = line.strip()
- peptides.append(pep)
- seq = ''.join(peptides)
- with open(output[0], 'w') as out:
- out.write(params.name + ' concatenated peptides from basic approach\n')
- out.write(seq)
diff --git a/Pipeline/envs/config.yaml b/Pipeline/envs/config.yaml
deleted file mode 100644
index 9e90fa9e6403436fd057d94bf98fcb226db91791..0000000000000000000000000000000000000000
--- a/Pipeline/envs/config.yaml
+++ /dev/null
@@ -1,20 +0,0 @@
-output_dir: 'test'
-
-k: 15
-ba_threshold: 0.638
-population: ['World'] # must be a list of strings
-instance_start_size: 'all' # used for benchmarking; start size from where instance is iteratively increased
-logging: True
-approach: 'hog' # choose either hog or og
-min_hits: 1 # choose min number of hits for an allele / haplotype to be covered
-
-peptides: '../OptiVax_Data/Peptides/30_filtered_mhc_peptides.pep'
-frequency_data: '../OptiVax_Data/Frequencies/IEDB_population_frequency2392_normalized.pkl'
-binding_affinities: '../OptiVax_Data/BindingAffinities/25June_mhc1_netmhc-4.1_pred_affinity_pivot.pkl.gz'
-# optional: if use embedded peptides add non-embedded peptides here
-# embedded length is either an int value
-embedding_length: 0
-# unembedded is either the path to the unembedded peptides or False
-unembedded: False
-# this is either a path to the embedded epitope features or an empty string ''
-embedded_epitope_features: ''
diff --git a/Pipeline/envs/graph.yaml b/Pipeline/envs/graph.yaml
deleted file mode 100644
index 4d75039d0748d47585eefa113c3c130277274f0f..0000000000000000000000000000000000000000
--- a/Pipeline/envs/graph.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-channels:
- - conda-forge
-dependencies:
- - networkx = 2.8
- - pydot = 1.4.2
diff --git a/Pipeline/envs/ilp.yaml b/Pipeline/envs/ilp.yaml
deleted file mode 100644
index 0e2ce12a348d9126f28d958b0e3fed1e838ac219..0000000000000000000000000000000000000000
--- a/Pipeline/envs/ilp.yaml
+++ /dev/null
@@ -1,10 +0,0 @@
-channels:
- - conda-forge
- - gurobi
-dependencies:
- - contextlib2=21.6.0
- - pandas = 1.4.2
- - networkx = 2.8
- - pydot = 1.4.2
- - gurobi = 9.5.1
- - python = 3.9
diff --git a/Pipeline/scripts/aho_corasick_trie.py b/Pipeline/scripts/aho_corasick_trie.py
deleted file mode 100644
index c307609d5062851333402f32b00c3c21bc08d313..0000000000000000000000000000000000000000
--- a/Pipeline/scripts/aho_corasick_trie.py
+++ /dev/null
@@ -1,149 +0,0 @@
-import sys
-import pickle
-import pydot
-import networkx as nx
-
-
-def log(string):
- if logging_enabled:
- print(string)
-
-
-def get_next():
- global counter
- counter += 1
- return str(counter)
-
-
-def get_slink(c, node, graph):
- cur_node = node
- # loop until slink found and we return
- while True:
- for i in graph.successors(cur_node):
- if graph.nodes[i]['char'] == c:
- return i
- # if we are at root node and root has no child labeled with c, we return root node as slink
- if cur_node == '0':
- return cur_node
- # follow the suffix links further
- else:
- cur_node = graph.nodes[cur_node]['slink']
-
-
-def add_slinks(graph):
- queue = ['0']
- while queue:
- cur = queue.pop(0)
- for i in graph.successors(cur):
- queue.append(i)
- # children of root node have suffix link back to root
- if cur == '0':
- graph.nodes[i]['slink'] = '0'
- # for all other nodes, follow suffix link of parent and check for child with same label as node i
- else:
- pred_slink = graph.nodes[cur]['slink']
- char = graph.nodes[i]['char']
- graph.nodes[i]['slink'] = get_slink(char, pred_slink, graph)
-
-
-def get_or_add_node(c, prefix, node, graph):
- for i in graph.successors(node):
- if graph.nodes[i]['char'] == c:
- return i
- next_node = get_next()
- graph.add_node(next_node, char=c, string=prefix)
- graph.add_edge(node, next_node, weight=1)
- return next_node
-
-
-def calc_lps(word):
- lps_table = [0] * len(word)
- lps_len = 0 # longest prefix suffix length is 0 at beginning
- i = 1 # first entry is always 0 so we start at index 1
- while i < len(word):
- if word[i] == word[lps_len]:
- lps_len += 1
- lps_table[i] = lps_len
- i += 1
- else:
- if lps_len > 0:
- lps_len = lps_table[lps_len-1]
- else:
- i += 1
- return lps_table
-
-
-def insert(word, graph):
- cur_node = '0'
- word_nodes = [cur_node]
- lps = calc_lps(word)
- prefix = ''
- for c, l in zip(word, lps):
- prefix += c
- cur_node = get_or_add_node(c, prefix, cur_node, graph)
- word_nodes.append(cur_node)
- graph.nodes[cur_node]['ancestor'] = word_nodes[l]
- return cur_node
-
-
-def main(count, strings, path, graph_out, leaves_out, logging=False, dot_bool=False):
- global logging_enabled
- logging_enabled = logging
-
- global counter
- counter = -1
-
- DG = nx.DiGraph()
- root = get_next()
- # we calculate ancestor here already for linear time hog construction
- DG.add_node(root, string='', ancestor='')
-
- log('Build trie')
- leaves = {}
- for i, entry in enumerate(strings):
- word = entry
- log('Insert word ' + word)
- leaves[word] = insert(word, DG)
- log('Built trie')
-
- log('Add slinks')
- add_slinks(DG)
-
- nx.write_gpickle(DG, graph_out)
- with open(leaves_out, 'wb') as handle:
- pickle.dump(leaves, handle, protocol=pickle.HIGHEST_PROTOCOL)
-
- # draw graph
- if dot_bool:
- print('Write dot')
- dot = nx.drawing.nx_pydot.to_pydot(DG)
-
- for i, node in enumerate(dot.get_nodes()):
- attr = node.get_attributes()
- # use int here cause root is first node in list and it is easier to check for index here
- if i == 0:
- node.set_label('0 root')
- else:
- node.set_label(node.get_name() + ', ' + attr['string'])
- edge = pydot.Edge(node.get_name(), attr['slink'], color='blue', style='dashed')
- dot.add_edge(edge, )
-
- dot.write_png(path + count + '_aho_corasick.png')
-
-
-if __name__ == '__main__':
- count = int(snakemake.wildcards['pep_count'])
- strings = snakemake.params['pep'][:count]
- path = snakemake.params['path']
- logging = snakemake.params['log']
- out1 = snakemake.output[0]
- out2 = snakemake.output[1]
-
- # if count <= 30:
- # coloring = True
- # else:
- # coloring = False
- # TODO
- coloring = False
-
- main(str(count), strings, path, out1, out2, logging=logging, dot_bool=coloring)
diff --git a/Pipeline/scripts/binding_affinities_for_embedded_peptide.py b/Pipeline/scripts/binding_affinities_for_embedded_peptide.py
deleted file mode 100644
index 11f07aaa19cc4df229610d78c999bc3cbc8a3b7a..0000000000000000000000000000000000000000
--- a/Pipeline/scripts/binding_affinities_for_embedded_peptide.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import pandas as pd
-import numpy as np
-
-embedded_epitope_features = snakemake.input['embedded_epitope_features']
-ba_file = snakemake.input['ba_predictions']
-
-embedded_peptides = snakemake.params['pep']
-all_embedded = pd.read_pickle(embedded_epitope_features)
-ba_affinities = pd.read_pickle(ba_file)
-
-mapped_peptides = pd.DataFrame(all_embedded.loc[embedded_peptides]['peptide']).reset_index(level=0).set_index('peptide')
-same_pep = list(set([p for p in mapped_peptides.index if p in ba_affinities.index]))
-mapped_peptides = mapped_peptides.loc[same_pep]
-filtered = ba_affinities.reset_index()
-filtered.Peptide = filtered.Peptide.apply(lambda x: mapped_peptides.loc[x]['embedded'] if x in same_pep else np.nan)
-filtered = filtered[filtered['Peptide'].notna()]
-filtered = filtered.set_index(['Peptide'])
-for embed in mapped_peptides['embedded']:
- if embed not in filtered.index:
- print(embed, ' is not in filtered index!!!')
- exit(-1)
-filtered.to_pickle(snakemake.output[0], compression='gzip')
diff --git a/Pipeline/scripts/create_output_feature_table.py b/Pipeline/scripts/create_output_feature_table.py
deleted file mode 100644
index e7f7fee5faa1320445cdf3a27c05cfcda7c902f0..0000000000000000000000000000000000000000
--- a/Pipeline/scripts/create_output_feature_table.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import sys
-import pandas as pd
-
-
-def collect_features(peptides, all_features):
- ilp_out_features = []
- for pep in peptides:
- all = all_features.loc[pep]
- dict = {'Peptide': pep, 'Protein': all['protein'],
- 'Start_pos': all['start_pos'], 'Pep_len': len(pep),
- 'Mutation_prob': all['perc_mutated'], 'Glyco_prob': all['glyco_probs'],
- 'Cleavage_prob': all['crosses_cleavage'], 'SARS-CoV-1_peptide': all['In_SARS_Cov1']}
- ilp_out_features.append(dict)
- return ilp_out_features
-
-
-def read_peptides(file):
- peptides = []
- with open(file) as f:
- for line in f:
- peptides.append(line.strip('\n'))
- return peptides
-
-
-# all epitope freatures based on gifford data
-all_epi_features_file = sys.argv[1]
-# ilp peptide output
-peptides_mhcI_file = sys.argv[2]
-peptides_mhcII_file = sys.argv[3]
-
-all_epi_features = pd.read_pickle(all_epi_features_file)
-peptides_mhcI = read_peptides(peptides_mhcI_file)
-peptides_mhcII = read_peptides(peptides_mhcII_file)
-
-with pd.ExcelWriter('peptides_output/all_peptide_vaccine_features.xlsx', engine='openpyxl') as writer:
- features = collect_features(peptides_mhcI, all_epi_features)
- df = pd.DataFrame(features)
- df.to_excel(writer, sheet_name='Sheet1')
-
- features = collect_features(peptides_mhcII, all_epi_features)
- df = pd.DataFrame(features)
- df.to_excel(writer, sheet_name='Sheet2')
diff --git a/Pipeline/scripts/create_vac_from_og_sol.py b/Pipeline/scripts/create_vac_from_og_sol.py
deleted file mode 100644
index 8b2321747f90414ca79a7745a270a729d8966358..0000000000000000000000000000000000000000
--- a/Pipeline/scripts/create_vac_from_og_sol.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import sys
-import networkx as nx
-import read_peptides
-
-# lp output
-sol_file = snakemake.input['sol']
-# og
-og_file = snakemake.input['og']
-# ilp chosen pep output
-pep_file = snakemake.input['peptides']
-
-edges = []
-with open(sol_file) as f:
- for line in f:
- if line.startswith('#'):
- continue
- l = line.strip('\n').split(' ')
- # filter out hit variables named by alleles; DRB1 is not labelled with HLA prefix
- if 'HLA' in l[0] or 'DRB1' in l[0]:
- continue
- # filter for traversed edges
- if round(float(l[1])) == 0:
- continue
- edges.append(l[0])
-
-edge_dict = {}
-og = nx.read_gpickle(og_file)
-for edge in og.edges():
- key = '_'.join(edge)
- edge_dict[key] = edge
-
-# create subgraph from edges chosen by ILP in order to find eulerian path to construct vaccine sequence
-sub_og = nx.DiGraph()
-sub_og.add_edges_from([edge_dict[e] for e in edge_dict if e in edges])
-print(sub_og)
-print(nx.is_eulerian(sub_og))
-seq = ''
-if nx.has_eulerian_path(sub_og):
- print('Has Eulerian path')
- for e in nx.eulerian_path(sub_og):
- print(e)
- node_a, node_b = e
- length = og.edges[e]['weight']
- seq += node_b[len(node_b)-length:]
-else:
- print('Attention! No Eulerian path!!')
- exit(-1)
-
-print(seq)
-print(len(seq))
-
-chosen_peptides = read_peptides.main(pep_file)
-for pep in chosen_peptides:
- if not pep in seq:
- print('{} does not occur in overall sequence!'.format(pep))
- exit(-1)
-
-with open(snakemake.output[0], 'w') as out:
- out.write('> HOGVAX MHC optimized combined peptide vaccine sequence with overlaps\n')
- out.write(seq + '\n')
- out.write('> Concat MHC optimized combined peptide vaccine sequence concatenated\n')
- out.write(''.join(chosen_peptides))
diff --git a/Pipeline/scripts/create_vac_seq_from_sol.py b/Pipeline/scripts/create_vac_seq_from_sol.py
deleted file mode 100644
index 87da9d15c6cfd2d35278262af01f25daa13e9fd2..0000000000000000000000000000000000000000
--- a/Pipeline/scripts/create_vac_seq_from_sol.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import sys
-import networkx as nx
-import read_peptides
-
-# lp output
-sol_file = snakemake.input['sol']
-# hog
-hog_file = snakemake.input['hog']
-# ilp chosen pep output
-pep_file = snakemake.input['peptides']
-
-edges = []
-with open(sol_file) as f:
- for line in f:
- if line.startswith('#'):
- continue
- l = line.strip('\n').split(' ')
- # filter out hit variables named by alleles; DRB1 is not labelled with HLA prefix
- if 'HLA' in l[0] or 'DRB1' in l[0]:
- continue
- # filter for traversed edges
- if round(float(l[1])) == 0:
- continue
- edges.append(l[0])
-
-edge_dict = {}
-hog = nx.read_gpickle(hog_file)
-for edge in hog.edges():
- node_a, node_b = edge
- key = hog.nodes[node_a]['string'] + '_' + hog.nodes[node_b]['string']
- edge_dict[key] = edge
-
-# create subgraph from edges chosen by ILP in order to find eulerian path to construct vaccine sequence
-sub_hog = nx.MultiDiGraph()
-sub_hog.add_edges_from([edge_dict[e] for e in edge_dict if e in edges])
-print(sub_hog)
-print(nx.is_eulerian(sub_hog))
-seq = ''
-for e in nx.eulerian_circuit(sub_hog):
- if hog.edges[e]['is_slink'] == 'yes':
- continue
- else:
- node_a, node_b = e
- length = hog.edges[e]['weight']
- node_seq = hog.nodes[node_b]['string']
- seq += node_seq[len(node_seq)-length:]
-
-print(seq)
-print(len(seq))
-
-chosen_peptides = read_peptides.main(pep_file)
-for pep in chosen_peptides:
- if not pep in seq:
- print('{} does not occur in overall sequence!'.format(pep))
- exit(-1)
-
-with open(snakemake.output[0], 'w') as out:
- out.write('> HOGVAX optimized combined peptide vaccine sequence with overlaps\n')
- out.write(seq + '\n')
- out.write('> Concat optimized combined peptide vaccine sequence concatenated\n')
- out.write(''.join(chosen_peptides))
diff --git a/Pipeline/scripts/draw_output.py b/Pipeline/scripts/draw_output.py
deleted file mode 100644
index a3578de125a870b881af564d0c7f526d571658c2..0000000000000000000000000000000000000000
--- a/Pipeline/scripts/draw_output.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import networkx as nx
-
-
-def draw(G, vals, path):
- dot = nx.drawing.nx_pydot.to_pydot(G)
- for i, e in enumerate(dot.get_edges()):
- print(e)
- attr = e.get_attributes()
- e.set_label(attr['weight'])
-
- node_a, node_b = e.obj_dict['points']
- if round(vals[node_a, node_b], 0) == 1.0:
- e.set_color('red')
- dot.get_node(node_a)[0].set_color('red')
- if node_b == 'Sink':
- dot.get_node(node_b)[0].set_color('red')
- dot.write_png(path)
-
-
-def draw_colored_graph(graph, vals, path, draw_all=False):
- if draw_all:
- SG = graph
- else:
- SG = graph.edge_subgraph([(node_a, node_b) for node_a, node_b in vals if round(vals[node_a, node_b], 0) == 1.0])
- draw(SG, vals, path)
-
-
-def draw_hog(hog, leaves, vals, path, draw_all=False):
- if draw_all:
- SG = hog
- else:
- SG = hog.edge_subgraph([(node_a, node_b) for node_a, node_b in vals if round(vals[node_a, node_b], 0) == 1.0])
-
- dot = nx.drawing.nx_pydot.to_pydot(SG)
- for i, e in enumerate(dot.get_edges()):
- attr = e.get_attributes()
- e.set_label(attr['weight'])
- if attr['is_slink'] == 'yes':
- e.set_color('blue')
- e.set_style('dashed')
-
- node_a, node_b = e.obj_dict['points']
- if round(vals[node_a, node_b], 0) == 1.0:
- e.set_color('red')
- if node_a in leaves.values():
- dot.get_node(node_a)[0].set_style('filled')
- if node_b in leaves.values():
- dot.get_node(node_b)[0].set_style('filled')
- dot.get_node(node_a)[0].set_color('red')
- dot.get_node(node_b)[0].set_color('red')
-
- for i, node in enumerate(dot.get_nodes()):
- attr = node.get_attributes()
- node.set_label(node.get_name() + ', ' + attr['string'] + ', ' + attr['interval_size'])
-
- dot.write_png(path)
diff --git a/Pipeline/scripts/filter_substrings_in_ba_predictions.py b/Pipeline/scripts/filter_substrings_in_ba_predictions.py
deleted file mode 100644
index 24b722865c9032983cd1f6bfcc21f00a5bb4a4fd..0000000000000000000000000000000000000000
--- a/Pipeline/scripts/filter_substrings_in_ba_predictions.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import gzip
-import numpy as np
-import pandas as pd
-import find_superstrings
-import read_peptides
-
-
-def compare_row_entries(df, row_super):
- data = np.vstack([df.values, row_super.values])
- max_row = np.max(data, axis=0).reshape(row_super.shape)
- new_row = pd.DataFrame(max_row, index=row_super.index, columns=row_super.columns)
- return new_row
-
-
-ba_file = snakemake.input['ba_predictions']
-ba_df = pd.read_pickle(gzip.open(ba_file))
-peptides = snakemake.params['pep']
-print(len(peptides))
-
-# first read in all peptides and identify superstrings
-superstrings = find_superstrings.main(peptides)
-print(len(superstrings))
-
-ba_df.replace([np.inf, -np.inf], np.nan, inplace=True)
-if ba_df.isnull().values.any():
- ba_df.fillna(0, inplace=True)
-for super in superstrings:
- new_row = compare_row_entries(ba_df.loc[superstrings[super]], ba_df.loc[[super]])
- ba_df.loc[[super]] = new_row
-
-ba_df.to_pickle(snakemake.output[0], compression='gzip')
diff --git a/Pipeline/scripts/find_superstrings.py b/Pipeline/scripts/find_superstrings.py
deleted file mode 100644
index 4fa74a4032e3627e79916610e08b6ff05bf8cd11..0000000000000000000000000000000000000000
--- a/Pipeline/scripts/find_superstrings.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import sys
-import read_peptides
-
-def main(peptides):
- # sort peptides in reversed order
- sorted_peptides = sorted(peptides, key=len, reverse=True)
- # first read in all peptides and identify superstrings
- superstrings = {}
- for i, peptide in enumerate(sorted_peptides):
- if i % 1000 == 0:
- print(i, 'of', len(sorted_peptides))
- for j in range(i+1, len(sorted_peptides)):
- sec_peptide = sorted_peptides[j]
- if sec_peptide in peptide:
- if peptide in superstrings:
- superstrings[peptide].append(sec_peptide)
- else:
- superstrings[peptide] = [sec_peptide]
-
- return superstrings
-
-if __name__ == '__main__':
- peptide_file = sys.argv[1]
- peptides = read_peptides.main(peptide_file)
- main(peptides)
diff --git a/Pipeline/scripts/get_all_peptides_incl_substrings.py b/Pipeline/scripts/get_all_peptides_incl_substrings.py
deleted file mode 100644
index 530d763e4ac0fd3900c233abb4cad8d024df598b..0000000000000000000000000000000000000000
--- a/Pipeline/scripts/get_all_peptides_incl_substrings.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import sys
-import re
-import read_peptides, find_superstrings
-
-
-def get_all_peptides(all_peptides, vax_peptides):
- superstrings_mhc = find_superstrings.main(all_peptides)
- pep_incl_substr = vax_peptides
- for pep in vax_peptides:
- if pep in superstrings_mhc:
- pep_incl_substr = pep_incl_substr + superstrings_mhc[pep]
-
- print(len(set(pep_incl_substr)))
- return set(pep_incl_substr)
-
-
-def remove_embedding(peptides, strip):
- stripped = [p[strip:-strip] for p in peptides]
- return stripped
-
-
-# use pipeline output containing all chosen peptides
-peptide_file = snakemake.input['chosen_peptides']
-ilp_peptides = list(set(read_peptides.main(peptide_file)))
-
-# optional for embedding use embedding length as third argument
-if snakemake.params['embed_len']:
- print('################### Get all peptides inc substrings: embedded will be executed! ##################')
- ilp_peptides = remove_embedding(ilp_peptides, snakemake.params['embed_len'])
-
-# use pipeline input of all peptides from which ilp may choose
-all_peptides = snakemake.params['pep']
-
-with open(snakemake.output[0], 'w') as file:
- file.write('\n'.join(get_all_peptides(all_peptides, ilp_peptides)))
diff --git a/Pipeline/scripts/linear_time_hog.py b/Pipeline/scripts/linear_time_hog.py
deleted file mode 100644
index be3bedea55fc97d4f5bf6316938928e5c1eb82df..0000000000000000000000000000000000000000
--- a/Pipeline/scripts/linear_time_hog.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import sys
-import pickle
-import pydot
-import networkx as nx
-import aho_corasick_trie
-from collections import defaultdict
-
-
-def log(string):
- if logging_enabled:
- print(string)
-
-
-def contract_ac(unmarked_nodes, slinks, graph):
- for node in unmarked_nodes:
- log('Removing node ' + node)
- parent = list(graph.pred[node])[0]
- for succ in graph.successors(node):
- w = len(graph.nodes[succ]['string']) - len(graph.nodes[parent]['string'])
- graph.add_edge(parent, succ, weight=w)
- graph.nodes[succ]['char'] = graph.nodes[node]['char']
-
- if node in slinks:
- for link_node in slinks[node]:
- if link_node in graph.nodes():
- graph.nodes[link_node]['slink'] = graph.nodes[node]['slink']
- graph.remove_node(node)
- return graph
-
-
-def get_incoming_slinks(unmarked_nodes, graph):
- incoming_slinks = {}
- for node in graph.nodes():
- # skip root node
- if node == '0':
- continue
- # slink is incoming edge to slink_suc
- slink_suc = graph.nodes[node]['slink']
- # skip nodes that will not be removed
- if slink_suc not in unmarked_nodes:
- continue
-
- if slink_suc in incoming_slinks:
- incoming_slinks[slink_suc].append(node)
- else:
- incoming_slinks[slink_suc] = [node]
- return incoming_slinks
-
-
-def mark_nodes(leaves, graph):
- # root node is initially marked
- marked_nodes = {'0'}
- # for each node initialize child nodes as empty set
- children = defaultdict(lambda: set())
- for leaf in leaves.values():
- log('Current leaf ' + leaf)
- cur_node = leaf
- marked_nodes.add(cur_node)
- # while current node is not root node
- while cur_node != '0':
- interval = graph.nodes[cur_node]['interval_size']
- for child in children[cur_node]:
- interval -= graph.nodes[child]['interval_size']
- if interval > 0:
- marked_nodes.add(cur_node)
- children[cur_node] = set()
- children[graph.nodes[cur_node]['ancestor']].add(cur_node)
- cur_node = graph.nodes[cur_node]['slink']
-
- return marked_nodes
-
-
-def compute_interval_size(node, graph):
- log('Compute interval size for ' + node)
- if graph.out_degree(node) == 0:
- log('Is leaf ' + node)
- graph.nodes[node]['interval_size'] = 0
- return 1
- else:
- num_leaves = sum(compute_interval_size(suc, graph) for suc in graph.successors(node))
- graph.nodes[node]['interval_size'] = num_leaves
- return num_leaves
-
-
-def main(aho_corasick, leaves, pep_count, peptides, path, logging=False, dot_bool=False):
- global logging_enabled
- logging_enabled = logging
-
- with open(leaves, 'rb') as handle:
- leaves_dict = pickle.load(handle)
-
- aho_corasick = nx.read_gpickle(aho_corasick)
-
- # compute |R(u)| for each node u starting with root = 0
- log('Compute interval sizes')
- compute_interval_size('0', aho_corasick)
- # mark nodes
- log('Execute linear time algorithm')
- md_nodes = mark_nodes(leaves_dict, aho_corasick)
- log('Marked nodes' + ', '.join(md_nodes))
-
- # get nodes that we have to remove from aho corasick to get hog
- rm_nodes = set(aho_corasick.nodes) - md_nodes
- log('Nodes to be removed' + ', '.join(rm_nodes))
-
- # need to temporary store suffix link nodes for correct contraction
- incoming_slinks = get_incoming_slinks(rm_nodes, aho_corasick)
-
- # remove nodes and contract edges
- log('Contract aho corasick')
- hog = contract_ac(rm_nodes, incoming_slinks, aho_corasick)
-
- # set suffix links as explicit edges -> need this for ILP subtour elimination constraints
- nx.set_edge_attributes(hog, 'no', name='is_slink')
- slinks = [(i, hog.nodes[i]['slink'], {'weight': 0, 'is_slink': 'yes'}) for i in hog.nodes if i != '0']
- hog.add_edges_from(slinks)
-
- # write hog
- nx.write_gpickle(hog, path + pep_count + '_hog.gpickle')
-
- if dot_bool:
- dot = nx.drawing.nx_pydot.to_pydot(hog)
- for i, e in enumerate(dot.get_edges()):
- attr = e.get_attributes()
- e.set_label(attr['weight'])
- if attr['is_slink'] == 'yes':
- e.set_color('blue')
- e.set_style('dashed')
-
- for i, node in enumerate(dot.get_nodes()):
- attr = node.get_attributes()
- node.set_label(node.get_name() + ', ' + attr['string'] + ', ' + attr['interval_size'])
-
- dot.write_png(path + pep_count + '_hog.png')
-
-
-if __name__ == '__main__':
- aho_corasick = snakemake.input['ac']
- leaves = snakemake.input['leaves']
- path = snakemake.params['path']
- pep_count = int(snakemake.wildcards['pep_count'])
- peptides = snakemake.params['pep'][:pep_count]
- logging = snakemake.params['log']
-
- # if pep_count <= 30:
- # coloring = True
- # else:
- # coloring = False
- coloring = False
-
- main(aho_corasick, leaves, str(pep_count), peptides, path, logging=logging, dot_bool=coloring)
diff --git a/Pipeline/scripts/networkx_basic_graph.py b/Pipeline/scripts/networkx_basic_graph.py
deleted file mode 100644
index 3d70f5142e285d903bed12afcef67566815572e2..0000000000000000000000000000000000000000
--- a/Pipeline/scripts/networkx_basic_graph.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import itertools
-import networkx as nx
-
-
-def calculate_overlaps(p_a, p_b):
- ov = len(p_b)
- for i in range(min(len(p_a), len(p_b))):
- if p_a[-(i+1):] == p_b[:i+1]:
- ov = len(p_b) - (i+1)
- return ov
-
-
-pep_count = int(snakemake.wildcards['pep_count'])
-peptides = snakemake.params['pep'][:pep_count]
-
-DG = nx.DiGraph()
-DG.add_nodes_from(['Source', 'Sink'])
-
-for peptide_a, peptide_b in itertools.product(peptides, peptides):
- if peptide_a == peptide_b:
- continue
- overlap = calculate_overlaps(peptide_a, peptide_b)
- DG.add_edge(peptide_a, peptide_b, weight=overlap)
-
-DG.add_edges_from([('Source', p, {'weight': len(p)}) for p in peptides])
-DG.add_edges_from([(p, 'Sink', {'weight': 0}) for p in peptides])
-
-nx.write_gpickle(DG, snakemake.output[0])
diff --git a/Pipeline/scripts/read_peptides.py b/Pipeline/scripts/read_peptides.py
deleted file mode 100644
index 58eb33be566a7381d2b55e6c4f9a058965552dc8..0000000000000000000000000000000000000000
--- a/Pipeline/scripts/read_peptides.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import sys
-
-
-def main(file):
- peptides = []
- for line in open(file, 'r'):
- if line.startswith('#'):
- continue
- pep = line.strip()
- peptides.append(pep)
- return peptides
-
-if __name__ == '__main__':
- f = sys.argv[1]
- main(f)
\ No newline at end of file
diff --git a/Pipeline/scripts/vaccine_data.py b/Pipeline/scripts/vaccine_data.py
deleted file mode 100644
index 45ad1a3af52a43b8d7a5942d6d8c7f1138ceddd2..0000000000000000000000000000000000000000
--- a/Pipeline/scripts/vaccine_data.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import vaccine_ilp_og
-import vaccine_ilp_hog
-import vaccine_ilp_naive
-import gzip
-import pickle
-import networkx as nx
-import pandas as pd
-import gurobipy as gp
-from gurobipy import GRB
-
-
-def binarize_entries(df, th):
- df[df < th] = 0.0
- df[df >= th] = 1.0
- return df
-
-
-path = snakemake.params['path']
-k = int(snakemake.params['k'])
-populations = snakemake.params['population']
-peptides_count = snakemake.wildcards['pep_count']
-peptides = snakemake.params['pep'][:int(peptides_count)]
-min_hits = int(snakemake.params['min_hits'])
-
-print('Read frequencies')
-f_data = pd.read_pickle(snakemake.input['frequency_data'])
-type = snakemake.params['type']
-
-print('Read ba data')
-ba_threshold = float(snakemake.params['binding_affinity_threshold'])
-ba_matrix = pd.read_pickle(snakemake.input['binding_affinity_data'])
-print('Binarize')
-ba_matrix = binarize_entries(ba_matrix, ba_threshold)
-print('Calc shared alleles')
-full_known_alleles = gp.tuplelist(allele for allele in f_data.keys() if allele in ba_matrix.keys())
-print(full_known_alleles)
-
-# if int(snakemake.wildcards['pep_count']) <= 30:
-# coloring = True
-# else:
-# coloring = False
-coloring = False
-
-if snakemake.params['approach'] == 'og':
- cost_graph = nx.read_gpickle(snakemake.input['overlap_graph'])
-
- vaccine_ilp_og.solve_vaccine_problem(k=k,
- alleles=full_known_alleles,
- freq_vector=f_data,
- B_matrix=ba_matrix,
- peptides=peptides,
- pep_count=peptides_count,
- graph=cost_graph,
- populations=populations,
- path=path,
- subtour_el='boeckler',
- coloring=coloring)
-
-elif snakemake.params['approach'] == 'hog':
- print('Start HOGVAX')
- hog = nx.read_gpickle(snakemake.input['hog'])
- with open(snakemake.input['leaves'], 'rb') as handle:
- leaves_dict = pickle.load(handle)
-
- vaccine_ilp_hog.solve_msks_hog(k=k,
- alleles=full_known_alleles,
- freq_vector=f_data,
- B_matrix=ba_matrix,
- leaves=leaves_dict,
- pep_count=peptides_count,
- graph=hog,
- min_hits=min_hits,
- populations=populations,
- path=path,
- logging=False,
- coloring=coloring)
-
-elif snakemake.params['approach'] == 'concat':
- print('Start concat ilp')
- vaccine_ilp_naive.solve_vaccine_problem(k=k,
- peptides=peptides,
- pep_count=peptides_count,
- alleles=full_known_alleles,
- freq_vector=f_data,
- B_matrix=ba_matrix,
- populations=populations,
- path=path)
-
-else:
- print('Method is unclear, choose between og and hog for overlap graph or hierarchical overlap graph, respectively.')
-
-
-print('Possible maximum for population %s: %g' % (populations, sum(f_data[i][pop] for pop in populations for i in f_data if i in ba_matrix.keys())))
-# calculate maximum for given set of peptides --> if take all peptides from input set
-# frequencies = set()
-# for peptide in peptides:
-# for pop in populations:
-# for locus in full_known_alleles:
-# hit = f_data[locus][pop] * ba_matrix[locus][peptide]
-# if hit > 0:
-# frequencies.add((locus, f_data[locus][pop]))
-#
-# total_max_peptides = sum(f for _, f in frequencies)
-# print('Possible maximum for given set of peptides: %g' % total_max_peptides)
diff --git a/Pipeline/scripts/vaccine_ilp_hog.py b/Pipeline/scripts/vaccine_ilp_hog.py
deleted file mode 100644
index ebb19fc3c198932d4728e6d4c9f31e144d7201bd..0000000000000000000000000000000000000000
--- a/Pipeline/scripts/vaccine_ilp_hog.py
+++ /dev/null
@@ -1,163 +0,0 @@
-import numpy
-import random
-import draw_output
-import gurobipy as gp
-import networkx as nx
-from gurobipy import GRB
-
-
-def log(string):
- if logging_enabled:
- print(string)
-
-
-# subtour elimination version 2
-def subtour_elim_boekler_paper(model, where):
- global save_for_later
- if where == GRB.Callback.MIPSOL:
- vals = model.cbGetSolution(model._x_edges)
- graph = model._graph
- # find cycle
- subtours = find_subtour(vals, graph)
- # sum over all incoming edges of node v in W <= sum of all incoming edges to node set W
- if subtours:
- for subtour in subtours:
- log('Subtours ' + ', '.join(subtour))
- sorted_sub_int = sorted(list(map(int, subtour)))
- subtour = [str(x) for x in sorted_sub_int]
- print('SUBTOUR!!!', subtour)
- save_for_later.append(subtour)
- # for each subtour that does not contain the root node, we require an incoming edge from the outside
- for node in subtour:
- model.cbLazy(gp.quicksum(model._x_edges.select('*', node)) <=
- gp.quicksum(model._x_edges[node_outer, node_inner] for node_outer, node_inner in model._x_edges
- if node_inner in subtour and node_outer not in subtour),
- 'At least one incoming edge to subtour ' + '_'.join(subtour) + ' from the outside')
- # first add constraint only for single node, if this does not work, add it for all nodes in cycle
- break
-
-
-def find_subtour(vals, graph):
- random.seed(42)
- numpy.random.seed(42)
- # chosen edges
- edges = gp.tuplelist((i, j) for i, j in vals.keys() if vals[i, j] >= 0.5)
- log('Subtours edges')
- log(edges)
- subgraph = graph.edge_subgraph(edges).copy()
- # finding strongly connected components with networkx contains a random factor: algorithm starts from random node
- # generate sorted list of strongly connected components to remove random factor of scc algorithm
- scc = sorted(nx.strongly_connected_components(subgraph), key=len, reverse=True)
- subtours = []
- for c in scc:
- if '0' not in c:
- subtours.append(c)
- return subtours
-
-
-def solve_msks_hog(k, alleles, freq_vector, B_matrix, leaves, pep_count, graph, path, min_hits=1, populations=['World'], logging=False, coloring=False):
- global logging_enabled
- logging_enabled = logging
-
- # create new model
- m = gp.Model('ivp_on_hog')
- # set time limit to 48 hours
- m.setParam('TimeLimit', 172800)
- m.setParam('Seed', 42)
- # m.setParam('PoolSearchMode', 2)
- # m.setParam('PoolSolutions', 5)
- # m.setParam('SolFiles', path + 'lp_out/' + pep_count)
-
- # create hit variables
- log('Create hit variables')
- hit = gp.tupledict()
- for allele in alleles:
- hit[allele] = m.addVar(vtype=GRB.BINARY, name='_'.join(allele).replace(':', '-'))
-
- # create edge variables
- log('Create edge variables')
- x_edges = gp.tupledict()
- for node_a, node_b in sorted(graph.edges):
- x_edges[node_a, node_b] = m.addVar(vtype=GRB.INTEGER,
- name=graph.nodes[node_a]['string'] + '_' + graph.nodes[node_b]['string'])
-
- # the objective function is to maximize population coverage
- log('Set objective function')
- m.setObjective(gp.quicksum(hit[allele] * freq_vector[allele][pop] for pop in populations for allele in alleles),
- GRB.MAXIMIZE)
-
- # subject to the following constraints
- log('Add >>At least one outgoing edge from root<< constraint')
- m.addConstr(x_edges.sum('0', '*') >= 1, 'At least one outgoing edge from root')
-
- log('Add >>Sum of edge weights below threshold k<< constraint')
- m.addConstr(gp.quicksum(x_edges[node_a, node_b] * graph.edges[node_a, node_b]['weight']
- for node_a, node_b in sorted(graph.edges)) <= k, 'Sum of edge weights below threshold k')
-
-
- log('Add flow conservation for nodes')
- for node in sorted(graph.nodes):
- m.addConstr(x_edges.sum('*', node) == x_edges.sum(node, '*'),
- 'Flow conservation at node ' + str(node))
-
- '''
- without this update function, the data will be cached in some intermediate storage that might require more memory
- than the final storage to which the ilp will be transferred by update() and optimize().
- '''
- m.update()
- log('Add allele hit constraint')
- for allele in alleles:
- # check for peptides (leaf nodes) with non-zero entry in B matrix for current allele
- hit_leaves = list(filter(lambda x: 1.0 == B_matrix[allele][x], leaves.keys()))
- # only sum over such peptides instead of summing over all peptides -> only beneficial for ba threshold > 0.xx
- m.addConstr(hit[allele] * min_hits <= gp.quicksum(x_edges.sum('*', leaves[leaf]) for leaf in hit_leaves),
- 'Peptide-hit for allele ' + '_'.join(allele))
- m.update()
-
- # sub tour elimination with callbacks
- m._x_edges = x_edges
- m._graph = graph
- m.Params.LazyConstraints = 1
- # keep subtour to add them after solving to lp file: lazy constraints are not added to lp file otherwise
- global save_for_later
- save_for_later = []
- chosen_pep = []
- # Solve
- log('Now optimize:')
- m.optimize(subtour_elim_boekler_paper)
-
- if m.Status == GRB.OPTIMAL:
- sol = m.getAttr('X', x_edges)
- for node_a, node_b in x_edges:
- # print('%s -> %s: %g' % (node_a, node_b, sol[node_a, node_b]))
- traversals = round(sol[node_a, node_b])
- for i in range(traversals):
- if node_a in leaves.values():
- chosen_pep.append(graph.nodes[node_a]['string'])
- if node_b in leaves.values():
- chosen_pep.append(graph.nodes[node_b]['string'])
-
- m.write(path + 'lp_out/' + pep_count + '_vaccine_ilp_hog.sol')
- m.write(path + 'lp_out/' + pep_count + '_vaccine_ilp_hog.json')
-
- if coloring:
- draw_output.draw_hog(graph, leaves, sol, path + 'pep_out/' + pep_count + '_colored_hog.png', True)
-
- # write chosen peptides to output file, remove empty string from root node
- if '' in chosen_pep:
- chosen_pep.remove('')
- chosen_pep = set(chosen_pep)
- with open(path + 'pep_out/' + pep_count + '_chosen_peptides_hog.txt', 'w') as file:
- file.write('\n'.join(chosen_pep))
-
- # after solving add lazy constraints to lp file
- for i, subtour in enumerate(save_for_later):
- for node in subtour:
- m.addConstr(gp.quicksum(m._x_edges.select('*', node)) <=
- gp.quicksum(m._x_edges[node_outer, node_inner] for node_outer, node_inner in m._x_edges
- if node_inner in subtour and node_outer not in subtour),
- 'Subtour elimination ' + node)
- # first add constraint only for single node, if this does not work, add it for all nodes in cycle
- break
-
- m.write(path + 'lp_out/' + pep_count + '_vaccine_ilp_hog.lp')
diff --git a/Pipeline/scripts/vaccine_ilp_naive.py b/Pipeline/scripts/vaccine_ilp_naive.py
deleted file mode 100644
index a6d93f4b425b06744f54f98b46df347247ebad5d..0000000000000000000000000000000000000000
--- a/Pipeline/scripts/vaccine_ilp_naive.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import gurobipy as gp
-from gurobipy import GRB
-
-
-def solve_vaccine_problem(k, peptides, pep_count, alleles, freq_vector, B_matrix, min_hits=1, populations=['World'], path=''):
- # create new model
- m = gp.Model('naive_vaccine_design')
-
- # create peptide variables
- x = m.addVars(peptides, vtype=GRB.BINARY, name='peptide')
-
- # create hit variables
- hit = m.addVars(alleles, vtype=GRB.BINARY, name='allele_hit')
-
- # maximize population coverage
- m.setObjective(gp.quicksum(hit[allele] * freq_vector[allele][pop] for pop in populations for allele in alleles),
- GRB.MAXIMIZE)
-
- # add constraints
- m.addConstrs(x[i] <= 1 for i in peptides)
-
- m.addConstr(gp.quicksum(x[i] * len(i) for i in peptides) <= k, 'Sum of peptide lengths smaller k')
-
- m.addConstrs(hit[a] * min_hits <= gp.quicksum(x[i] * B_matrix[a][i] for i in peptides) for a in alleles)
-
- m.optimize()
- m.write(path + 'lp_out/' + pep_count + '_vaccine_ilp_concat.lp')
- m.write(path + 'lp_out/' + pep_count + '_vaccine_ilp_concat.sol')
- m.write(path + 'lp_out/' + pep_count + '_vaccine_ilp_concat.json')
-
- chosen_pep = []
- if m.Status == GRB.OPTIMAL:
- sol = m.getAttr('X', x)
- for peptide in x:
- print('%s: %g' % (peptide, sol[peptide]))
- if sol[peptide]:
- chosen_pep.append(peptide)
-
- chosen_pep = set(chosen_pep)
- with open(path + 'pep_out/' + pep_count + '_chosen_peptides_concat.txt', 'w') as file:
- file.write('\n'.join(chosen_pep))
diff --git a/Pipeline/scripts/vaccine_ilp_og.py b/Pipeline/scripts/vaccine_ilp_og.py
deleted file mode 100644
index e8d94fbe6c5fc8e6592206fa736f5420ca0f394e..0000000000000000000000000000000000000000
--- a/Pipeline/scripts/vaccine_ilp_og.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import draw_output
-import gurobipy as gp
-from gurobipy import GRB
-
-
-def log(string):
- if logging_enabled:
- print(string)
-
-
-# subtour elimination version 1
-def subtour_elim_cycle(model, where):
- global save_for_later
- if where == GRB.Callback.MIPSOL:
- vals = model.cbGetSolution(model._x_edges)
- # find cycle
- subtour_edges = find_subtour(vals)
- # add sub tour elimination constr. for specific sub tour
- if subtour_edges:
- model.cbLazy(gp.quicksum(model._x_edges[i, j] for i, j in subtour_edges) <= len(subtour_edges) - 1)
- save_for_later.append(subtour_edges)
-
-
-# subtour elimination version 2
-def subtour_elim_boekler_paper(model, where):
- global save_for_later
- if where == GRB.Callback.MIPSOL:
- vals = model.cbGetSolution(model._x_edges)
- # find cycle
- subtour_edges = find_subtour(vals)
- # sum over all incoming edges of node v in W <= sum of all incoming edges to node set W
- if subtour_edges:
- subtour_nodes = [node for node, _ in subtour_edges]
- save_for_later.append(subtour_nodes)
- for node in subtour_nodes:
- model.cbLazy(gp.quicksum(model._x_edges.select('*', node)) <=
- gp.quicksum(model._x_edges[node_outer, node_inner] for node_outer, node_inner in model._x_edges
- if node_inner in subtour_nodes and node_outer not in subtour_nodes))
- # first add constraint only for single node, if this does not work, add it for all nodes in cycle
- break
-
-
-def find_subtour(vals):
- # chosen edges
- edges = gp.tuplelist((i, j) for i, j in vals.keys() if vals[i, j])
- nodes = ['Source'] + [node for node, _ in edges if node != 'Source']
- while nodes:
- current, nextn = edges.select(nodes[0], '*')[0]
- nodes.remove(current)
- curr_tour_edges = [(current, nextn)]
- while nextn != 'Sink':
- if nextn not in nodes:
- return curr_tour_edges
- else:
- nodes.remove(nextn)
- current, nextn = edges.select(nextn, '*')[0]
- curr_tour_edges.append((current, nextn))
- return None
-
-
-def solve_vaccine_problem(k, alleles, freq_vector, B_matrix, peptides, pep_count, graph, path, min_hits=1, populations=['World'], subtour_el='cycle', logging=False, coloring=False):
- global logging_enabled
- logging_enabled = logging
-
- # create new model
- m = gp.Model('vaccine_overlap_design')
- # m.setParam('TimeLimit', 36000)
-
- # create allele hit variables
- log('Create allele hit variables')
- hit = gp.tupledict()
- for allele in alleles:
- hit[allele] = m.addVar(vtype=GRB.BINARY, name='_'.join(allele).replace(':', '-'))
-
- # create edge variables
- log('Create edge variables')
- x_edges = gp.tupledict()
- for node_a, node_b in graph.edges():
- x_edges[node_a, node_b] = m.addVar(vtype=GRB.BINARY, name=node_a + '_' + node_b)
-
- # the objective function is to maximize population coverage
- log('Set objective function')
- m.setObjective(gp.quicksum(hit[allele] * freq_vector[allele][pop] for pop in populations for allele in alleles),
- GRB.MAXIMIZE)
-
- # subject to the following constraints
- log('Add constraints')
- m.addConstr(x_edges.sum('Source', '*') == 1, 'Exactly one outgoing edge from the source')
- m.addConstr(x_edges.sum('*', 'Sink') == 1, 'Exactly one incoming edge to the sink')
-
- log('Add >>At most one outgoing edge constraint<<')
- for node_a in graph.nodes():
- if node_a == 'Source' or node_a == 'Sink':
- continue
- m.addConstr(x_edges.sum(node_a, '*') <= 1, 'At most one outgoing edge from ' + node_a)
- '''
- Due to flow conservation, we might not need constraint of
- 'at most one incoming edge per node' -> skip it here;
- '''
- m.addConstr(x_edges.sum('*', node_a) == x_edges.sum(node_a, '*'), 'Flow conservation at node ' + node_a)
-
- log('Sum of edge weights below threshold k constraint')
- m.addConstr(gp.quicksum(x_edges[node_a, node_b] * graph.edges[node_a, node_b]['weight']
- for node_a, node_b in graph.edges()) <= k, 'Sum of edge weights below threshold k')
-
- '''
- without this update function, the data will be cached in some intermediate storage that might require more memory
- than the final storage to which the ilp will be transferred by update() and optimize().
- '''
- m.update()
- log('Allele hit constraint')
- for allele in alleles:
- # check for those peptides with non-zero entry in B matrix for current allele
- hit_nodes = list(filter(lambda x: 1.0 == B_matrix[allele][x], peptides))
- # only sum over such peptides instead of summing over all peptides -> only beneficial for ba threshold > 0.xx
- m.addConstr(hit[allele] * min_hits <= gp.quicksum(x_edges.sum(node_a, '*') for node_a in hit_nodes),
- 'Peptide-hit for allele ' + '_'.join(allele))
- m.update()
-
- # sub tour elimination with callbacks
- m._x_edges = x_edges
- m.Params.LazyConstraints = 1
- # keep subtour to add them after solving to lp file: lazy constraints are not added to lp file otherwise
- global save_for_later
- save_for_later = []
- chosen_pep = []
- # Solve
- log('Now optimize:')
- if subtour_el == 'cycle':
- m.optimize(subtour_elim_cycle)
- else:
- m.optimize(subtour_elim_boekler_paper)
- if m.Status == GRB.OPTIMAL:
- sol = m.getAttr('X', x_edges)
- for node_a, node_b in x_edges:
- # print('%s -> %s: %g' % (node_a, node_b, sol[node_a, node_b]))
- if sol[node_a, node_b]:
- chosen_pep.append(node_a)
- m.write(path + 'lp_out/' + pep_count + '_vaccine_ilp_og.sol')
- m.write(path + 'lp_out/' + pep_count + '_vaccine_ilp_og.json')
-
- if coloring:
- draw_output.draw_colored_graph(graph, sol, path + 'pep_out/' + pep_count + '_colored_og.png')
-
- # write chosen peptides to output file
- with open(path + 'pep_out/' + pep_count + '_chosen_peptides_og.txt', 'w') as file:
- chosen_pep.remove('Source')
- file.write('\n'.join(chosen_pep))
-
- # after solving add lazy constraints to lp file
- for i, subtour in enumerate(save_for_later):
- if subtour_el == 'cycle':
- m.addConstr(gp.quicksum(m._x_edges[i, j] for i, j in subtour) <= len(subtour) - 1,
- 'Subtour elimination ' + str(i))
- else:
- for node in subtour:
- m.addConstr(gp.quicksum(m._x_edges.select('*', node)) <=
- gp.quicksum(m._x_edges[node_outer, node_inner] for node_outer, node_inner in m._x_edges
- if node_inner in subtour and node_outer not in subtour),
- 'Subtour elimination ' + node)
- # first add constraint only for single node, if this does not work, add it for all nodes in cycle
- break
-
- m.write(path + 'lp_out/' + pep_count + '_vaccine_ilp_og.lp')
diff --git a/README.md b/README.md
index 5b4ba2b3536f667c4813bdf9a8a9e747daff25dd..2d399398fa544708e6b0dd5061afc1e2951559f3 100644
--- a/README.md
+++ b/README.md
@@ -1,69 +1,4 @@
-# Abstract
-Vaccination is the key component to overcoming the global COVID-19 pandemic. Peptide vaccines present a safe and cost-efficient alternative to traditional vaccines. They are rapidly produced and adapted for new viral variants. The vaccine's efficacy essentially depends on two components: the peptides included in the vaccine and the ability of major histocompatibility complex (MHC) molecules to bind and present these peptides to cells of the immune system. Due to the high diversity of MHC alleles and their diverging specificities in binding peptides, choosing a set of peptides that maximizes population coverage is a challenging task. Further, peptide vaccines are limited in their size allowing only for a small set of peptides to be included. Thus, they might fail to immunize a large part of the human population or protect against upcoming viral variants. Here, we present HOGVAX, a combinatorial optimization approach to select peptides that maximize population coverage. Furthermore, we exploit overlaps between peptide sequences to include a large number of peptides in a limited space and thereby also cover rare MHC alleles. We model this task as a theoretical problem, which we call the *Maximal Scoring k-Superstring Problem*. Additionally, HOGVAX is able to consider haplotype frequencies to take linkage disequilibrium between MHC loci into account. Our vaccine formulations contain significantly more peptides compared to vaccine sequences built from concatenated peptides. We predicted over 98% population coverage for our vaccine candidates of MHC class I and II based on single-allele and haplotype frequencies. Moreover, we predicted high numbers of per-individual presented peptides leading to a robust immunity in the face of new virus variants.
-
-# Execute HOGVAX
-HOGVAX uses the Gurobi solver for which a license is required. Further information can be found [here](https://www.gurobi.com/academia/academic-program-and-licenses/). For the use of HOGVAX, we recommend the stand-alone version. The pipeline was used for producing the results of the master thesis. In any case, Conda is required.
-
-## Data
-HLA haplotype and allele frequencies, binding affinity predictions, and input peptides are available in [Sciebo](https://uni-duesseldorf.sciebo.de/s/jRTn9Vpff78kLqi) (~2GB). You can use the [download script](download_data.sh) to download and extract the data directly.
-
-## HOGVAX GitHub Repository
-the repo will be hosted on [GitHub](https://github.com/AlBi-HHU/hogvax) in the future.
-
-## Snakemake Pipeline
-The pipeline can be used to execute HOGVAX or the OCG approach. Additionally, the concatenation approach is always executed. Use the the [config file](Pipeline/envs/config.yaml) to adjust parameters for your execution. The pipeline can optionally be used for regional context embedding computations. Open a shell in the [Pipeline](Pipeline/) directory and execute the pipeline after adjusting the config file as follows:
-
-```shell
-snakemake --cores <cpu cores> --use-conda
-```
-
-In your specified output directory, you can find the computed vaccine sequences and chosen peptides in `ilp/pep_out/`.
-
-## Stand-alone Version
-Open a shell in the [HOGVAX](HOGVAX/) folder and create the conda environment from the `yaml` file for proper execution of HOGVAX.
-
-```shell
-conda env create -f hogvax_env.yaml
-conda activate hogvax_env
-```
-
-To execute HOGVAX, call the python script with the necessary arguments, see the [example arguments](HOGVAX/example_arguments.txt).
-
-```shell
-python hogvax.py <arguments>
-```
-The `--help` argument gives the following list of arguments.
-
-```angular2html
- -h, --help show this help message and exit
- --k K, -k K Maximal length of vaccine sequence
- --populations POPULATIONS [POPULATIONS ...], -pop POPULATIONS [POPULATIONS ...]
- Target population(s). Default "World"
- --peptides PEPTIDES, -pep PEPTIDES
- Preprocessed peptide file with every peptide in a new
- line.
- --allele-frequencies F_DATA, -af F_DATA
- (Normalized) allele frequency file.
- --ba-threshold BA_THRESHOLD, -t BA_THRESHOLD
- Binding affinities are converted to binary data, where
- everything >= BA_THRESHOLD is set to 1.
- --binding-affinities BA_MATRIX, -ba BA_MATRIX
- Binding affinity file for input peptides and alleles.
- --required_epitopes REQUIRED_EPITOPES, -epi REQUIRED_EPITOPES
- File of peptides you want to be present in vaccine
- --min-hits MIN_HITS, -mh MIN_HITS
- Minimum number of hits for an allele to be covered
- --maximize-peptides Maximize number of peptides in the vaccine in a second
- optimization
- --embedding-length EMBEDDING_LENGTH
- Set length of embedding if used
- --embedded-peptides EMBEDDED_PEPTIDES
- File containing embedded peptides
- --embedded-epitope_features EMBEDDED_EPITOPE_FEATURES
- Path to embedded epitope features
- --outdir OUTDIR, -o OUTDIR
- Output directory
- --verbose [LOGGING_ENABLED], -v [LOGGING_ENABLED]
-```
\ No newline at end of file
+# Moved to GitHub
+HOGVAX was moved to GitHub: https://github.com/AlBi-HHU/HOGVAX
\ No newline at end of file
diff --git a/SupplementaryResults/CombinedComputations/41947_chosen_peptides_hog_inc_substrings.txt b/SupplementaryResults/CombinedComputations/41947_chosen_peptides_hog_inc_substrings.txt
deleted file mode 100644
index db501bdc9e6d5617ecef7c651bf032f9a7996796..0000000000000000000000000000000000000000
--- a/SupplementaryResults/CombinedComputations/41947_chosen_peptides_hog_inc_substrings.txt
+++ /dev/null
@@ -1,1095 +0,0 @@
-ELKFNPPALQDAYYRARAGE
-EKWESGVKDCVVLHSYF
-TQQLIRAAE
-KYAISAKNRARTV
-VTQQLIRAAEIRASANLAA
-GVKDCVVL
-QQIELKFNPPALQDA
-FIASFRLFARTRSMWSF
-LMWLSYFI
-MNLKYAISAKNRA
-IELKFNPPALQDA
-AYILFTRFFY
-LHSYFTSD
-IELKFNPPALQDAY
-TITQMNLKYAISAKN
-YRARAGEAANFCAL
-ARKTLNSLE
-DCVMYASA
-EKMADQAM
-CVVLHSYFTSDYYQ
-WFLAYILFT
-NVIPTITQMNLKYAISAK
-ESGVKDCVVLHSYFTSD
-LISMMGFKMN
-ARKTLNSL
-PTHLSVDTKFKTEG
-KDCVVLHSYFTSD
-AKNRARTV
-VVLHSYFTS
-WFLAYILF
-HAGTDLEGNF
-QQIELKFNPPALQD
-FLAYILFTRFFYV
-KWESGVKDCVVLHSYFT
-IASFRLFARTRSM
-FNPPALQDAYYRARAGE
-ITQMNLKYAISAKNRARTV
-AYTKRNVIPTITQMNLKYAISAKNR
-KNRARTVAGV
-NPPALQDAYYRARAGE
-LSYFIASFRLFARTRS
-GVKDCVVLHSYFTSD
-RAGEAANF
-TISLAGSYK
-VKDCVVLHSYFTSDYYQ
-AYYRARAGEAANFCAL
-WESGVKDCVVLHSY
-CLVGLMWLSYFIASFRLFART
-PPALQDAYYRARAGEA
-LQDAYYRARAGEAANFC
-KDCVVLHSYFTSDY
-EHFIETISL
-TKRNVIPTITQMNLKY
-TAQNSVRVLQKAA
-CLVGLMWLSYFIASFRLF
-NSVRVLQKAAITIL
-FAPSASAFFGMSR
-AYTKRNVIPTITQMNLKYAI
-YILFTRFF
-QDAYYRARAGEAANFCAL
-LKFNPPALQDAYYRARA
-SAKNRART
-LISMMGFKMNYQV
-RRLISMMGFKMNY
-FIETISLAGS
-YQVNGYPN
-ELKFNPPALQDAYY
-LNMARKTLN
-PQIAQFAPSA
-SVDTKFKTEG
-TKRNVIPTITQMNLKYAISAKNRAR
-SEKSYELQT
-VLHSYFTS
-APTHLSVDTKFKTEGLCV
-ESGVKDCVVLHSYFTSDYY
-LHSYFTSDYYQLYS
-RDAAMQRKLEKMA
-QQIELKFNPPALQDAYYRAR
-AEWFLAYIL
-TQMNLKYAISAKNRARTVAGV
-NPPALQDAYYRARAGEA
-AQNSVRVLQKAAIT
-NYMPYFFT
-QIAQFAPS
-AYTKRNVIPTITQMNLKYAISA
-AYYRARAGEAANFC
-SEKSYELQTP
-QQIELKFNPPALQDAYYRARAGEA
-SYFIASFRLFART
-IELKFNPPALQDAYYRARAGE
-MGFKMNYQVNGYP
-VVLHSYFTSDYYQ
-ISAKNRARTVAGVS
-TAQNSVRVLQKAAITI
-HAGTDLEGN
-YYRARAGEAANFCALI
-PTITQMNLKYAISAKNRARTVA
-DCVVLHSYFTSDYYQL
-TQQLIRAAEI
-PYFFTLLLQ
-APTHLSVDTKFKTEGLC
-QLIRAAEIRASAN
-IPTITQMNLKYAISAKNRART
-DAYYRARAGEAANFCALIL
-RNVIPTITQMNLKYAI
-QDAYYRARAGEAANFCALI
-QFAPSASAFF
-SNEKQEILGT
-LMWLSYFIASFRLFART
-VLHSYFTSD
-FKMNYQVN
-QIPFAMQM
-SVDTKFKTEGLCV
-LPTGVHAGTDLEG
-QDAYYRARAGEAANFCALIL
-RSEKSYELQ
-YVTQQLIRAAEIRAS
-ALQDAYYRARAGEAANFC
-LKFNPPALQDAYYRARAGEA
-QMNLKYAISAKNRARTV
-LKYAISAKN
-KFKTEGLCVD
-RAGEAANFC
-ITQMNLKYAISAK
-TGVHAGTDL
-IRAAEIRASANLAA
-RNVIPTITQMNLKYAISAKN
-YFIASFRLFARTRSMW
-ELKFNPPALQDAYYRAR
-DCVMYASAV
-SYFIASFRLFARTRSMWS
-LKFNPPALQDAYYR
-SAKNRARTVAGVSI
-IELKFNPPALQDAYYRARAGEA
-FKTEGLCV
-RNVIPTITQMNLKY
-KMADQAMTQ
-AYTKRNVIPTITQMNLKYA
-LQQIELKFNPPALQDAY
-IASFRLFARTRSMW
-PALQDAYYRARAGEAA
-VTQQLIRAAEIRASAN
-QMNLKYAI
-KWESGVKDCVVLHS
-LMWLSYFIASFRLFA
-NLKYAISAKNRARTVAGV
-MMGFKMNYQVNGYP
-HSYFTSDYYQLYS
-PTHLSVDTKFKTE
-RSEKSYELQTPFE
-QQIELKFNPPALQDAY
-YVTQQLIRAAEIRASANLAA
-QQIELKFNPPALQ
-LQQIELKFNPPALQDAYYRARAGEA
-GVKDCVVLHSYFTS
-SFRLFART
-TKRNVIPTITQMNLKYAIS
-LQTPFEIKL
-SMMGFKMNYQVNGYPN
-TKFKTEGLCV
-QDAYYRARAGEAA
-NMARKTLNS
-NVIPTITQMNLKYAISAKN
-LMWLSYFIASFRL
-VIPTITQMNLKYA
-TKRNVIPTITQMNLKYAISAKN
-LVGLMWLSYF
-QLIRAAEIR
-IAQFAPSASAFFGMS
-LKFNPPALQDAYYRARAG
-RSEKSYELQT
-ISNEKQEI
-SYFIASFRLFARTRSMWSF
-LVGLMWLSYFIASFRL
-VLHSYFTSDYYQLYS
-EHFIETIS
-YASAVVLLI
-PYFFTLLL
-KDCVVLHSYFTSDYYQL
-QMNLKYAISAKNRAR
-PALQDAYYRARAGEAANFCA
-LSLNMARKTLNSL
-QQLIRAAE
-IPTITQMNLKYAISAK
-RNVIPTITQMNLKYAISAK
-LVGLMWLSYFIASFRLF
-FIASFRLFARTRSM
-MMGFKMNYQVNGYPNM
-DLEGNFYG
-GLMWLSYFI
-LQQIELKFNPPAL
-SLNMARKT
-LKYAISAK
-CVMYASAV
-VIPTITQMNLKYAIS
-ITQMNLKY
-TITQMNLKY
-WESGVKDCVVLHSYFTS
-IISNEKQEILGTV
-ERSEKSYEL
-QFAPSASAF
-YFIASFRLFARTR
-ARTVAGVS
-MEIDFLEL
-LKFNPPALQDAYYRAR
-AQFAPSASA
-AISAKNRARTVAGV
-AQFAPSASAFFGMS
-ISMMGFKMNY
-TITQMNLK
-QQLIRAAEIRASAN
-TITQMNLKYAISAKNRAR
-AYILFTRF
-KRNVIPTITQMNLKYAISAKN
-DCVVLHSYFTSDYY
-AYTKRNVIPTITQMN
-VTQQLIRAAE
-KFNPPALQDAYYRARAGE
-RRLISMMGFKMNYQV
-LNMARKTLNS
-VGLMWLSYFI
-IELKFNPPALQDAYY
-RLISMMGFKMNYQVN
-THLSVDTKFKTEGLC
-QAMTQMYKQ
-LQIPFAMQMA
-FRLFARTRS
-KTEGLCVD
-IAQFAPSA
-TAQNSVRVLQKAAI
-TQMNLKYAISAKNR
-RLISMMGFK
-TITQMNLKYAISAKNRART
-YYRARAGEAANFCA
-GLMWLSYFIASFRLF
-PTGVHAGT
-QNSVRVLQKAAIT
-QFAPSASAFFGMS
-EKQEILGTVS
-SLAGSYKD
-NLKYAISAK
-KDCVVLHSYF
-LQDAYYRARAGEAANFCA
-TGVHAGTD
-GVHAGTDLE
-MPYFFTLLL
-KLEKMADQAM
-SFRLFARTR
-YTKRNVIPTITQMNLKYAI
-MNLKYAISAKNRARTVAGV
-IETISLAG
-MNLKYAISAKNRARTV
-VIPTITQMNLKYAISAKN
-VGLMWLSYFIASFRLFAR
-TDLEGNFY
-NMARKTLN
-NPPALQDAYYRARAGEAANF
-IAQFAPSASAFFGMSR
-TITQMNLKYAISAKNR
-ALQDAYYRARAGE
-QIELKFNPPALQDAY
-DAYYRARAGEAANFCAL
-CVVLHSYFTSDYYQL
-EKWESGVKDCVVLHS
-KRNVIPTITQMNLKYAISAKNRAR
-IVTRCLNRV
-LMWLSYFIASFRLF
-LKFNPPALQDAYYRARAGEAA
-LQDAYYRARAGEAAN
-KDCVVLHSYFTSDYY
-QQIELKFNPPALQDAYYRARAG
-QIAQFAPSASAFF
-CLVGLMWLSYFIASFRLFA
-QMNLKYAISAKNRARTVAGV
-VTQQLIRAAEIRASANLA
-VTQQLIRAAEIRASA
-KYAISAKNRARTVAG
-GFKMNYQVN
-LQQIELKFNPPALQDAYYRARAGE
-TQMNLKYAISAKNRART
-IISNEKQEILGTVS
-ELKFNPPALQDAY
-NPPALQDAYYRAR
-VKDCVVLHSYFTSD
-LVGLMWLSYFIASFRLFARTRS
-NVIPTITQMNLKYAISA
-LIRAAEIRASANLA
-LQDAYYRARAGEAA
-SFRLFARTRSMWS
-FIASFRLFARTRS
-VKDCVVLHSYFTSDY
-RAGEAANFCA
-YTKRNVIPTITQMNLKYAISA
-VVLHSYFTSD
-AQNSVRVL
-YFIASFRLFARTRS
-QFAPSASAFFGMSR
-ETISLAGSYK
-VIPTITQMNLKYAISAKNRA
-WLTNIFGTVYEKL
-ESGVKDCVVLHSYFTSDY
-DAYYRARAGEAAN
-EHFIETISLA
-EEHFIETIS
-NLKYAISAKNRARTV
-CLVGLMWLSYFIASFRLFARTRS
-AGTDLEGNF
-LQDAYYRARAGEA
-GLMWLSYFIASFR
-AYTKRNVIPTITQMNLKYAISAK
-NPPALQDAYYRARAGEAA
-YVTQQLIR
-WLSYFIASFRLFAR
-RKTLNSLE
-TKRNVIPTITQMNLKYAISA
-YFIASFRLFARTRSMWSF
-KWESGVKDCVVLHSYFTSDY
-CLVGLMWLSYFIAS
-RLISMMGFKMNYQVNGY
-RNVIPTITQMNLK
-YRARAGEAANFCA
-ALQDAYYRARAGEAA
-LISMMGFKMNYQVNGY
-CLVGLMWLSYFIA
-EKQEILGTV
-TITQMNLKYA
-NVIPTITQMNLKYAISAKNRAR
-CLVGLMWLSYFIASFRLFAR
-KFNPPALQDAYYRAR
-LIRAAEIRASANLAA
-TITQMNLKYAISAKNRA
-MNYQVNGYPN
-LISMMGFKMNYQVNGYP
-SFRLFARTRS
-RNVIPTITQMNLKYAISAKNR
-AQFAPSAS
-KQEILGTVSW
-QIELKFNPPALQDAYYRARAGEAA
-ITQMNLKYAISAKNRARTVAGV
-ESGVKDCVVLHSYF
-NRARTVAGV
-IASFRLFARTRSMWSF
-APTHLSVDTKFKTE
-ETISLAGS
-MWLSYFIASFRLFAR
-SGVKDCVVLHSYFTS
-AQNSVRVLQKAAITI
-NLKYAISAKNRART
-YELQTPFE
-QMNLKYAISAKNRARTVA
-QEILGTVSW
-SGVKDCVVLHSYF
-FNPPALQDAYYRARAG
-AEWFLAYI
-IPTITQMNLKYAISAKNRA
-EILGTVSW
-AISAKNRAR
-QDAYYRARAGEAANFC
-YELQTPFEIK
-EHFIETISLAGSYK
-IISNEKQE
-VKDCVVLHSYFTSDYYQL
-TISLAGSY
-KYAISAKNRARTVA
-IAQFAPSASA
-FIETISLA
-QQIELKFNPPALQDAYYR
-LKYAISAKNR
-PALQDAYYRARAGEAANF
-NEKQEILGTV
-QIAQFAPSASAFFG
-MMGFKMNYQVNGYPN
-AMTQMYKQ
-YFIASFRLFARTRSM
-ALQDAYYRARAGEAANFCALIL
-LQDAYYRARAGEAANFCALIL
-YAISAKNRARTVAG
-TKRNVIPTITQMNLKYA
-QWLTNIFGTVYEKLK
-EEHFIETISL
-WLTNIFGTVYEKLK
-ASFRLFARTRSMWS
-PPALQDAYYRARAGEAAN
-RSEKSYEL
-YMPYFFTLLL
-NMARKTLNSL
-SFRLFARTRSMWSF
-ARAGEAANFC
-TKFKTEGLC
-VLHSYFTSDY
-LISMMGFKM
-PPALQDAYYRARAGEAANFC
-QLIRAAEIRASANLA
-EEHFIETISLAGSY
-MNYQVNGYP
-RARAGEAANFCAL
-SYELQTPF
-GTDLEGNFY
-TITQMNLKYAISAKNRARTVA
-NVIPTITQMNLKYAISAKNR
-TNIVTRCLNR
-ISLAGSYKDW
-PTGVHAGTD
-QIELKFNPPALQDAYYRARAGEA
-QIELKFNPPALQDAYYRARAGE
-SVDTKFKTE
-CLVGLMWL
-LQQIELKFNPPALQDAYYRAR
-IETISLAGSYKDW
-QNSVRVLQKAAITIL
-ISMMGFKMNYQVNGY
-ITQMNLKYAISAKNRARTVAG
-QQIELKFNPPALQDAYY
-VVLHSYFTSDYYQLY
-YAISAKNRAR
-LQDAYYRARAGEAANFCAL
-QQIELKFNPPALQDAYYRARAGEAA
-ITQMNLKYA
-YAISAKNRA
-AQFAPSASAFFGM
-YTKRNVIPTITQMNLKYA
-LKYAISAKNRARTVA
-RRLISMMGFKMNYQVNG
-HSYFTSDY
-YRARAGEAANFCALI
-SGVKDCVV
-ESGVKDCVVLHSYFTS
-TQQLIRAAEIRASANLAA
-RLISMMGFKM
-LHSYFTSDYYQLY
-GVKDCVVLHSYFTSDYY
-KYAISAKNRARTVAGVS
-TQMNLKYAISAKN
-EKWESGVKDCVVLH
-MWLSYFIASFRLFARTR
-VGLMWLSYFIASFR
-VGLMWLSYF
-MWLSYFIASFRLFA
-QTPFEIKL
-PTITQMNLKYAISAKNRA
-MNYQVNGY
-ELKFNPPALQDAYYRARAG
-FKMNYQVNGYPNM
-IPTITQMNLKYAISAKNRAR
-MGFKMNYQV
-TQQLIRAA
-IISNEKQEILGTVSW
-FPLKLRGTA
-GFKMNYQV
-KLEKMADQ
-PPALQDAYYRARAGEAA
-TQQLIRAAEIRASANL
-YTKRNVIPTITQM
-IPFAMQMAY
-YFFTLLLQ
-TGVHAGTDLE
-LQIPFAMQM
-LQTPFEIK
-TKRNVIPTITQMNLKYAISAK
-IELKFNPPALQDAYYRA
-DCVVLHSYF
-LEKMADQA
-SGVKDCVVLHSYFTSD
-VTQQLIRAA
-AGEAANFC
-SYFIASFRLFARTRS
-SMMGFKMNYQ
-KDCVVLHSYFTSDYYQLY
-GVKDCVVLHSYFT
-ITQMNLKYAI
-YRARAGEAANFCALIL
-ADQAMTQM
-QIPFAMQMA
-IPTITQMNLKYAIS
-NLKYAISAKNRARTVAG
-TAQNSVRVLQKAAIT
-YTKRNVIPTITQMNLKYAISAKN
-PQIAQFAPSASAFFGM
-MWLSYFIASFRLFARTRSM
-THLSVDTKFKTEG
-GVHAGTDLEG
-MMGFKMNYQ
-YAISAKNRARTVAGV
-GFKMNYQVNG
-PLKLRGTAV
-SLNMARKTLNSLE
-SYFIASFRLFARTRSMW
-DTKFKTEGL
-AQNSVRVLQKAAITIL
-KYAISAKNRARTVAGV
-VHAGTDLE
-KMNYQVNGYP
-KWESGVKDCVVLHSY
-MWLSYFIASFRLFARTRS
-KRNVIPTITQMNLKYAI
-PTGVHAGTDL
-GEAANFCA
-AYTKRNVIPTITQMNL
-SEKSYELQ
-ITQMNLKYAISAKNRARTVA
-VKDCVVLHSY
-SGVKDCVVLHSYFTSDY
-TAQNSVRV
-MNLKYAISAKNRAR
-TNIVTRCL
-TTTNIVTRCLNRV
-DCVVLHSY
-MGFKMNYQ
-RNVIPTITQMNLKYAISAKNRAR
-YTKRNVIPTITQMNLKYAISAKNRA
-AQFAPSASAF
-YYRARAGEAANFCALIL
-LSLNMARK
-VGLMWLSYFIASFRLFARTR
-TQMNLKYAISAKNRARTV
-KMNYQVNG
-SGVKDCVVLHSYFTSDYYQ
-RRLISMMGFKMNYQ
-KFNPPALQDAYYR
-FAPSASAF
-RLISMMGFKMNYQ
-CVVLHSYFTSDYY
-VTQQLIRAAEIRA
-LPTGVHAGT
-MARKTLNSLE
-ISAKNRART
-NIVTRCLN
-LNMARKTL
-CVVLHSYF
-EAANFCAL
-FNPPALQDAYYRA
-TQMNLKYAISAKNRARTVAG
-QIELKFNPPALQDAYYRARAG
-ARAGEAAN
-PTITQMNLKYAISAKNRARTV
-FKMNYQVNG
-GLMWLSYFIASFRLFAR
-FRLFARTRSMWSF
-TQQLIRAAEIRAS
-LKFNPPALQDAYYRA
-SYFTSDYY
-QIAQFAPSA
-QQLIRAAEIRASANL
-APTHLSVDTKFKTEGL
-LAGSYKDW
-CLVGLMWLSYFIASFR
-MEIDFLELA
-FAPSASAFF
-NPPALQDAYYRARAG
-LSYFIASFRLFARTR
-PQIAQFAP
-MWLSYFIASFRLFART
-PLSLNMARKTLNS
-RAGEAANFCALIL
-ISAKNRARTVAGVSI
-CLVGLMWLSYFIASFRLFARTR
-WESGVKDCVVLHSYFTSD
-ALQDAYYRARAGEAANF
-GFKMNYQVNGYPN
-IAQFAPSASAFFGM
-LQQIELKFNPPALQDAYY
-KWESGVKDCVVLHSYF
-GVKDCVVLHSYFTSDY
-QQIELKFNPPALQDAYYRA
-IETISLAGS
-MNLKYAISAK
-ITQMNLKYAISAKNRART
-QEILGTVS
-RARAGEAANFCALIL
-QLIRAAEI
-MMGFKMNYQV
-MARKTLNSL
-YAISAKNRARTVA
-WESGVKDCVVLHSYF
-IAQFAPSASAFFG
-YMPYFFTL
-VKDCVVLHSYFTSDYYQLY
-NEKQEILG
-ALQDAYYRARAGEAANFCALI
-TKFKTEGL
-RARTVAGVSI
-VMYASAVV
-PTITQMNLKYAIS
-YFIASFRLFARTRSMWS
-ALQDAYYRARAGEA
-LAYILFTR
-FNPPALQDAYYRAR
-SAKNRARTV
-AYTKRNVIPTITQMNLKY
-LHSYFTSDY
-LPTGVHAGTD
-EKSYELQT
-QQLIRAAEIRASA
-IAQFAPSAS
-YVTQQLIRAAEIRASANL
-ASFRLFARTRSMWSF
-LISMMGFKMNYQVNG
-IPTITQMNLKYAI
-KDCVVLHSYFTSDYYQ
-LHSYFTSDYY
-QIELKFNPPALQDA
-QQLIRAAEIRASANLA
-QAMTQMYK
-VIPTITQMNLKYAISAKNRAR
-CVVLHSYFTS
-RLISMMGFKMNYQV
-YYRARAGE
-TQMNLKYA
-NYQVNGYPN
-VIPTITQMNLKYAI
-QIAQFAPSAS
-MYASAVVL
-CLVGLMWLS
-QQLIRAAEIR
-ERSEKSYE
-NIVTRCLNR
-LAYILFTRF
-YYRARAGEAA
-VAEWFLAYIL
-FIASFRLFARTRSMWS
-PLSLNMARKT
-RTVAGVSI
-ETISLAGSY
-TKRNVIPTITQMNLKYAISAKNRA
-AQNSVRVLQKAAI
-AISAKNRARTVAGVS
-QIELKFNPPALQDAYYRA
-TNIVTRCLN
-LSYFIASFRLFART
-PALQDAYYRARAGEA
-NLKYAISAKNRARTVAGVS
-WLSYFIASFRLFA
-SMMGFKMNYQVNG
-SLAGSYKDW
-LKYAISAKNRARTVAG
-QIELKFNPPALQD
-HFIETISLA
-YAISAKNR
-YYRARAGEAANFC
-EHFIETISLAGSY
-PTITQMNLKYAISAKNRAR
-GVKDCVVLHSYFTSDYYQ
-TKRNVIPTITQMN
-YTKRNVIPTITQMNLKYAISAK
-LVGLMWLSYFIASF
-LSLNMARKTL
-TKRNVIPTITQMNLKYAISAKNR
-DTKFKTEGLC
-TQMNLKYAIS
-ARAGEAANF
-YTKRNVIPTITQMNLKYAISAKNR
-ARTVAGVSI
-ESGVKDCVVL
-KYAISAKNR
-PTITQMNLKYAISAKNRART
-SGVKDCVVLHSYFT
-LKYAISAKNRARTVAGV
-QQIELKFNPPALQDAYYRARA
-ELKFNPPALQDAYYRARA
-AMTQMYKQA
-IETISLAGSY
-AGTDLEGNFY
-HFIETISLAGSYK
-KLEKMADQA
-LMWLSYFIASFRLFARTR
-AGTDLEGN
-AYTKRNVIPTITQM
-NLKYAISAKNRAR
-ELKFNPPALQDAYYRARAGEA
-FIETISLAG
-PFAMQMAY
-VAEWFLAY
-AQFAPSASAFFGMSR
-LSVDTKFKTEGLCV
-QIELKFNPPALQDAYYR
-AYTKRNVIPTITQMNLK
-MTQMYKQA
-TAQNSVRVL
-KWESGVKDCVVLH
-KYAISAKNRA
-SLNMARKTLN
-ESGVKDCVV
-LVGLMWLSYFIASFRLFART
-VHAGTDLEG
-KSYELQTP
-RAAEIRASANLAA
-VTQQLIRA
-LSVDTKFK
-ISNEKQEIL
-KRNVIPTITQMNLKYAISAK
-LQQIELKFNPPALQDAYYRARAG
-NYQVNGYP
-VGLMWLSYFIASF
-AYYRARAGEAANF
-IELKFNPPALQDAYYRARA
-QQLIRAAEIRASANLAA
-DCVVLHSYFT
-ITQMNLKYAISAKNRAR
-KDCVVLHS
-SAVVLLIL
-MPYFFTLLLQ
-PTHLSVDTKFKTEGL
-LSYFIASFRLFAR
-LSVDTKFKTEGLC
-DAYYRARAGEAANFC
-CLVGLMWLSY
-KNRARTVA
-YRARAGEA
-IRAAEIRASANLA
-EKWESGVKDCVVLHSY
-NVIPTITQMNLKYAI
-LQDAYYRARAGEAANF
-VTQQLIRAAEIRASANL
-SYELQTPFE
-EEHFIETISLAGS
-RNVIPTITQMNLKYAISAKNRART
-PTITQMNLKYAISAKN
-KRNVIPTITQMNLKY
-SVDTKFKT
-ASAVVLLIL
-MGFKMNYQVNGYPNM
-GLMWLSYFIASFRLFARTR
-AYTKRNVIPTITQMNLKYAIS
-LSVDTKFKTE
-MADQAMTQ
-SMMGFKMNYQVNGYPNM
-IVTRCLNR
-NVIPTITQMNLKYA
-VGLMWLSYFIASFRLFART
-NEKQEILGT
-FNPPALQDAYYRARAGEAAN
-AYYRARAGEAANFCALIL
-AKNRARTVAG
-KMADQAMTQM
-LKFNPPALQDAYYRARAGE
-SMMGFKMNYQVNGYP
-LQQIELKFNPPALQDAYYRA
-SYFIASFRLFARTR
-AGEAANFCALILA
-AYYRARAGEAANFCALI
-IPTITQMNLKYAISAKN
-PQIAQFAPSASAFF
-ISMMGFKMNYQVNGYPN
-TITQMNLKYAISA
-LAYILFTRFF
-VKDCVVLHS
-FKMNYQVNGY
-GVKDCVVLHS
-NSVRVLQKAAITI
-YTKRNVIPTITQMNL
-KMADQAMT
-VIPTITQMNLKYAISAKNR
-GLMWLSYFIASFRLFART
-QMNLKYAISA
-ALQDAYYRARAGEAANFCA
-VCTNYMPYFFTLL
-NLKYAISAKNRARTVA
-DCVVLHSYFTSDYYQ
-ISMMGFKMNYQVNG
-VLHSYFTSDYYQLY
-VKDCVVLH
-VHAGTDLEGN
-LVGLMWLSYFIASFRLFAR
-YTKRNVIPTITQMNLKYAIS
-ITQMNLKYAISAKN
-KFNPPALQDAYYRARAGEAA
-VIPTITQMNLKYAISA
-MNLKYAISAKNRARTVAG
-VDTKFKTEGL
-AKNRARTVAGVSI
-PALQDAYYRARAGEAANFC
-QQIELKFNPPALQDAYYRARAGE
-KFKTEGLCV
-WLSYFIASFRLFARTRS
-SMMGFKMNYQVNGY
-PTHLSVDTKFKTEGLCV
-VGLMWLSY
-ISAKNRAR
-YELQTPFEI
-YVTQQLIRAAEIRASA
-PALQDAYYRARAG
-YMPYFFTLL
-DCVVLHSYFTSDY
-DTKFKTEGLCVDI
-ISMMGFKMN
-IELKFNPPALQDAYYRARAG
-ARAGEAANFCALI
-HAGTDLEG
-LSYFIASFRLFARTRSM
-AGEAANFCA
-CLVGLMWLSYFIASF
-ERSEKSYELQTPF
-ELQTPFEI
-AYILFTRFF
-SGVKDCVVL
-IISNEKQEI
-QQLIRAAEI
-HLSVDTKFKTEGLC
-PTITQMNLKYAISAK
-IISNEKQEIL
-IPTITQMNLKYAISA
-YYRARAGEAANFCAL
-LSLNMARKT
-KRNVIPTITQMNLKYAISAKNRA
-MGFKMNYQVN
-NVIPTITQMNLKYAISAKNRART
-VDTKFKTEG
-PPALQDAYYRARA
-PQIAQFAPSASAFFG
-HSYFTSDYY
-ASFRLFARTRSMW
-PALQDAYYRARAGEAAN
-VTRCLNRV
-RRLISMMGFKMNYQVNGY
-VIPTITQMNLKYAISAKNRART
-HFIETISLAG
-WESGVKDCVVLHSYFT
-ELQTPFEIKL
-EWFLAYIL
-DMSKFPLKLRGTA
-PTITQMNLKYAISA
-IELKFNPPALQDAYYR
-QMNLKYAISAKNRARTVAG
-LQQIELKFNPPALQDAYYRARA
-NYMPYFFTLL
-RRLISMMG
-GEAANFCAL
-GLMWLSYFIASFRL
-NRARTVAGVS
-VDTKFKTE
-ELKFNPPALQDAYYRA
-RRLISMMGF
-GVHAGTDL
-VGLMWLSYFIASFRL
-IELKFNPPALQDAYYRARAGEAA
-KRNVIPTITQMNL
-LMWLSYFIASFRLFARTRS
-VKDCVVLHSYFTS
-KNRARTVAG
-TQQLIRAAEIRASANLA
-QIELKFNPPALQDAYYRARA
-YVTQQLIRAAEIRASANLA
-QAMTQMYKQA
-KYAISAKN
-QMNLKYAISAKNR
-SGVKDCVVLHSYFTSDYY
-PLKLRGTA
-RLFARTRS
-GTDLEGNF
-FIASFRLFARTRSMW
-KDCVVLHSY
-WESGVKDCVVLHS
-RARAGEAANFCALI
-LMWLSYFIASFRLFAR
-ALQDAYYRARAGEAAN
-LQIPFAMQ
-ISAKNRARTVAGV
-FLAYILFTR
-IPTITQMNLKYAISAKNR
-SIISNEKQ
-WLSYFIASFRLFARTRSM
-HLSVDTKFKTEGLCV
-QLIRAAEIRASANL
-ISMMGFKMNYQVN
-PLSLNMARK
-NPPALQDAYYRARAGEAAN
-TDLEGNFYG
-SNEKQEILG
-RARTVAGV
-KSYELQTPFE
-SIISNEKQE
-YVTQQLIRA
-EIDFLELA
-QIELKFNPPALQDAYYRAR
-SGWTFGAGAALQI
-SYFIASFRLFARTRSM
-DRDAAMQRKLEKM
-CVVLHSYFT
-KFNPPALQDAYYRARAG
-NVIPTITQMNLKYAIS
-ILFTRFFY
-DAYYRARAGEAANF
-KQEILGTV
-LEKMADQAM
-ISNEKQEILGTVSW
-TITQMNLKYAISAKNRARTV
-LQQIELKFNPPALQD
-ITQMNLKYAISAKNR
-LVGLMWLSYFIASFR
-LTNIFGTVYEKLK
-HLSVDTKFKTEGL
-LSVDTKFKT
-PPALQDAYYRARAGEAANF
-IPFAMQMA
-NVIPTITQMNLKY
-VGLMWLSYFIASFRLFARTRS
-ESGVKDCV
-APSASAFF
-CVVLHSYFTSDYYQLY
-KWESGVKDCVVLHSYFTSD
-PQIAQFAPSASAF
-YRARAGEAA
-AYTKRNVIPTITQ
-QMNLKYAISAKNRARTVAGVS
-LQQIELKFNPPALQ
-WESGVKDCVVLHSYFTSDY
-VTQQLIRAAEIRAS
-FNPPALQDAYYRARA
-NIVTRCLNRV
-LQQIELKFNPPALQDAYYR
-APTHLSVDTKFKTEG
-MMGFKMNY
-QDAYYRARAGEAANF
-RLISMMGFKMNYQVNG
-ISMMGFKM
-AISAKNRARTVAG
-QDAYYRARAGEAANFCA
-SGVKDCVVLH
-VIPTITQMNLKYAISAK
-PPALQDAYYRARAGE
-YILFTRFFY
-YAISAKNRARTVAGVS
-GLMWLSYF
-TQMNLKYAISAKNRAR
-IELKFNPPALQDAYYRAR
-QDAYYRARAGEAAN
-MADQAMTQM
-RARAGEAANF
-GFKMNYQVNGYPNM
-RRLISMMGFKMNYQVN
-ELQTPFEIK
-EKSYELQTP
-KFKTEGLC
-ALQDAYYRARAGEAANFCAL
-DTKFKTEG
-AYTKRNVIPTITQMNLKYAISAKN
-QIAQFAPSASAFFGM
-PALQDAYYRARAGE
-RNVIPTITQMNLKYAISAKNRA
-LIRAAEIRASANL
-NVIPTITQMNLKYAISAKNRA
-AGEAANFCAL
-QMNLKYAISAKNRART
-PTHLSVDTKFKTEGLC
-TQQLIRAAEIRASAN
-LISMMGFKMNYQVN
-WFLAYILFTR
-THLSVDTKFKTEGL
-SAKNRARTVAGVS
-RARTVAGVS
-MARKTLNS
-QNSVRVLQKAAITI
-QFAPSASA
-GLMWLSYFIASFRLFARTRS
-ISNEKQEILGTVS
-VAEWFLAYI
-QMNLKYAIS
-GVKDCVVLH
-ELKFNPPALQDAYYR
-ERSEKSYELQTPFE
-QIAQFAPSASAFFGMS
-QWLTNIFGTVYEKL
-VKDCVVLHSYFTSDYY
-DAYYRARAGEAANFCALI
-MNLKYAIS
-VGLMWLSYFIASFRLF
-LKYAISAKNRARTV
-TQMNLKYAISAKNRA
-QWLTNIFGTVYEK
-YASAVVLL
-DCVVLHSYFTSDYYQLY
-NRARTVAG
-YRARAGEAAN
-EKWESGVKDCVVL
-FRLFARTR
-TKRNVIPTITQMNL
-SLNMARKTL
-KRNVIPTITQMNLK
-SNEKQEILGTVSW
-THLSVDTKFKTEGLCV
-VLHSYFTSDYYQL
-FNPPALQDAYYRARAGEAA
-ERSEKSYELQ
-SNEKQEIL
-LQQIELKFNPPALQDA
-ISAKNRARTV
-KQEILGTVS
-PQIAQFAPS
-MMGFKMNYQVNGY
-WLSYFIASFRLFARTR
-NLKYAISA
-PLSLNMAR
-QMNLKYAISAKNRA
-TISLAGSYKD
-VVLHSYFT
-KFNPPALQDAYYRARA
-NLKYAISAKN
-LVGLMWLS
-FLAYILFTRF
-YVTQQLIRAAEIRA
-ELKFNPPALQDAYYRARAGEAA
-MNLKYAISA
-AISAKNRARTVAGVSI
-YTKRNVIPTITQMN
-EKQEILGT
-RLISMMGF
-RNVIPTITQMNLKYA
-ISLAGSYKD
-ESGVKDCVVLHSYFT
-VHAGTDLEGNFYG
-MNLKYAISAKNRART
-PTITQMNLKYAISAKNR
-LVGLMWLSYFIASFRLFARTR
-RRLISMMGFK
-RNVIPTITQMNLKYAISA
-KRNVIPTITQMNLKYAIS
-HFIETISL
-KFNPPALQDAYYRARAGEA
-SVRVLQKAAITIL
-AISAKNRA
-KMNYQVNGY
-TQQLIRAAEIRASA
-MPYFFTLL
-ESGVKDCVVLHSY
-ISLAGSYK
-LQDAYYRARAGEAANFCALI
-IASFRLFARTRSMWS
-RNVIPTITQMNLKYAIS
-FEYVSQPF
-AISAKNRART
-DAYYRARAGEAANFCA
-VGLMWLSYFIASFRLFA
-TQMNLKYAI
-LVGLMWLSYFIASFRLFA
-FKTEGLCVD
-GTDLEGNFYG
-QIPFAMQMAY
-LPTGVHAG
-MGFKMNYQVNGYPN
-EEHFIETI
-ISMMGFKMNYQVNGYP
-RARAGEAAN
-ARAGEAANFCALIL
-APTHLSVDTKFKT
-MNLKYAISAKNRARTVA
-MWLSYFIASFRLF
-TQMNLKYAISAKNRARTVA
-LKYAISAKNRART
-LVGLMWLSY
-FLAYILFT
-WLSYFIASFRLFART
-KSYELQTPF
-KWESGVKDCVVLHSYFTS
-ITQMNLKYAISAKNRA
-NYMPYFFTL
-GVKDCVVLHSYFTSDYYQL
-LKFNPPALQDAYY
-GLMWLSYFIASFRLFA
-SMMGFKMNY
-QLIRAAEIRASANLAA
-PPALQDAYYRARAG
-ASAVVLLI
-AKNRARTVA
-YVTQQLIRAAEIRASAN
-YYRARAGEA
-MYASAVVLL
-SMMGFKMN
-TITQMNLKYAISAK
-SAKNRARTVA
-FNPPALQDAYYRARAGEA
-EEHFIETISLAGSYK
-KRNVIPTITQMNLKYAISAKNR
-EKSYELQTPF
-KRNVIPTITQMNLKYAISA
-LISMMGFK
-FPLKLRGT
-LKLRGTAV
-CLVGLMWLSYFIASFRL
-TKRNVIPTITQMNLKYAI
-MNLKYAISAKNRARTVAGVS
-TKRNVIPTITQMNLK
-QIELKFNPPALQDAYY
-LVGLMWLSYFIAS
-KFNPPALQDAYYRA
-ISNEKQEILG
-YTKRNVIPTITQMNLKY
-YVTQQLIRAAEIR
-KRNVIPTITQMNLKYA
-NPPALQDAYYRARA
-VVLHSYFTSDYYQL
-YTKRNVIPTITQMNLK
-AYYRARAGEAANFCA
-YVTQQLIRAA
-LKYAISAKNRARTVAGVS
-LIRAAEIR
\ No newline at end of file
diff --git a/SupplementaryResults/CombinedComputations/41947_vaccine_sequence_hog.txt b/SupplementaryResults/CombinedComputations/41947_vaccine_sequence_hog.txt
deleted file mode 100644
index 90b4f7cbf02e7aa9631e51821cbdd2c80308415b..0000000000000000000000000000000000000000
--- a/SupplementaryResults/CombinedComputations/41947_vaccine_sequence_hog.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-> HOGVAX optimized combined peptide vaccine sequence with overlaps
-ISPDAVTAYNGYLTSSSKTPEEHFIETISLAGSYKDWPQIAQFAPSASAFFGMSREKWESGVKDCVVLHSYFTSDYYQLYSTQSPLSLNMARKTLNSLEDCVMYASAVVLLILYRRLISMMGFKMNYQVNGYPNMSYSLFDMSKFPLKLRGTAVLRQWLTNIFGTVYEKLKSAPTHLSVDTKFKTEGLCVDINKVVSTTTNIVTRCLNRVCTNYMPYFFTLLLQYVTQQLIRAAEIRASANLAATERSEKSYELQTPFEIKLQQIELKFNPPALQDAYYRARAGEAANFCALILASGWTFGAGAALQIPFAMQMAYTKRNVIPTITQMNLKYAISAKNRARTVAGVSIISNEKQEILGTVSWTAQNSVRVLQKAAITILTAFGLVAEWFLAYILFTRFFYVLGLPTGVHAGTDLEGNFYGCLVGLMWLSYFIASFRLFARTRSMWSFEYVSQPFDRDAAMQRKLEKMADQAMTQMYKQARSQMEIDFLELA
-> Concat optimized combined peptide vaccine sequence concatenated
-LQQIELKFNPPALQDPQIAQFAPSASAFKQEILGTVSWCLVGLMWLSYFIASFRLFDAYYRARAGEAANFCALILPTITQMNLKYAISAKNRARTVLAGSYKDWLQQIELKFNPPALQDASLAGSYKDWIISNEKQEILGTVSVTQQLIRAAEIRASANLAADCVMYASAAPTHLSVDTKFKTEGLCVMYASAVVLAMTQMYKQAFKMNYQVNGYPNMTAQNSVRVLPQIAQFAPSQIAQFAPSASAFFGMKSYELQTPFESMMGFKMNYQVNGYPNMYVTQQLIRAAEIRAALQDAYYRARAGEAANFCALILIRAAEIRASANLAAIASFRLFARTRSMWSFRRLISMMGFKLQQIELKFNPPALQKWESGVKDCVVLHSYFTYVTQQLIRAADLEGNFYGMWLSYFIASFRLFARTRSISAKNRARTVAGVSIAYTKRNVIPTITQMNLKYAISQMNLKYAISAKNRARTVAGVSLIRAAEIRASANLAAYAISAKNRARTVAGVSVCTNYMPYFFTLLIETISLAGSYKDWNPPALQDAYYRARAGEAANNSVRVLQKAAITILFAPSASAFFGMSRSNEKQEILGTVSWAYTKRNVIPTITQMNLKYAISAVHAGTDLEGNFYGIISNEKQEIITQMNLKYAISAKNRARTVACLVGLMWLSYFIASFRLQIPFAMQMATAQNSVRVLQKAAIQQLIRAAEIRASANLAAYVTQQLIRAIAQFAPSASAFFGMSRYTKRNVIPTITQMNLKYAISAKNRWFLAYILFTRPPALQDAYYRARAGEAANFCCLVGLMWLSPLKLRGTAARAGEAANFCALILEEHFIETISLAGSYKIPTITQMNLKYAISAKNRARTKWESGVKDCVVLHSYFTSDYSFRLFARTRSMWSFLPTGVHAGTDMWLSYFIASFRLFARTRSMLQQIELKFNPPALQDAYYRARAGERSEKSYETAQNSVRVLQKAAITIIISNEKQERRLISMMGFKMNYQVNGYSLNMARKTLNSLESAKNRARTVAGVSIYVTQQLIRAAEIRASANLCLVGLMWLSYFIASFRLFAVKDCVVLHSYFTSDYYQLYCLVGLMWLSYFIASLKYAISAKNRARTVAGVSCLVGLMWLSYQMNLKYAISAKNRARTVAGVLISMMGFKMNYQVNGYQQIELKFNPPALQDAYYRARAGEAAYVTQQLIRAAEIRASAPQIAQFAPSASAFFRNVIPTITQMNLKYAISAKNRARLVGLMWLSYFIASFRLFARTRSEEHFIETISLAGSGTDLEGNFYGALQDAYYRARAGEAANFCALIHFIETISLAGSYKMYASAVVLLARTVAGVSIISNEKQEILGTVSWLQQIELKFNPPALQDAYYRARAGEITQMNLKYAISAKNRARTVAGVAPTHLSVDTKFKTEGLCAQNSVRVLQKAAITILEIDFLELACVMYASAVLHSYFTSDYYQLYSSYFIASFRLFARTRSMWSFYVTQQLIRAAEIRASCLVGLMWLSYFIASFIISNEKQEILPQIAQFAPSARDAAMQRKLEKMAQDAYYRARAGEAANFCALILAISAKNRARTVAGVSIVMYASAVVQWLTNIFGTVYEKLKTQQLIRAAEIRASANLAARRLISMMGFKMNYQRSEKSYELQTPFEYVTQQLIRAAEIRASANTKRNVIPTITQMNLKYAISAKNRARAAEIRASANLAAKWESGVKDCVVLHSYFTSKWESGVKDCVVLHSYFDCVMYASAVYYRARAGEAANFCALILYVTQQLIRAAEIRSIISNEKQQNSVRVLQKAAITILAPTHLSVDTKFKTECLVGLMWLSYFIASFRLFARTAYTKRNVIPTITQMNLKYAAQFAPSASAFFGMSRKMADQAMTQMTDLEGNFYGLPTGVHAGTDLEGIELKFNPPALQDAYYRARAGEAASYFIASFRLFARTRSMWISLAGSYKDWIPFAMQMAYLQDAYYRARAGEAANFCALILIAQFAPSASAFFGMSRRLISMMGFKMNYQVNTAQNSVRVLQKAARRLISMMGFLISMMGFKMNYQVNGYPSGVKDCVVLHSYFTSDYYQMMGFKMNYQVNGYPNMMNLKYAISAKNRARTVAGVSYMPYFFTLLLAYTKRNVIPTITQCVVLHSYFTSDYYQLYWLSYFIASFRLFARTRSMELQTPFEIKLEKWESGVKDCVVLHSYYTKRNVIPTITQMNLKYAISAKNRAIISNEKQEILGTVSWKRNVIPTITQMNLKYAISAKNRARDRDAAMQRKLEKMTKRNVIPTITQMNLKYAISAKNRARRTVAGVSIAYTKRNVIPTITQMNLKYAISAKERSEKSYELQTPFEFRLFARTRSMWSFLQQIELKFNPPALQDAYYRAPLKLRGTAVHLSVDTKFKTEGLCVEEHFIETILQQIELKFNPPALQDAYYRAPTHLSVDTKFKTEGQEILGTVSWQAMTQMYKQAQIPFAMQMAYTHLSVDTKFKTEGLCVISMMGFKMNYQVNGYPNVLHSYFTSDYYQLYSLQQIELKFNPPALLTNIFGTVYEKLKVGLMWLSYFIASFRLFARTRSYFIASFRLFARTRSMWSFAYTKRNVIPTITQMNLKYAISAKNRLPTGVHAGFPLKLRGTAWLTNIFGTVYEKLKDTKFKTEGLCVDIYMPYFFTLLLKLRGTAVQWLTNIFGTVYEKFIASFRLFARTRSMWSFVAEWFLAYILAYYRARAGEAANFCALILGLMWLSYFIASFRLFARTRSRARTVAGVSIEKWESGVKDCVVLLPTGVHAGTALQDAYYRARAGEAANFCALCLVGLMWLCLVGLMWLSYFIASFRLFARTRSAYTKRNVIPTITQMNTAQNSVRVLQKAAITGFKMNYQVNGYPNMSIISNEKQEASAVVLLILSMMGFKMNYQVNGYPNPQIAQFAPSASAFFGRRLISMMGFKMNYQVNGIISNEKQEILGTVLSVDTKFKTEGLCVAYTKRNVIPTITQMNLKYAISAKNFLAYILFTRFFYVYVTQQLIRCLVGLMWLSYFIASFRLFARCLVGLMWLSYFIASFRLAYTKRNVIPTITQMNLRRLISMMGFKMNYQVERSEKSYELRAGEAANFCALILYASAVVLLIPALQDAYYRARAGEAANFCLQQIELKFNPPALQDAYYRARAFEYVSQPFMGFKMNYQVNGYPNMVIPTITQMNLKYAISAKNRARTPALQDAYYRARAGEAANFCAEKWESGVKDCVVLHSQFAPSASAFFGMSRQIELKFNPPALQDAYYRARAGEAAAGEAANFCALILALQQIELKFNPPALQDAYYAYTKRNVIPTITQMNLKQLIRAAEIRASANLAAEHFIETISLAGSYKYVTQQLIRAAEIRASANLAAAPTHLSVDTKFKTEGLELKFNPPALQDAYYRARAGEAARNVIPTITQMNLKYAISAKNRARTSYFIASFRLFARTRSMWSRARAGEAANFCALILLKFNPPALQDAYYRARAGEAAAYTKRNVIPTITQMNLKYNLKYAISAKNRARTVAGVSNYMPYFFTLLMEIDFLELRRLISMMGFKMNYKFNPPALQDAYYRARAGEAAAQNSVRVLQKAAITIEEHFIETISNPPALQDAYYRARAGEAANFEILGTVSWEEHFIETISLAGSYITQMNLKYAISAKNRARTVAGYFFTLLLQYVTQQLIRAAEIRASANLAQTPFEIKLASAVVLLIPPALQDAYYRARAGEAANFSYFIASFRLFARTRSMYRARAGEAANFCALILTQMNLKYAISAKNRARTVAGVEKWESGVKDCVVLHRRLISMMGAISAKNRARTVAGVSSVDTKFKTEGLCVHSYFTSDYYQLYSYASAVVLLPLSLNMARKTLNSMPYFFTLLLQFNPPALQDAYYRARAGEAAAYTKRNVIPTITQMLSYFIASFRLFARTRSMRLISMMGFKMNYQVNGYKLEKMADQAMSGWTFGAGAALQIDMSKFPLKLRGTASVRVLQKAAITILLQQIELKFNPPALQDAYYRARAGEAVLHSYFTSDYYQLYGVKDCVVLHSYFTSDYYQLPTHLSVDTKFKTEGLCVYELQTPFEIKNVIPTITQMNLKYAISAKNRARTSGVKDCVVLHSYFTSDYYAPTHLSVDTKFKTPYFFTLLLQTAQNSVRVLQQIELKFNPPALQDAYYRARASFRLFARTRSMWSFVVLHSYFTSDYYQLYWESGVKDCVVLHSYFTSDYESGVKDCVVLHSYFTSDYKDCVVLHSYFTSDYYQLYPQIAQFAPALQDAYYRARAGEAANFCAQIAQFAPSASAFFGMSERSEKSYELQTPFLMWLSYFIASFRLFARTRSEKWESGVKDCVVLHSYFEEHFIETISLCLVGLMWLSYFIASFRLFARTRPTITQMNLKYAISAKNRARTMTQMYKQATTTNIVTRCLNRVFNPPALQDAYYRARAGEAANMEIDFLELAKWESGVKDCVVLHSYFTSDQWLTNIFGTVYEKLCLVGLMWLSYFIAERSEKSYELQTITQMNLKYAISAKNRARTVAISMMGFKMNYQVNGYPESGVKDCVVLHSYFTSDYYGVKDCVVLHSYFTSDYYQKYAISAKNRARTVAGVSPFAMQMAYAYTKRNVIPTITQMNLKYAILQQIELKFNPPALQDAYVKDCVVLHSYFTSDYYQLPQIAQFAPSASAFFGMLSLNMARKTLNSLAKNRARTVAGVSILQTPFEIKLPTITQMNLKYAISAKNRARTVAQQIELKFNPPALQDAYYRARAGEASAVVLLILDCVVLHSYFTSDYYQLY
\ No newline at end of file
diff --git a/SupplementaryResults/CombinedComputations/hit_hist_mhc1_Combined.csv b/SupplementaryResults/CombinedComputations/hit_hist_mhc1_Combined.csv
deleted file mode 100644
index 0357aa133bcb888cb1aec97ee0d203cbca82e7ad..0000000000000000000000000000000000000000
--- a/SupplementaryResults/CombinedComputations/hit_hist_mhc1_Combined.csv
+++ /dev/null
@@ -1,53 +0,0 @@
-,count,prob
-0,0,0.0
-1,1,1.1271576454168899e-05
-2,2,9.184902162729291e-05
-3,3,0.013021122902304885
-4,4,0.00209473315343291
-5,5,0.005917125451690525
-6,6,0.020494421487969497
-7,7,0.019503272622992382
-8,8,0.028486998056936864
-9,9,0.05961998458703115
-10,10,0.03912729617590892
-11,11,0.05015994349717297
-12,12,0.06903966498840747
-13,13,0.07696695307883539
-14,14,0.05696434534463272
-15,15,0.06858987219001793
-16,16,0.08932612403417163
-17,17,0.053213134589637806
-18,18,0.05794027391464695
-19,19,0.08217727631479205
-20,20,0.0401524657892574
-21,21,0.036128608395606325
-22,22,0.04001397620412572
-23,23,0.029844474295751787
-24,24,0.015245321540241028
-25,25,0.02000283474963957
-26,26,0.012647647527411102
-27,27,0.005258464271491665
-28,28,0.004326352634977892
-29,29,0.002161033557783765
-30,30,0.0007909675968822278
-31,31,0.00044133618035007707
-32,32,0.0001724388036393076
-33,33,4.5122428272960904e-05
-34,34,1.6439533869298958e-05
-35,35,5.031668147617412e-06
-36,36,1.2356280212770568e-06
-37,37,5.86205861320914e-07
-38,38,0.0
-39,39,0.0
-40,40,0.0
-41,41,0.0
-42,42,0.0
-43,43,0.0
-44,44,0.0
-45,45,0.0
-46,46,0.0
-47,47,0.0
-48,48,0.0
-49,49,0.0
-50,50,0.0
-51,51,0.0
diff --git a/SupplementaryResults/CombinedComputations/hit_hist_mhc2_Combined.csv b/SupplementaryResults/CombinedComputations/hit_hist_mhc2_Combined.csv
deleted file mode 100644
index 38da4bee4b0ef122c898dc8de4e4515978227b77..0000000000000000000000000000000000000000
--- a/SupplementaryResults/CombinedComputations/hit_hist_mhc2_Combined.csv
+++ /dev/null
@@ -1,398 +0,0 @@
-,count,prob
-0,0,0.0007243433400952906
-1,1,0.014885742639532804
-2,2,0.019507630785920414
-3,3,0.017474705383599104
-4,4,0.008843217748788402
-5,5,0.020781655403095702
-6,6,0.03292508469472294
-7,7,0.016883875960736965
-8,8,0.014820899585693117
-9,9,0.005949623636914083
-10,10,0.006754939451606302
-11,11,0.0101567353185798
-12,12,0.014271345575631492
-13,13,0.007744249049454156
-14,14,0.020599972037271812
-15,15,0.025128145572793196
-16,16,0.009540393868010854
-17,17,0.008249663471868457
-18,18,0.004059030553326011
-19,19,0.015270719413900574
-20,20,0.002697911073053284
-21,21,0.008597064480062054
-22,22,0.0038416781998314034
-23,23,0.013111011218753906
-24,24,0.015997999029488136
-25,25,0.012164069362286326
-26,26,0.015649389600556708
-27,27,0.0074565786295851776
-28,28,0.015154416956053782
-29,29,0.003713595549594871
-30,30,0.011970196740797927
-31,31,0.004080614421715299
-32,32,0.007158334906220716
-33,33,0.005400271554255336
-34,34,0.009595297802666729
-35,35,0.004515510260208585
-36,36,0.005563365449253851
-37,37,0.012642785480997024
-38,38,0.00958631592284183
-39,39,0.009583418066430736
-40,40,0.007116045516848406
-41,41,0.0033720075184625236
-42,42,0.0073694892989234345
-43,43,0.011788252774555722
-44,44,0.026056292450397714
-45,45,0.011641090559484413
-46,46,0.012539469014545394
-47,47,0.0066851430090249546
-48,48,0.022769338836215576
-49,49,0.010932842609617457
-50,50,0.024194622219603387
-51,51,0.01670133044785422
-52,52,0.018240532491242235
-53,53,0.010520678113468107
-54,54,0.01844413805294535
-55,55,0.007271633364483284
-56,56,0.010552452994347618
-57,57,0.016342485276994913
-58,58,0.008566809744123835
-59,59,0.011675942894455591
-60,60,0.013044186117088092
-61,61,0.0062746165937871455
-62,62,0.006340580708275876
-63,63,0.014549271618452184
-64,64,0.004741411328712042
-65,65,0.005314948051474561
-66,66,0.00973306297026134
-67,67,0.0017047335047484966
-68,68,0.006606334440703958
-69,69,0.00402962461480823
-70,70,0.005792204874037294
-71,71,0.002832027891788934
-72,72,0.010429396960383107
-73,73,0.0030937020873403665
-74,74,0.00783506999248173
-75,75,0.0042561689586959085
-76,76,0.003620523119224851
-77,77,0.002575804187273225
-78,78,0.0020342857935578694
-79,79,0.0013221775404518863
-80,80,0.005192080858247845
-81,81,0.004023838943961698
-82,82,0.003730905542930535
-83,83,0.003879143920230482
-84,84,0.0023765282679353216
-85,85,0.0016513661363588867
-86,86,0.0036476941467093833
-87,87,0.0010377953621737502
-88,88,0.003265970869476515
-89,89,0.0024577705600102107
-90,90,0.0026312212385898493
-91,91,0.0016369154743639946
-92,92,0.006874769519627707
-93,93,0.0018241655613680792
-94,94,0.005290177050181119
-95,95,0.0028975364237482804
-96,96,0.0018182482779463805
-97,97,0.0016998239735334531
-98,98,0.00155070870767842
-99,99,0.0008618346746215543
-100,100,0.001015505257822371
-101,101,0.004021098027542306
-102,102,0.0009760575215940696
-103,103,0.00134721374269414
-104,104,0.0009759325283437079
-105,105,0.0006125292804202072
-106,106,0.0009341559146669635
-107,107,0.0006199580006714205
-108,108,0.0004746150420312386
-109,109,0.0005463595219076837
-110,110,0.0003012447462523442
-111,111,0.00029270125771039705
-112,112,0.0011654010080659669
-113,113,0.0003628276156394898
-114,114,0.0007712531901685733
-115,115,0.0005692756963982802
-116,116,0.0002781443245554417
-117,117,0.00016007809361808767
-118,118,0.000882369044062463
-119,119,0.00022246029847093615
-120,120,0.0006041599241163189
-121,121,0.00048609432211481265
-122,122,0.00029217158973790817
-123,123,0.00027448054427056
-124,124,0.00024996615980153763
-125,125,0.0016624074711140205
-126,126,0.006227192545163455
-127,127,0.002907255534373722
-128,128,0.0031637345038517304
-129,129,0.0012195078940228695
-130,130,0.004559602715663477
-131,131,0.0011207884776601052
-132,132,0.002970058349323239
-133,133,0.0010110458346951302
-134,134,0.0013493371011060577
-135,135,0.0023429801489307078
-136,136,0.0023294107880408206
-137,137,0.001282540107573138
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diff --git a/SupplementaryResults/GenotypeComputations/37435_chosen_peptides_hog_inc_substrings.txt b/SupplementaryResults/GenotypeComputations/37435_chosen_peptides_hog_inc_substrings.txt
deleted file mode 100644
index 93b486135b549938581cc4342906c657cfa3cd64..0000000000000000000000000000000000000000
--- a/SupplementaryResults/GenotypeComputations/37435_chosen_peptides_hog_inc_substrings.txt
+++ /dev/null
@@ -1,518 +0,0 @@
-SFRLFARTRSMWSFNPET
-YFIASFRLFARTRS
-GDSGFAAYSRYRIGN
-SGFAAYSRYRIGNYKL
-AGDSGFAAYSRYRIGNYKL
-QMNLKYAISAKNRARTVA
-FAAYSRYRIGNYKLNT
-ARTRSMWSFNPETNILLNV
-ARTRSMWSFNPETNI
-ASFRLFARTRSMWSFNPETNI
-AVKRTIKGTHHWL
-LSYFIASFRLFART
-GLVEVEKGVLPQLEQP
-GLVEVEKGVLPQLEQ
-MWSFNPETNILLNVPLH
-SAVKRTIKGTHHWL
-LVEVEKGVLPQLEQPYV
-SFNPETNILLNVPLHGT
-SMMGFKMNYQVNGYPN
-KRTIKGTHHWLLL
-TQMNLKYAISAKNRARTVAG
-TYRRLISMMGFKMNY
-PETNILLNVPLHG
-FKMNYQVNGYPNM
-IASFRLFARTRSMWSFN
-AGDSGFAAYSRYR
-ASFRLFARTRSMW
-AVKRTIKGTHHWLLL
-LISMMGFKMNYQVNG
-KGVLPQLEQPYVF
-SMWSFNPETNILLNV
-FKMNYQVNGYPNMF
-DSGFAAYSRYRIG
-KATYKPNTWCIRCLW
-SYFIASFRLFARTRS
-ARTRSMWSFNPET
-EKGVLPQLEQPYVF
-EVEKGVLPQLEQPYVF
-APTHLSVDTKFKTE
-RTRSMWSFNPETNILL
-LMWLSYFIASFRLF
-DKLQFTSLEIPRR
-HLSVDTKFKTEGLC
-EKGVLPQLEQPYVFIK
-KLQFTSLEIPRRN
-LQTYVTQQLIRAAE
-PTHLSVDTKFKTE
-RLFARTRSMWSFNP
-FARTRSMWSFNPETNILLNVP
-TRSMWSFNPETNILLNVP
-TQMNLKYAISAKNRARTVA
-LQTYVTQQLIRAA
-SMMGFKMNYQVNG
-SMWSFNPETNILLNVP
-FNPETNILLNVPLHGT
-ETNILLNVPLHGT
-THLSVDTKFKTEGL
-LFARTRSMWSFNPETN
-SFRLFARTRSMWSFNPETNILL
-FIASFRLFARTRSM
-TCGLVEVEKGVLPQLEQPYVFIK
-EKGVLPQLEQPYVFIKR
-FIASFRLFARTRS
-RSMWSFNPETNILLNVPLHGT
-QLIRAAEIRASAN
-MTYRRLISMMGFK
-CGLVEVEKGVLPQLEQ
-GLVEVEKGVLPQL
-ASFRLFARTRSMWSFNPET
-FRLFARTRSMWSF
-GDSGFAAYSRYRI
-FARTRSMWSFNPETNILLNVPL
-SMWSFNPETNILLNVPLH
-YFIASFRLFARTR
-VAGDSGFAAYSRYRIGNYKLNT
-SFRLFARTRSMWSFNPETNILLNV
-QMNLKYAISAKNRA
-SAVKRTIKGTHHW
-RLFARTRSMWSFNPETNILLN
-QWLTNIFGTVYEKLK
-YRRLISMMGFKMNYQ
-LFARTRSMWSFNPETNILLNV
-EVEKGVLPQLEQPYVFIK
-RSMWSFNPETNILLNVP
-NLKYAISAKNRARTV
-AGDSGFAAYSRYRIGNYKLNT
-SFRLFARTRSMWSF
-QMNLKYAISAKNRAR
-TQMNLKYAISAKNRA
-FIASFRLFARTRSMWS
-SYFIASFRLFARTR
-LVEVEKGVLPQLEQPYVFIK
-ATYKPNTWCIRCLW
-LQSLQTYVTQQLIRAAE
-QQLIRAAEIRASANL
-WSFNPETNILLNV
-WLTNIFGTVYEKLK
-FARTRSMWSFNPE
-TRSMWSFNPETNILL
-SFNPETNILLNVP
-TQMNLKYAISAKNRAR
-DSGFAAYSRYRIGNYK
-VTQQLIRAAEIRAS
-AAYSRYRIGNYKL
-ARTRSMWSFNPETNIL
-SMMGFKMNYQVNGYPNMF
-IASFRLFARTRSMWSFNPE
-IRAAEIRASANLA
-GVLPQLEQPYVFIK
-MMGFKMNYQVNGYPN
-AGDSGFAAYSRYRIGNYK
-SMWSFNPETNILLN
-TQMNLKYAISAKNRART
-APTHLSVDTKFKT
-MNLKYAISAKNRA
-MWSFNPETNILLN
-GVLPQLEQPYVFIKRS
-TCGLVEVEKGVLPQ
-DSGFAAYSRYRIGNYKLN
-NLKYAISAKNRART
-CGLVEVEKGVLPQLE
-VEVEKGVLPQLEQPY
-RSMWSFNPETNILLNVPL
-LIRAAEIRASANLAA
-LISMMGFKMNYQV
-FARTRSMWSFNPETNILLNVPLHG
-THLSVDTKFKTEGLC
-VEKGVLPQLEQPYV
-MNLKYAISAKNRART
-RLFARTRSMWSFNPETNILL
-LIRAAEIRASANLA
-VAGDSGFAAYSRYRIGNY
-TQQLIRAAEIRASA
-KGVLPQLEQPYVFIK
-MGFKMNYQVNGYPNM
-VEVEKGVLPQLEQPYV
-IASFRLFARTRSMWSFNPETNI
-AAYSRYRIGNYKLN
-WSFNPETNILLNVPLH
-FARTRSMWSFNPETNI
-MNLKYAISAKNRAR
-LIRAAEIRASANL
-MMGFKMNYQVNGY
-CGLVEVEKGVLPQLEQPY
-IASFRLFARTRSMWSFNP
-LSVDTKFKTEGLC
-GDSGFAAYSRYRIG
-SLQTYVTQQLIRAA
-ARTRSMWSFNPETNILL
-RTRSMWSFNPETNIL
-TQQLIRAAEIRASAN
-IRAAEIRASANLAA
-SMWSFNPETNILLNVPLHGT
-FIASFRLFARTRSMWSFNPE
-RTRSMWSFNPETNI
-VEKGVLPQLEQPYVFIK
-GLVEVEKGVLPQLE
-GFKMNYQVNGYPNMF
-TRSMWSFNPETNILLN
-MWLSYFIASFRLFA
-PTHLSVDTKFKTEGLC
-QLIRAAEIRASANL
-YFIASFRLFARTRSMWS
-SMWSFNPETNILLNVPLHG
-RLFARTRSMWSFN
-QMNLKYAISAKNRARTVAG
-LFARTRSMWSFNPETNI
-YFIASFRLFARTRSMW
-WSFNPETNILLNVPLHG
-SFRLFARTRSMWSFNPETNILLN
-YFIASFRLFARTRSMWSFN
-SGFAAYSRYRIGNY
-TRSMWSFNPETNILLNVPL
-SFRLFARTRSMWS
-ASFRLFARTRSMWSFNP
-FIASFRLFARTRSMWSFNPETNI
-LQSLQTYVTQQLI
-MNLKYAISAKNRARTVA
-IASFRLFARTRSMWS
-CGLVEVEKGVLPQL
-FRLFARTRSMWSFNPET
-PQLEQPYVFIKRS
-ISMMGFKMNYQVNGYP
-TRSMWSFNPETNILLNVPLHG
-YFIASFRLFARTRSMWSF
-CGLVEVEKGVLPQ
-GDSGFAAYSRYRIGNYKLNT
-LMWLSYFIASFRL
-LPQLEQPYVFIKRS
-FRLFARTRSMWSFNPETNI
-SFNPETNILLNVPLH
-VTQQLIRAAEIRA
-IASFRLFARTRSMWSFNPET
-LFARTRSMWSFNP
-ARTRSMWSFNPETN
-PTHLSVDTKFKTEG
-LFARTRSMWSFNPETNILL
-GLVEVEKGVLPQLEQPYV
-YRRLISMMGFKMN
-QQLIRAAEIRASA
-MGFKMNYQVNGYP
-RTRSMWSFNPETN
-SFRLFARTRSMWSFNPETNI
-RAAEIRASANLAA
-NLKYAISAKNRARTVAG
-SMMGFKMNYQVNGYPNM
-SFRLFARTRSMWSFNPE
-SYFIASFRLFART
-GDSGFAAYSRYRIGNY
-LKYAISAKNRARTVA
-VEVEKGVLPQLEQPYVFIKR
-VEKGVLPQLEQPYVF
-IASFRLFARTRSMWSFNPETNILL
-RSMWSFNPETNILL
-EKGVLPQLEQPYVFIKRS
-QWLTNIFGTVYEK
-IASFRLFARTRSM
-VEVEKGVLPQLEQPYVFI
-VKRTIKGTHHWLLL
-ASFRLFARTRSMWS
-CGLVEVEKGVLPQLEQPYVFIKR
-KGVLPQLEQPYVFI
-GDSGFAAYSRYRIGNYKL
-ASFRLFARTRSMWSFNPETN
-RLFARTRSMWSFNPE
-IASFRLFARTRSMWSFNPETNIL
-THLSVDTKFKTEG
-SFNPETNILLNVPL
-TQMNLKYAISAKNRARTV
-LKYAISAKNRARTVAG
-TYRRLISMMGFKMNYQ
-KMNYQVNGYPNMF
-VEKGVLPQLEQPYVFIKRS
-LSYFIASFRLFARTR
-QSLQTYVTQQLIRA
-LSYFIASFRLFAR
-RLQSLQTYVTQQL
-FRLFARTRSMWSFNPE
-QSLQTYVTQQLIR
-CGLVEVEKGVLPQLEQPYVFIKRS
-AAYSRYRIGNYKLNT
-AGDSGFAAYSRYRIGNYKLN
-TQQLIRAAEIRAS
-FIASFRLFARTRSMWSFNPETN
-MGFKMNYQVNGYPNMF
-MNLKYAISAKNRARTV
-AGDSGFAAYSRYRIGN
-VKRTIKGTHHWLLLT
-QTYVTQQLIRAAE
-NPETNILLNVPLHGT
-EVEKGVLPQLEQPYVFI
-YFIASFRLFARTRSM
-EKGVLPQLEQPYVFI
-FRLFARTRSMWSFNPETNIL
-SMMGFKMNYQVNGY
-RSMWSFNPETNILLNVPLHG
-YDKLQFTSLEIPR
-ARTRSMWSFNPETNILLNVPLHG
-RLFARTRSMWSFNPETNIL
-LQSLQTYVTQQLIR
-DKLQFTSLEIPRRN
-YAISAKNRARTVA
-RTRSMWSFNPETNILLNVPL
-CGLVEVEKGVLPQLEQPYVF
-GFAAYSRYRIGNY
-CGLVEVEKGVLPQLEQP
-ISMMGFKMNYQVNGY
-GFAAYSRYRIGNYKLNT
-VAGDSGFAAYSRYR
-LKYAISAKNRARTV
-YFIASFRLFARTRSMWSFNP
-FAAYSRYRIGNYKL
-LQSLQTYVTQQLIRA
-VEVEKGVLPQLEQPYVFIK
-NLKYAISAKNRAR
-ASFRLFARTRSMWSFN
-SFRLFARTRSMWSFNPETN
-YRRLISMMGFKMNY
-ARTRSMWSFNPETNILLNVP
-WLSYFIASFRLFART
-RLISMMGFKMNYQ
-LKYAISAKNRART
-RTIKGTHHWLLLT
-KLQFTSLEIPRRNV
-FIASFRLFARTRSMW
-QWLTNIFGTVYEKL
-LVEVEKGVLPQLEQPYVFIKRS
-ASFRLFARTRSMWSFNPETNILL
-GVLPQLEQPYVFI
-FIASFRLFARTRSMWSFN
-DSGFAAYSRYRIGNYKLNT
-RLFARTRSMWSFNPETNI
-RTRSMWSFNPETNILLNVPLH
-SAVKRTIKGTHHWLL
-LMWLSYFIASFRLFAR
-RTRSMWSFNPETNILLNVPLHG
-VEKGVLPQLEQPYVFIKR
-GFAAYSRYRIGNYKLN
-ATYKPNTWCIRCL
-KYAISAKNRARTV
-ASFRLFARTRSMWSF
-FNPETNILLNVPL
-IASFRLFARTRSMWSFNPETN
-ISMMGFKMNYQVNGYPN
-TCGLVEVEKGVLP
-ISMMGFKMNYQVN
-VAGDSGFAAYSRYRIG
-NPETNILLNVPLH
-VEVEKGVLPQLEQP
-LFARTRSMWSFNPET
-ASFRLFARTRSMWSFNPE
-FAAYSRYRIGNYKLN
-YFIASFRLFARTRSMWSFNPETNI
-FRLFARTRSMWSFNPETNILL
-TQMNLKYAISAKNR
-TCGLVEVEKGVLPQL
-FIASFRLFARTRSMWSFNPET
-LISMMGFKMNYQVNGYPN
-GLVEVEKGVLPQLEQPYVFIK
-YFIASFRLFARTRSMWSFNPET
-GDSGFAAYSRYRIGNYK
-RLQSLQTYVTQQLIRAA
-LVEVEKGVLPQLEQPYVFIKR
-AGDSGFAAYSRYRI
-RRLISMMGFKMNY
-RSMWSFNPETNILLNVPLH
-MMGFKMNYQVNGYPNMF
-FARTRSMWSFNPETNILLNVPLH
-MWLSYFIASFRLF
-SLQTYVTQQLIRA
-KRTIKGTHHWLLLT
-MWSFNPETNILLNVPLHG
-VLPQLEQPYVFIKR
-SFNPETNILLNVPLHG
-HLSVDTKFKTEGL
-CGLVEVEKGVLPQLEQPYV
-TRSMWSFNPETNIL
-FARTRSMWSFNPETNILLN
-WLSYFIASFRLFA
-LVEVEKGVLPQLEQPY
-FARTRSMWSFNPETN
-GFAAYSRYRIGNYKL
-ISMMGFKMNYQVNGYPNM
-QQLIRAAEIRASAN
-FRLFARTRSMWSFNP
-AVKRTIKGTHHWLL
-KYAISAKNRARTVAG
-TRSMWSFNPETNI
-LFARTRSMWSFNPETNILLNVPL
-VAGDSGFAAYSRYRIGNYKL
-EVEKGVLPQLEQPY
-QMNLKYAISAKNR
-WLSYFIASFRLFAR
-KATYKPNTWCIRCL
-EVEKGVLPQLEQPYV
-YDKLQFTSLEIPRRN
-RLQSLQTYVTQQLI
-GLVEVEKGVLPQLEQPYVFIKRS
-LYQPPQTSITSAV
-VLPQLEQPYVFIK
-TQMNLKYAISAKN
-MWSFNPETNILLNVPL
-SGFAAYSRYRIGNYKLN
-GLVEVEKGVLPQLEQPYVF
-LISMMGFKMNYQVNGY
-RSMWSFNPETNIL
-SFRLFARTRSMWSFN
-GLVEVEKGVLPQLEQPYVFIKR
-MFDAYVNTFSSTF
-AISAKNRARTVAG
-KATYKPNTWCIRC
-GFAAYSRYRIGNYK
-SMWSFNPETNILL
-SFRLFARTRSMWSFNP
-LQFTSLEIPRRNV
-WSFNPETNILLNVPL
-MNLKYAISAKNRARTVAG
-ARTRSMWSFNPETNILLNVPLH
-LVEVEKGVLPQLEQPYVFI
-TYKPNTWCIRCLW
-MWSFNPETNILLNVPLHGT
-YDKLQFTSLEIPRRNV
-IASFRLFARTRSMWSF
-TCGLVEVEKGVLPQLEQPYVFIKR
-LVEVEKGVLPQLE
-FARTRSMWSFNPET
-FRLFARTRSMWSFN
-TYRRLISMMGFKMN
-LISMMGFKMNYQVNGYPNM
-MWLSYFIASFRLFAR
-LQSLQTYVTQQLIRAA
-VAGDSGFAAYSRYRIGNYK
-SVDTKFKTEGLCV
-TYRRLISMMGFKM
-CGLVEVEKGVLPQLEQPYVFI
-FARTRSMWSFNPETNILL
-TCGLVEVEKGVLPQLEQPYVFI
-RLQSLQTYVTQQLIRAAE
-RLFARTRSMWSFNPETN
-WSFNPETNILLNVP
-TRSMWSFNPETNILLNVPLH
-PTHLSVDTKFKTEGL
-DKLQFTSLEIPRRNV
-EKGVLPQLEQPYV
-GFKMNYQVNGYPN
-SFRLFARTRSMWSFNPETNIL
-QSLQTYVTQQLIRAAE
-LFARTRSMWSFNPE
-DSGFAAYSRYRIGN
-FRLFARTRSMWSFNPETNILLNV
-VAGDSGFAAYSRYRIGN
-RSMWSFNPETNILLNV
-GVLPQLEQPYVFIKR
-YVTQQLIRAAEIRAS
-KGVLPQLEQPYVFIKRS
-MTYRRLISMMGFKM
-FNPETNILLNVPLHG
-LVEVEKGVLPQLEQP
-LISMMGFKMNYQVN
-GLVEVEKGVLPQLEQPY
-VEVEKGVLPQLEQ
-MWSFNPETNILLNV
-AYSRYRIGNYKLN
-LFARTRSMWSFNPETNILLN
-FIASFRLFARTRSMWSFNP
-TCGLVEVEKGVLPQLE
-RLQSLQTYVTQQLIRA
-MMGFKMNYQVNGYPNM
-LFARTRSMWSFNPETNIL
-YAISAKNRARTVAG
-AGDSGFAAYSRYRIGNY
-DSGFAAYSRYRIGNYKL
-TCGLVEVEKGVLPQLEQP
-MGFKMNYQVNGYPN
-FRLFARTRSMWSFNPETNILLN
-TCGLVEVEKGVLPQLEQ
-SGFAAYSRYRIGN
-YSRYRIGNYKLNT
-SAVKRTIKGTHHWLLL
-SMWSFNPETNILLNVPL
-MMGFKMNYQVNGYP
-RTRSMWSFNPETNILLN
-HLSVDTKFKTEGLCV
-ISMMGFKMNYQVNGYPNMF
-QSLQTYVTQQLIRAA
-VLYQPPQTSITSA
-YFIASFRLFARTRSMWSFNPETN
-LFARTRSMWSFNPETNILLNVPLH
-TCGLVEVEKGVLPQLEQPYVFIKRS
-VEVEKGVLPQLEQPYVFIKRS
-EVEKGVLPQLEQPYVFIKR
-LVEVEKGVLPQLEQ
-WSFNPETNILLNVPLHGT
-QMNLKYAISAKNRARTV
-RTRSMWSFNPETNILLNVP
-FAAYSRYRIGNYK
-LVEVEKGVLPQLEQPYVF
-GDSGFAAYSRYRIGNYKLN
-RLQSLQTYVTQQLIR
-RRLISMMGFKMNYQ
-TCGLVEVEKGVLPQLEQPYV
-ISMMGFKMNYQVNG
-EVEKGVLPQLEQP
-YDKLQFTSLEIPRR
-GLVEVEKGVLPQLEQPYVFI
-VEVEKGVLPQLEQPYVF
-VAGDSGFAAYSRYRIGNYKLN
-KGVLPQLEQPYVFIKR
-LISMMGFKMNYQVNGYPNMF
-VEKGVLPQLEQPYVFI
-LTNIFGTVYEKLK
-LSVDTKFKTEGLCV
-VEKGVLPQLEQPY
-LFARTRSMWSFNPETNILLNVP
-VLPQLEQPYVFIKRS
-VAGDSGFAAYSRY
-TRSMWSFNPETNILLNV
-RLFARTRSMWSFNPETNILLNVP
-TCGLVEVEKGVLPQLEQPYVF
-MWSFNPETNILLNVP
-VAGDSGFAAYSRYRI
-ASFRLFARTRSMWSFNPETNIL
-IASFRLFARTRSMW
-SGFAAYSRYRIGNYKLNT
-FRLFARTRSMWSFNPETN
-NLKYAISAKNRARTVA
-GFKMNYQVNGYPNM
-WLTNIFGTVYEKL
-VKRTIKGTHHWLL
-ARTRSMWSFNPETNILLNVPL
-DSGFAAYSRYRIGNY
-RLFARTRSMWSFNPET
-RLFARTRSMWSFNPETNILLNV
-EVEKGVLPQLEQPYVFIKRS
-LMWLSYFIASFRLFA
-KYAISAKNRARTVA
-FARTRSMWSFNPETNIL
-TCGLVEVEKGVLPQLEQPY
-RTRSMWSFNPETNILLNV
-NPETNILLNVPLHG
-AGDSGFAAYSRYRIG
-LPQLEQPYVFIKR
-SGFAAYSRYRIGNYK
-AYSRYRIGNYKLNT
-SMMGFKMNYQVNGYP
-FIASFRLFARTRSMWSF
-CGLVEVEKGVLPQLEQPYVFIK
-PETNILLNVPLHGT
-ARTRSMWSFNPETNILLN
-SLQTYVTQQLIRAAE
-YVTQQLIRAAEIRA
-FNPETNILLNVPLH
-LISMMGFKMNYQVNGYP
-QMNLKYAISAKNRART
-YFIASFRLFARTRSMWSFNPE
-YVTQQLIRAAEIR
-FARTRSMWSFNPETNILLNV
-RSMWSFNPETNILLN
\ No newline at end of file
diff --git a/SupplementaryResults/GenotypeComputations/37435_vaccine_sequence_hog.txt b/SupplementaryResults/GenotypeComputations/37435_vaccine_sequence_hog.txt
deleted file mode 100644
index 51912d4177250060825259e5c56de964ddd21e5f..0000000000000000000000000000000000000000
--- a/SupplementaryResults/GenotypeComputations/37435_vaccine_sequence_hog.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-> HOGVAX optimized combined peptide vaccine sequence with overlaps
-RLQSLQTYVTQQLIRAAEIRASANLAAPTHLSVDTKFKTEGLCVCSVIDLLLSTMTYRRLISMMGFKMNYQVNGYPNMFDAYVNTFSSTFELTALRVYDKLQFTSLEIPRRNVLQVKSVLYQPPQTSITSAVKRTIKGTHHWLLLTILMWLSYFIASFRLFARTRSMWSFNPETNILLNVPLHGTCGLVEVEKGVLPQLEQPYVFIKRSVRMYIFFASRQRKYKGIKIQWLTNIFGTVYEKLKATYKPNTWCIRCLWMTQMNLKYAISAKNRARTVAGDSGFAAYSRYRIGNYKLNTVA
-> Concat optimized combined peptide vaccine sequence concatenated
-TYRRLISMMGFKMNYQLVEVEKGVLPQLEQPYVFIKRSVKRTIKGTHHWLLLTETNILLNVPLHGTSFNPETNILLNVPLHGTNPETNILLNVPLHGTTYRRLISMMGFKMFKMNYQVNGYPNMFLIRAAEIRASANLVAGDSGFAAYSRYRILISMMGFKMNYQVNGTCGLVEVEKGVLPQLEQMWLSYFIASFRLFARKATYKPNTWCIRCLLMWLSYFIASFRLFARTQQLIRAAEIRASAYFIASFRLFARTRSMWKATYKPNTWCIRCWLSYFIASFRLFARRLQSLQTYVTQQLITQMNLKYAISAKNVKRTIKGTHHWLLLLSVDTKFKTEGLCVLSYFIASFRLFARTTCGLVEVEKGVLPQLEQPYVFARTRSMWSFNPETNILLNVPLHGPTHLSVDTKFKTEGLCTCGLVEVEKGVLPQLEQPYVFIKEVEKGVLPQLEQPYVFIKRSGFKMNYQVNGYPNMFPQLEQPYVFIKRSTCGLVEVEKGVLPVAGDSGFAAYSRYRIGMFDAYVNTFSSTFLSYFIASFRLFARTRFIASFRLFARTRSMWSFNPETNITCGLVEVEKGVLPQSLQTYVTQQLIRAAEQTYVTQQLIRAAERSMWSFNPETNILLNVPLHGTVAGDSGFAAYSRYRIGNYKSMMGFKMNYQVNGYPNMFFRLFARTRSMWSFNPETNILLNVYFIASFRLFARTRSMWSCGLVEVEKGVLPQLEQPYVFIKRSYFIASFRLFARTRSMWSFNPETGVLPQLEQPYVFIKRSMTYRRLISMMGFKLMWLSYFIASFRLFTRSMWSFNPETNILLNVPLHGYDKLQFTSLEIPRRNVQWLTNIFGTVYEKLFARTRSMWSFNPETNILLNVPLHWLTNIFGTVYEKLKISMMGFKMNYQVNGYPNMFQQLIRAAEIRASANVAGDSGFAAYSRYRIGNYKLNTLIRAAEIRASANLAAYFIASFRLFARTRSMWSFNPETNVLPQLEQPYVFIKRSAAYSRYRIGNYKLNTQWLTNIFGTVYEKLKLMWLSYFIASFRLRLQSLQTYVTQQLIRAAERLFARTRSMWSFNPETNILLNVPLQSLQTYVTQQLIRAAEYFIASFRLFARTRSMWSFNPETCGLVEVEKGVLPQLEQPYVFIKRSKGVLPQLEQPYVFIKRSLPQLEQPYVFIKRSTCGLVEVEKGVLPQLEQPYVSAVKRTIKGTHHWLPTHLSVDTKFKTEGTQMNLKYAISAKNRARSYFIASFRLFARTRSSFRLFARTRSMWSFNPETNILLNVLFARTRSMWSFNPETNILLNVPLQTYVTQQLIRAAEVAGDSGFAAYSRYRIGNYKLNLISMMGFKMNYQVNGYPMGFKMNYQVNGYPNMFYFIASFRLFARTRSMTYRRLISMMGFKMNWSFNPETNILLNVPLHGTYFIASFRLFARTRSMWSFNPSVDTKFKTEGLCVFNPETNILLNVPLHGTEKGVLPQLEQPYVFIKRSTQMNLKYAISAKNRARTVRLFARTRSMWSFNPETNILLNVGDSGFAAYSRYRIGNYKLNTHLSVDTKFKTEGLCRLQSLQTYVTQQLIRTYKPNTWCIRCLWVEVEKGVLPQLEQPYVFIKRSFARTRSMWSFNPETNILLNVPLHAGDSGFAAYSRYRIGNYKLNTPTHLSVDTKFKTEGLKRTIKGTHHWLLLTLISMMGFKMNYQVNGYPNMFRRLISMMGFKMNYQYVTQQLIRAAEIRLISMMGFKMNYQVAPTHLSVDTKFKTYFIASFRLFARTRSMWSFNPETNILIRAAEIRASANLATHLSVDTKFKTEGLCYVTQQLIRAAEIRATYRRLISMMGFKMNYTQMNLKYAISAKNRATCGLVEVEKGVLPQLEQPYVFIKRAPTHLSVDTKFKTEHLSVDTKFKTEGLCVIASFRLFARTRSMWSFNPETNILLKMNYQVNGYPNMFSYFIASFRLFARTRIRAAEIRASANLAAAISAKNRARTVAGFARTRSMWSFNPETNILLNVPLHGTCGLVEVEKGVLPQLYDKLQFTSLEIPRGLVEVEKGVLPQLEQPYVFIKRSTCGLVEVEKGVLPQLEQPYIASFRLFARTRSMWSFNPETNILVLYQPPQTSITSARAAEIRASANLAATCGLVEVEKGVLPQLEQSLQTYVTQQLIRAAEYDKLQFTSLEIPRRRLISMMGFKMNYQYFIASFRLFARTRSVAGDSGFAAYSRYRIGNYKLQMNLKYAISAKNRARTVAGYFIASFRLFARTRSMWSFSMWSFNPETNILLNVPLHGTYSRYRIGNYKLNTSFRLFARTRSMWSFNPETNILLNMTYRRLISMMGFKMTQMNLKYAISAKNRARTVADSGFAAYSRYRIGNYKLNTQQLIRAAEIRASANLVTQQLIRAAEIRASVAGDSGFAAYSRYRIGNSAVKRTIKGTHHWLLTQMNLKYAISAKNRARTTQMNLKYAISAKNRSAVKRTIKGTHHWLLLWLSYFIASFRLFARTSAVKRTIKGTHHWLMWLSYFIASFRLFAPTHLSVDTKFKTERTRSMWSFNPETNILLNVPLHGYRRLISMMGFKMNYQTCGLVEVEKGVLPQLEQPYVFISGFAAYSRYRIGNYKLNTFAAYSRYRIGNYKLNTLQFTSLEIPRRNVLKYAISAKNRARTVAGTCGLVEVEKGVLPQLEQPYAISAKNRARTVAGYDKLQFTSLEIPRRNLISMMGFKMNYQVNMNLKYAISAKNRARTVAGPETNILLNVPLHGTTQQLIRAAEIRASVAGDSGFAAYSRYRIGNYMWSFNPETNILLNVPLHGTNLKYAISAKNRARTVAGAYSRYRIGNYKLNTTQQLIRAAEIRASANKYAISAKNRARTVAGYFIASFRLFARTRSMWSFNRLQSLQTYVTQQLIRAQLIRAAEIRASANLKLQFTSLEIPRRNVVEKGVLPQLEQPYVFIKRSRTIKGTHHWLLLTRLQSLQTYVTQQLLYQPPQTSITSAVLISMMGFKMNYQVNGYPNMIASFRLFARTRSMWSFNPETNIMMGFKMNYQVNGYPNMFVAGDSGFAAYSRYLTNIFGTVYEKLKSFRLFARTRSMWSFNPETNILLVAGDSGFAAYSRYRRLQSLQTYVTQQLIRAAATYKPNTWCIRCLWLISMMGFKMNYQVNGYDKLQFTSLEIPRRNVGFAAYSRYRIGNYKLNTRSMWSFNPETNILLNVPLHGASFRLFARTRSMWSFNPETNILLQWLTNIFGTVYEKLLFARTRSMWSFNPETNILLNVPLYVTQQLIRAAEIRASLISMMGFKMNYQVNGYPNTQMNLKYAISAKNRARTVAGKATYKPNTWCIRCLWAVKRTIKGTHHWLLL
\ No newline at end of file
diff --git a/SupplementaryResults/GenotypeComputations/4519_chosen_peptides_hog_inc_substrings.txt b/SupplementaryResults/GenotypeComputations/4519_chosen_peptides_hog_inc_substrings.txt
deleted file mode 100644
index bb0bdafd3398de33a1c2f8e61f3497e40640935f..0000000000000000000000000000000000000000
--- a/SupplementaryResults/GenotypeComputations/4519_chosen_peptides_hog_inc_substrings.txt
+++ /dev/null
@@ -1,114 +0,0 @@
-TTLPVNVAF
-HFIETISLA
-FFTLLLQL
-GTDLEGNFY
-KTIQPRVEK
-LPTGVHAG
-IETISLAGS
-MPYFFTLL
-FGTTLDSK
-TNYDLSVVN
-PYFFTLLL
-YFFTLLLQ
-FTGYRVTK
-YDLSVVNAR
-GYRVTKNSK
-AGTDLEGNFY
-IKTIQPRVE
-TGYRVTKN
-HFIETISLAG
-FIETISLA
-EEHFIETISL
-WIFGTTLDSK
-TGYRVTKNS
-EHFIETISL
-HAGTDLEGN
-WIFGTTLDS
-NYDLSVVNA
-AGTDLEGNF
-TTLPVNVA
-MPYFFTLLLQ
-NYDLSVVN
-YMPYFFTL
-LVAEWFLAY
-HAGTDLEG
-IETISLAGSY
-NYMPYFFT
-FTSLEIPR
-QFTSLEIP
-ELPTGVHA
-DLSVVNAR
-GTTLDSKT
-PTGVHAGT
-DLEGNFYG
-QFTSLEIPR
-LQFTSLEI
-VAEWFLAYI
-IKTIQPRVEK
-YDKLQFTSL
-YDLSVVNA
-TIQPRVEK
-VAEWFLAY
-TISLAGSY
-IKTIQPRV
-KTIQPRVE
-DLSVVNARL
-IFGTTLDSKT
-HSYFTSDY
-YRVTKNSK
-AEWFLAYI
-FGTTLDSKT
-GTDLEGNF
-EHFIETISLA
-WIFGTTLD
-HSYFTSDYY
-HFIETISL
-SYFTSDYY
-TNYDLSVVNA
-KLQFTSLE
-ISLAGSYK
-MPYFFTLLL
-LVAEWFLA
-KTTLPVNV
-EEHFIETI
-ETISLAGS
-IFGTTLDS
-KLQFTSLEI
-FIETISLAG
-IETISLAG
-TDLEGNFY
-GYRVTKNS
-TISLAGSYK
-NYMPYFFTLL
-ELPTGVHAG
-GWIFGTTL
-EEHFIETIS
-ETISLAGSY
-TGVHAGTD
-PYFFTLLLQ
-EHFIETIS
-YMPYFFTLL
-YDKLQFTS
-FTGYRVTKNS
-LPAPRTLL
-IFGTTLDSK
-LSVVNARL
-LVAEWFLAYI
-TLPVNVAF
-FTLLLQLC
-AYYFMRFR
-FTGYRVTKN
-FIETISLAGS
-FFTLLLQLC
-GWIFGTTLD
-TDLEGNFYG
-DKLQFTSLE
-DKLQFTSL
-KTTLPVNVA
-AGTDLEGN
-DKLQFTSLEI
-TGYRVTKNSK
-NYDLSVVNAR
-NYMPYFFTL
-ETISLAGSYK
-TNYDLSVV
\ No newline at end of file
diff --git a/SupplementaryResults/GenotypeComputations/4519_vaccine_sequence_hog.txt b/SupplementaryResults/GenotypeComputations/4519_vaccine_sequence_hog.txt
deleted file mode 100644
index 7d9c0299bff09f6839404a792ab92e857c25910b..0000000000000000000000000000000000000000
--- a/SupplementaryResults/GenotypeComputations/4519_vaccine_sequence_hog.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-> HOGVAX optimized combined peptide vaccine sequence with overlaps
-AKDTTEAYYFMRFRNYMPYFFTLLLQLCFTGYRVTKNSKITEHSYFTSDYYEEHFIETISLAGSYKDCVPLTNYDLSVVNARLPAPRTLLVAEWFLAYIKTIQPRVEKFKTTLPVNVAFELPTGVHAGTDLEGNFYGWIFGTTLDSKTEQCYDKLQFTSLEIPR
-> Concat optimized combined peptide vaccine sequence concatenated
-TGYRVTKNSIKTIQPRVEKLVAEWFLAYNYMPYFFTLLTTLPVNVATGYRVTKNSKFIETISLAGSHSYFTSDYIFGTTLDSKTGYRVTKNSKFTSLEIPRFIETISLAGPTGVHAGTAGTDLEGNLQFTSLEIIETISLAGSTNYDLSVVNATISLAGSYKLPAPRTLLDKLQFTSLEIAGTDLEGNFFFTLLLQLLSVVNARLWIFGTTLDSKEEHFIETIYRVTKNSKDKLQFTSLEIFGTTLDSKELPTGVHAGKLQFTSLEIMPYFFTLLLQEEHFIETISLFFTLLLQLCNYDLSVVNARHFIETISLAAGTDLEGNFYYDKLQFTSLNYDLSVVNATTLPVNVAFETISLAGSYKELPTGVHANYMPYFFTLQFTSLEIPREHFIETISLYDKLQFTSISLAGSYKTDLEGNFYAYYFMRFRDLSVVNARLTIQPRVEKKTIQPRVEKVAEWFLAYIAEWFLAYIGWIFGTTLFGTTLDSKTYDLSVVNARFTGYRVTKNSQFTSLEIPMPYFFTLLLNYMPYFFTTNYDLSVVDLEGNFYGHAGTDLEGEHFIETISLAHFIETISLAGHSYFTSDYYYMPYFFTLLLVAEWFLAYIDLSVVNARDKLQFTSLGTDLEGNFYFTGYRVTKNIKTIQPRVTLPVNVAFIKTIQPRVELPTGVHAGHAGTDLEGNSYFTSDYYETISLAGSYFTLLLQLCPYFFTLLLQTDLEGNFYGGWIFGTTLDTGVHAGTDFTGYRVTKMPYFFTLLGTTLDSKTKTTLPVNVTNYDLSVVNKTTLPVNVAWIFGTTLDSIETISLAGSYWIFGTTLDEEHFIETISLVAEWFLAYFFTLLLQ
\ No newline at end of file
diff --git a/SupplementaryResults/GenotypeComputations/evalvax-unlinked.csv b/SupplementaryResults/GenotypeComputations/evalvax-unlinked.csv
deleted file mode 100644
index c632210c1d9088db68de63377be9aba760b5335b..0000000000000000000000000000000000000000
--- a/SupplementaryResults/GenotypeComputations/evalvax-unlinked.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-,Method,Population Coverage,Number of Peptides
-0,MHCI OptiVax-Unlinked,0.9660236112979911,19
-1,MHCI HOGVAX,0.9458208799581866,114
-2,MHCII OptiVax-Unlinked,0.9824151601089331,19
-3,MHCII HOGVAX,0.9942225768510408,518
diff --git a/SupplementaryResults/GenotypeComputations/hit_hist_mhc1_HOGVAX.csv b/SupplementaryResults/GenotypeComputations/hit_hist_mhc1_HOGVAX.csv
deleted file mode 100644
index a7593ff77132140f03f342cea654e926145677b4..0000000000000000000000000000000000000000
--- a/SupplementaryResults/GenotypeComputations/hit_hist_mhc1_HOGVAX.csv
+++ /dev/null
@@ -1,22 +0,0 @@
-,count,prob
-0,0,0.0
-1,1,0.00020255576255769878
-2,2,0.027411885142501282
-3,3,0.06449908633430962
-4,4,0.16582164010285252
-5,5,0.23644302657750377
-6,6,0.2291970081736202
-7,7,0.15895121716901736
-8,8,0.06535014126005981
-9,9,0.03121017413576232
-10,10,0.01671387676629871
-11,11,0.0036948692548380464
-12,12,0.00047909031758776446
-13,13,2.5429003142817705e-05
-14,14,0.0
-15,15,0.0
-16,16,0.0
-17,17,0.0
-18,18,0.0
-19,19,0.0
-20,20,0.0
diff --git a/SupplementaryResults/GenotypeComputations/hit_hist_mhc1_OptiVax.csv b/SupplementaryResults/GenotypeComputations/hit_hist_mhc1_OptiVax.csv
deleted file mode 100644
index 38f5f3b1429d8379721e898fd9ed84470517f39b..0000000000000000000000000000000000000000
--- a/SupplementaryResults/GenotypeComputations/hit_hist_mhc1_OptiVax.csv
+++ /dev/null
@@ -1,23 +0,0 @@
-,count,prob
-0,0,0.0
-1,1,0.010899192265474547
-2,2,0.041103801087957796
-3,3,0.10355535446938997
-4,4,0.09670031709977213
-5,5,0.07490409101259721
-6,6,0.10969245679125345
-7,7,0.13666664812909482
-8,8,0.09467987025972693
-9,9,0.0857471847814406
-10,10,0.10295143293042615
-11,11,0.04662551180090322
-12,12,0.020340342733792375
-13,13,0.04382989385011436
-14,14,0.028842227526130044
-15,15,0.0020970738305962563
-16,16,0.0008097090192772844
-17,17,0.000536976937811024
-18,18,1.7915474231537395e-05
-19,19,0.0
-20,20,0.0
-21,21,0.0
diff --git a/SupplementaryResults/GenotypeComputations/hit_hist_mhc2_HOGVAX.csv b/SupplementaryResults/GenotypeComputations/hit_hist_mhc2_HOGVAX.csv
deleted file mode 100644
index ac2785f6c0a71dace61e75d83db3473b83f77725..0000000000000000000000000000000000000000
--- a/SupplementaryResults/GenotypeComputations/hit_hist_mhc2_HOGVAX.csv
+++ /dev/null
@@ -1,222 +0,0 @@
-,count,prob
-0,0,0.0007243433400952906
-1,1,0.000348344217983722
-2,2,0.010074772235027497
-3,3,0.007996187006127245
-4,4,0.013510442233751495
-5,5,0.01494617208757196
-6,6,0.011913443301796375
-7,7,0.021015337313933277
-8,8,0.024769760645987814
-9,9,0.023705974241968245
-10,10,0.04758796840344677
-11,11,0.04206787852597208
-12,12,0.03833786242531609
-13,13,0.03889501528027554
-14,14,0.029232288115579094
-15,15,0.04416553157090515
-16,16,0.020414891548962807
-17,17,0.03622040551742788
-18,18,0.03703903453013959
-19,19,0.03585096881093293
-20,20,0.029190054605506912
-21,21,0.029446074101852595
-22,22,0.01620238620246493
-23,23,0.018635512968206345
-24,24,0.01673607207711764
-25,25,0.01732909500523648
-26,26,0.017035298060289042
-27,27,0.009422892263480148
-28,28,0.010769090865721268
-29,29,0.009034453110284106
-30,30,0.014126851714381189
-31,31,0.009920917954564299
-32,32,0.01837962742119759
-33,33,0.015195140693126661
-34,34,0.01269984706656692
-35,35,0.008917815847588304
-36,36,0.0066424822845298605
-37,37,0.015832627492814947
-38,38,0.01588833092392022
-39,39,0.01684069173364043
-40,40,0.01705531964503526
-41,41,0.013329016750622638
-42,42,0.00735141151890402
-43,43,0.01005405239250535
-44,44,0.006159137739930592
-45,45,0.011026586561490576
-46,46,0.0048561354815500595
-47,47,0.0059360920071754855
-48,48,0.005933726610452106
-49,49,0.002272551019885064
-50,50,0.00329977912794622
-51,51,0.001973445877387569
-52,52,0.0026249717000590405
-53,53,0.0020467144438032253
-54,54,0.0013087747005764159
-55,55,0.0007495943444271725
-56,56,0.000610258658029598
-57,57,0.000598583758899675
-58,58,0.0006559360098209077
-59,59,0.0011713655376684332
-60,60,0.0007561477379639869
-61,61,0.000503998604831406
-62,62,0.0005982180262053602
-63,63,0.0004458414190156264
-64,64,0.00031790148522164524
-65,65,0.0005440532611522787
-66,66,0.00038344902720187417
-67,67,0.00033787164421348546
-68,68,0.0004299861702922356
-69,69,0.00015278918710551038
-70,70,0.0002433521494477967
-71,71,0.00020219930714475046
-72,72,0.000207433355258229
-73,73,0.0003276092450048971
-74,74,6.565784872865345e-05
-75,75,0.0001258919166970517
-76,76,6.664934959298636e-05
-77,77,5.603406837928056e-05
-78,78,7.044783013809096e-05
-79,79,6.665785772513478e-05
-80,80,5.210429622626144e-05
-81,81,0.00022167896716876172
-82,82,0.00020419787528560182
-83,83,0.00021324555239900345
-84,84,0.00014413529182533748
-85,85,0.00013312439696525267
-86,86,0.00020598624486725293
-87,87,0.0002934895553019923
-88,88,0.000431233468064618
-89,89,0.00031907974403618157
-90,90,0.0007478949447703576
-91,91,0.0018182139364493013
-92,92,0.0008046426273591338
-93,93,0.004780879469859005
-94,94,0.0030348856647684492
-95,95,0.003748942188145809
-96,96,0.0022308259249532677
-97,97,0.0013904557748943825
-98,98,0.0044974984534597254
-99,99,0.00607666857799963
-100,100,0.004702010430706125
-101,101,0.005418170325534121
-102,102,0.004318972839536711
-103,103,0.00319081438263366
-104,104,0.004319231897112163
-105,105,0.0024824781887678145
-106,106,0.0037228395586819084
-107,107,0.001900752742276425
-108,108,0.0018134195364905678
-109,109,0.0026797725902257025
-110,110,0.0011631394787478381
-111,111,0.0022474297723176456
-112,112,0.001269041946420687
-113,113,0.0014542655251338877
-114,114,0.0014338235862648224
-115,115,0.0006837062201872204
-116,116,0.00109434201426816
-117,117,0.0005112123790076768
-118,118,0.0007056821485853642
-119,119,0.000541751281960144
-120,120,0.00048406972362912396
-121,121,0.0018789802511271526
-122,122,0.0006855016270510503
-123,123,0.0013874852148992277
-124,124,0.0004256434272633195
-125,125,0.00021497596589232614
-126,126,0.0007131986898786018
-127,127,0.00027686570897755646
-128,128,0.0012997602570730992
-129,129,0.00037394400473888753
-130,130,0.0003539724658877963
-131,131,0.00034847926620845167
-132,132,8.049231557445065e-05
-133,133,0.0005342724228232282
-134,134,0.00033471361434074624
-135,135,0.00012850476018022044
-136,136,0.0001124677160004392
-137,137,1.949576138163055e-05
-138,138,0.00011027352981313737
-139,139,0.00010166697970363418
-140,140,2.923431802352307e-05
-141,141,8.629175201684926e-05
-142,142,1.5585178271279246e-05
-143,143,6.732561261239946e-06
-144,144,1.5501402816593523e-05
-145,145,2.4436009463476604e-05
-146,146,5.30090309409674e-05
-147,147,1.731688963221624e-05
-148,148,1.7824601844250058e-06
-149,149,1.3298165552525085e-05
-150,150,6.671487490923372e-06
-151,151,3.472987346257432e-05
-152,152,9.213426206562791e-06
-153,153,1.060283248350869e-05
-154,154,4.117410013469232e-06
-155,155,2.86023424866716e-06
-156,156,1.1180393570775552e-05
-157,157,4.196481947926252e-06
-158,158,4.982053817552275e-06
-159,159,1.7932050884168508e-06
-160,160,1.1579686841823348e-05
-161,161,4.839412367351208e-06
-162,162,4.4414909563701225e-06
-163,163,1.4350416220220076e-06
-164,164,2.507144264763906e-06
-165,165,0.0
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diff --git a/SupplementaryResults/GenotypeComputations/min_hit_number_bar_mhc2_HOGVAX.html b/SupplementaryResults/GenotypeComputations/min_hit_number_bar_mhc2_HOGVAX.html
deleted file mode 100644
index a61810679d332c396ddf8c5c66256d534431074a..0000000000000000000000000000000000000000
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diff --git a/SupplementaryResults/GenotypeComputations/min_hit_number_bar_mhc2_OptiVax.html b/SupplementaryResults/GenotypeComputations/min_hit_number_bar_mhc2_OptiVax.html
deleted file mode 100644
index 722e8b5ce055d55f4d1b3d32a10d2fa49ac8350b..0000000000000000000000000000000000000000
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diff --git a/SupplementaryResults/GenotypeComputations/unlinked_pop_coverage_bar.html b/SupplementaryResults/GenotypeComputations/unlinked_pop_coverage_bar.html
deleted file mode 100644
index c3de5b443b9c2d2f95c2e05487b891e171a0522a..0000000000000000000000000000000000000000
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diff --git a/SupplementaryResults/HaplotypeComputations/37435_chosen_peptides_hog_inc_substrings.txt b/SupplementaryResults/HaplotypeComputations/37435_chosen_peptides_hog_inc_substrings.txt
deleted file mode 100644
index c56251420149f625e910fa7f72e82c8976d9bd10..0000000000000000000000000000000000000000
--- a/SupplementaryResults/HaplotypeComputations/37435_chosen_peptides_hog_inc_substrings.txt
+++ /dev/null
@@ -1,747 +0,0 @@
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-SWVMRIMTWLDMVDTS
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-MRIMTWLDMVDTS
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-SKETLYCIDGALLTKS
-AAAYYVGYLQPRT
-MPASWVMRIMTWLD
-TYRRLISMMGFKMN
-LLLTILTSLLVLVQSTQW
-TQMNLKYAISAKNRARTVA
-ITSKETLYCIDGAL
-VKRTIKGTHHWLL
-TNILLNVPLHGTI
-TRSMWSFNPETNILLNVP
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-WVMRIMTWLDMVDTSL
-TRSMWSFNPETNILLNVPL
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-SYFIASFRLFARTRS
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-TSLSGFKLKDCVMY
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-WLLLTILTSLLVLVQSTQW
-DEVARDLSLQFKR
-YMPASWVMRIMTWLDM
-ASFRLFARTRSMWS
-MNLKYAISAKNRARTVA
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-DSGFAAYSRYRIGNY
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-MRIMTWLDMVDTSL
-LFARTRSMWSFNPETNIL
-SGFAAYSRYRIGNYKLNT
-RLFARTRSMWSFNP
-GTHHWLLLTILTS
-PASWVMRIMTWLDMV
-MVYMPASWVMRIMT
-ANLAATKMSECVLGQSK
-LFARTRSMWSFNPET
-AGDSGFAAYSRYRIG
-WLLLTILTSLLVLVQSTQ
-DTSLSGFKLKDCV
-AVKRTIKGTHHWL
-FNPETNILLNVPLHG
-LIRAAEIRASANLA
-FARTRSMWSFNPETNILLNVPLHGT
-SDEVARDLSLQFKRP
-RTRSMWSFNPETN
-TQQLIRAAEIRASANLA
-QMNLKYAISAKNRAR
-SLSGFKLKDCVMYA
-SMWSFNPETNILLNVPLH
-RLFARTRSMWSFNPETNILLN
-MVYMPASWVMRIM
-SFRLFARTRSMWSFNP
-DYTEISFMLWCKDGH
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-LFARTRSMWSFNP
-LDMVDTSLSGFKLKDCVMYAS
-DMVDTSLSGFKLKDCVM
-ILTSLLVLVQSTQ
-NMVYMPASWVMRIMTWLD
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-LYCIDGALLTKSS
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-HWLLLTILTSLLVLVQST
-RSMWSFNPETNILLNV
-KETLYCIDGALLTKSS
-VAGDSGFAAYSRY
-TSLSGFKLKDCVM
-KYAISAKNRARTVA
-LLTILTSLLVLVQSTQW
-DTSLSGFKLKDCVMY
-YFIASFRLFARTRSMWSFNPET
-NMVYMPASWVMRI
-LSGFKLKDCVMYA
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-IASFRLFARTRSMWSFNPETNILLN
-RLFARTRSMWSFN
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-WSFNPETNILLNVPL
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-LFARTRSMWSFNPETNILLNVPL
-RSMWSFNPETNILLNVP
-SFNPETNILLNVPLH
-ASFRLFARTRSMWSFNPETNIL
-DYTEISFMLWCKDGHVETF
-HWLLLTILTSLLV
-WCKDGHVETFYPKL
-DSGFAAYSRYRIGNYKLNT
-FIASFRLFARTRSMWSF
-TIDYTEISFMLWCKDGHVE
-FARTRSMWSFNPETNIL
-DTSLSGFKLKDCVMYAS
-VMRIMTWLDMVDTSL
-MVYMPASWVMRIMTWLD
-KHITSKETLYCIDGALL
-MLWCKDGHVETFY
-RLFARTRSMWSFNPETNIL
-AAYSRYRIGNYKL
-LAATKMSECVLGQSK
-ANLAATKMSECVLGQSKR
-ARTRSMWSFNPETNI
-AVKRTIKGTHHWLLL
-DMVDTSLSGFKLK
-TQMNLKYAISAKNR
-TEISFMLWCKDGHV
-NLKYAISAKNRARTV
-LFARTRSMWSFNPETNILLNVPLHG
-SAVKRTIKGTHHW
-FKLKDCVMYASAV
-VDTSLSGFKLKDCVMYA
-AGDSGFAAYSRYRIGNYKLNT
-SFRLFARTRSMWSFNPET
-FARTRSMWSFNPET
-ISMMGFKMNYQVNGYPNM
-MWLSYFIASFRLFAR
-YTEISFMLWCKDG
-ASWVMRIMTWLDM
-MVDTSLSGFKLKDCVMYAS
-HWLLLTILTSLLVLVQS
-LTILTSLLVLVQST
-SANLAATKMSECVLGQSKR
-HHWLLLTILTSLLVLVQS
-WSFNPETNILLNV
-AYSRYRIGNYKLNT
-YMPASWVMRIMTWL
-LISMMGFKMNYQVNGY
-PASWVMRIMTWLD
-QQLIRAAEIRASANLA
-SANLAATKMSECVL
-SMWSFNPETNILLNVPL
-MNLKYAISAKNRAR
-DSGFAAYSRYRIGN
-MVDTSLSGFKLKDCV
-QMNLKYAISAKNRARTVA
-LKYAISAKNRARTVA
-THHWLLLTILTSLLVLVQSTQW
-FIASFRLFARTRSMWSFNPET
-SYFIASFRLFARTRSMWSFNPETNI
-VDTSLSGFKLKDCVM
-SFNPETNILLNVPLHGT
-VTQQLIRAAEIRASANL
-EISFMLWCKDGHVET
-FIASFRLFARTRSMWSFNP
-ARTRSMWSFNPETNILLNVPLH
-RTIKGTHHWLLLT
-LLLTILTSLLVLV
-NLAATKMSECVLGQ
-ISFMLWCKDGHVET
-FARTRSMWSFNPETNILLNVP
-MVDTSLSGFKLKDCVMYASAV
-FRLFARTRSMWSFNPETNI
-LFARTRSMWSFNPETNILLNVP
-HITSKETLYCIDGALL
-DYTEISFMLWCKDGHVET
-FARTRSMWSFNPETNILL
-MWLSYFIASFRLF
-MPASWVMRIMTWLDM
-NLAATKMSECVLGQS
-ARTRSMWSFNPETNIL
-PETNILLNVPLHGTI
-FRLFARTRSMWSFNPETNIL
-TRSMWSFNPETNILLNVPLHG
-IASFRLFARTRSMWSFNPETNILL
-ANLAATKMSECVLG
-DTSLSGFKLKDCVMYA
-LMWLSYFIASFRLFARTR
-GDSGFAAYSRYRIGNYKLNT
-ASFRLFARTRSMW
-LAATKMSECVLGQS
-SFRLFARTRSMWSFNPETNI
-MGFKMNYQVNGYPNM
-SFMLWCKDGHVETFYP
-TQQLIRAAEIRASAN
-SWVMRIMTWLDMVDT
-TQMNLKYAISAKNRARTV
-WLSYFIASFRLFARTRS
-NMVYMPASWVMRIMTWL
-DYTEISFMLWCKDGHVETFYP
-TRSMWSFNPETNILLN
-LSGFKLKDCVMYASA
-MWLSYFIASFRLFA
-FNPETNILLNVPLH
-VDTSLSGFKLKDC
-SYFIASFRLFARTRSMW
-KYAISAKNRARTV
-YFIASFRLFARTRSMW
-IASFRLFARTRSM
-QWLTNIFGTVYEKL
-SANLAATKMSECVLG
-ISMMGFKMNYQVNGYPNMF
-AGDSGFAAYSRYRI
-MNLKYAISAKNRART
-FARTRSMWSFNPETN
-LMWLSYFIASFRLFARTRSM
-IASFRLFARTRSMWSFNPE
-TSLSGFKLKDCVMYA
-ISMMGFKMNYQVNGYPN
-SMWSFNPETNILL
-ARTRSMWSFNPETNILL
-SGFAAYSRYRIGN
-VAGDSGFAAYSRYRIGN
-LSYFIASFRLFAR
-YTEISFMLWCKDGH
-QQLIRAAEIRASAN
-SGFAAYSRYRIGNYK
-IKGTHHWLLLTIL
-EVARDLSLQFKRPI
-LFARTRSMWSFNPETNI
-SMMGFKMNYQVNGYPNMF
-LFARTRSMWSFNPETNILL
-VYMPASWVMRIMT
-VAGDSGFAAYSRYRIG
-LLLTILTSLLVLVQ
-NLKYAISAKNRAR
-MGFKMNYQVNGYP
-SMWSFNPETNILLNVPLHG
-RIMTWLDMVDTSLS
-AYSRYRIGNYKLN
-LTILTSLLVLVQSTQW
-TQQLIRAAEIRASA
-ETLYCIDGALLTKSS
-LFARTRSMWSFNPE
-IASFRLFARTRSMWSFNP
-IASFRLFARTRSMWSF
-ASFRLFARTRSMWSFNPET
-MVDTSLSGFKLKDCVMYA
-LFARTRSMWSFNPETN
-SFRLFARTRSMWSFNPETNIL
-MWSFNPETNILLNVPLHGT
-WLLLTILTSLLVL
-SECVLGQSKRVDFC
-IASFRLFARTRSMWS
-YFIASFRLFARTRSMWSFNPE
-ANLAATKMSECVL
-FNPETNILLNVPLHGT
-LMWLSYFIASFRLFA
-RLFARTRSMWSFNPET
-MMGFKMNYQVNGYPNM
-RTRSMWSFNPETNILLNVPLH
-LKYAISAKNRART
-YFIASFRLFARTRSMWSFN
-TRSMWSFNPETNI
-ASFRLFARTRSMWSFNPETN
-SLSGFKLKDCVMY
-RTRSMWSFNPETNI
-LFARTRSMWSFNPETNILLNVPLH
-MWSFNPETNILLNVP
-TIDYTEISFMLWC
-VYMPASWVMRIMTW
-IASFRLFARTRSMWSFNPETNIL
-THHWLLLTILTSL
-AAYSRYRIGNYKLNT
-FIASFRLFARTRS
-LTNIFGTVYEKLK
-SFMLWCKDGHVETFY
-NLAATKMSECVLGQSK
-WLSYFIASFRLFARTRSMWSF
-YAISAKNRARTVAG
-RLFARTRSMWSFNPETNI
-ISFMLWCKDGHVE
-MWLSYFIASFRLFARTRS
-FAAYSRYRIGNYKLNT
-ANLAATKMSECVLGQS
-YFIASFRLFARTR
-GDSGFAAYSRYRIGNY
-ISMMGFKMNYQVN
-MMGFKMNYQVNGYPNMF
-LLTILTSLLVLVQS
-SFRLFARTRSMWSF
-SYFIASFRLFARTRSMWSFNP
-SKETLYCIDGALLTK
-SGFKLKDCVMYASA
-HWLLLTILTSLLVLVQSTQ
-ARTRSMWSFNPETN
-GDSGFAAYSRYRIGN
-CKDGHVETFYPKL
-GFKMNYQVNGYPN
-SWVMRIMTWLDMVDTSL
-KETLYCIDGALLTKS
-WLLLTILTSLLVLVQST
-FRLFARTRSMWSFNPETNILL
-YRRLISMMGFKMNY
-HWLLLTILTSLLVLV
-VAGDSGFAAYSRYRI
-IASFRLFARTRSMWSFNPET
-WVMRIMTWLDMVD
-TLYCIDGALLTKS
-DMVDTSLSGFKLKDCVMYASA
-TEISFMLWCKDGHVETF
-VMRIMTWLDMVDT
-VTQQLIRAAEIRAS
-LKYAISAKNRARTV
-HHWLLLTILTSLLVLVQST
-RLFARTRSMWSFNPETNILLNVP
-LSYFIASFRLFARTRSMW
-FMLWCKDGHVETFYP
-IASFRLFARTRSMWSFNPETN
-FRLFARTRSMWSFNPETN
-WSFNPETNILLNVPLHGT
-MMGFKMNYQVNGYP
-TILTSLLVLVQSTQ
-SFRLFARTRSMWSFNPETN
-TEISFMLWCKDGHVET
-SMMGFKMNYQVNG
-NMVYMPASWVMRIM
-LDMVDTSLSGFKLKDCVMYASA
-LKYAISAKNRARTVAG
-MTYRRLISMMGFK
-TQMNLKYAISAKNRA
-PASWVMRIMTWLDM
-RTRSMWSFNPETNIL
-WLSYFIASFRLFARTRSM
-KGTHHWLLLTILT
-MWLSYFIASFRLFART
-LISMMGFKMNYQVNGYPNM
-SYFIASFRLFARTRSMWSFNPE
-AAYSRYRIGNYKLN
-RAAEIRASANLAA
-YTEISFMLWCKDGHVETFYP
-FAAYSRYRIGNYKL
-LLLTILTSLLVLVQS
-SWVMRIMTWLDMV
-RSMWSFNPETNILLNVPLHG
-SYFIASFRLFARTRSMWSF
-IDYTEISFMLWCKDGHVET
-MNLKYAISAKNRARTVAG
-FARTRSMWSFNPETNILLN
-WLSYFIASFRLFARTRSMWS
-RRLISMMGFKMNY
-NPETNILLNVPLHGTI
-THHWLLLTILTSLL
-VTQQLIRAAEIRASAN
-DSGFAAYSRYRIG
-RLFARTRSMWSFNPETNILLNVPL
-QMNLKYAISAKNRARTV
-FKMNYQVNGYPNM
-TRSMWSFNPETNILL
-RLISMMGFKMNYQ
-IDYTEISFMLWCKDGHV
-GDSGFAAYSRYRIGNYKL
-TIDYTEISFMLWCKDGHVETFY
-AATKMSECVLGQS
-SYFIASFRLFARTRSM
-PETNILLNVPLHG
-WVMRIMTWLDMVDTS
-FARTRSMWSFNPETNILLNVPLH
-NPETNILLNVPLHG
-LISMMGFKMNYQV
-GFKMNYQVNGYPNMF
-HITSKETLYCIDG
-FRLFARTRSMWSFNPETNILLNVP
-ANLAATKMSECVLGQ
-WCKDGHVETFYPKLQ
-TQMNLKYAISAKN
-KGTHHWLLLTILTS
-GFKMNYQVNGYPNM
-ECVLGQSKRVDFC
-YFIASFRLFARTRSMWSFNPETNIL
-GFAAYSRYRIGNYKLNT
-THHWLLLTILTSLLVLVQS
-YSRYRIGNYKLNT
-HITSKETLYCIDGAL
-SFRLFARTRSMWSFNPETNILLNV
-RIMTWLDMVDTSL
-FARTRSMWSFNPETNILLNV
-VKRTIKGTHHWLLL
-SFNPETNILLNVPLHGTI
-LFARTRSMWSFNPETNILLN
-HHWLLLTILTSLLVLVQSTQW
-SMMGFKMNYQVNGY
-LDMVDTSLSGFKLKD
-LLLTILTSLLVLVQST
-SFNPETNILLNVP
-ETLYCIDGALLTK
-TRSMWSFNPETNILLNVPLH
-TIDYTEISFMLWCKDGHVETFYP
-RTRSMWSFNPETNILLN
-EISFMLWCKDGHVE
-THHWLLLTILTSLLVLVQST
-TIDYTEISFMLWCKDG
-SLSGFKLKDCVMYASAV
-MVDTSLSGFKLKDCVM
-RTRSMWSFNPETNILLNV
-LDMVDTSLSGFKLKDCV
-QLIRAAEIRASAN
-LMWLSYFIASFRLF
-SFRLFARTRSMWS
-RTRSMWSFNPETNILLNVPLHGT
-LSYFIASFRLFARTRSMWS
-VDTSLSGFKLKDCVMYAS
-MVYMPASWVMRIMTW
-AGDSGFAAYSRYRIGNYKLN
-ASFRLFARTRSMWSFNPETNI
-DYTEISFMLWCKD
-SGFKLKDCVMYAS
-VTQQLIRAAEIRASANLA
-FRLFARTRSMWSFNPE
-LSYFIASFRLFARTRSMWSF
-EVARDLSLQFKRP
-LDMVDTSLSGFKL
-ASFRLFARTRSMWSFNPETNILL
-LLTILTSLLVLVQST
-ARTRSMWSFNPETNILLNVPL
-FIASFRLFARTRSMWSFNPE
-LSGFKLKDCVMYAS
-MNLKYAISAKNRA
-IDYTEISFMLWCKDGHVETFYP
-ISFMLWCKDGHVETFYP
-ASFRLFARTRSMWSF
-IDYTEISFMLWCKDGH
-TLYCIDGALLTKSS
-VAGDSGFAAYSRYRIGNY
-ARTRSMWSFNPETNILLNVPLHGT
-VARDLSLQFKRPI
-RSMWSFNPETNILL
-AVKRTIKGTHHWLL
-LAATKMSECVLGQSKR
-LDMVDTSLSGFKLKDCVMYA
-GFAAYSRYRIGNYK
-VDTSLSGFKLKDCVMYASAV
-QWLTNIFGTVYEKLK
-DMVDTSLSGFKLKDC
-WLSYFIASFRLFARTR
-RLFARTRSMWSFNPETNILL
-QMNLKYAISAKNRARTVAG
-DSGFAAYSRYRIGNYKLN
-TSLSGFKLKDCVMYASAV
-KHITSKETLYCIDGA
-TIDYTEISFMLWCK
-HWLLLTILTSLLVLVQ
-MWLSYFIASFRLFARTR
-NPETNILLNVPLH
-ASFRLFARTRSMWSFN
-FRLFARTRSMWSF
-LDMVDTSLSGFKLKDCVMY
-VDTSLSGFKLKDCV
-GDSGFAAYSRYRIGNYKLN
-YFIASFRLFARTRSMWSFNP
-SYFIASFRLFART
-IASFRLFARTRSMW
-RLFARTRSMWSFNPE
-QLIRAAEIRASANLA
-YFIASFRLFARTRSMWS
-SDEVARDLSLQFK
-GDSGFAAYSRYRIGNYK
-FIASFRLFARTRSMWSFNPETNI
-SDEVARDLSLQFKRPI
-LIRAAEIRASANL
-FARTRSMWSFNPE
-NMVYMPASWVMRIMT
-DMVDTSLSGFKLKDCVMYA
-FIASFRLFARTRSMWSFN
-VTQQLIRAAEIRASA
-QLIRAAEIRASANLAA
-MRIMTWLDMVDTSLS
-FRLFARTRSMWSFN
-QQLIRAAEIRASANL
-NLKYAISAKNRARTVA
-DYTEISFMLWCKDG
-TIDYTEISFMLWCKD
-SFMLWCKDGHVET
-SYFIASFRLFARTRSMWS
-SFRLFARTRSMWSFNPETNILLN
-YTEISFMLWCKDGHVETFY
-TEISFMLWCKDGHVETFYP
-FARTRSMWSFNPETNILLNVPLHG
-LDMVDTSLSGFKLK
-TIKGTHHWLLLTIL
-LISMMGFKMNYQVNGYPN
-ETLYCIDGALLTKS
-SFNPETNILLNVPL
-AISAKNRARTVAG
-MWSFNPETNILLN
-IASFRLFARTRSMWSFN
-WSFNPETNILLNVPLHGTI
-FMLWCKDGHVETF
-VAGDSGFAAYSRYRIGNYKLN
-PETNILLNVPLHGT
-LLTILTSLLVLVQSTQ
-NPETNILLNVPLHGT
-LDMVDTSLSGFKLKDC
-EISFMLWCKDGHVETFYP
-SFRLFARTRSMWSFNPE
-VDTSLSGFKLKDCVMY
-ILTSLLVLVQSTQW
-ATKMSECVLGQSK
-AAAYYVGYLQPRTF
-FIASFRLFARTRSMWSFNPETN
-QMNLKYAISAKNRART
-RLFARTRSMWSFNPETNILLNV
-LISMMGFKMNYQVNGYPNMF
-MSECVLGQSKRVD
-THHWLLLTILTSLLV
-MSECVLGQSKRVDF
-MWSFNPETNILLNVPLH
-TIDYTEISFMLWCKDGH
-YFIASFRLFARTRSMWSF
-SYFIASFRLFARTRSMWSFN
-TIDYTEISFMLWCKDGHVETF
-IDYTEISFMLWCKDGHVETFY
-TSLSGFKLKDCVMYAS
-AGDSGFAAYSRYRIGNYKL
-WLLLTILTSLLVLVQS
-ITSKETLYCIDGA
-TYRRLISMMGFKMNY
-MLWCKDGHVETFYP
-MVDTSLSGFKLKDC
-LMWLSYFIASFRL
-MTYRRLISMMGFKM
-NLAATKMSECVLG
-FNPETNILLNVPLHGTI
-AATKMSECVLGQSK
-EISFMLWCKDGHV
-LMWLSYFIASFRLFART
-SMWSFNPETNILLNVP
-SMMGFKMNYQVNGYPN
-DYTEISFMLWCKDGHVETFY
-YTEISFMLWCKDGHVETF
-MMGFKMNYQVNGYPN
-FNPETNILLNVPL
-MPASWVMRIMTWLDMV
-LMWLSYFIASFRLFAR
-ISFMLWCKDGHVETF
-HHWLLLTILTSLL
-DEVARDLSLQFKRP
-SDEVARDLSLQFKR
-DMVDTSLSGFKLKD
-KETLYCIDGALLTK
-QWLTNIFGTVYEK
-DYTEISFMLWCKDGHVE
-GFAAYSRYRIGNYKLN
-AAYYVGYLQPRTF
-IDYTEISFMLWCKDGHVETF
-IASFRLFARTRSMWSFNPETNI
-TSKETLYCIDGALL
-KHITSKETLYCIDGAL
-LLLTILTSLLVLVQSTQ
-LWCKDGHVETFYP
-HHWLLLTILTSLLVLV
-FIASFRLFARTRSMWS
-ARTRSMWSFNPETNILLNVPLHG
-ATKMSECVLGQSKR
-LDMVDTSLSGFKLKDCVM
-MWLSYFIASFRLFARTRSMW
-FRLFARTRSMWSFNPETNILLNV
-MWSFNPETNILLNVPLHG
-MVDTSLSGFKLKDCVMY
-ISMMGFKMNYQVNGYP
-FARTRSMWSFNPETNILLNVPL
-DTSLSGFKLKDCVM
-MGFKMNYQVNGYPN
-TQMNLKYAISAKNRAR
-SFRLFARTRSMWSFN
-AGDSGFAAYSRYRIGNYK
-AGDSGFAAYSRYRIGN
-NMVYMPASWVMRIMTW
-THHWLLLTILTSLLVL
-YRRLISMMGFKMN
-YFIASFRLFARTRS
-WSFNPETNILLNVPLHG
-SAVKRTIKGTHHWLLL
-NLKYAISAKNRART
-SWVMRIMTWLDMVD
-RSMWSFNPETNILLN
-GFAAYSRYRIGNYKL
-TSLSGFKLKDCVMYASA
-SANLAATKMSECVLGQS
-DTSLSGFKLKDCVMYASAV
-IDYTEISFMLWCKDGHVE
-EISFMLWCKDGHVETFY
-FMLWCKDGHVETFY
-MMGFKMNYQVNGY
-NLKYAISAKNRARTVAG
-SECVLGQSKRVDF
-FAAYSRYRIGNYK
-VYMPASWVMRIMTWLD
-CKDGHVETFYPKLQ
-WLLLTILTSLLVLV
-SLSGFKLKDCVMYAS
-MGFKMNYQVNGYPNMF
-TRSMWSFNPETNIL
-AGDSGFAAYSRYR
-ASFRLFARTRSMWSFNP
-MWLSYFIASFRLFARTRSMWS
-ARTRSMWSFNPETNILLNVP
-DMVDTSLSGFKLKDCV
-SMWSFNPETNILLNV
-VAGDSGFAAYSRYRIGNYKLNT
-LSYFIASFRLFART
-IDYTEISFMLWCK
-ETNILLNVPLHGT
-LTILTSLLVLVQSTQ
-MVDTSLSGFKLKDCVMYASA
-DSGFAAYSRYRIGNYK
-TILTSLLVLVQST
-YMPASWVMRIMTW
-TIDYTEISFMLWCKDGHV
-SFNPETNILLNVPLHG
-TQQLIRAAEIRASANL
-ISMMGFKMNYQVNG
-AGDSGFAAYSRYRIGNY
-LMWLSYFIASFRLFARTRS
-HWLLLTILTSLLVL
-RTRSMWSFNPETNILLNVPLHG
-GFKLKDCVMYASA
-KETLYCIDGALLT
-SKETLYCIDGALL
-RSMWSFNPETNILLNVPL
-TQQLIRAAEIRAS
-MWSFNPETNILLNV
-LLTILTSLLVLVQ
-IDYTEISFMLWCKD
-SFMLWCKDGHVETF
-VYMPASWVMRIMTWL
-ISMMGFKMNYQVNGY
-WSFNPETNILLNVP
-YFIASFRLFARTRSMWSFNPETN
-MVDTSLSGFKLKD
-IRAAEIRASANLAA
-TYRRLISMMGFKM
-IMTWLDMVDTSLS
-SGFAAYSRYRIGNY
-THHWLLLTILTSLLVLVQSTQ
-WLSYFIASFRLFA
-ARTRSMWSFNPETNILLNV
-SKETLYCIDGALLT
-TEISFMLWCKDGHVETFY
-KHITSKETLYCIDG
-WCKDGHVETFYPK
-DSGFAAYSRYRIGNYKL
-IRAAEIRASANLA
-NLAATKMSECVLGQSKR
-LISMMGFKMNYQVNGYP
-LSYFIASFRLFARTRSMWSFN
-LFARTRSMWSFNPETNILLNV
-LSYFIASFRLFARTRSM
-MNLKYAISAKNRARTV
-KRTIKGTHHWLLL
-HHWLLLTILTSLLVLVQ
-ITSKETLYCIDGALL
-RSMWSFNPETNIL
-QMNLKYAISAKNRA
-SYFIASFRLFARTR
-WLSYFIASFRLFAR
-LIRAAEIRASANLAA
-KDGHVETFYPKLQ
-TSKETLYCIDGAL
-TIKGTHHWLLLTI
-KYAISAKNRARTVAG
-SYFIASFRLFARTRSMWSFNPET
-RLFARTRSMWSFNPETN
-SAVKRTIKGTHHWLL
-LISMMGFKMNYQVNG
-LSYFIASFRLFARTRS
-YFIASFRLFARTRSM
-MPASWVMRIMTWL
-ASFRLFARTRSMWSFNPE
-SGFAAYSRYRIGNYKLN
-RSMWSFNPETNILLNVPLHGT
-THHWLLLTILTSLLVLV
-FIASFRLFARTRSMWSFNPETNIL
-TKMSECVLGQSKR
-FKMNYQVNGYPNMF
-SLSGFKLKDCVMYASA
-ARTRSMWSFNPETNILLN
-GFKLKDCVMYASAV
-MWLSYFIASFRLFARTRSM
-SKETLYCIDGALLTKSS
-VAGDSGFAAYSRYRIGNYKL
-KHITSKETLYCID
-WLTNIFGTVYEKL
-HWLLLTILTSLLVLVQSTQW
-HHWLLLTILTSLLVLVQSTQ
-TILTSLLVLVQSTQW
-VDTSLSGFKLKDCVMYASA
-RRLISMMGFKMNYQ
-YFIASFRLFARTRSMWSFNPETNI
-HHWLLLTILTSLLVL
-MWSFNPETNILLNVPL
-LISMMGFKMNYQVN
-MTYRRLISMMGFKMN
-FRLFARTRSMWSFNPETNILLN
-DMVDTSLSGFKLKDCVMYAS
-RTRSMWSFNPETNILLNVP
-RTRSMWSFNPETNILL
-TQMNLKYAISAKNRARTVAG
-SMMGFKMNYQVNGYP
-TRSMWSFNPETNILLNVPLHGT
-MVYMPASWVMRIMTWL
-ASWVMRIMTWLDMV
-YMPASWVMRIMTWLD
-SMMGFKMNYQVNGYPNM
-QQLIRAAEIRASA
-FAAYSRYRIGNYKLN
-SYFIASFRLFARTRSMWSFNPETN
-SANLAATKMSECV
-FIASFRLFARTRSM
-ISFMLWCKDGHVETFY
-GDSGFAAYSRYRI
\ No newline at end of file
diff --git a/SupplementaryResults/HaplotypeComputations/37435_vaccine_sequence_hog.txt b/SupplementaryResults/HaplotypeComputations/37435_vaccine_sequence_hog.txt
deleted file mode 100644
index 38ac6957a5b199b74bc137c6fc73fd266c41638d..0000000000000000000000000000000000000000
--- a/SupplementaryResults/HaplotypeComputations/37435_vaccine_sequence_hog.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-> MHC optimized combined peptide vaccine sequence with overlaps
-ADKASGHNLAKHITSKETLYCIDGALLTKSSDEVARDLSLQFKRPIHFYSKWYIRVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCAAAYYVGYLQPRTFLLKTAYFNMVYMPASWVMRIMTWLDMVDTSLSGFKLKDCVMYASAVKRTIKGTHHWLLLTILTSLLVLVQSTQWMTYRRLISMMGFKMNYQVNGYPNMFHYTQMNLKYAISAKNRARTVAGDSGFAAYSRYRIGNYKLNTLMWLSYFIASFRLFARTRSMWSFNPETNILLNVPLHGTIDYTEISFMLWCKDGHVETFYPKLQWLTNIFGTVYEKLKPV
-> MHC optimized combined peptide vaccine sequence concatenated
-WCKDGHVETFYPKLFAAYSRYRIGNYKLNTHITSKETLYCIDGALLLMWLSYFIASFRLFARTRSMLYCIDGALLTKSSTQMNLKYAISAKNRARTVAKYAISAKNRARTVAGMGFKMNYQVNGYPNMFTIDYTEISFMLWCKDGIRAAEIRASANLAAETNILLNVPLHGTIPETNILLNVPLHGTILKYAISAKNRARTVAGLLLTILTSLLVLVQSTQWLMWLSYFIASFRLFARTSWVMRIMTWLDMVDTSMMGFKMNYQVNGYPNMFLIRAAEIRASANLAALISMMGFKMNYQVTIDYTEISFMLWCKDGHVEGTHHWLLLTILTSRLISMMGFKMNYQTSLSGFKLKDCVMYASAVMPASWVMRIMTWLDMVLDMVDTSLSGFKLKMTYRRLISMMGFKMNTHHWLLLTILTSLLVLVAGDSGFAAYSRYRIGNYKLNMVYMPASWVMRIMTWLSYFIASFRLFARTRSMWSFNPETAAYSRYRIGNYKLNTWCKDGHVETFYPKLQLISMMGFKMNYQVNGSGFAAYSRYRIGNYKLNTSMMGFKMNYQVNGYPNMFLSYFIASFRLFARTRSMWSFVTQQLIRAAEIRASTIDYTEISFMLWCKDGHVETFYLISMMGFKMNYQVNGYPNMFSFNPETNILLNVPLHGTILAATKMSECVLGQSKRKMNYQVNGYPNMFTIDYTEISFMLWCKDGHVQWLTNIFGTVYEKYRRLISMMGFKMNYRLFARTRSMWSFNPETNILLNVPLLLTILTSLLVLVQSTQWNMVYMPASWVMRIMTFKLKDCVMYASAVVAGDSGFAAYSRYRIARTRSMWSFNPETNILLNVPLHGTMSECVLGQSKRVDSANLAATKMSECVLGQSKRRTIKGTHHWLLLTLMWLSYFIASFRLFARTQMNLKYAISAKNRARTVSAVKRTIKGTHHWLSKETLYCIDGALLTKSSWLSYFIASFRLFARTRSMWSFTLYCIDGALLTKSSRAAEIRASANLAASANLAATKMSECVLGQSQLIRAAEIRASANLAASFMLWCKDGHVETFYPMVDTSLSGFKLKDCVMYASAVWLSYFIASFRLFARTRSMWSVAGDSGFAAYSRYRIGNAVKRTIKGTHHWLLLIKGTHHWLLLTILTEISFMLWCKDGHVETFYPNMVYMPASWVMRIMTWLDSECVLGQSKRVDFCGFKLKDCVMYASAVAAAYYVGYLQPRTSWVMRIMTWLDMVDTIKGTHHWLLLTILFARTRSMWSFNPETNILLNVPLYTEISFMLWCKDGHVETFYPVAGDSGFAAYSRYRIGNYKFARTRSMWSFNPETNILLNVPLHGTLMWLSYFIASFRLFANMVYMPASWVMRIQMNLKYAISAKNRARTVAGANLAATKMSECVLGQSKRTIDYTEISFMLWCKDTQMNLKYAISAKNSWVMRIMTWLDMVDTSLKGTHHWLLLTILTSMWSFNPETNILLNVPLHGTTHHWLLLTILTSLLVTQQLIRAAEIRASFRLFARTRSMWSFNPETNILLNHWLLLTILTSLLVLVQSTQWTQMNLKYAISAKNRARSKETLYCIDGALLTKLWCKDGHVETFYPDMVDTSLSGFKLKDCVMYASAKRTIKGTHHWLLLVAGDSGFAAYSRYRIGNYFIASFRLFARTRSMWSFNPETNILQWLTNIFGTVYEKLKSAVKRTIKGTHHWLLLKDGHVETFYPKLQTIDYTEISFMLWCKDGHVETFYPVTQQLIRAAEIRASAKGTHHWLLLTILTSQQLIRAAEIRASANLASDEVARDLSLQFKATKMSECVLGQSKRWSFNPETNILLNVPLHGTLDMVDTSLSGFKLKDTNILLNVPLHGTIITSKETLYCIDGALLTHHWLLLTILTSLLVLVQSTQAAAYYVGYLQPRTFECVLGQSKRVDFCASWVMRIMTWLDMVAISAKNRARTVAGMVYMPASWVMRIMTWLDMLDMVDTSLSGFKLLSYFIASFRLFARTRSMWSFNRTRSMWSFNPETNILLNVPLHGTKETLYCIDGALLTKSSSANLAATKMSECVLGSDEVARDLSLQFKRVAGDSGFAAYSRYTYRRLISMMGFKMNLDMVDTSLSGFKLKDCVMYASTQMNLKYAISAKNRAVAGDSGFAAYSRYRIGNYKLNTWSFNPETNILLNVPLHGTITSKETLYCIDGALLNLAATKMSECVLGQSKRHHWLLLTILTSLLVLVQSTQWSYFIASFRLFARTRSMWSFNLISMMGFKMNYQVNGYQWLTNIFGTVYEKLNPETNILLNVPLHGTITQMNLKYAISAKNRSLSGFKLKDCVMYASAVRLFARTRSMWSFNPETNILLNVPLDMVDTSLSGFKLKDCVRIMTWLDMVDTSLSSECVLGQSKRVDFYAISAKNRARTVAGKHITSKETLYCIDGAGFKMNYQVNGYPNMFYMPASWVMRIMTWLDMLDMVDTSLSGFKLKDCVMYTHHWLLLTILTSLLVNLKYAISAKNRARTVAGVYMPASWVMRIMTWLDMTHHWLLLTILTSLLVLVQSAVKRTIKGTHHWLLDTSLSGFKLKDCVMYASAVMWLSYFIASFRLFARTRSMWTIDYTEISFMLWCKEVARDLSLQFKRPIETLYCIDGALLTKSSMWSFNPETNILLNVPLHGTDYTEISFMLWCKDGHVETFYPCKDGHVETFYPKLQLMWLSYFIASFRLFARTRSAYSRYRIGNYKLNTVARDLSLQFKRPIKHITSKETLYCIDGGFAAYSRYRIGNYKLNTLDMVDTSLSGFKLKDCVMYASATIDYTEISFMLWCAAYYVGYLQPRTFVKRTIKGTHHWLLLMVDTSLSGFKLKDCVMYASALFARTRSMWSFNPETNILLNVPLHGRSMWSFNPETNILLNVPLHGTSANLAATKMSECVLGQSKMSECVLGQSKRVDFVMRIMTWLDMVDTSLIMTWLDMVDTSLSMRIMTWLDMVDTSLSAATKMSECVLGQSKRFNPETNILLNVPLHGTIIASFRLFARTRSMWSFNPETNILLVTQQLIRAAEIRASANTKMSECVLGQSKRVAGDSGFAAYSRYRIGNYKLNFARTRSMWSFNPETNILLNVPLHGMVYMPASWVMRIMTWLDTHHWLLLTILTSLMLWCKDGHVETFYPSYFIASFRLFARTRSMWSFNPLISMMGFKMNYQVNMWLSYFIASFRLFARTRSMVAGDSGFAAYSRYRSWVMRIMTWLDMVLTSLLVLVQSTQWTHHWLLLTILTSLLVLVQSTTQQLIRAAEIRASANLASYFIASFRLFARTRSMWSFNPETNLISMMGFKMNYQVNGYPNMSGFKLKDCVMYASAVWLTNIFGTVYEKLKWLLLTILTSLLVLVQSTQWLDMVDTSLSGFKLKDCLFARTRSMWSFNPETNILLNVPLHAGDSGFAAYSRYRIGNYKLNTQLIRAAEIRASANLASFRLFARTRSMWSFNPETNILLNVSYFIASFRLFARTRSMWSFNPETNITQMNLKYAISAKNRARTSKETLYCIDGALLTKSTYRRLISMMGFKMNYYRRLISMMGFKMNYQSWVMRIMTWLDMVDTTHHWLLLTILTSLLVLVQSLDMVDTSLSGFKLKDCVMYAWVMRIMTWLDMVDTSLMRIMTWLDMVDTSLNMVYMPASWVMRIMMNLKYAISAKNRARTVAGSDEVARDLSLQFKRPISKETLYCIDGALLYSRYRIGNYKLNTEISFMLWCKDGHVETFYPMTYRRLISMMGFKLTNIFGTVYEKLKTIKGTHHWLLLTILTQMNLKYAISAKNRARTVAGGDSGFAAYSRYRIGNYKLNTMPASWVMRIMTWLDMWCKDGHVETFYPKKHITSKETLYCIDGALTRSMWSFNPETNILLNVPLHGTASFRLFARTRSMWSFNPETNILLNIASFRLFARTRSMWSFNPETNILMTYRRLISMMGFKMSANLAATKMSECVFRLFARTRSMWSFNPETNILLNVPSDEVARDLSLQFKRPLMWLSYFIASFRLLSGFKLKDCVMYASAVLTILTSLLVLVQSTQWPASWVMRIMTWLDMVSKETLYCIDGALLTSYFIASFRLFARTRSMWSFNPEILTSLLVLVQSTQWTIDYTEISFMLWCKDGHVETDEVARDLSLQFKRPIISMMGFKMNYQVNGYPNMFNMVYMPASWVMRIMTWTIDYTEISFMLWCKDGHTIDYTEISFMLWCKDGHVETFVAGDSGFAAYSRYRIGKHITSKETLYCIDGALLSANLAATKMSECVLGQDSGFAAYSRYRIGNYKLNTVTQQLIRAAEIRASANLIASFRLFARTRSMWSFNPETNILLNVDTSLSGFKLKDCVMYASAVTHHWLLLTILTSLLVLVTHHWLLLTILTSLLVLVQSTQWISFMLWCKDGHVETFYPTILTSLLVLVQSTQWLDMVDTSLSGFKLKDCVMLMWLSYFIASFRLFARTRRRLISMMGFKMNYQLISMMGFKMNYQVNGYPVTQQLIRAAEIRASANLAIDYTEISFMLWCKDGHVETFYPKHITSKETLYCIDSAVKRTIKGTHHWMWLSYFIASFRLFARTRSMWSSFRLFARTRSMWSFNPETNILLNVPSANLAATKMSECVLYFIASFRLFARTRSMWSFNPETNILYFIASFRLFARTRSMWSFNPETNILISMMGFKMNYQVNGYPNFKMNYQVNGYPNMFFMLWCKDGHVETFYPLMWLSYFIASFRLF
\ No newline at end of file
diff --git a/SupplementaryResults/HaplotypeComputations/4519_chosen_peptides_hog_inc_substrings.txt b/SupplementaryResults/HaplotypeComputations/4519_chosen_peptides_hog_inc_substrings.txt
deleted file mode 100644
index 4e6334c1ca36012b9b6a2af30b61b365f65ec26a..0000000000000000000000000000000000000000
--- a/SupplementaryResults/HaplotypeComputations/4519_chosen_peptides_hog_inc_substrings.txt
+++ /dev/null
@@ -1,104 +0,0 @@
-VAEWFLAY
-WFLAYILFT
-AYILFTRFFY
-VMYASAVVL
-VAEWFLAYIL
-EWFLAYILF
-ADQAMTQM
-FLAYILFTRF
-YRVTKNSK
-YMPYFFTLL
-LRPDTRYVL
-SYFTSDYY
-MYASAVVL
-LKRRVVFN
-MEIDFLELA
-KMADQAMT
-LAYILFTRF
-KSYELQTP
-GTDLEGNFY
-SYELQTPFE
-YILFTRFF
-ISLAGSYK
-AYILFTRFF
-LVAEWFLA
-YILFTRFFY
-GTDLEGNF
-YVFTGYRV
-TISLAGSYK
-SLAGSYKD
-ETISLAGSYK
-KLKDCVMY
-LRPDTRYV
-QMEIDFLEL
-AEWFLAYILF
-VMYASAVVLL
-VKDCVVLH
-TGYRVTKN
-VMYASAVV
-YASAVVLL
-ETISLAGSY
-FLAYILFT
-MPYFFTLL
-AGTDLEGNF
-LVAEWFLAYI
-MPYFFTLLL
-EKSYELQTPF
-ETISLAGS
-YLKRRVVF
-EIDFLELA
-WFLAYILF
-HEHEIAWY
-GYRVTKNS
-RNYVFTGYR
-NYLKRRVV
-HSYFTSDYY
-AGTDLEGNFY
-MADQAMTQM
-FLAYILFTR
-HSYFTSDY
-TDLEGNFY
-AEWFLAYI
-AYILFTRF
-VAEWFLAYI
-IETISLAG
-AGTDLEGN
-WFLAYILFTR
-MYASAVVLL
-EKSYELQT
-TISLAGSYKD
-PYFFTLLL
-LKDCVMYA
-LAGSYKDW
-QMEIDFLE
-SYELQTPF
-LAYILFTRFF
-MEIDFLEL
-RNYVFTGY
-GYRVTKNSK
-VFTGYRVT
-SLAGSYKDW
-MADQAMTQ
-AEWFLAYIL
-EHEHEIAW
-EKSYELQTP
-RPDTRYVL
-TISLAGSY
-YMPYFFTL
-NYVFTGYR
-EHEHEIAWY
-VHAGTDLE
-KSYELQTPFE
-ILFTRFFY
-YASAVVLLI
-EHFIETIS
-ISLAGSYKD
-YLKRRVVFN
-ASAVVLLI
-LAYILFTR
-KSYELQTPF
-EWFLAYIL
-NYLKRRVVF
-ISLAGSYKDW
-YELQTPFE
-LVAEWFLAY
\ No newline at end of file
diff --git a/SupplementaryResults/HaplotypeComputations/4519_vaccine_sequence_hog.txt b/SupplementaryResults/HaplotypeComputations/4519_vaccine_sequence_hog.txt
deleted file mode 100644
index 7a4eeb7808e43bf8c60d96215fd4f76a5e12d5c0..0000000000000000000000000000000000000000
--- a/SupplementaryResults/HaplotypeComputations/4519_vaccine_sequence_hog.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-> MHC optimized combined peptide vaccine sequence with overlaps
-HQSEKSYELQTPFENYLKRRVVFNRNYVFTGYRVTKNSKVKDCVVLHSYFTSDYYCRARKMADQAMTQMEIDFLELAALRPDTRYVLVAEWFLAYILFTRFFYMPYFFTLLLCTGVHAGTDLEGNFYLKLKDCVMYASAVVLLIPCEHFIETISLAGSYKDWEHEHEIAWYA
-> MHC optimized combined peptide vaccine sequence concatenated
-WFLAYILFASAVVLLILVAEWFLAYISYELQTPFEMEIDFLELAVHAGTDLESYFTSDYYLAYILFTRFLKRRVVFNVAEWFLAYILNYLKRRVVFIETISLAGYLKRRVVFAEWFLAYILSLAGSYKDWYILFTRFFYGYRVTKNSHEHEIAWYYELQTPFEHSYFTSDYYMADQAMTQEKSYELQTPFLAYILFTRFFLRPDTRYVLVAEWFLAYWFLAYILFTMYASAVVLLKSYELQTPFMADQAMTQMKMADQAMTYASAVVLLPYFFTLLLYRVTKNSKLAGSYKDWEHFIETISEKSYELQTQMEIDFLELRPDTRYVLYASAVVLLIADQAMTQMKLKDCVMYAGTDLEGNFYFLAYILFTRFETISLAGSYKAGTDLEGNGYRVTKNSKETISLAGSYQMEIDFLELYVFTGYRVAYILFTRFFKSYELQTPFERNYVFTGYTISLAGSYKAGTDLEGNFEWFLAYILFMPYFFTLLLVMYASAVVETISLAGSVMYASAVVLLEHEHEIAWNYVFTGYRGTDLEGNFYEKSYELQTPISLAGSYKDEHEHEIAWYYLKRRVVFNRNYVFTGYRISLAGSYKDWAYILFTRFFYTISLAGSYKDYMPYFFTLMEIDFLELEIDFLELAHSYFTSDYILFTRFFYTGYRVTKNYMPYFFTLLFLAYILFTRLKDCVMYAVFTGYRVTTDLEGNFYVMYASAVVLAEWFLAYILFVAEWFLAYIMPYFFTLLWFLAYILFTRVKDCVVLHRPDTRYVLLVAEWFLANYLKRRVV
\ No newline at end of file
diff --git a/SupplementaryResults/HaplotypeComputations/evalvax-unlinked.csv b/SupplementaryResults/HaplotypeComputations/evalvax-unlinked.csv
deleted file mode 100644
index ad4ba39314405eba931408c5937ba742d1b23405..0000000000000000000000000000000000000000
--- a/SupplementaryResults/HaplotypeComputations/evalvax-unlinked.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-,Method,Population Coverage,Number of Peptides
-0,MHCI OptiVax-Unlinked,0.9666857340308445,19
-1,MHCI HOGVAX,0.9820975545275613,104
-2,MHCII OptiVax-Unlinked,0.9834908363096779,19
-3,MHCII HOGVAX,0.9938390508022868,747
diff --git a/SupplementaryResults/HaplotypeComputations/hit_hist_mhc1_HOGVAX.csv b/SupplementaryResults/HaplotypeComputations/hit_hist_mhc1_HOGVAX.csv
deleted file mode 100644
index 0cc54d80c94821726a967c1e32f7ec2d4f974416..0000000000000000000000000000000000000000
--- a/SupplementaryResults/HaplotypeComputations/hit_hist_mhc1_HOGVAX.csv
+++ /dev/null
@@ -1,32 +0,0 @@
-,count,prob
-0,0,0.0
-1,1,6.388313597939749e-05
-2,2,0.014778873460310895
-3,3,0.021863132432731507
-4,4,0.05325373445138106
-5,5,0.0906366971586285
-6,6,0.1298635490032129
-7,7,0.11404484841685239
-8,8,0.101049942736289
-9,9,0.07466925626510672
-10,10,0.05317658168608239
-11,11,0.05118970067647056
-12,12,0.03531446399572807
-13,13,0.07188484427531149
-14,14,0.05415233121392212
-15,15,0.06315520933322681
-16,16,0.031272569570579674
-17,17,0.02236729222601468
-18,18,0.008897312676976717
-19,19,0.005016595828717148
-20,20,0.0025967805660766057
-21,21,0.0004231316164607776
-22,22,0.0003193202074049682
-23,23,8.726813413223876e-06
-24,24,1.2222531117986737e-06
-25,25,0.0
-26,26,0.0
-27,27,0.0
-28,28,0.0
-29,29,0.0
-30,30,0.0
diff --git a/SupplementaryResults/HaplotypeComputations/hit_hist_mhc1_OptiVax.csv b/SupplementaryResults/HaplotypeComputations/hit_hist_mhc1_OptiVax.csv
deleted file mode 100644
index 992c91b934979c9aa5da3be4414344bbbd44fcde..0000000000000000000000000000000000000000
--- a/SupplementaryResults/HaplotypeComputations/hit_hist_mhc1_OptiVax.csv
+++ /dev/null
@@ -1,23 +0,0 @@
-,count,prob
-0,0,0.0
-1,1,0.010896073297388321
-2,2,0.040511381907138246
-3,3,0.10070430865157046
-4,4,0.08630085645447184
-5,5,0.058261278648598674
-6,6,0.10608394700676058
-7,7,0.14854575476561746
-8,8,0.09927766090617475
-9,9,0.08388530267160951
-10,10,0.11045148196709026
-11,11,0.049257736567144376
-12,12,0.01736896047723383
-13,13,0.049231593317433785
-14,14,0.035242780902360604
-15,15,0.0026057771538341742
-16,16,0.000739349750928372
-17,17,0.0006121260958668993
-18,18,2.3629458775881027e-05
-19,19,0.0
-20,20,0.0
-21,21,0.0
diff --git a/SupplementaryResults/HaplotypeComputations/hit_hist_mhc2_HOGVAX.csv b/SupplementaryResults/HaplotypeComputations/hit_hist_mhc2_HOGVAX.csv
deleted file mode 100644
index 14eafd76a8976f749b97814a4f1b88c706d3b38e..0000000000000000000000000000000000000000
--- a/SupplementaryResults/HaplotypeComputations/hit_hist_mhc2_HOGVAX.csv
+++ /dev/null
@@ -1,304 +0,0 @@
-,count,prob
-0,0,0.0007243433400952906
-1,1,0.006630679030492481
-2,2,0.004958597937113099
-3,3,0.014036480885440695
-4,4,0.01111527198734647
-5,5,0.011124278882619671
-6,6,0.008819760688469546
-7,7,0.018725168995058418
-8,8,0.035885683342960405
-9,9,0.025393345384722613
-10,10,0.03327658842042873
-11,11,0.03128769201703498
-12,12,0.02522355398788677
-13,13,0.02310542341271465
-14,14,0.02425812460019546
-15,15,0.027615118671068594
-16,16,0.015327784419121623
-17,17,0.015322962635956082
-18,18,0.02630222354278663
-19,19,0.03142429518615678
-20,20,0.02007683086603199
-21,21,0.02472012602284874
-22,22,0.01108776718190808
-23,23,0.01898001502210075
-24,24,0.01716669004296503
-25,25,0.024169932910977278
-26,26,0.027610298613727016
-27,27,0.01596517523360075
-28,28,0.015982448290704002
-29,29,0.020389820356289848
-30,30,0.01133343618903039
-31,31,0.017334487266211864
-32,32,0.01553278646737954
-33,33,0.011088425216786865
-34,34,0.01185072726199553
-35,35,0.00551772435476419
-36,36,0.00885551578835632
-37,37,0.004989607605046302
-38,38,0.006103094443492572
-39,39,0.009391264612582788
-40,40,0.0046312277417244994
-41,41,0.005319222053515223
-42,42,0.006102831205343555
-43,43,0.0030191281090801245
-44,44,0.008123817135522099
-45,45,0.008978522270375286
-46,46,0.010387942270869342
-47,47,0.01268763328540431
-48,48,0.005336155765232573
-49,49,0.008901008977932714
-50,50,0.0029376577812654553
-51,51,0.014565501974913615
-52,52,0.01613454553538942
-53,53,0.012139721978782695
-54,54,0.011115738137797886
-55,55,0.009520949456391462
-56,56,0.005278680938814369
-57,57,0.005712094791474097
-58,58,0.004679570846602061
-59,59,0.006382965492917614
-60,60,0.004134543335825792
-61,61,0.0020050842271935963
-62,62,0.010042062864468383
-63,63,0.002409882386728439
-64,64,0.0048310415761936756
-65,65,0.004421719240136601
-66,66,0.0031085166532161188
-67,67,0.005408145043138998
-68,68,0.0018511256733665332
-69,69,0.004718002353488408
-70,70,0.0016625777387462794
-71,71,0.0018097471950789445
-72,72,0.0027131249421990557
-73,73,0.0017629129082980175
-74,74,0.0014920538026312981
-75,75,0.0015021187419813043
-76,76,0.0006818530212183363
-77,77,0.0007619569584083917
-78,78,0.0005090311974938651
-79,79,0.00044908964568805696
-80,80,0.0004230678179821656
-81,81,0.0003983955326194456
-82,82,0.0006834611531846918
-83,83,0.00035405646931922785
-84,84,0.00020733865291811424
-85,85,0.00022477924793205977
-86,86,0.00031326310434855864
-87,87,0.00014296089761660163
-88,88,0.00032935064737732596
-89,89,0.00014812804455417415
-90,90,0.00016602176934770352
-91,91,9.733806326437042e-05
-92,92,0.00022149285115307922
-93,93,0.00011608071146550419
-94,94,0.00012159901658402917
-95,95,0.00011983762625042452
-96,96,5.8573175383761865e-05
-97,97,6.92169176943618e-05
-98,98,1.597474653430315e-05
-99,99,9.426706651402673e-05
-100,100,3.0225514918861645e-05
-101,101,7.275836445758062e-05
-102,102,5.105141862251564e-05
-103,103,7.307049461949229e-05
-104,104,3.352435642844737e-05
-105,105,4.481285297243846e-05
-106,106,3.9975496395391306e-05
-107,107,2.3045659282811338e-05
-108,108,0.00024723901497740306
-109,109,0.000124225082718453
-110,110,0.0001433385300971062
-111,111,0.00036805269966732907
-112,112,0.0007092852170630087
-113,113,0.0006715840176834061
-114,114,0.0008717251797643063
-115,115,0.0003619859124953997
-116,116,0.000568499130610011
-117,117,0.0005376314061981873
-118,118,0.0015767534230497159
-119,119,0.0018222025366017114
-120,120,0.0009681201672248605
-121,121,0.0009525081636898286
-122,122,0.000715855572869627
-123,123,0.0005146235090635387
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deleted file mode 100644
index 46b727167b720549c65bc88b7abc41883af99f65..0000000000000000000000000000000000000000
--- a/SupplementaryResults/HaplotypeComputations/min_hit_dictmhc1_HOGVAX.csv
+++ /dev/null
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--- a/SupplementaryResults/HaplotypeComputations/min_hit_dictmhc1_OptiVax.csv
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deleted file mode 100644
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--- a/SupplementaryResults/HaplotypeComputations/min_hit_dictmhc2_HOGVAX.csv
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deleted file mode 100644
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diff --git a/SupplementaryResults/HaplotypeMinHitsHOGVAX/hit_hist_mhc1_HOGVAX.csv b/SupplementaryResults/HaplotypeMinHitsHOGVAX/hit_hist_mhc1_HOGVAX.csv
deleted file mode 100644
index ea03024efdeca7425faf91f87c308a7c557c48ce..0000000000000000000000000000000000000000
--- a/SupplementaryResults/HaplotypeMinHitsHOGVAX/hit_hist_mhc1_HOGVAX.csv
+++ /dev/null
@@ -1,48 +0,0 @@
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diff --git a/SupplementaryResults/HaplotypeMinHitsHOGVAX/hit_hist_mhc2_HOGVAX.csv b/SupplementaryResults/HaplotypeMinHitsHOGVAX/hit_hist_mhc2_HOGVAX.csv
deleted file mode 100644
index 6f30b3c2170f960a36d4f206e875eac416ca6e8a..0000000000000000000000000000000000000000
--- a/SupplementaryResults/HaplotypeMinHitsHOGVAX/hit_hist_mhc2_HOGVAX.csv
+++ /dev/null
@@ -1,448 +0,0 @@
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diff --git a/SupplementaryResults/SingleAlleleComputations/37435_chosen_peptides_hog_inc_substrings.txt b/SupplementaryResults/SingleAlleleComputations/37435_chosen_peptides_hog_inc_substrings.txt
deleted file mode 100644
index b6767c7c0ddc12b10a3f1ea3551679210d9fecb2..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SingleAlleleComputations/37435_chosen_peptides_hog_inc_substrings.txt
+++ /dev/null
@@ -1,1112 +0,0 @@
-KNRARTVAGVSICST
-MWSFNPETNILLNVPLHGT
-LFARTRSMWSFNPETNI
-FIASFRLFARTRSMWSFN
-KHFYWFFSNYLKRRVVFNGVSF
-SYFIASFRLFARTRSMWSFNPE
-TITQMNLKYAISAKNRARTV
-SSRLSFKELLVYA
-GIAIAMACLVGLMWLSYFIASFRL
-ACLVGLMWLSYFIASF
-TRSMWSFNPETNI
-VVDADSKIVQLSE
-ETNILLNVPLHGT
-RLFARTRSMWSFN
-LKYAISAKNRART
-PKDMTYRRLISMMGFKMN
-WFFSNYLKRRVVFNGVSFST
-KGTHHWLLLTILT
-SFCAFAVDAAKAYKD
-VTQQLIRAAEIRAS
-AIAMACLVGLMWLSYFIASFRL
-WLSYFIASFRLFA
-KIVQLSEISMDNSP
-MWLSYFIASFRLFART
-IAYIICISTKHFYWFFSNY
-SMWSFNPETNILLNVPLH
-PETNILLNVPLHGTI
-YIICISTKHFYWFF
-YASALWEIQQVVDADSKI
-VTQQLIRAAEIRASAN
-LWEIQQVVDADSKIVQ
-NLKYAISAKNRARTVAG
-FARTRSMWSFNPETNI
-QQVVDADSKIVQLSE
-AMACLVGLMWLSY
-DVNLHSSRLSFKELLVY
-FCAFAVDAAKAYK
-KDMTYRRLISMMGF
-GIPKDMTYRRLISMMGF
-IRAAEIRASANLA
-AFAVDAAKAYKDYLASGGQ
-FRLFARTRSMWSFNP
-YFIASFRLFARTRS
-ALWEIQQVVDADSKIVQL
-YLKRRVVFNGVSF
-ALWEIQQVVDADSK
-AVDAAKAYKDYLA
-HFYWFFSNYLKRRVVFNGVSFS
-VGLMWLSYFIASFRLFA
-VKRTIKGTHHWLL
-WSFNPETNILLNV
-YAISAKNRARTVAG
-TQMNLKYAISAKNRARTV
-SFRLFARTRSMWSFNP
-WFFSNYLKRRVVFNGVSFS
-LVGLMWLSYFIASF
-RLSFKELLVYAADPA
-KNRARTVAGVSICS
-FARTRSMWSFNPETNILLNVPL
-GIAIAMACLVGLMWLSYFI
-SFRLFARTRSMWS
-RTRSMWSFNPETNIL
-SFCAFAVDAAKAYKDYLASGG
-QDVNLHSSRLSFKEL
-HSSRLSFKELLVYAADPA
-QDVNLHSSRLSFK
-ISAKNRARTVAGVSICS
-RNVIPTITQMNLKYA
-LFARTRSMWSFNPETNILL
-RTRSMWSFNPETNI
-YAISAKNRARTVAGVSIC
-IAYIICISTKHFYWFFSNYL
-GLMWLSYFIASFRLFARTR
-ISAKNRARTVAGVSI
-MWLSYFIASFRLFARTRSMWSFNP
-QLIRAAEIRASAN
-YWFFSNYLKRRVVFNGVSFST
-SFNPETNILLNVPLHGT
-FARTRSMWSFNPETNILLNV
-AFAVDAAKAYKDYLAS
-SAKNRARTVAGVSICS
-ASFRLFARTRSMWSFNPETN
-LSFCAFAVDAAKAY
-SFRLFARTRSMWSFNPE
-FKELLVYAADPAM
-STKHFYWFFSNYLKRRVVFNG
-YFIASFRLFARTRSMWSFNPE
-YFIASFRLFARTR
-IAIAMACLVGLMWL
-IPTITQMNLKYAISAKNRARTVA
-GIAIAMACLVGLMWLS
-FFSNYLKRRVVFN
-SALWEIQQVVDADSK
-TQMNLKYAISAKNRART
-TRSMWSFNPETNILLNVPL
-KYAISAKNRARTVA
-WFFSNYLKRRVVFNGVSF
-DMTYRRLISMMGF
-YWFFSNYLKRRVVFNGV
-LSYFIASFRLFARTRSMWSFNP
-ISTKHFYWFFSNYLKRRV
-AMACLVGLMWLSYFIA
-RNVIPTITQMNLKYAIS
-AVDAAKAYKDYLASGGQP
-AYIICISTKHFYWFFS
-SNYLKRRVVFNGVSFS
-SAKNRARTVAGVSI
-KYAISAKNRARTVAGVSIC
-MNLKYAISAKNRA
-TVLSFCAFAVDAAKAYK
-AIAMACLVGLMWLSY
-IAMACLVGLMWLSY
-VVDADSKIVQLSEISM
-SMWSFNPETNILLNV
-IPTITQMNLKYAISAKNRAR
-WLSYFIASFRLFARTRS
-SFRLFARTRSMWSFNPET
-PTITQMNLKYAISAKNRA
-IAYIICISTKHFYWFFS
-KGTHHWLLLTILTS
-SFRLFARTRSMWSF
-STKHFYWFFSNYLK
-IASFRLFARTRSMWSFNPETN
-IQQVVDADSKIVQ
-CAFAVDAAKAYKD
-VHNQDVNLHSSRLSF
-IICISTKHFYWFF
-NVIPTITQMNLKYAISAKNR
-WEIQQVVDADSKIVQLS
-QLIRAAEIRASANL
-SYFIASFRLFARTRSMWSFNPET
-NLKYAISAKNRART
-TKRNVIPTITQMNL
-HNQDVNLHSSRLSFKEL
-AKNRARTVAGVSI
-MACLVGLMWLSYFIASFRLF
-HNQDVNLHSSRLSFKE
-KYAISAKNRARTVAGVS
-IAIAMACLVGLMWLSY
-SRLSFKELLVYAAD
-GLMWLSYFIASFRLFARTRSMWSFN
-KAYKDYLASGGQP
-LVGLMWLSYFIASFRLFA
-LKYAISAKNRARTVAGV
-FYWFFSNYLKRRVVFNGVS
-CLVGLMWLSYFIASFRLFARTR
-LSFCAFAVDAAKAYKDYL
-DVNLHSSRLSFKEL
-ASFRLFARTRSMWSFN
-QQVVDADSKIVQLS
-RTRSMWSFNPETNILLNV
-HFYWFFSNYLKRRVVFN
-SFNPETNILLNVPLHGTI
-YRRLISMMGFKMNY
-SFCAFAVDAAKAYKDYLASGGQP
-TITQMNLKYAISAKNRAR
-AFAVDAAKAYKDYLASGG
-ASALWEIQQVVDADSKIVQL
-RTRSMWSFNPETNILLN
-FCAFAVDAAKAYKDYLASGGQ
-VIPTITQMNLKYAISAKNRARTVA
-TQMNLKYAISAKNRARTVA
-LKRRVVFNGVSFS
-AKNRARTVAGVSICST
-YLKRRVVFNGVSFS
-NPETNILLNVPLH
-SFKELLVYAADPA
-NPETNILLNVPLHG
-TVLSFCAFAVDAAKA
-ASFRLFARTRSMWS
-DADSKIVQLSEISMDN
-FCAFAVDAAKAYKDYLAS
-LTILTSLLVLVQSTQ
-WSFNPETNILLNVPL
-IPTITQMNLKYAISAKNRA
-WLSYFIASFRLFARTR
-ACLVGLMWLSYFIASFR
-KRTIKGTHHWLLLT
-SMWSFNPETNILLNVPLHG
-YFIASFRLFARTRSMWSF
-CISTKHFYWFFSNYLKRRVV
-NQDVNLHSSRLSFK
-ICISTKHFYWFFSNYLKRR
-FRLFARTRSMWSFNPE
-LMWLSYFIASFRLFARTRSMWSF
-LLTILTSLLVLVQST
-KIVQLSEISMDNS
-QQVVDADSKIVQLSEI
-RSMWSFNPETNILLNVPL
-VDADSKIVQLSEI
-LVGLMWLSYFIASFRLFAR
-LSYFIASFRLFARTRS
-VLSFCAFAVDAAKAYKDYLAS
-LHSSRLSFKELLVYAAD
-CAFAVDAAKAYKDYL
-ICISTKHFYWFFSNYLKRRV
-MWLSYFIASFRLFARTRSMW
-TYRRLISMMGFKM
-AMACLVGLMWLSYF
-YIICISTKHFYWFFSNYLKR
-IICISTKHFYWFFSNYLKRRV
-AVDAAKAYKDYLASGGQ
-GIPKDMTYRRLISMMGFKMN
-QQLIRAAEIRASANLAA
-TVLSFCAFAVDAAKAYKDYLA
-AYIICISTKHFYW
-NYLKRRVVFNGVSF
-AKAYKDYLASGGQ
-VLSFCAFAVDAAKAYKDYLASG
-FNPETNILLNVPLH
-VLSFCAFAVDAAKAY
-QLIRAAEIRASANLA
-GLMWLSYFIASFRLFA
-DMTYRRLISMMGFKMNY
-WLSYFIASFRLFARTRSMWSFN
-FSNYLKRRVVFNGV
-FAVDAAKAYKDYLAS
-HHWLLLTILTSLLVL
-DAAKAYKDYLASG
-THHWLLLTILTSLLVLVQS
-WLTNIFGTVYEKLK
-ISTKHFYWFFSNYL
-DMTYRRLISMMGFKM
-ITQMNLKYAISAKNRA
-IAMACLVGLMWLSYFIA
-KYAISAKNRARTVAG
-MWLSYFIASFRLFAR
-CLVGLMWLSYFIASFRLF
-IPKDMTYRRLISM
-AFAVDAAKAYKDY
-QMNLKYAISAKNRARTV
-ILTSLLVLVQSTQW
-ITQMNLKYAISAKNRARTVAG
-VIPTITQMNLKYA
-RLFARTRSMWSFNPET
-TQMNLKYAISAKNR
-IAMACLVGLMWLSYFIASF
-IAYIICISTKHFYWFFSN
-YIICISTKHFYWFFSNYLK
-GIPKDMTYRRLISMMGFKM
-LWEIQQVVDADSKIVQL
-VTQQLIRAAEIRASA
-SALWEIQQVVDADSKIV
-MACLVGLMWLSYFIAS
-PKDMTYRRLISMM
-STKHFYWFFSNYLKRRVVFNGVS
-TKHFYWFFSNYLKRRVVFNG
-KRNVIPTITQMNLKYAISA
-NPETNILLNVPLHGTI
-LWEIQQVVDADSKI
-WEIQQVVDADSKIVQL
-RAAEIRASANLAA
-YWFFSNYLKRRVVF
-WLLLTILTSLLVLVQS
-IAYIICISTKHFYWFFSNYLK
-ITQMNLKYAISAKNR
-MACLVGLMWLSYFI
-ALWEIQQVVDADS
-TVLSFCAFAVDAAKAYKDYL
-WSFNPETNILLNVPLH
-TQQLIRAAEIRASANL
-EIQQVVDADSKIV
-VLSFCAFAVDAAKAYKDYLASGGQ
-THHWLLLTILTSLLVLV
-FSNYLKRRVVFNG
-KYAISAKNRARTV
-ALWEIQQVVDADSKI
-LSFCAFAVDAAKAYKDY
-SAVKRTIKGTHHWLLLT
-WEIQQVVDADSKIVQ
-HHWLLLTILTSLLVLVQSTQW
-NLHSSRLSFKELLVY
-VGLMWLSYFIASFRLF
-RLFARTRSMWSFNPE
-GLMWLSYFIASFRLFART
-LMWLSYFIASFRLFA
-PKDMTYRRLISMMGF
-QVVDADSKIVQLSE
-KNRARTVAGVSIC
-FAVDAAKAYKDYLASG
-YIICISTKHFYWFFSN
-DSKIVQLSEISMD
-NVIPTITQMNLKYAISAKN
-CAFAVDAAKAYKDYLASG
-RLSFKELLVYAADPAMH
-GIAIAMACLVGLMWLSYFIASF
-FNPETNILLNVPL
-AIAMACLVGLMWLSYF
-YAISAKNRARTVA
-YASALWEIQQVVDADS
-PETNILLNVPLHGT
-TQMNLKYAISAKN
-SAKNRARTVAGVSICST
-FCAFAVDAAKAYKDYLASGGQP
-GIAIAMACLVGLM
-QVVDADSKIVQLSEISM
-FIASFRLFARTRSMWSF
-SYFIASFRLFARTRSMWSF
-VLSFCAFAVDAAKAYKD
-EIQQVVDADSKIVQLSEI
-DSKIVQLSEISMDNS
-KHFYWFFSNYLKRRVVFN
-KDMTYRRLISMMGFKM
-VNLHSSRLSFKELLVYAA
-FYWFFSNYLKRRVVFNGVSFST
-TKHFYWFFSNYLKRRVVF
-SRLSFKELLVYAADPA
-TKHFYWFFSNYLKRRVV
-NRARTVAGVSICS
-CAFAVDAAKAYKDYLASGGQP
-PTITQMNLKYAISAKNRARTVAG
-IAIAMACLVGLMWLSYFI
-YASALWEIQQVVDA
-HSSRLSFKELLVYAADPAM
-QQVVDADSKIVQLSEISM
-KHFYWFFSNYLKRR
-ASALWEIQQVVDA
-ADSKIVQLSEISMD
-GLMWLSYFIASFRLFARTRSMW
-TQMNLKYAISAKNRA
-AMACLVGLMWLSYFIASFRL
-TVLSFCAFAVDAAK
-ITQMNLKYAISAK
-NLKYAISAKNRARTVAGV
-AISAKNRARTVAGVSICST
-ACLVGLMWLSYFI
-HHWLLLTILTSLLVLV
-LSYFIASFRLFARTRSMWS
-FFSNYLKRRVVFNGVSFS
-HWLLLTILTSLLVLVQST
-TKHFYWFFSNYLKRRVVFNGVS
-TIAYIICISTKHFYWFFSN
-AFAVDAAKAYKDYL
-LVGLMWLSYFIASFRLFARTR
-YLKRRVVFNGVSFST
-AKAYKDYLASGGQP
-KRNVIPTITQMNLKYAISAKNRA
-IAIAMACLVGLMWLSYFIASFRL
-AVDAAKAYKDYLAS
-ARTRSMWSFNPETNILLNVP
-NLKYAISAKNRARTVAGVS
-TKHFYWFFSNYLKRRVVFNGVSF
-VDADSKIVQLSEIS
-HWLLLTILTSLLVLV
-LSYFIASFRLFARTRSMWSF
-SRLSFKELLVYAADPAMH
-HWLLLTILTSLLVLVQSTQW
-AISAKNRARTVAGVSI
-ITQMNLKYAISAKNRARTVA
-IPTITQMNLKYAIS
-GLMWLSYFIASFRLFARTRSMWS
-IAMACLVGLMWLSYF
-AISAKNRARTVAGVSICS
-TIAYIICISTKHFYWFFSNYLKR
-WFFSNYLKRRVVFNGVS
-RLSFKELLVYAADP
-ICISTKHFYWFFSNYLK
-KDMTYRRLISMMGFKMN
-LLLTILTSLLVLVQSTQ
-SRLSFKELLVYAADPAM
-KRNVIPTITQMNLKY
-ASFRLFARTRSMWSFNPETNI
-CLVGLMWLSYFIASF
-QVVDADSKIVQLS
-MTYRRLISMMGFKMNY
-LTILTSLLVLVQSTQW
-VHNQDVNLHSSRLSFKE
-RLFARTRSMWSFNP
-VVDADSKIVQLSEI
-QDVNLHSSRLSFKELLV
-IPTITQMNLKYAISA
-VDAAKAYKDYLASG
-KHFYWFFSNYLKRRVVFNGVS
-TIAYIICISTKHFYWFFSNY
-IAYIICISTKHFYWFFSNYLKR
-KDMTYRRLISMMGFKMNY
-SFCAFAVDAAKAYKDYLASGGQ
-LLLTILTSLLVLVQSTQW
-VQLSEISMDNSPNL
-AISAKNRARTVAGVSIC
-AAKAYKDYLASGG
-MWSFNPETNILLNVPLH
-ARTRSMWSFNPETN
-LWEIQQVVDADSKIV
-VGLMWLSYFIASFRLFARTR
-FRLFARTRSMWSFNPET
-FCAFAVDAAKAYKDYLA
-VIPTITQMNLKYAI
-LFARTRSMWSFNPETNILLNV
-GLMWLSYFIASFR
-SALWEIQQVVDADSKI
-VLSFCAFAVDAAK
-ASALWEIQQVVDADSKIVQ
-LLTILTSLLVLVQ
-VNLHSSRLSFKELL
-RARTVAGVSICST
-MWSFNPETNILLN
-TRSMWSFNPETNILLNV
-ISTKHFYWFFSNYLKRRVVF
-SALWEIQQVVDADSKIVQ
-IAIAMACLVGLMWLSYFIASF
-FYWFFSNYLKRRVVFNGV
-RNVIPTITQMNLKYAISAKN
-TQQLIRAAEIRAS
-THHWLLLTILTSLL
-LVGLMWLSYFIASFRL
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-ASALWEIQQVVDADSK
-LMWLSYFIASFRLFARTRS
-LHSSRLSFKELLVYAA
-FFSNYLKRRVVFNGV
-DAAKAYKDYLASGG
-IQQVVDADSKIVQLS
-VLSFCAFAVDAAKAYK
-SFRLFARTRSMWSFNPETNI
-TRSMWSFNPETNILL
-YFIASFRLFARTRSMWSFNP
-QWLTNIFGTVYEK
-IQQVVDADSKIVQLSEI
-TNILLNVPLHGTI
-DSKIVQLSEISMDN
-AMACLVGLMWLSYFIASF
-YAISAKNRARTVAGVSI
-FYWFFSNYLKRRVVFNGVSFS
-STKHFYWFFSNYLKRRVVFNGVSF
-WFFSNYLKRRVVFNG
-RLFARTRSMWSFNPETNILLN
-ASALWEIQQVVDADSKIV
-YWFFSNYLKRRVV
-LHSSRLSFKELLVYA
-HNQDVNLHSSRLSF
-ACLVGLMWLSYFIASFRLF
-SMWSFNPETNILLN
-QQVVDADSKIVQLSEIS
-TKHFYWFFSNYLKRR
-VHNQDVNLHSSRLS
-QDVNLHSSRLSFKELL
-IRAAEIRASANLAA
-LSYFIASFRLFARTRSMW
-GIAIAMACLVGLMW
-LSFKELLVYAADP
-LFARTRSMWSFNPET
-WLTNIFGTVYEKL
-SFNPETNILLNVPLHG
-VIPTITQMNLKYAIS
-NVIPTITQMNLKYAISAKNRA
-FYWFFSNYLKRRVVFN
-VHNQDVNLHSSRLSFK
-SFNPETNILLNVPL
-FIASFRLFARTRSMWSFNP
-ICISTKHFYWFFSNYL
-SYFIASFRLFARTRS
-AIAMACLVGLMWLSYFIASF
-QMNLKYAISAKNRARTVAGV
-PTITQMNLKYAISA
-TVLSFCAFAVDAAKAYKD
-NRARTVAGVSICST
-AVDAAKAYKDYLASG
-ISTKHFYWFFSNYLK
-RNVIPTITQMNLKYAISAKNR
-EIQQVVDADSKIVQ
-CISTKHFYWFFSNYLKRRV
-LHSSRLSFKELLVYAADPA
-TIAYIICISTKHF
-FRLFARTRSMWSFNPETNIL
-FIASFRLFARTRSMWSFNPETN
-IASFRLFARTRSMWSFNPE
-IASFRLFARTRSMWSFN
-TKRNVIPTITQMNLK
-WLSYFIASFRLFAR
-RLSFKELLVYAAD
-VLSFCAFAVDAAKAYKDYL
-AYIICISTKHFYWFFSNY
-TYRRLISMMGFKMN
-LMWLSYFIASFRLFARTRSMWS
-TITQMNLKYAISAKN
-AFAVDAAKAYKDYLASGGQP
-SYFIASFRLFARTR
-FFSNYLKRRVVFNGVSFST
-LSFCAFAVDAAKAYKDYLASGG
-AMACLVGLMWLSYFI
-IICISTKHFYWFFSNYLKR
-RLSFKELLVYAADPAM
-SKIVQLSEISMDNS
-NYLKRRVVFNGVS
-ITQMNLKYAISAKNRAR
-VDAAKAYKDYLASGG
-HSSRLSFKELLVY
-ILTSLLVLVQSTQ
-WLSYFIASFRLFARTRSMW
-CLVGLMWLSYFIASFRLFART
-TIAYIICISTKHFY
-RSMWSFNPETNIL
-KRNVIPTITQMNLKYAISAK
-MTYRRLISMMGFKM
-ISTKHFYWFFSNYLKRR
-GIAIAMACLVGLMWLSYFIA
-YFIASFRLFARTRSMW
-AISAKNRARTVAGV
-SNYLKRRVVFNGVS
-MWSFNPETNILLNV
-DADSKIVQLSEIS
-MNLKYAISAKNRARTVAGV
-PKDMTYRRLISMMG
-VIPTITQMNLKYAISAKNRART
-EIQQVVDADSKIVQLS
-IICISTKHFYWFFSNYLK
-MWSFNPETNILLNVPLHGTI
-VDAAKAYKDYLASGGQP
-HFYWFFSNYLKRRVVF
-SSRLSFKELLVYAADPAM
-NVIPTITQMNLKYAISA
-IAMACLVGLMWLSYFI
-IASFRLFARTRSM
-STKHFYWFFSNYLKRRVVF
-TYRRLISMMGFKMNY
-SSRLSFKELLVYAA
-STVLSFCAFAVDAA
-SFKELLVYAADPAMH
-TIAYIICISTKHFYWFFS
-RTRSMWSFNPETNILLNVPLH
-YASALWEIQQVVDADSK
-LWEIQQVVDADSK
-YWFFSNYLKRRVVFNGVSFS
-MACLVGLMWLSYFIASFR
-SRLSFKELLVYAADP
-AFAVDAAKAYKDYLASG
-RRLISMMGFKMNY
-GTHHWLLLTILTSL
-VIPTITQMNLKYAISAKNR
-VIPTITQMNLKYAISAKNRAR
-STKHFYWFFSNYLKRRVV
-GLMWLSYFIASFRLF
-TIKGTHHWLLLTI
-MWSFNPETNILLNVPLHG
-FIASFRLFARTRSMWSFNPE
-IAMACLVGLMWLSYFIASFRL
-ISAKNRARTVAGVSICST
-MTYRRLISMMGFKMN
-LVGLMWLSYFIASFRLFARTRS
-QWLTNIFGTVYEKL
-RNVIPTITQMNLKYAISAKNRA
-GIPKDMTYRRLIS
-WLSYFIASFRLFARTRSMWSFNP
-ISAKNRARTVAGVS
-VIPTITQMNLKYAISAKN
-VGLMWLSYFIASF
-RSMWSFNPETNILLNVPLH
-HHWLLLTILTSLLVLVQS
-WLLLTILTSLLVLV
-FRLFARTRSMWSFNPETNILL
-WSFNPETNILLNVPLHGTI
-NQDVNLHSSRLSFKEL
-SNYLKRRVVFNGVSF
-CISTKHFYWFFSNYLK
-GIPKDMTYRRLISMMGFK
-AYIICISTKHFYWF
-WFFSNYLKRRVVFNGV
-FYWFFSNYLKRRVVFNGVSF
-THHWLLLTILTSLLVLVQST
-MWLSYFIASFRLFA
-IICISTKHFYWFFSN
-WLLLTILTSLLVLVQ
-FCAFAVDAAKAYKDYLASG
-FAVDAAKAYKDYL
-ARTRSMWSFNPETNI
-FAVDAAKAYKDYLA
-FAVDAAKAYKDYLASGG
-VNLHSSRLSFKELLVYA
-MACLVGLMWLSYFIASFRL
-LSYFIASFRLFART
-SSRLSFKELLVYAADPAMH
-NVIPTITQMNLKYAISAKNRAR
-IPKDMTYRRLISMMGF
-HNQDVNLHSSRLS
-LSFKELLVYAADPAM
-FSNYLKRRVVFNGVS
-FIASFRLFARTRSMWSFNPET
-KHFYWFFSNYLKR
-WFFSNYLKRRVVFN
-KRNVIPTITQMNLKYAISAKNR
-LVGLMWLSYFIASFRLFART
-FRLFARTRSMWSF
-LIRAAEIRASANLAA
-STKHFYWFFSNYLKRRV
-TVLSFCAFAVDAA
-VVDADSKIVQLSEIS
-DVNLHSSRLSFKELLVYAA
-FNPETNILLNVPLHG
-SMWSFNPETNILL
-TQMNLKYAISAKNRAR
-LLTILTSLLVLVQSTQ
-IAYIICISTKHFY
-VHNQDVNLHSSRL
-NLHSSRLSFKELLVYA
-DADSKIVQLSEISMDNS
-YWFFSNYLKRRVVFN
-AFAVDAAKAYKDYLA
-SFNPETNILLNVP
-MWLSYFIASFRLFARTRSMWS
-IAMACLVGLMWLSYFIASFR
-SAVKRTIKGTHHWL
-KRTIKGTHHWLLL
-IPTITQMNLKYAISAKNRARTVAG
-FARTRSMWSFNPETNILLNVP
-FYWFFSNYLKRRVVF
-NVIPTITQMNLKYAISAK
-KYAISAKNRARTVAGVSI
-MWSFNPETNILLNVPL
-IQQVVDADSKIVQL
-CISTKHFYWFFSNYLKR
-LLLTILTSLLVLVQS
-HHWLLLTILTSLLV
-YASALWEIQQVVD
-YFIASFRLFARTRSMWS
-ALWEIQQVVDADSKIVQ
-IAIAMACLVGLMWLSYFIAS
-LSYFIASFRLFARTRSMWSFNPE
-FYWFFSNYLKRRVVFNG
-AVDAAKAYKDYLASGG
-HFYWFFSNYLKRRVVFNGVSFST
-IASFRLFARTRSMWSFNPET
-PTITQMNLKYAISAKNRAR
-CISTKHFYWFFSNYL
-LLLTILTSLLVLV
-TRSMWSFNPETNILLNVPLH
-PETNILLNVPLHG
-LFARTRSMWSFNPETNIL
-WLSYFIASFRLFARTRSMWSF
-WEIQQVVDADSKI
-ARTRSMWSFNPETNILLN
-KDMTYRRLISMMG
-FSNYLKRRVVFNGVSF
-SRLSFKELLVYAA
-AYIICISTKHFYWFFSNYLK
-CLVGLMWLSYFIASFRLFARTRSM
-SALWEIQQVVDAD
-LSFCAFAVDAAKAYKD
-IAYIICISTKHFYW
-RTRSMWSFNPETN
-AYIICISTKHFYWFF
-IPKDMTYRRLISMMG
-AYIICISTKHFYWFFSNYLKR
-GTHHWLLLTILTS
-ASFRLFARTRSMWSF
-DVNLHSSRLSFKELLVYA
-FRLFARTRSMWSFN
-QQLIRAAEIRASANLA
-PTITQMNLKYAISAK
-SFCAFAVDAAKAYKDYLA
-FARTRSMWSFNPETN
-LSFCAFAVDAAKAYKDYLASGGQP
-VNLHSSRLSFKELLV
-WLSYFIASFRLFARTRSMWSFNPE
-LSFCAFAVDAAKAYK
-STKHFYWFFSNYLKRRVVFN
-SAVKRTIKGTHHWLLL
-TIAYIICISTKHFYWF
-LLTILTSLLVLVQS
-NVIPTITQMNLKYA
-YAISAKNRARTVAGV
-HWLLLTILTSLLVLVQS
-VKRTIKGTHHWLLL
-TITQMNLKYAISAKNRA
-LSFCAFAVDAAKA
-LSFCAFAVDAAKAYKDYLASG
-HFYWFFSNYLKRRVV
-ITIAYIICISTKH
-LWEIQQVVDADSKIVQLSEI
-IQQVVDADSKIVQLSE
-LMWLSYFIASFRL
-LMWLSYFIASFRLF
-SNYLKRRVVFNGVSFST
-CLVGLMWLSYFIASFR
-ICISTKHFYWFFSNY
-FSNYLKRRVVFNGVSFST
-WLSYFIASFRLFARTRSM
-SFRLFARTRSMWSFNPETN
-YWFFSNYLKRRVVFNGVSF
-SYFIASFRLFARTRSMWSFNP
-RTRSMWSFNPETNILLNVPL
-PTITQMNLKYAISAKNRARTVA
-QLSEISMDNSPNL
-RTIKGTHHWLLLTI
-AVKRTIKGTHHWLLLT
-FKELLVYAADPAMH
-FSNYLKRRVVFNGVSFS
-LFARTRSMWSFNPE
-STVLSFCAFAVDA
-FARTRSMWSFNPETNILLN
-YWFFSNYLKRRVVFNG
-LMWLSYFIASFRLFARTR
-CAFAVDAAKAYKDYLAS
-QQLIRAAEIRASANL
-IPKDMTYRRLISMMGFKMN
-TVLSFCAFAVDAAKAY
-KHFYWFFSNYLKRRVVFNGVSFST
-STVLSFCAFAVDAAK
-SAKNRARTVAGVS
-VTQQLIRAAEIRA
-IAIAMACLVGLMW
-YFIASFRLFARTRSMWSFNPET
-GIAIAMACLVGLMWLSYF
-IPTITQMNLKYAISAKN
-RNVIPTITQMNLK
-WEIQQVVDADSKIV
-HWLLLTILTSLLVLVQSTQ
-NLHSSRLSFKELL
-DAAKAYKDYLASGGQ
-SYFIASFRLFARTRSMW
-LHSSRLSFKELLVY
-HHWLLLTILTSLL
-ISAKNRARTVAGVSIC
-TILTSLLVLVQSTQW
-CLVGLMWLSYFIASFRLFAR
-GIAIAMACLVGLMWLSY
-VGLMWLSYFIASFRLFAR
-ISAKNRARTVAGV
-DMTYRRLISMMGFK
-NQDVNLHSSRLSF
-AVKRTIKGTHHWL
-HFYWFFSNYLKRRV
-SFCAFAVDAAKAYKDYLAS
-RSMWSFNPETNILLN
-LLTILTSLLVLVQSTQW
-LSYFIASFRLFARTR
-LFARTRSMWSFNP
-IAYIICISTKHFYWF
-FARTRSMWSFNPETNILL
-KYAISAKNRARTVAGV
-WFFSNYLKRRVVF
-PTITQMNLKYAIS
-AIAMACLVGLMWLSYFI
-AIAMACLVGLMWLS
-TITQMNLKYAISAKNR
-ICISTKHFYWFFSNYLKR
-QMNLKYAISAKNRARTVAG
-RLFARTRSMWSFNPETNILL
-VGLMWLSYFIASFRL
-IPTITQMNLKYAISAK
-GLMWLSYFIASFRLFAR
-CISTKHFYWFFSNYLKRR
-HFYWFFSNYLKRR
-RNVIPTITQMNLKYAI
-WLLLTILTSLLVLVQSTQ
-LFARTRSMWSFNPETN
-IAIAMACLVGLMWLSYF
-HFYWFFSNYLKRRVVFNGV
-FIASFRLFARTRSMWS
-IASFRLFARTRSMW
-QLIRAAEIRASANLAA
-HFYWFFSNYLKRRVVFNG
-ADSKIVQLSEISMDNS
-NPETNILLNVPLHGT
-VDADSKIVQLSEISM
-SYFIASFRLFART
-DADSKIVQLSEISMD
-AIAMACLVGLMWL
-ITQMNLKYAISAKNRARTV
-FAVDAAKAYKDYLASGGQP
-KDMTYRRLISMMGFK
-CLVGLMWLSYFIASFRLFARTRS
-SFNPETNILLNVPLH
-LWEIQQVVDADSKIVQLS
-AMACLVGLMWLSYFIASFR
-DVNLHSSRLSFKELLVYAAD
-FRLFARTRSMWSFNPETNILLN
-ASALWEIQQVVDAD
-LMWLSYFIASFRLFART
-LIRAAEIRASANLA
-MACLVGLMWLSYF
-ACLVGLMWLSYFIASFRL
-KHFYWFFSNYLKRRVVF
-NVIPTITQMNLKYAIS
-FARTRSMWSFNPET
-KRNVIPTITQMNLK
-WLSYFIASFRLFARTRSMWS
-FCAFAVDAAKAYKDY
-TRSMWSFNPETNILLN
-ASALWEIQQVVDADSKI
-ACLVGLMWLSYFIASFRLFA
-SFCAFAVDAAKAYKDYLASG
-LTILTSLLVLVQST
-ADSKIVQLSEISM
-YIICISTKHFYWFFSNYLKRR
-LTNIFGTVYEKLK
-FAVDAAKAYKDYLASGGQ
-LSFCAFAVDAAKAYKDYLASGGQ
-KHFYWFFSNYLKRRV
-VNLHSSRLSFKELLVYAAD
-TKHFYWFFSNYLKRRVVFNGVSFS
-FNPETNILLNVPLHGTI
-FCAFAVDAAKAYKD
-ALWEIQQVVDADSKIVQLS
-LKYAISAKNRARTVA
-VIPTITQMNLKYAISAKNRARTV
-RSMWSFNPETNILLNVP
-AIAMACLVGLMWLSYFIA
-SAVKRTIKGTHHWLL
-LSYFIASFRLFARTRSMWSFN
-AYIICISTKHFYWFFSNYL
-IPTITQMNLKYAISAKNRART
-IICISTKHFYWFFSNY
-LHSSRLSFKELLVYAADP
-FYWFFSNYLKRRVV
-HNQDVNLHSSRLSFK
-MNLKYAISAKNRAR
-LSFCAFAVDAAKAYKDYLA
-VIPTITQMNLKYAISAKNRA
-VDAAKAYKDYLASGGQ
-HWLLLTILTSLLV
-LIRAAEIRASANL
-YFIASFRLFARTRSM
-THHWLLLTILTSLLVLVQSTQ
-VIPTITQMNLKYAISAK
-GIPKDMTYRRLISM
-LFARTRSMWSFNPETNILLN
-HHWLLLTILTSLLVLVQSTQ
-QQVVDADSKIVQL
-CLVGLMWLSYFIAS
-TKHFYWFFSNYLKR
-SFKELLVYAADPAM
-KRNVIPTITQMNL
-GTHHWLLLTILTSLL
-IICISTKHFYWFFSNYL
-VLSFCAFAVDAAKAYKDY
-HFYWFFSNYLKRRVVFNGVSF
-IVQLSEISMDNSP
-HHWLLLTILTSLLVLVQST
-GIPKDMTYRRLISMMGFKMNY
-AMACLVGLMWLSYFIAS
-ARTRSMWSFNPETNILLNV
-KRNVIPTITQMNLKYA
-TILTSLLVLVQST
-IPKDMTYRRLISMMGFKMNY
-WLSYFIASFRLFART
-IASFRLFARTRSMWSF
-YIICISTKHFYWFFSNYL
-SALWEIQQVVDADSKIVQL
-TKHFYWFFSNYLKRRVVFN
-ADSKIVQLSEISMDN
-VNLHSSRLSFKEL
-TILTSLLVLVQSTQ
-ASFRLFARTRSMWSFNP
-LWEIQQVVDADSKIVQLSE
-TKHFYWFFSNYLKRRV
-GLMWLSYFIASFRLFARTRSMWSF
-SYFIASFRLFARTRSM
-LMWLSYFIASFRLFAR
-SSRLSFKELLVYAADPA
-VLSFCAFAVDAAKAYKDYLA
-NLHSSRLSFKELLVYAAD
-FARTRSMWSFNPE
-VIPTITQMNLKYAISA
-FRLFARTRSMWSFNPETNI
-FIASFRLFARTRS
-MNLKYAISAKNRARTV
-TKRNVIPTITQMN
-IPTITQMNLKYAI
-HWLLLTILTSLLVL
-QDVNLHSSRLSFKE
-KRNVIPTITQMNLKYAISAKN
-VDAAKAYKDYLAS
-IICISTKHFYWFFSNYLKRR
-IPTITQMNLKYAISAKNRARTV
-TITQMNLKYAISAKNRART
-NYLKRRVVFNGVSFS
-VGLMWLSYFIASFRLFART
-TQQLIRAAEIRASA
-ASFRLFARTRSMWSFNPET
-CAFAVDAAKAYKDYLA
-QQLIRAAEIRASA
-THHWLLLTILTSLLVL
-ASFRLFARTRSMW
-FRLFARTRSMWSFNPETN
-PTITQMNLKYAISAKNRARTV
-ARTRSMWSFNPETNIL
-NLHSSRLSFKELLV
-NQDVNLHSSRLSFKELL
-IAIAMACLVGLMWLSYFIASFR
-FFSNYLKRRVVFNGVS
-SFCAFAVDAAKAYKDYL
-VGLMWLSYFIASFR
-SMWSFNPETNILLNVPL
-MNLKYAISAKNRARTVA
-THHWLLLTILTSLLV
-QWLTNIFGTVYEKLK
-CLVGLMWLSYFIASFRL
-HSSRLSFKELLVYAADPAMH
-MNLKYAISAKNRARTVAG
-CISTKHFYWFFSN
-TITQMNLKYAISAKNRARTVAG
-ISTKHFYWFFSNYLKR
-WLLLTILTSLLVL
-IAIAMACLVGLMWLS
-NVIPTITQMNLKY
-TIAYIICISTKHFYWFFSNYL
-GIPKDMTYRRLISMM
-PKDMTYRRLISMMGFKM
-CAFAVDAAKAYKDY
-TKHFYWFFSNYLKRRVVFNGV
-LKYAISAKNRARTVAGVS
-FRLFARTRSMWSFNPETNILLNV
-EIQQVVDADSKIVQLSE
-FNPETNILLNVPLHGT
-MWLSYFIASFRLFARTRSM
-YFIASFRLFARTRSMWSFN
-MTYRRLISMMGFK
-VGLMWLSYFIASFRLFARTRSM
-VGLMWLSYFIASFRLFARTRS
-TQMNLKYAISAKNRARTVAG
-LFARTRSMWSFNPETNILLNVPL
-YIICISTKHFYWFFSNY
-AAKAYKDYLASGGQ
-SFCAFAVDAAKAYK
-SAVKRTIKGTHHW
-ASFRLFARTRSMWSFNPE
-CISTKHFYWFFSNY
-MWLSYFIASFRLFARTRSMWSF
-AYIICISTKHFYWFFSN
-RNVIPTITQMNLKYAISAK
-ISTKHFYWFFSNY
-LLLTILTSLLVLVQST
-FIASFRLFARTRSM
-WSFNPETNILLNVPLHGT
-SNYLKRRVVFNGV
-YIICISTKHFYWFFS
-EIQQVVDADSKIVQL
-AKNRARTVAGVSIC
-TRSMWSFNPETNILLNVP
-WLLLTILTSLLVLVQSTQW
-MACLVGLMWLSYFIASF
-ICISTKHFYWFFS
-QMNLKYAISAKNRARTVA
-FARTRSMWSFNPETNILLNVPLH
-KRNVIPTITQMNLKYAI
-MWSFNPETNILLNVP
-QVVDADSKIVQLSEI
-GIPKDMTYRRLISMMG
-TKHFYWFFSNYLKRRVVFNGVSFST
-MNLKYAISAKNRART
-WLLLTILTSLLVLVQST
-VLSFCAFAVDAAKAYKDYLASGGQP
-ARTRSMWSFNPETNILL
-WSFNPETNILLNVPLHG
-DVNLHSSRLSFKELLV
-TIAYIICISTKHFYW
-ARTRSMWSFNPETNILLNVPL
-QMNLKYAISAKNRAR
-RTRSMWSFNPETNILL
-ICISTKHFYWFFSN
-NYLKRRVVFNGVSFST
-PTITQMNLKYAISAKN
-TVLSFCAFAVDAAKAYKDY
-IPKDMTYRRLISMMGFK
-DVNLHSSRLSFKE
-HSSRLSFKELLVYAAD
-ITQMNLKYAISAKN
-MWLSYFIASFRLF
-ITQMNLKYAISAKNRART
-LFARTRSMWSFNPETNILLNVP
-LVGLMWLSYFIASFRLFARTRSM
-YAISAKNRARTVAGVS
-TIAYIICISTKHFYWFFSNYLK
-LSFCAFAVDAAKAYKDYLAS
-LVGLMWLSYFIASFR
-GLMWLSYFIASFRLFARTRS
-HSSRLSFKELLVYAA
-HFYWFFSNYLKRRVVFNGVS
-LMWLSYFIASFRLFARTRSMWSFN
-FIASFRLFARTRSMW
-ACLVGLMWLSYFIA
-KELLVYAADPAMH
-FFSNYLKRRVVFNG
-SMWSFNPETNILLNVP
-WSFNPETNILLNVP
-ACLVGLMWLSYFIAS
-GLMWLSYFIASFRLFARTRSM
-KHFYWFFSNYLKRRVVFNG
-HWLLLTILTSLLVLVQ
-RNVIPTITQMNLKYAISA
-AYIICISTKHFYWFFSNYLKRR
-STKHFYWFFSNYLKRRVVFNGV
-IAYIICISTKHFYWFF
-AISAKNRARTVAGVS
-PTITQMNLKYAISAKNR
-SKIVQLSEISMDN
-YASALWEIQQVVDAD
-WEIQQVVDADSKIVQLSE
-SSRLSFKELLVYAAD
-STKHFYWFFSNYLKR
-SSRLSFKELLVYAADP
-DADSKIVQLSEISM
-KHFYWFFSNYLKRRVV
-NLKYAISAKNRARTV
-WEIQQVVDADSKIVQLSEI
-SYFIASFRLFARTRSMWS
-NQDVNLHSSRLSFKE
-TIAYIICISTKHFYWFF
-ISTKHFYWFFSNYLKRRVV
-PKDMTYRRLISMMGFKMNY
-LLLTILTSLLVLVQ
-DVNLHSSRLSFKELL
-FFSNYLKRRVVFNGVSF
-LSFKELLVYAADPA
-TITQMNLKYAISAKNRARTVA
-AIAMACLVGLMWLSYFIASFR
-IPTITQMNLKYAISAKNR
-QMNLKYAISAKNRART
-NVIPTITQMNLKYAI
-ASALWEIQQVVDADS
-IASFRLFARTRSMWS
-RLFARTRSMWSFNPETNIL
-QMNLKYAISAKNR
-SFRLFARTRSMWSFN
-TITQMNLKYAISA
-VKRTIKGTHHWLLLT
-HSSRLSFKELLVYAADP
-FYWFFSNYLKRRV
-SFCAFAVDAAKAY
-NLHSSRLSFKELLVYAA
-AIAMACLVGLMWLSYFIAS
-IAMACLVGLMWLS
-MWLSYFIASFRLFARTRS
-PTITQMNLKYAISAKNRART
-ETNILLNVPLHGTI
-FCAFAVDAAKAYKDYL
-IAIAMACLVGLMWLSYFIA
-RSMWSFNPETNILL
-LVGLMWLSYFIAS
-QMNLKYAISAKNRA
-YRRLISMMGFKMN
-LSYFIASFRLFAR
-KHFYWFFSNYLKRRVVFNGV
-CLVGLMWLSYFIA
-TQQLIRAAEIRASAN
-SALWEIQQVVDADS
-IPKDMTYRRLISMMGFKM
-STKHFYWFFSNYLKRR
-THHWLLLTILTSLLVLVQ
-YIICISTKHFYWF
-TQMNLKYAISAKNRARTVAGV
-RNVIPTITQMNLKY
-LSYFIASFRLFARTRSM
-RLFARTRSMWSFNPETN
-AAKAYKDYLASGGQP
-THHWLLLTILTSL
-TITQMNLKYAISAK
-LKYAISAKNRARTVAG
-IAMACLVGLMWLSYFIAS
-YWFFSNYLKRRVVFNGVS
-GIAIAMACLVGLMWLSYFIASFR
-RLFARTRSMWSFNPETNILLNV
-RLFARTRSMWSFNPETNI
-KRNVIPTITQMNLKYAIS
-LKRRVVFNGVSFST
-HSSRLSFKELLVYA
-MWLSYFIASFRLFARTRSMWSFN
-KRRVVFNGVSFST
-CAFAVDAAKAYKDYLASGG
-VQLSEISMDNSPN
-LKYAISAKNRARTV
-VLSFCAFAVDAAKA
-QVVDADSKIVQLSEIS
-DMTYRRLISMMGFKMN
-DAAKAYKDYLASGGQP
-NQDVNLHSSRLSFKELLV
-SAKNRARTVAGVSIC
-NLKYAISAKNRARTVA
-HHWLLLTILTSLLVLVQ
-IASFRLFARTRSMWSFNP
-ARTRSMWSFNPET
-LMWLSYFIASFRLFARTRSMW
-IICISTKHFYWFFS
-QQLIRAAEIRASAN
-ARTRSMWSFNPETNILLNVPLH
-LTILTSLLVLVQS
-MWLSYFIASFRLFARTR
-GIAIAMACLVGLMWL
-SYFIASFRLFARTRSMWSFN
-VNLHSSRLSFKELLVY
-RTIKGTHHWLLLT
-VTQQLIRAAEIRASANL
-LVGLMWLSYFIASFRLF
-FCAFAVDAAKAYKDYLASGG
-AKNRARTVAGVSICS
-PKDMTYRRLISMMGFK
-TRSMWSFNPETNIL
-STKHFYWFFSNYL
-RTRSMWSFNPETNILLNVP
-CAFAVDAAKAYKDYLASGGQ
-RNVIPTITQMNLKYAISAKNRAR
-ALWEIQQVVDADSKIV
-GLMWLSYFIASFRL
-AVKRTIKGTHHWLL
-AISAKNRARTVAG
-TKHFYWFFSNYLK
-LTSLLVLVQSTQW
-LSFKELLVYAADPAMH
-CLVGLMWLSYFIASFRLFA
-IPKDMTYRRLISMM
-KHFYWFFSNYLKRRVVFNGVSFS
-NLKYAISAKNRAR
-GIAIAMACLVGLMWLSYFIAS
-MACLVGLMWLSYFIA
-AVKRTIKGTHHWLLL
-LHSSRLSFKELLV
-VLSFCAFAVDAAKAYKDYLASGG
-RSMWSFNPETNILLNV
-SFCAFAVDAAKAYKDY
-ITQMNLKYAISAKNRARTVAGV
-FARTRSMWSFNPETNIL
\ No newline at end of file
diff --git a/SupplementaryResults/SingleAlleleComputations/37435_vaccine_sequence_hog.txt b/SupplementaryResults/SingleAlleleComputations/37435_vaccine_sequence_hog.txt
deleted file mode 100644
index da148e8c0cc191032c177860a6cd2532ba127ce0..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SingleAlleleComputations/37435_vaccine_sequence_hog.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-> HOGVAX optimized combined peptide vaccine sequence with overlaps
-ECSFGGVTQQLIRAAEIRASANLAASAVKRTIKGTHHWLLLTILTSLLVLVQSTQWLTNIFGTVYEKLKDGVVHNQDVNLHSSRLSFKELLVYAADPAMHWHNMFTPLVPFWITIAYIICISTKHFYWFFSNYLKRRVVFNGVSFSTKRNVIPTITQMNLKYAISAKNRARTVAGVSICSTVLSFCAFAVDAAKAYKDYLASGGQPASIVAGGIAIAMACLVGLMWLSYFIASFRLFARTRSMWSFNPETNILLNVPLHGTIAGIPKDMTYRRLISMMGFKMNYQGAYASALWEIQQVVDADSKIVQLSEISMDNSPNLG
-> Concat optimized combined peptide vaccine sequence concatenated
-SAKNRARTVAGVSICSTGLMWLSYFIASFRLFARTRSMWSFNSAVKRTIKGTHHWLLLTGIPKDMTYRRLISMMGFSFRLFARTRSMWSFNPETNIKYAISAKNRARTVAGVSSTKHFYWFFSNYLKRRVVFNGVSFSFKELLVYAADPAMHQMNLKYAISAKNRARTVAGVTVLSFCAFAVDAAKAYKDYLLHSSRLSFKELLVYAADQQVVDADSKIVQLSEISTIAYIICISTKHFYWFFSNYLKMACLVGLMWLSYFIASFRLFVQLSEISMDNSPNIPTITQMNLKYAISAKNRARTVAGVGLMWLSYFIASFRLFARTRSMAISAKNRARTVAGVSICSVTQQLIRAAEIRASSSRLSFKELLVYAADPAMHDVNLHSSRLSFKELLVYAASTVLSFCAFAVDAYWFFSNYLKRRVVFNGVSFSTDSKIVQLSEISMDNSSMWSFNPETNILLNVPLHGQLSEISMDNSPNLLIRAAEIRASANLAAETNILLNVPLHGTITIAYIICISTKHFYWFFSNLLTILTSLLVLVQSTQWRTIKGTHHWLLLTMTYRRLISMMGFKMNYVLSFCAFAVDAAKAYKDYLASGGQGIAIAMACLVGLMWLSYSTKHFYWFFSNYLKRRVVFASFRLFARTRSMWSFNPETNYASALWEIQQVVDAAVDAAKAYKDYLASGGQPIRAAEIRASANLAADADSKIVQLSEISMDRTIKGTHHWLLLTILMWLSYFIASFRLFARTRSMWSFNTIAYIICISTKHFYWFFSNYKRNVIPTITQMNLKYHHWLLLTILTSLLVLVQSTQWQWLTNIFGTVYEKLHHWLLLTILTSLLVLVQSTQSAVKRTIKGTHHWEIQQVVDADSKIVQLSEITKHFYWFFSNYLKRRVVFNGVSFSTTIAYIICISTKHFYKAYKDYLASGGQPTRSMWSFNPETNILLNVPLHVHNQDVNLHSSRLYRRLISMMGFKMNYSAVKRTIKGTHHWLNQDVNLHSSRLSFKELLVFARTRSMWSFNPETNILLNVPLHIAYIICISTKHFYWFFSNYLKRFARTRSMWSFNPETNILLNVPLTVLSFCAFAVDAAKAYKCLVGLMWLSYFIASFRLFARWLTNIFGTVYEKLKKYAISAKNRARTVAGVSIMACLVGLMWLSYFIASFRLMNLKYAISAKNRARTVAGVCLVGLMWLSYFIASFRLFARTRSMWLSYFIASFRLFARTRSMWSFNPFFSNYLKRRVVFNGVSFSTAIAMACLVGLMWLSYFIASFRLKRTIKGTHHWLLLTTKRNVIPTITQMNLKKGTHHWLLLTILTSMWSFNPETNILLNVPLHGTIHSSRLSFKELLVYAADPANYLKRRVVFNGVSFSTSFNPETNILLNVPLHGTIAYIICISTKHFYWFFSNYLKRRCLVGLMWLSYFIASFRLFARTRSMGIPKDMTYRRLISMMGFKMAMACLVGLMWLSYFIASFRLICISTKHFYWFFSNYLKRRVGTHHWLLLTILTSVIPTITQMNLKYAISAKNRARTVAYASALWEIQQVVDTIKGTHHWLLLTIKRNVIPTITQMNLKYAISAKACLVGLMWLSYFIASFRLFAKRNVIPTITQMNLKYAISVLSFCAFAVDAAKAYKDYLATHHWLLLTILTSLLVLVQSTQGTHHWLLLTILTSLNLKYAISAKNRARTVAGVSTIAYIICISTKHFYWFFSNYLKRAAKAYKDYLASGGQPGIPKDMTYRRLISMMGTVLSFCAFAVDAAKAYKDSTVLSFCAFAVDAAGIPKDMTYRRLISMMQVVDADSKIVQLSEISMSAVKRTIKGTHHWLLLMWLSYFIASFRLFARTRSMWSFNGIPKDMTYRRLISMMGFKMNTHHWLLLTILTSLLVLYIICISTKHFYWFFSNYLKRRKIVQLSEISMDNSVIPTITQMNLKYAISAKNRARYAISAKNRARTVAGVSICAISAKNRARTVAGVSICTYRRLISMMGFKMNYCISTKHFYWFFSNYLKRRVQQLIRAAEIRASANLTHHWLLLTILTSLLVLVQSHFYWFFSNYLKRRVVFNGVSFSTWFFSNYLKRRVVFNGVSFSTSAVKRTIKGTHHWLLQQLIRAAEIRASANLATIAYIICISTKHFVIPTITQMNLKYAISAKNRARTVLWEIQQVVDADSKIVQLSEMWSFNPETNILLNVPLHGTDVNLHSSRLSFKELLVYAADLHSSRLSFKELLVYAADPAHNQDVNLHSSRLSFKELSYFIASFRLFARTRSMWSFNPETLFARTRSMWSFNPETNILLNVYASALWEIQQVVDADSKILWEIQQVVDADSKIVQLSEIKRNVIPTITQMNLKYAISAKNRQWLTNIFGTVYEKAISAKNRARTVAGVSICSTTHHWLLLTILTSLLVLVQDVNLHSSRLSFKELLVKGTHHWLLLTILTLLLTILTSLLVLVQSTQWFRLFARTRSMWSFNPETNILTHHWLLLTILTSLLRNVIPTITQMNLKYAISAKNRARTILTSLLVLVQSTQWIPKDMTYRRLISMMGFKMNYIICISTKHFYWFFSNYLKRRIAMACLVGLMWLSYFIASFRLIPTITQMNLKYAISAKNRARTVAGIAIAMACLVGLMWLSYFIVHNQDVNLHSSRLSFGIAIAMACLVGLMWLHSSRLSFKELLVYAADPFYWFFSNYLKRRVVFNGVSFSTTVLSFCAFAVDAAKAYKDYLAKRNVIPTITQMNLKYAISAKNRATIAYIICISTKHFYWFFTQMNLKYAISAKNRARTVAGVVTQQLIRAAEIRASAKYAISAKNRARTVAGVSICVQLSEISMDNSPNLFCAFAVDAAKAYKDYLASGGQPALWEIQQVVDADSKIVQLGIAIAMACLVGLMWLSGIAIAMACLVGLMWLSYFIASGIAIAMACLVGLMWLVHNQDVNLHSSRLSFKEPTITQMNLKYAISAKNRARTVAGCISTKHFYWFFSNYLKRRVVDVNLHSSRLSFKELLVYITQMNLKYAISAKNRARTVAGVLSFKELLVYAADPAMHTHHWLLLTILTSLLVPKDMTYRRLISMMGFKMNYAFAVDAAKAYKDYLASGGQPTHHWLLLTILTSLLVLVCAFAVDAAKAYKDYLASGGQPAVKRTIKGTHHWLLLTTIAYIICISTKHFYWFRLFARTRSMWSFNPETNILLQQVVDADSKIVQLSEISMIQQVVDADSKIVQLSEIILTSLLVLVQSTQWDMTYRRLISMMGFKMNYLTILTSLLVLVQSTQWFNPETNILLNVPLHGTITKRNVIPTITQMNVDAAKAYKDYLASGGQPTVLSFCAFAVDAAKITQMNLKYAISAKNRARTVAGKRNVIPTITQMNLKYADADSKIVQLSEISMDNQLIRAAEIRASANLAAAKAYKDYLASGGQPSNYLKRRVVFNGVSFSTVLSFCAFAVDAAKAYKDYLASGGVVDADSKIVQLSEISMVDADSKIVQLSEISMVLSFCAFAVDAAKAYKDYLASGNVIPTITQMNLKYAISAKNRARGIAIAMACLVGLMWLSYFIATITQMNLKYAISAKNRARTVAGVHNQDVNLHSSRLSADSKIVQLSEISMDNSFRLFARTRSMWSFNPETNILLNVCLVGLMWLSYFIASFRLFARTLFARTRSMWSFNPETNILLNVPLSTKHFYWFFSNYLKRRVVFNGVTQQLIRAAEIRAKRNVIPTITQMNLKYAIKRNVIPTITQMNLKYAISARLSFKELLVYAADPAMHSTVLSFCAFAVDAAKGIPKDMTYRRLISMMGFKMNYRAAEIRASANLAASFCAFAVDAAKAYKDYLASGGQPASFRLFARTRSMWSFNPETNIVNLHSSRLSFKELLVYAADTNILLNVPLHGTIGIAIAMACLVGLMWLSYFIASFRLFARTRSMWSFNPETNILLNVCLVGLMWLSYFIASFRLFARTRDVNLHSSRLSFKELLVYAKRNVIPTITQMNLKYAISAKNQQVVDADSKIVQLSEIKHFYWFFSNYLKRRVVFNGVSFSTTHHWLLLTILTSLLVLVQSTSTKHFYWFFSNYLKRRVVFNGVSAYIICISTKHFYWFFSNYLKRAKNRARTVAGVSICSTYASALWEIQQVVDADHNQDVNLHSSRLSFKEGLMWLSYFIASFRLFARTRSMWSLWEIQQVVDADSKIVQLSTVLSFCAFAVDAAKAYKDYHWLLLTILTSLLVLVQSTQWTVLSFCAFAVDAAKAYVTQQLIRAAEIRASANNQDVNLHSSRLSFKELKRNVIPTITQMNLKKRRVVFNGVSFSTSMWSFNPETNILLNVPLHLVGLMWLSYFIASFRLFARTRSMSKIVQLSEISMDNSTIAYIICISTKHFYWFFSNYLGIPKDMTYRRLISMMGFKTQQLIRAAEIRASANLYFIASFRLFARTRSMWSFNPETASALWEIQQVVDADSKIVKIVQLSEISMDNSPWLLLTILTSLLVLVQSTQWWLSYFIASFRLFARTRSMWSFNPERRLISMMGFKMNYTIAYIICISTKHFYWFTKHFYWFFSNYLKRRVVFNGVSFSFRLFARTRSMWSFNPETNILLNVKRTIKGTHHWLLLTFSNYLKRRVVFNGVSFSTFIASFRLFARTRSMWSFNPETNSYFIASFRLFARTRSMWSFNPEIVQLSEISMDNSPQWLTNIFGTVYEKLKNQDVNLHSSRLSFKELLACLVGLMWLSYFIASFRLFGLMWLSYFIASFRLFARTRSMWSRLSFKELLVYAADPAMHALWEIQQVVDADSKIVQLSIAIAMACLVGLMWLSYFIASFRLITIAYIICISTKHARTRSMWSFNPETNILLNVPLHLFARTRSMWSFNPETNILLNVPWSFNPETNILLNVPLHGTITKRNVIPTITQMNLDADSKIVQLSEISMDNSIASFRLFARTRSMWSFNPETNWLSYFIASFRLFARTRSMWSFNPRNVIPTITQMNLKYAISAKNRAGTHHWLLLTILTSLLGIPKDMTYRRLISISTKHFYWFFSNYLKRRVVFNLHSSRLSFKELLVYAADNLKYAISAKNRARTVAGVLSFCAFAVDAAKAYKDYLASGGQPISTKHFYWFFSNYLKRRVVGLMWLSYFIASFRLFARTRSMFKELLVYAADPAMHTKHFYWFFSNYLKRRVVFNGVSFGIAIAMACLVGLMWLSYFIASFRLTIAYIICISTKHFYWFFSLKRRVVFNGVSFSTDAAKAYKDYLASGGQPGIAIAMACLVGLMWLSYFNRARTVAGVSICSTHSSRLSFKELLVYAADPAMHGIAIAMACLVGLMDADSKIVQLSEISMHSSRLSFKELLVYAADPAMMWSFNPETNILLNVPLHGYASALWEIQQVVDADSKFAVDAAKAYKDYLASGGQPIICISTKHFYWFFSNYLKRRVSALWEIQQVVDADSKIVQLSTKHFYWFFSNYLKRRVVFNGVISAKNRARTVAGVSICSTRTRSMWSFNPETNILLNVPLHVIPTITQMNLKYAISAKNRARTTVLSFCAFAVDAAKALTNIFGTVYEKLKKELLVYAADPAMHVLSFCAFAVDAAKAYKDYLASGGQPLTSLLVLVQSTQWVLSFCAFAVDAAKAYKDYLASASALWEIQQVVDADSKIVQLVTQQLIRAAEIRASANLRSMWSFNPETNILLNVPLHVHNQDVNLHSSRLSFKFIASFRLFARTRSMWSFNPETKDMTYRRLISMMGFKMNYCLVGLMWLSYFIASFRLFAWEIQQVVDADSKIVQLSEIGIAIAMACLVGLMWLSYFIASFRQDVNLHSSRLSFKELLVYLKRRVVFNGVSFSTLKYAISAKNRARTVAGVSNPETNILLNVPLHGTIGLMWLSYFIASFRLFARTRSMWSFYASALWEIQQVVDADSASALWEIQQVVDADSKIQQLIRAAEIRASANLAASTKHFYWFFSNYLKRRVVFNFRLFARTRSMWSFNPETNIPETNILLNVPLHGTIASALWEIQQVVDADSKIVQLSYFIASFRLFARTRSMWSFNPERARTVAGVSICSTKNRARTVAGVSICSTGIPKDMTYRRLISM
\ No newline at end of file
diff --git a/SupplementaryResults/SingleAlleleComputations/4512_chosen_peptides_hog_inc_substrings.txt b/SupplementaryResults/SingleAlleleComputations/4512_chosen_peptides_hog_inc_substrings.txt
deleted file mode 100644
index ba90caee8ea31649ea7722b28e4875462a908b1b..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SingleAlleleComputations/4512_chosen_peptides_hog_inc_substrings.txt
+++ /dev/null
@@ -1,122 +0,0 @@
-ARAGEAAN
-FLAYILFT
-WFLAYILF
-QFAPSASA
-EILGTVSW
-IPFAMQMAYR
-FIETISLAG
-PYFFTLLLQ
-GEAANFCAL
-SYELQTPF
-NYMPYFFT
-TQMNLKYAI
-AQFAPSASAF
-EIDFLELA
-LVAEWFLAYI
-LAGSYKDW
-SYELQTPFE
-SQMEIDFLEL
-WFLAYILFT
-HSYFTSDY
-MPYFFTLLLQ
-LAYILFTR
-PYFFTLLL
-NYMPYFFTLL
-QFAPSASAF
-VAEWFLAYIL
-FHQKLLKS
-FLAYILFTR
-QMEIDFLEL
-MEIDFLELA
-PFAMQMAYR
-ETISLAGSYK
-KSYELQTP
-YMPYFFTLL
-YASAVVLL
-AGEAANFCA
-TISLAGSY
-QFHQKLLK
-MARKTLNSL
-SLAGSYKDW
-ARAGEAANF
-YRARAGEAA
-FEYVSQPF
-ASAVVLLI
-FAPSASAF
-RARAGEAAN
-TISLAGSYK
-LHSYFTSDY
-AGEAANFC
-RQFHQKLLK
-YMPYFFTL
-QEILGTVSW
-QFHQKLLKS
-SDYDYYRY
-VAEWFLAY
-PLKLRGTA
-AQFAPSASA
-EWFLAYILFT
-MPYFFTLL
-LHSYFTSD
-KMADQAMTQ
-SYFTSDYY
-KSYELQTPF
-RQFHQKLLKS
-FAMQMAYR
-ARKTLNSL
-QMEIDFLE
-GEAANFCA
-ISLAGSYK
-RKTLNSLE
-AEWFLAYIL
-MARKTLNS
-RAGEAANFC
-FIETISLA
-LVAEWFLA
-ARAGEAANFC
-MPYFFTLLL
-SLAGSYKD
-IPFAMQMAY
-MADQAMTQ
-LVAEWFLAY
-RQFHQKLL
-IPFAMQMA
-HSYFTSDYY
-KSYELQTPFE
-PFAMQMAY
-AQFAPSAS
-MADQAMTQM
-ISDYDYYRY
-FPLKLRGTA
-ARKTLNSLE
-TQMNLKYA
-SQMEIDFL
-ETISLAGSY
-IETISLAG
-ISDYDYYR
-QEILGTVS
-SQMEIDFLE
-YASAVVLLI
-VAEWFLAYI
-AEWFLAYI
-MEIDFLEL
-FPLKLRGT
-YRARAGEAAN
-EWFLAYIL
-AEWFLAYILF
-ETISLAGS
-ISLAGSYKD
-YELQTPFE
-ISLAGSYKDW
-YFFTLLLQ
-KMADQAMT
-MNLKYAIS
-FIETISLAGS
-EWFLAYILF
-RAGEAANF
-ADQAMTQM
-NYMPYFFTL
-EAANFCAL
-YRARAGEA
-QMNLKYAI
-IETISLAGS
\ No newline at end of file
diff --git a/SupplementaryResults/SingleAlleleComputations/4512_vaccine_sequence_hog.txt b/SupplementaryResults/SingleAlleleComputations/4512_vaccine_sequence_hog.txt
deleted file mode 100644
index 2e1921621c0c6f6139a4958049f4d3e406d9e628..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SingleAlleleComputations/4512_vaccine_sequence_hog.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-> HOGVAX optimized combined peptide vaccine sequence with overlaps
-LHSYFTSDYYMARKTLNSLEKMADQAMTQMNLKYAISDYDYYRYASAVVLLIPFAMQMAYRARAGEAANFCALVAEWFLAYILFTRQFHQKLLKSYELQTPFEYVSQPFPLKLRGTASQMEIDFLELAQFAPSASAFIETISLAGSYKDWNYMPYFFTLLLQEILGTVSW
-> Concat optimized combined peptide vaccine sequence concatenated
-MNLKYAISMADQAMTQMLVAEWFLAVAEWFLAYIQMEIDFLELMPYFFTLLLQPLKLRGTAAEWFLAYILFLAYILFTRSQMEIDFLARAGEAANFAQFAPSASARAGEAANFCFAMQMAYRKSYELQTPFARAGEAANFCSYFTSDYYRARAGEAANETISLAGSTISLAGSYKFLAYILFTMARKTLNSLSQMEIDFLELARAGEAANQFAPSASAFYELQTPFEETISLAGSYMEIDFLELALAGSYKDWLHSYFTSDVAEWFLAYILRKTLNSLESLAGSYKDWAQFAPSASAFKMADQAMTQEIDFLELAPYFFTLLLQFIETISLAGSISLAGSYKDWGEAANFCALEWFLAYILFTETISLAGSYKMEIDFLELFIETISLALAYILFTRLVAEWFLAYINYMPYFFTLIPFAMQMAYLVAEWFLAYKSYELQTPFHQKLLKSISDYDYYRYKMADQAMTYRARAGEAAFPLKLRGTAQEILGTVSYASAVVLLEAANFCALGEAANFCAPFAMQMAYRAGEAANFCAEWFLAYILFMPYFFTLLLFPLKLRGTFIETISLAGEWFLAYILFKSYELQTPFEYASAVVLLIIPFAMQMAYRISLAGSYKRQFHQKLLKSTQMNLKYAIWFLAYILFTFEYVSQPFISLAGSYKDNYMPYFFTLLRQFHQKLLYRARAGEAANASAVVLLISQMEIDFLEQFHQKLLKSQMNLKYAIYFFTLLLQIETISLAGSYRARAGEAARKTLNSLEADQAMTQMQEILGTVSWLHSYFTSDYYMPYFFTLLISDYDYYRNYMPYFFTAGEAANFCAEILGTVSWRQFHQKLLKSYELQTPFESDYDYYRYAQFAPSASFAPSASAFHSYFTSDYYMARKTLNSIPFAMQMAARKTLNSLMADQAMTQHSYFTSDYTQMNLKYAMPYFFTLL
\ No newline at end of file
diff --git a/SupplementaryResults/SingleAlleleComputations/hit_hist_mhc1_HOGVAX.csv b/SupplementaryResults/SingleAlleleComputations/hit_hist_mhc1_HOGVAX.csv
deleted file mode 100644
index 4a9fc47f812c97e495f3aec4dfe303997acb779e..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SingleAlleleComputations/hit_hist_mhc1_HOGVAX.csv
+++ /dev/null
@@ -1,37 +0,0 @@
-,count,prob
-0,0,0.0
-1,1,6.269462716217183e-05
-2,2,0.013057629579716753
-3,3,0.01094845169305867
-4,4,0.023480130854754908
-5,5,0.05629529194077506
-6,6,0.05470943327621871
-7,7,0.0884937927496057
-8,8,0.08835531042996772
-9,9,0.09765851649522583
-10,10,0.09944995061767473
-11,11,0.05360637004419369
-12,12,0.07523610000582681
-13,13,0.03920115724537604
-14,14,0.061659494768365755
-15,15,0.06842381747048956
-16,16,0.02603304930725867
-17,17,0.05930497878300048
-18,18,0.019108516827054695
-19,19,0.04599088588507463
-20,20,0.007096921806478777
-21,21,0.0036771317727097605
-22,22,0.005679322441360689
-23,23,0.0014287518274726284
-24,24,0.0008372375151831832
-25,25,9.82915917146112e-05
-26,26,1.3652446350569562e-05
-27,27,9.16387353280676e-05
-28,28,7.206910061552974e-07
-29,29,4.851329884824014e-07
-30,30,2.734385935082626e-07
-31,31,0.0
-32,32,0.0
-33,33,0.0
-34,34,0.0
-35,35,0.0
diff --git a/SupplementaryResults/SingleAlleleComputations/hit_hist_mhc1_OptiVax.csv b/SupplementaryResults/SingleAlleleComputations/hit_hist_mhc1_OptiVax.csv
deleted file mode 100644
index ad7cbe7086c882e58445808eced4b810e50a8724..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SingleAlleleComputations/hit_hist_mhc1_OptiVax.csv
+++ /dev/null
@@ -1,23 +0,0 @@
-,count,prob
-0,0,0.0
-1,1,6.416124789068595e-05
-2,2,0.013362564025906591
-3,3,0.017452135857063542
-4,4,0.04122818442788108
-5,5,0.09377708384829216
-6,6,0.11292430889645957
-7,7,0.14318529061023885
-8,8,0.1457310938919302
-9,9,0.14106764085949985
-10,10,0.11676295909723841
-11,11,0.06789344308964511
-12,12,0.05629421961585755
-13,13,0.03838144650199308
-14,14,0.008613846795624467
-15,15,0.00269464169652035
-16,16,0.00045011977162413017
-17,17,0.00010552776776621763
-18,18,1.1331998562802441e-05
-19,19,0.0
-20,20,0.0
-21,21,0.0
diff --git a/SupplementaryResults/SingleAlleleComputations/hit_hist_mhc2_HOGVAX.csv b/SupplementaryResults/SingleAlleleComputations/hit_hist_mhc2_HOGVAX.csv
deleted file mode 100644
index d23c3697c9c4dc674a09f0cc13794d7243f8e6c7..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SingleAlleleComputations/hit_hist_mhc2_HOGVAX.csv
+++ /dev/null
@@ -1,630 +0,0 @@
-,count,prob
-0,0,0.0007243433400952906
-1,1,0.0001135917863423405
-2,2,0.009488251076711189
-3,3,0.0005541284914525836
-4,4,0.005085832605845956
-5,5,0.00033537209717378466
-6,6,0.0002854844868397968
-7,7,0.0012074435036220697
-8,8,0.0010781705358010242
-9,9,0.0003155558954779009
-10,10,0.0008506448810096089
-11,11,0.00014018937431918644
-12,12,0.0002354884455660465
-13,13,0.00010862969748830998
-14,14,0.0005105682047151309
-15,15,0.0012534996017802614
-16,16,0.0045081225217771465
-17,17,0.007902131917990898
-18,18,0.003157285551661632
-19,19,0.008504085271183183
-20,20,0.001984065134195753
-21,21,0.0007508799352935602
-22,22,0.00502702217566278
-23,23,0.003945691071247989
-24,24,0.0008733360484515099
-25,25,0.019182186544079643
-26,26,0.0008802986592470978
-27,27,0.008520635413025673
-28,28,0.000671962621064875
-29,29,0.001340943541270994
-30,30,0.0012562025850689374
-31,31,0.0051496512021831226
-32,32,0.002432398593016844
-33,33,0.0045026528536975105
-34,34,0.0016468322218299593
-35,35,0.0012643889190415798
-36,36,0.011544526276292476
-37,37,0.004858463304106108
-38,38,0.014686724889861023
-39,39,0.005596655714560191
-40,40,0.005839066504029155
-41,41,0.0036768286869104994
-42,42,0.008055560138913275
-43,43,0.0021867215077185443
-44,44,0.002613555738516801
-45,45,0.0036602095021105807
-46,46,0.004292093874261087
-47,47,0.002224435274816202
-48,48,0.0053922532901391285
-49,49,0.0022227085549461237
-50,50,0.011970809700274478
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+++ /dev/null
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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diff --git a/SupplementaryResults/SpikeHOGVAX/5064_chosen_peptides_hogvax_inc_substrings.txt b/SupplementaryResults/SpikeHOGVAX/5064_chosen_peptides_hogvax_inc_substrings.txt
deleted file mode 100644
index 49376de98e6803d4ec7675e996a0abf2fa3d29f8..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SpikeHOGVAX/5064_chosen_peptides_hogvax_inc_substrings.txt
+++ /dev/null
@@ -1,1180 +0,0 @@
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-APGQTGKIADYNYKLPDD
-FVFKNIDGYFKIYSKH
-NNLDSKVGGNYNYLY
-PDDFTGCVIAWNSN
-GGNYNYLYRLFRKSNLKPFE
-YKLPDDFTGCVIAWNSNNLDS
-MAYRFNGIGVTQN
-NSNNLDSKVGGNYNYLYRLFR
-CTFEYVSQPFLMDLE
-GDEVRQIAPGQTGKIA
-GGNYNYLYRLFRKS
-VKQLSSNFGAISSVLNDILS
-SSVLNDILSRLDKVEAE
-GCVIAWNSNNLDSKVGGNYNYLYR
-HTPINLVRDLPQG
-NYNYLYRLFRKSNLKPFERDI
-AMQMAYRFNGIGV
-AISSVLNDILSRLDK
-YNYKLPDDFTGCVIAWNSNNLDSKV
-PGQTGKIADYNYKLP
-APGQTGKIADYNYKLPDDFTGCV
-CVIAWNSNNLDSKVGGNYNYLYRL
-NYNYLYRLFRKSNLK
-KVGGNYNYLYRLFRKSNLKPFE
-NYKLPDDFTGCVIAWNS
-KVGGNYNYLYRLFRKSN
-CTFEYVSQPFLMDL
-NYLYRLFRKSNLKPFE
-AWNSNNLDSKVGGNYNYLYRLFRK
-TFEYVSQPFLMDL
-FEYVSQPFLMDLE
-LSSNFGAISSVLNDI
-QTGKIADYNYKLPDDFTG
-GAISSVLNDILSRL
-NSNNLDSKVGGNYNYLYRL
-VGGNYNYLYRLFRKSNLKP
-QIPFAMQMAYRFNG
-EVRQIAPGQTGKIADY
-HWFVTQRNFYEPQ
-YNYKLPDDFTGCVIA
-YNYKLPDDFTGCV
-IDGYFKIYSKHTPINLVRD
-NYKLPDDFTGCVIAWNSNNLD
-DFTGCVIAWNSNNLDSK
-RNFYEPQIITTDN
-REFVFKNIDGYFKIYSK
-AISSVLNDILSRLDKVE
-QIAPGQTGKIADYNYK
-DYNYKLPDDFTGCVIA
-VFKNIDGYFKIYSKHTPINLV
-IRGDEVRQIAPGQTGKIADYNYK
-WFVTQRNFYEPQI
-VRQIAPGQTGKIADYNYK
-GAISSVLNDILSR
-GAISSVLNDILSRLDK
-AWNSNNLDSKVGGNYNY
-FKNIDGYFKIYSKHTPINLVRD
-LSSNFGAISSVLNDILSRLDKV
-LYRLFRKSNLKPF
-GDEVRQIAPGQTGKIADYNYKL
-AWNSNNLDSKVGGNYNYLYR
-AGAALQIPFAMQMAY
-QQLIRAAEIRASANL
-GAISSVLNDILSRLDKV
-FKNIDGYFKIYSKHTPINLVR
-LFRKSNLKPFERD
-GKIADYNYKLPDDFTGCVI
-HWFVTQRNFYEPQIIT
-VIAWNSNNLDSKVGGNYNYLYRLFR
-ALQIPFAMQMAYRFN
-NNLDSKVGGNYNYLYRLFRKSNLK
-KNIDGYFKIYSKHTP
-REFVFKNIDGYFKIYSKHTPINLV
-FVIRGDEVRQIAPGQ
-VRQIAPGQTGKIADYNYKLPD
-ALQIPFAMQMAYR
-NLDSKVGGNYNYLYRLFRKSNLKP
-GAGAALQIPFAMQMAYR
-VGGNYNYLYRLFRKSNLKPF
-HWFVTQRNFYEPQI
-SNNLDSKVGGNYNYL
-SNFGAISSVLNDILSRLDKVEAE
-SKHTPINLVRDLPQG
-RQIAPGQTGKIADYNYKLPDDF
-PINLVRDLPQGFS
-NFGAISSVLNDILSRL
-LQIPFAMQMAYRF
-WNSNNLDSKVGGN
-GKIADYNYKLPDDFTGCVIAWN
-NNLDSKVGGNYNYLYRLFRK
-AWNSNNLDSKVGG
-SVLNDILSRLDKV
-DDFTGCVIAWNSNNLDS
-KHTPINLVRDLPQ
-VIRGDEVRQIAPGQTGKIA
-YRFNGIGVTQNVL
-DFTGCVIAWNSNNLDSKVGGNY
-QTGKIADYNYKLPDDFTGCVIAWN
-FGAISSVLNDILSRLDKVE
-GNYNYLYRLFRKSNLK
-QQLIRAAEIRASANLA
-DGYFKIYSKHTPINLVRD
-WNSNNLDSKVGGNYNYLYRLFRKS
-AISSVLNDILSRLD
-VLNDILSRLDKVEA
-KVGGNYNYLYRLFRKSNL
-QLIRAAEIRASAN
-VIAWNSNNLDSKVGGN
-FTGCVIAWNSNNLDSKVGGNYNYL
-TFGAGAALQIPFAMQMAY
-FKIYSKHTPINLVR
-DGYFKIYSKHTPINLV
-PDDFTGCVIAWNSNNLDSK
-EVRQIAPGQTGKIAD
-FVFKNIDGYFKIYSKHTPINLVR
-SSVLNDILSRLDKVE
-DYNYKLPDDFTGCVIAWNSNNL
-LSSNFGAISSVLNDILSRLDKVE
-NFGAISSVLNDILS
-WNSNNLDSKVGGNYNYLYRLFRK
-IAPGQTGKIADYNYKLPDDFT
-IADYNYKLPDDFT
-QMAYRFNGIGVTQ
-LPDDFTGCVIAWNSNNLDSKVGGNY
-SFVIRGDEVRQIAPGQTGKIA
-GAGAALQIPFAMQ
-TGCVIAWNSNNLDSKVGGNYN
-QIAPGQTGKIADYNYKLPDDFTGCV
-DFTGCVIAWNSNN
-VIAWNSNNLDSKVGGNYNYLY
-WNSNNLDSKVGGNYNYLY
-TGCVIAWNSNNLDSKVGGN
-LPDDFTGCVIAWNSNNLDS
-PGQTGKIADYNYKL
-YSKHTPINLVRDL
-CTFEYVSQPFLMD
-DEVRQIAPGQTGKIADYNYKLPD
-NNLDSKVGGNYNYLYRLFRKSNL
-THWFVTQRNFYEPQIITTDN
-NYKLPDDFTGCVIAWNSNNLDS
-KLPDDFTGCVIAWNSNNLDSK
-LSSNFGAISSVLNDIL
-ADYNYKLPDDFTGCVI
-FVTQRNFYEPQIITTD
-SNFGAISSVLNDILSRL
-GNYNYLYRLFRKSNL
-AWNSNNLDSKVGGNYNYLYRL
-RGDEVRQIAPGQT
-SNNLDSKVGGNYNYLYRL
-FTGCVIAWNSNNLDSKVGGNYN
-VTQQLIRAAEIRASANLA
-GAALQIPFAMQMAYRFNGIG
-AALQIPFAMQMAYRFNG
-YVTQQLIRAAEIRASANLAA
-RGDEVRQIAPGQTGKIADY
-SVLNDILSRLDKVE
-VRQIAPGQTGKIADYN
-NIDGYFKIYSKHTPIN
-TQQLIRAAEIRASANLA
-YNYKLPDDFTGCVIAWNSNN
-FAMQMAYRFNGIG
-TFGAGAALQIPFAMQMAYR
-GNYNYLYRLFRKSNLKPFER
-VRQIAPGQTGKIADYNYKLP
-QIAPGQTGKIADY
-NFGAISSVLNDILSRLDKVEAEV
-LSSNFGAISSVLNDILSRL
-IRAAEIRASANLA
-FGAISSVLNDILS
-GAISSVLNDILSRLDKVEAE
-QQLIRAAEIRASANLAA
-ISSVLNDILSRLDKV
-FTGCVIAWNSNNLDSKVGGNYNY
-IAPGQTGKIADYNY
-RGDEVRQIAPGQTGKIADYN
-DFTGCVIAWNSNNLDSKVGGNYNY
-NYKLPDDFTGCVI
-GAALQIPFAMQMA
-FKNIDGYFKIYSKH
-AWNSNNLDSKVGGNYNYLYRLFRKS
-IPFAMQMAYRFNGIGVTQN
-PGQTGKIADYNYKLPDDFTGC
-FTGCVIAWNSNNLDSKVGGNY
-YLYRLFRKSNLKPFER
-QIAPGQTGKIADYNYKLPDDFTG
-ADYNYKLPDDFTGCVIA
-IAWNSNNLDSKVG
-PGQTGKIADYNYKLPDDF
-NDILSRLDKVEAE
-FVTQRNFYEPQIITTDNTF
-IADYNYKLPDDFTGCV
-IRGDEVRQIAPGQTGKIADYNYKL
-IAWNSNNLDSKVGGNYNYLYRLFR
-IRGDEVRQIAPGQTGKIAD
-LPDDFTGCVIAWNSNNLDSKVGGN
-FGAGAALQIPFAMQMAY
-DEVRQIAPGQTGK
-VIAWNSNNLDSKVGGNYN
-AMQMAYRFNGIGVT
-GAALQIPFAMQMAYRFNGI
-QMAYRFNGIGVTQN
-QLSSNFGAISSVLNDILSRLDKVE
-TFGAGAALQIPFAMQMA
-LQIPFAMQMAYRFNGI
-KLPDDFTGCVIAW
-QQLIRAAEIRASA
-SSNFGAISSVLNDILSRLD
-DSKVGGNYNYLYRLFRKSNL
-GDEVRQIAPGQTGKIADYNYKLPDD
-APGQTGKIADYNYKLPDDFTG
-LIRAAEIRASANLAA
-VGGNYNYLYRLFRKSNLK
-QLIRAAEIRASANL
-KNIDGYFKIYSKHTPIN
-YFKIYSKHTPINLV
-YNYLYRLFRKSNLKPFE
-SSVLNDILSRLDKV
-NLDSKVGGNYNYL
-VFKNIDGYFKIYSKHTPI
-VKQLSSNFGAISS
-VIAWNSNNLDSKVGGNYNYL
-VGGNYNYLYRLFRK
-NNLDSKVGGNYNYL
-LQIPFAMQMAYRFNGIG
-FVFKNIDGYFKIYSKHTPIN
-GYFKIYSKHTPINL
-LPDDFTGCVIAWNSNNLDSKVGG
-FVFKNIDGYFKIYSKHTPINL
-APGQTGKIADYNYKL
-AMQMAYRFNGIGVTQ
-SNNLDSKVGGNYNY
-FVFKNIDGYFKIYSKHTPINLV
-GQTGKIADYNYKLPD
-VTQQLIRAAEIRASANLAA
-LNDILSRLDKVEA
-GCVIAWNSNNLDSKVGGN
-SKHTPINLVRDLPQGFS
-FKNIDGYFKIYSKHTPINLV
-FTGCVIAWNSNNLDSKVGGN
-VFKNIDGYFKIYSK
-SNFGAISSVLNDILS
-SFVIRGDEVRQIAPGQTGKIADYN
-AISSVLNDILSRLDKVEAE
-ADYNYKLPDDFTG
-SVLNDILSRLDKVEAEV
-TGCVIAWNSNNLDS
-GKIADYNYKLPDDFTGCVIAWNS
-FVFKNIDGYFKIYSKHTP
-CVIAWNSNNLDSKVGGNY
-FTGCVIAWNSNNLDSKVGGNYNYLY
-RQIAPGQTGKIADYN
-GQTGKIADYNYKL
-LPDDFTGCVIAWNSNN
-GYFKIYSKHTPIN
-DSKVGGNYNYLYRLFRKSNLKP
-DFTGCVIAWNSNNLDSKVGG
-FVIRGDEVRQIAP
-SFVIRGDEVRQIAPGQTGKIADY
-DYNYKLPDDFTGCVIAWNSNN
-FVTQRNFYEPQIITTDNT
-LSSNFGAISSVLN
-KNIDGYFKIYSKHTPINL
-GAGAALQIPFAMQMAY
-GKIADYNYKLPDDFTGCVIAWNSNN
-KIADYNYKLPDDFTGCVIAWNSNNL
-KLPDDFTGCVIAWNSNNLDSKVG
-DYNYKLPDDFTGCVIAWNSNNLDS
-YYVGYLQPRTFLL
-GDEVRQIAPGQTGKIADYNY
-AGAALQIPFAMQMA
-ALQIPFAMQMAYRF
-NLDSKVGGNYNYLYRLFRKSN
-NSNNLDSKVGGNYN
-AALQIPFAMQMAYRFN
-GAALQIPFAMQMAYRFNGIGV
-YRLFRKSNLKPFERDIS
-KIADYNYKLPDDFTGC
-NYLYRLFRKSNLKP
-QIPFAMQMAYRFNGIGVTQN
-EVRQIAPGQTGKIADYN
-IADYNYKLPDDFTGCVIA
-WNSNNLDSKVGGNYNYL
-TFGAGAALQIPFAMQMAYRFN
-SSNFGAISSVLNDILSRLDKVEA
-CVIAWNSNNLDSKVGGNYNYLY
-AMQMAYRFNGIGVTQN
-LQTYVTQQLIRAAE
-SNFGAISSVLNDILSRLDKV
-QIAPGQTGKIADYNYKLPDD
-VKQLSSNFGAISSVLNDIL
-LDSKVGGNYNYLYRLFRK
-CVIAWNSNNLDSKVGG
-KIADYNYKLPDDFTG
-KQLSSNFGAISSVLNDILSRLDKV
-GAALQIPFAMQMAYRFNGIGVT
-GDEVRQIAPGQTGKIADYNYKLPD
-TFGAGAALQIPFAMQ
-SFVIRGDEVRQIAP
-SKHTPINLVRDLPQ
-RLFRKSNLKPFERDIS
-CVIAWNSNNLDSK
-YNYLYRLFRKSNLK
-YLYRLFRKSNLKPFERDI
-KQLSSNFGAISSVLNDI
-DEVRQIAPGQTGKIADYNYKL
-TGCVIAWNSNNLDSKVGGNYNYLYR
-GAALQIPFAMQMAYRFN
-TQQLIRAAEIRAS
-GAGAALQIPFAMQMAYRFNGIG
-SNFGAISSVLNDILSRLDKVEAEV
-RQIAPGQTGKIADYNYK
-GAISSVLNDILSRLDKVE
-SNFGAISSVLNDILSRLD
-RNFYEPQIITTDNT
-GKIADYNYKLPDDFT
-DYNYKLPDDFTGCVIAWNS
-NYLYRLFRKSNLKPF
-AWNSNNLDSKVGGN
-EVRQIAPGQTGKIA
-CVIAWNSNNLDSKVGGNYNYL
-FYEPQIITTDNTF
-YVTQQLIRAAEIRASANL
-GYFKIYSKHTPINLVR
-GCVIAWNSNNLDSKVGGNYN
-NSNNLDSKVGGNYNY
-SNNLDSKVGGNYNYLYR
-QIPFAMQMAYRFNGI
-VTQRNFYEPQIITTD
-VGGNYNYLYRLFRKSNLKPFE
-SKVGGNYNYLYRLFRKSNLKPFE
-AGAALQIPFAMQMAYRFNG
-VKQLSSNFGAISSVLNDILSR
-GQTGKIADYNYKLPDDFT
-KIADYNYKLPDDFTGCVIAWNSNN
-FKNIDGYFKIYSKHTPINL
-FGAISSVLNDILSRLDKVEA
-FRKSNLKPFERDIS
-AGAALQIPFAMQMAYRFNGIGVT
-QIPFAMQMAYRFNGIGVTQNVL
-YKLPDDFTGCVIAWN
-DEVRQIAPGQTGKIADYN
-KQLSSNFGAISSVLNDILSRL
-SFVIRGDEVRQIA
-PDDFTGCVIAWNSNNLDSKVGG
-FKNIDGYFKIYSKHTP
-NYKLPDDFTGCVIAWN
-VKQLSSNFGAISSVL
-GDEVRQIAPGQTGKIADYNYK
-NYNYLYRLFRKSNLKPFER
-PGQTGKIADYNYKLPDDFT
-IDGYFKIYSKHTPI
-LFRKSNLKPFERDIS
-AGAALQIPFAMQM
-SFVIRGDEVRQIAPGQT
-IRGDEVRQIAPGQT
-FGAGAALQIPFAMQMAYRF
-KQLSSNFGAISSVLNDILS
-VTQRNFYEPQIITTDN
-HTPINLVRDLPQGFS
-EFVFKNIDGYFKIYSKHTPIN
-AISSVLNDILSRLDKVEAEV
-NIDGYFKIYSKHTPINL
-AGAALQIPFAMQMAYRFN
-FGAGAALQIPFAMQMAYRFNGIG
-GQTGKIADYNYKLPDDFTGCVIAW
-SNFGAISSVLNDILSRLDK
-YFKIYSKHTPINLVR
-HWFVTQRNFYEPQIITTDN
-MQMAYRFNGIGVTQNV
-DDFTGCVIAWNSNNLDSKVGGN
-IRGDEVRQIAPGQTGKIADYNY
-GCVIAWNSNNLDSKVGGNYNYL
-IAPGQTGKIADYNYKLP
-NFGAISSVLNDILSRLDKVE
-AYYVGYLQPRTFL
-VIAWNSNNLDSKVGGNYNYLYRL
-VGGNYNYLYRLFRKSNLKPFERD
-ALQIPFAMQMAYRFNGIG
-SSNFGAISSVLNDILSRL
-GCVIAWNSNNLDSKVGGNYNY
-KIYSKHTPINLVRD
-YNYKLPDDFTGCVIAWN
-IAWNSNNLDSKVGGNYNYLY
-ADYNYKLPDDFTGCVIAWNSNNL
-IAPGQTGKIADYNYKL
-VIRGDEVRQIAPGQTG
-CVIAWNSNNLDSKVGGNYN
-YRLFRKSNLKPFERD
-SKHTPINLVRDLP
-DGYFKIYSKHTPI
-VIRGDEVRQIAPGQTGKI
-SNNLDSKVGGNYNYLYRLFR
-QIAPGQTGKIADYNYKLP
-IADYNYKLPDDFTGCVIAWNSN
-FVFKNIDGYFKIY
-NIDGYFKIYSKHT
-FGAISSVLNDILSRLDKV
-YNYLYRLFRKSNLKPFER
-IAPGQTGKIADYNYKLPDDFTGCV
-QTGKIADYNYKLPDDFTGCV
-RLFRKSNLKPFERDI
-FAMQMAYRFNGIGVTQ
-PGQTGKIADYNYKLPDDFTG
-KVGGNYNYLYRLFRKSNLKPFER
-EVRQIAPGQTGKIADYNYKLPD
-PDDFTGCVIAWNSNNLDSKVGGNYN
-GNYNYLYRLFRKSNLKPFERD
-TQQLIRAAEIRASANLAA
-KVGGNYNYLYRLFRKSNLKPFERD
-FTGCVIAWNSNNLDS
-KIADYNYKLPDDFTGCVIAWNSN
-KQLSSNFGAISSVLNDILSRLDK
-LPDDFTGCVIAWNS
-GDEVRQIAPGQTGKI
-RLFRKSNLKPFER
-LQIPFAMQMAYRFN
-NFGAISSVLNDILSRLDKVEAE
-GDEVRQIAPGQTGK
-FVIRGDEVRQIAPGQTGKI
-NFYEPQIITTDNT
-GAGAALQIPFAMQMAYRFN
-YNYKLPDDFTGCVIAWNSN
-THWFVTQRNFYEPQIIT
-FGAGAALQIPFAMQ
-RQIAPGQTGKIADYNY
-GKIADYNYKLPDDFTGCVIAWNSN
-YKLPDDFTGCVIAWNSNNLDSK
-SSNFGAISSVLNDILSRLDKV
-DFTGCVIAWNSNNL
-GCVIAWNSNNLDSK
-QLSSNFGAISSVLNDILSRL
-YVTQQLIRAAEIRASAN
-RGDEVRQIAPGQTGK
-RGDEVRQIAPGQTGKIADYNYKLPD
-QTGKIADYNYKLPD
-SKVGGNYNYLYRLFRKSN
-ALQIPFAMQMAYRFNGIGVT
-LQIPFAMQMAYRFNGIGVTQ
-LDSKVGGNYNYLYRLFRKSNLKP
-AWNSNNLDSKVGGNYNYL
-AALQIPFAMQMAYRFNGIGVT
-YNYKLPDDFTGCVIAWNSNNLDS
-VGYLQPRTFLLKY
-NYLYRLFRKSNLKPFERDIS
-RAAEIRASANLAA
-IPFAMQMAYRFNGIGVT
-AGAALQIPFAMQMAYRFNGIG
-VLNDILSRLDKVE
-FGAGAALQIPFAMQMAYRFNGI
-GNYNYLYRLFRKSNLKPF
-YLYRLFRKSNLKPF
-WNSNNLDSKVGGNYNYLYR
-GDEVRQIAPGQTGKIADYNYKLP
-IAPGQTGKIADYNYKLPDDFTG
-VGGNYNYLYRLFRKSN
-PFAMQMAYRFNGIGVTQ
-TFGAGAALQIPFAMQMAYRFNG
-FGAGAALQIPFAMQMA
-NSNNLDSKVGGNYNYL
-EVRQIAPGQTGKIADYNYKLPDDF
-FVIRGDEVRQIAPGQT
-EFVFKNIDGYFKIYSK
-GAALQIPFAMQMAYRFNGIGVTQ
-IRGDEVRQIAPGQ
-GKIADYNYKLPDDFTG
-LYRLFRKSNLKPFE
-IAPGQTGKIADYNYKLPDDFTGC
-APGQTGKIADYNYK
-YFKIYSKHTPINLVRD
-TGKIADYNYKLPDDFTGCVIA
-RGDEVRQIAPGQTGKIADYNY
-DDFTGCVIAWNSNNLDSKVGGNYNY
-CVIAWNSNNLDSKVGGNYNY
-QIPFAMQMAYRFNGIGVTQ
-FGAISSVLNDILSR
-DDFTGCVIAWNSNNLDSK
-APGQTGKIADYNYKLP
-QQLIRAAEIRASAN
-RQIAPGQTGKIADYNYKLPDD
-YLYRLFRKSNLKPFERDIS
-IAPGQTGKIADYN
-FVIRGDEVRQIAPGQTG
-TQRNFYEPQIITTDNT
-THWFVTQRNFYEPQ
-AALQIPFAMQMAYRFNGIG
-TFGAGAALQIPFAMQM
-DSKVGGNYNYLYR
-NFGAISSVLNDILSRLDKV
-DDFTGCVIAWNSNNLDSKVG
-REFVFKNIDGYFKIYSKHTPIN
-ALQIPFAMQMAYRFNGI
-KLPDDFTGCVIAWNSNNL
-TGCVIAWNSNNLDSKVGGNYNYLY
-WNSNNLDSKVGGNYNYLYRL
-NYKLPDDFTGCVIAWNSNNLDSKV
-PFAMQMAYRFNGIGV
-QIPFAMQMAYRFNGIGV
-LQIPFAMQMAYRFNGIGVT
-AISSVLNDILSRLDKVEA
-SSNFGAISSVLND
-WFVTQRNFYEPQIITTDNT
-NNLDSKVGGNYNYLYRL
-KLPDDFTGCVIAWNSNN
-NYKLPDDFTGCVIAW
-APGQTGKIADYNYKLPDDFTGC
-IAWNSNNLDSKVGGNYNYLYRL
-KLPDDFTGCVIAWNSNNLDSKV
-QIPFAMQMAYRFN
-IRGDEVRQIAPGQTGKIADYNYKLP
-AGAALQIPFAMQMAYR
-TGKIADYNYKLPDDFTGC
-FKNIDGYFKIYSK
-VGGNYNYLYRLFRKSNL
-YNYLYRLFRKSNLKPFERD
-AALQIPFAMQMAYRFNGIGV
-PDDFTGCVIAWNSNNLDSKVGGNY
-VKQLSSNFGAISSVLN
-LDSKVGGNYNYLYRLFR
-RGDEVRQIAPGQTGKIA
-DSKVGGNYNYLYRLFRK
-KLPDDFTGCVIAWNSNNLDS
-VIRGDEVRQIAPGQTGKIAD
-KHTPINLVRDLPQG
-QLSSNFGAISSVLND
-PDDFTGCVIAWNSNNLDSKVGGN
-NNLDSKVGGNYNYLYR
-YNYKLPDDFTGCVIAWNSNNLDSK
-YNYKLPDDFTGCVIAWNS
-GKIADYNYKLPDDFTGC
-GNYNYLYRLFRKSNLKP
-THWFVTQRNFYEPQII
-KVGGNYNYLYRLF
-IAWNSNNLDSKVGGNYNYLYR
-VFKNIDGYFKIYSKHT
-SNNLDSKVGGNYNYLYRLFRK
-TYVTQQLIRAAEI
-AWNSNNLDSKVGGNYNYLYRLF
-PGQTGKIADYNYKLPDDFTGCVI
-DDFTGCVIAWNSNNL
-DSKVGGNYNYLYRLFRKS
-TQRNFYEPQIITTD
-IYSKHTPINLVRD
-SFVIRGDEVRQIAPGQTGKI
-NYKLPDDFTGCVIA
-WNSNNLDSKVGGNYNY
-IPFAMQMAYRFNGI
-VTQQLIRAAEIRA
-NDILSRLDKVEAEV
-RGDEVRQIAPGQTGKI
-IRGDEVRQIAPGQTGKIADYN
-FAMQMAYRFNGIGV
-PDDFTGCVIAWNSNNLD
-SSVLNDILSRLDKVEAEV
-QLSSNFGAISSVLNDILS
-GQTGKIADYNYKLPDDFTGCVIAWN
-NYKLPDDFTGCVIAWNSNNLDSK
-GDEVRQIAPGQTGKIADY
-QLSSNFGAISSVLNDILSRLDK
-FVIRGDEVRQIAPGQTGKIADY
-QIPFAMQMAYRFNGIG
-DSKVGGNYNYLYRLFRKSN
-DYNYKLPDDFTGCVIAWNSNNLDSK
-LIRAAEIRASANL
-EVRQIAPGQTGKIADYNYKLP
-NIDGYFKIYSKHTPINLVR
-ADYNYKLPDDFTGCVIAWN
-VTQRNFYEPQIIT
-QLIRAAEIRASANLA
-KNIDGYFKIYSKH
-DFTGCVIAWNSNNLDSKVG
-YKLPDDFTGCVIAW
-QIAPGQTGKIADYNY
-RQIAPGQTGKIAD
-HWFVTQRNFYEPQIITTD
-NFGAISSVLNDIL
-YKLPDDFTGCVIAWNSNNLDSKV
-AYYVGYLQPRTFLL
-QMAYRFNGIGVTQNVL
-YKLPDDFTGCVIAWNSN
-GCVIAWNSNNLDSKV
-IAWNSNNLDSKVGGNYNYL
-DEVRQIAPGQTGKIADY
-LSSNFGAISSVLNDILSR
-SFVIRGDEVRQIAPGQ
-VIAWNSNNLDSKV
-MAYRFNGIGVTQNVL
-TPINLVRDLPQGF
-AWNSNNLDSKVGGNYNYLY
-YVGYLQPRTFLLK
-VIAWNSNNLDSKVGGNYNYLYRLF
-NYLYRLFRKSNLK
-VIAWNSNNLDSKVGG
-NSNNLDSKVGGNYNYLYRLF
-NFGAISSVLNDILSRLDK
-DEVRQIAPGQTGKIADYNYKLPDDF
-NNLDSKVGGNYNY
-CVIAWNSNNLDSKVGGNYNYLYR
-QTGKIADYNYKLP
-VTQQLIRAAEIRASAN
-YNYKLPDDFTGCVIAWNSNNLD
-ADYNYKLPDDFTGCVIAWNSNNLDS
-PGQTGKIADYNYKLPDDFTGCVIA
-NIDGYFKIYSKHTPINLVRD
-EFVFKNIDGYFKI
-SKVGGNYNYLYRLFRKSNLKPF
-QRNFYEPQIITTDNT
-NYLYRLFRKSNLKPFERD
-LDSKVGGNYNYLYRLFRKSN
-EVRQIAPGQTGKIADYNYK
-KNIDGYFKIYSKHT
-QIAPGQTGKIADYNYKLPDDFTGC
-YVTQQLIRAAEIR
-VKQLSSNFGAISSV
-VRQIAPGQTGKIADYNY
-KNIDGYFKIYSKHTPI
-IPFAMQMAYRFNGIG
-LSSNFGAISSVLNDILSRLDK
-KHTPINLVRDLPQGF
-NYNYLYRLFRKSNLKPFERD
-APGQTGKIADYNYKLPDDFT
-THWFVTQRNFYEPQIITTD
-EVRQIAPGQTGKIADYNYKLPDDFT
-GCVIAWNSNNLDSKVG
-PDDFTGCVIAWNSNNLDS
-ISSVLNDILSRLDKVE
-KIADYNYKLPDDFTGCV
-YRLFRKSNLKPFER
-DFTGCVIAWNSNNLDSKV
-LSSNFGAISSVLNDILS
-SNNLDSKVGGNYN
-IPFAMQMAYRFNGIGVTQNVL
-NYKLPDDFTGCVIAWNSNNL
-GKIADYNYKLPDDF
-SVLNDILSRLDKVEA
-AWNSNNLDSKVGGNY
-GGNYNYLYRLFRKSNLKPFERD
-VFKNIDGYFKIYSKHTPIN
-VKQLSSNFGAISSVLNDILSRLD
-VIRGDEVRQIAPGQTGKIADYNY
-REFVFKNIDGYFKI
-GGNYNYLYRLFRKSNL
-THWFVTQRNFYEPQI
-GYFKIYSKHTPINLVRD
-FGAISSVLNDILSRLD
-NNLDSKVGGNYNYLYRLFRKSNLKP
-LSSNFGAISSVLNDILSRLD
-KQLSSNFGAISSVLNDILSRLD
-YVTQQLIRAAEIRAS
-LSSNFGAISSVLND
-WFVTQRNFYEPQIITTD
-THWFVTQRNFYEPQIITT
-RQIAPGQTGKIADYNYKLPDDFTG
-YKLPDDFTGCVIAWNSNNL
-DFTGCVIAWNSNNLDS
-QTGKIADYNYKLPDDFTGCVIAW
-KIADYNYKLPDDFTGCVI
-VIAWNSNNLDSKVGGNY
-FGAISSVLNDILSRLDKVEAEV
-AGAALQIPFAMQMAYRFNGI
-IADYNYKLPDDFTGCVIAWNSNNL
-RLFRKSNLKPFERD
-KIADYNYKLPDDFTGCVIA
-GCVIAWNSNNLDS
-YFKIYSKHTPINL
-AYYVGYLQPRTFLLK
-TGKIADYNYKLPDDFTGCV
-VTQRNFYEPQIITTDNT
-PFAMQMAYRFNGIGVTQN
-TGKIADYNYKLPDDFTGCVIAWNS
-KVGGNYNYLYRLFRKSNLKPF
-QLIRAAEIRASANLAA
-SSNFGAISSVLNDI
-YLYRLFRKSNLKPFERD
-NFGAISSVLNDILSR
-GGNYNYLYRLFRKSNLKPFERDI
-NNLDSKVGGNYNYLYRLFRKSN
-EVRQIAPGQTGKIADYNYKL
-VRQIAPGQTGKIADY
-KLPDDFTGCVIAWNS
-LPDDFTGCVIAWNSNNLD
-FVIRGDEVRQIAPGQTGKIADYN
-WFVTQRNFYEPQII
-IPFAMQMAYRFNGIGVTQ
-IPFAMQMAYRFNG
-NFGAISSVLNDILSRLDKVEA
-NFGAISSVLNDILSRLD
-DSKVGGNYNYLYRLFR
-FVTQRNFYEPQIITTDN
-FGAGAALQIPFAMQMAYRFN
-VKQLSSNFGAISSVLND
-DDFTGCVIAWNSNNLD
-FAMQMAYRFNGIGVT
-REFVFKNIDGYFKIYSKHT
-NYKLPDDFTGCVIAWNSNNLDSKVG
-IADYNYKLPDDFTGCVIAWNS
-FGAGAALQIPFAMQMAYR
-TQQLIRAAEIRASA
-MQMAYRFNGIGVTQ
-FVIRGDEVRQIAPGQTGKIADYNY
-VKQLSSNFGAISSVLNDILSRL
-GAGAALQIPFAMQMAYRFNGI
-VIRGDEVRQIAPGQTGKIADYN
-ISSVLNDILSRLDKVEAE
-GAALQIPFAMQMAYRFNG
-HTPINLVRDLPQGF
-IADYNYKLPDDFTGCVIAW
-NIDGYFKIYSKHTP
-EVRQIAPGQTGKI
-AALQIPFAMQMAYRF
-NFYEPQIITTDNTF
-IADYNYKLPDDFTGCVIAWNSNN
-NSNNLDSKVGGNY
-GGNYNYLYRLFRKSNLKPF
-LQIPFAMQMAYRFNG
-FVFKNIDGYFKIYSK
-NIDGYFKIYSKHTPI
-YKLPDDFTGCVIAWNSNNLDSKVGG
-TFEYVSQPFLMDLE
-NYLYRLFRKSNLKPFER
-DEVRQIAPGQTGKIADYNYKLP
-GAGAALQIPFAMQMAYRFNGIGVT
-VRQIAPGQTGKIAD
-VTQRNFYEPQIITT
-APGQTGKIADYNYKLPD
-YNYLYRLFRKSNLKP
-DEVRQIAPGQTGKIADYNYKLPDD
-TGKIADYNYKLPDDFTGCVIAW
-GNYNYLYRLFRKS
-NYKLPDDFTGCVIAWNSNN
-FVIRGDEVRQIAPG
-AALQIPFAMQMAYR
-ADYNYKLPDDFTGCV
-FVFKNIDGYFKIYS
-LQIPFAMQMAYRFNGIGV
-VIRGDEVRQIAPGQT
-LDSKVGGNYNYLYRL
-YNYLYRLFRKSNL
-QTGKIADYNYKLPDDFT
-VRQIAPGQTGKIADYNYKLPDDFT
-RGDEVRQIAPGQTGKIAD
-SNFGAISSVLNDI
-ADYNYKLPDDFTGCVIAWNSN
-SKVGGNYNYLYRLFR
-AALQIPFAMQMAYRFNGI
-WNSNNLDSKVGGNY
-KVGGNYNYLYRLFRK
-VRQIAPGQTGKIADYNYKLPDDF
-IDGYFKIYSKHTP
-TGKIADYNYKLPD
-IADYNYKLPDDFTGC
-VIAWNSNNLDSKVGGNYNYLYR
-KQLSSNFGAISSVLND
-DYNYKLPDDFTGCVIAWN
-KVGGNYNYLYRLFRKSNLK
-LDSKVGGNYNYLYRLFRKSNLK
-TPINLVRDLPQGFS
-WNSNNLDSKVGGNYN
-DDFTGCVIAWNSNNLDSKVGGNYN
-RQIAPGQTGKIADYNYKLPD
-TGCVIAWNSNNLDSKV
-FTGCVIAWNSNNL
-IAWNSNNLDSKVGGNYNY
-DSKVGGNYNYLYRLFRKSNLKPFE
-SFVIRGDEVRQIAPG
-QLSSNFGAISSVLNDI
-TGKIADYNYKLPDD
-DYNYKLPDDFTGCVIAWNSN
-RGDEVRQIAPGQTGKIADYNYKLP
-NNLDSKVGGNYNYLYRLF
-MQMAYRFNGIGVTQN
-PGQTGKIADYNYKLPDDFTGCVIAW
-RGDEVRQIAPGQTGKIADYNYKL
-IAPGQTGKIADYNYK
-LPDDFTGCVIAWNSNNL
-ALQIPFAMQMAYRFNGIGVTQ
-GNYNYLYRLFRKSN
-TGKIADYNYKLPDDFTGCVIAWN
-WNSNNLDSKVGGNYNYLYRLF
-YVTQQLIRAAEIRASA
-QRNFYEPQIITTDN
-TQQLIRAAEIRASANL
-TQQLIRAAEIRASAN
-VGGNYNYLYRLFRKSNLKPFER
-VIRGDEVRQIAPGQTGK
-DEVRQIAPGQTGKI
-VIAWNSNNLDSKVG
-IAWNSNNLDSKVGGNYNYLYRLFRK
-FAMQMAYRFNGIGVTQN
-TGKIADYNYKLPDDF
-EFVFKNIDGYFKIYSKHTPINLV
-ADYNYKLPDDFTGCVIAWNSNNLD
-TQRNFYEPQIITT
-SNNLDSKVGGNYNYLY
-FGAISSVLNDILSRLDK
-SSNFGAISSVLNDILSRLDKVEAE
-AALQIPFAMQMAYRFNGIGVTQ
-YKLPDDFTGCVIA
-IAPGQTGKIADYNYKLPDD
-KQLSSNFGAISSVLNDIL
-TGCVIAWNSNNLDSKVGGNYNYL
-APGQTGKIADYNYKLPDDFTGCVI
-QLSSNFGAISSVLNDILSRLDKV
-WNSNNLDSKVGGNYNYLYRLFRKSN
-DDFTGCVIAWNSNN
-FVTQRNFYEPQII
-FVIRGDEVRQIAPGQTGK
-CVIAWNSNNLDSKVGGN
-NLDSKVGGNYNYLYRLFRKSNLK
-NLDSKVGGNYNYLYRLF
-QMAYRFNGIGVTQNV
-PDDFTGCVIAWNSNNLDSKVG
-GKIADYNYKLPDDFTGCV
-GKIADYNYKLPDDFTGCVIA
-QRNFYEPQIITTD
-GCVIAWNSNNLDSKVGGNYNYLYRL
-KNIDGYFKIYSKHTPINLV
-YNYKLPDDFTGCVI
-AISSVLNDILSRL
-TFGAGAALQIPFAM
-TGCVIAWNSNNLDSKVGGNY
-IRGDEVRQIAPGQTGKIA
-ISSVLNDILSRLDK
-VTQQLIRAAEIRASA
-QIAPGQTGKIADYN
-DYNYKLPDDFTGCVI
-GGNYNYLYRLFRKSNLKPFER
-RQIAPGQTGKIADYNYKL
-DDFTGCVIAWNSNNLDSKV
-EFVFKNIDGYFKIYS
-SKVGGNYNYLYRLFRKSNL
-FVTQRNFYEPQIITT
-LDSKVGGNYNYLYRLFRKS
-IADYNYKLPDDFTGCVIAWN
-SSNFGAISSVLNDILSRLDK
-FTGCVIAWNSNNLDSKV
-PFAMQMAYRFNGI
-LYRLFRKSNLKPFERDI
-DFTGCVIAWNSNNLD
-TGCVIAWNSNNLD
-QIPFAMQMAYRFNGIGVT
-KIADYNYKLPDDFTGCVIAWN
-SVLNDILSRLDKVEAE
-GNYNYLYRLFRKSNLKPFERDI
-NNLDSKVGGNYNYLYRLFR
-SNFGAISSVLNDIL
-DYNYKLPDDFTGCVIAW
-PDDFTGCVIAWNSNNL
-VGGNYNYLYRLFR
-KNIDGYFKIYSKHTPINLVR
-IDGYFKIYSKHTPINLV
-LPDDFTGCVIAWNSNNLDSKV
-GGNYNYLYRLFRKSN
-TGKIADYNYKLPDDFTGCVIAWNSN
-REFVFKNIDGYFKIYS
-EFVFKNIDGYFKIYSKHTPINLVR
-FGAGAALQIPFAMQM
-CVIAWNSNNLDSKVG
-VIAWNSNNLDSKVGGNYNY
-NNLDSKVGGNYNYLYRLFRKS
-YNYLYRLFRKSNLKPFERDI
-WFVTQRNFYEPQIITTDN
-FTGCVIAWNSNNLD
-NLDSKVGGNYNYLYRLFR
-DFTGCVIAWNSNNLDSKVGGNYN
-QIAPGQTGKIADYNYKL
-VFKNIDGYFKIYS
-EFVFKNIDGYFKIYSKHTPINL
-LYRLFRKSNLKPFER
-IPFAMQMAYRFNGIGV
-KLPDDFTGCVIAWNSNNLDSKVGGN
-WFVTQRNFYEPQIIT
-SKHTPINLVRDLPQGF
-IDGYFKIYSKHTPINL
-DSKVGGNYNYLYRLFRKSNLK
-GKIADYNYKLPDDFTGCVIAW
-QTGKIADYNYKLPDDFTGCVI
-IADYNYKLPDDFTG
-KIADYNYKLPDDF
-KNIDGYFKIYSKHTPINLVRD
-VRQIAPGQTGKIADYNYKL
-LYRLFRKSNLKPFERD
-NYNYLYRLFRKSNLKPFERDIS
-SSNFGAISSVLNDIL
-VKQLSSNFGAISSVLNDI
-MQMAYRFNGIGVT
-KQLSSNFGAISSVLNDILSR
-NYNYLYRLFRKSN
-PFAMQMAYRFNGIGVTQNVL
-SKVGGNYNYLYRLFRK
-EFVFKNIDGYFKIY
-IAWNSNNLDSKVGGN
-QLSSNFGAISSVLNDIL
-KQLSSNFGAISSVLN
-TGKIADYNYKLPDDFTG
-EFVFKNIDGYFKIYSKH
-AALQIPFAMQMAY
-SNFGAISSVLNDILSR
-TGKIADYNYKLPDDFTGCVI
-VIRGDEVRQIAPGQTGKIADYNYKL
-LIRAAEIRASANLA
-QIAPGQTGKIADYNYKLPDDFT
-KIADYNYKLPDDFTGCVIAW
-AISSVLNDILSRLDKV
-QIPFAMQMAYRFNGIGVTQNV
-VTQQLIRAAEIRAS
-AWNSNNLDSKVGGNYNYLYRLFR
-FAMQMAYRFNGIGVTQNVL
-KQLSSNFGAISSV
-RQIAPGQTGKIADYNYKLPDDFT
-GAGAALQIPFAMQM
-PGQTGKIADYNYK
-GQTGKIADYNYKLPDDF
-FGAGAALQIPFAM
-PDDFTGCVIAWNSNNLDSKV
-WFVTQRNFYEPQIITT
-NLDSKVGGNYNYLY
-GCVIAWNSNNLDSKVGGNYNYLY
-DDFTGCVIAWNSN
-NIDGYFKIYSKHTPINLV
-YRLFRKSNLKPFERDI
-SFVIRGDEVRQIAPGQTGKIAD
-ADYNYKLPDDFTGCVIAW
-YKLPDDFTGCVIAWNSNN
-THWFVTQRNFYEP
-DEVRQIAPGQTGKIADYNYK
-IPFAMQMAYRFNGIGVTQNV
-DDFTGCVIAWNSNNLDSKVGG
-YYVGYLQPRTFLLKY
-GDEVRQIAPGQTGKIADYN
-IAWNSNNLDSKVGG
-TFGAGAALQIPFAMQMAYRFNGIG
-KLPDDFTGCVIAWNSN
-MQMAYRFNGIGVTQNVL
-AGAALQIPFAMQMAYRFNGIGV
-IAPGQTGKIADYNYKLPDDFTGCVI
-IRGDEVRQIAPGQTGK
-SSNFGAISSVLNDILS
-DYNYKLPDDFTGCVIAWNSNNLD
-FTGCVIAWNSNNLDSK
-FTGCVIAWNSNNLDSKVG
-IDGYFKIYSKHTPIN
-FGAISSVLNDILSRLDKVEAE
-LPDDFTGCVIAWNSNNLDSK
-REFVFKNIDGYFKIYSKH
-IRAAEIRASANLAA
-NLDSKVGGNYNYLYRLFRK
-EVRQIAPGQTGKIADYNYKLPDD
-VIRGDEVRQIAPG
-GQTGKIADYNYKLPDDFTGCV
-FVIRGDEVRQIAPGQTGKIAD
-NLDSKVGGNYNYLYRLFRKSNL
-FTGCVIAWNSNNLDSKVGG
-TFGAGAALQIPFAMQMAYRFNGI
-ADYNYKLPDDFTGCVIAWNSNN
-FGAISSVLNDILSRL
-EFVFKNIDGYFKIYSKHTPI
-NLDSKVGGNYNYLYRLFRKS
-VFKNIDGYFKIYSKHTP
-NYNYLYRLFRKSNLKPFE
-QIAPGQTGKIADYNYKLPD
-RQIAPGQTGKIADY
-TGKIADYNYKLPDDFT
-VFKNIDGYFKIYSKHTPINL
-RQIAPGQTGKIADYNYKLP
-YVTQQLIRAAEIRASANLA
-IYSKHTPINLVRDL
-GAALQIPFAMQMAYR
-AMQMAYRFNGIGVTQNVL
-DFTGCVIAWNSNNLDSKVGGN
-APGQTGKIADYNYKLPDDFTGCVIA
-GCVIAWNSNNLDSKVGG
-DDFTGCVIAWNSNNLDSKVGGNY
-LNDILSRLDKVEAEV
-GAALQIPFAMQMAYRF
-SKVGGNYNYLYRLFRKS
-GDEVRQIAPGQTGKIAD
-FVTQRNFYEPQIIT
-GQTGKIADYNYKLPDDFTGCVI
-APGQTGKIADYNYKLPDDF
-YNYLYRLFRKSNLKPFERDIS
-QTGKIADYNYKLPDDFTGCVIA
-NSNNLDSKVGGNYNYLYRLFRKSN
-DSKVGGNYNYLYRL
-AWNSNNLDSKVGGNYN
-PDDFTGCVIAWNS
-SSNFGAISSVLNDILSR
-SSNFGAISSVLNDILSRLDKVEAEV
-TGCVIAWNSNNLDSKVG
-KLPDDFTGCVIAWNSNNLDSKVGG
-GAGAALQIPFAMQMAYRFNGIGV
-VGGNYNYLYRLFRKS
-PGQTGKIADYNYKLPDDFTGCV
-ALQIPFAMQMAYRFNGIGV
-VKQLSSNFGAISSVLNDILSRLDKV
-CVIAWNSNNLDSKV
-VFKNIDGYFKIYSKHTPINLVR
-LPDDFTGCVIAWNSNNLDSKVG
-FAMQMAYRFNGIGVTQNV
-AYRFNGIGVTQNV
-DYNYKLPDDFTGCV
-HWFVTQRNFYEPQII
-KHTPINLVRDLPQGFS
-KVGGNYNYLYRLFR
-SNFGAISSVLNDILSRLDKVE
-NYLYRLFRKSNLKPFERDI
-TGCVIAWNSNNLDSK
-LYRLFRKSNLKPFERDIS
-TQRNFYEPQIITTDNTF
-QLSSNFGAISSVLN
-SSVLNDILSRLDKVEA
-FGAGAALQIPFAMQMAYRFNG
-DSKVGGNYNYLYRLF
-KLPDDFTGCVIAWN
-YVGYLQPRTFLLKY
-GQTGKIADYNYKLPDDFTGCVIA
-ISSVLNDILSRLDKVEA
-LDSKVGGNYNYLYRLFRKSNLKPF
-QRNFYEPQIITTDNTF
-KVGGNYNYLYRLFRKS
-PFAMQMAYRFNGIGVTQNV
-YKLPDDFTGCVIAWNS
-LDSKVGGNYNYLYRLFRKSNL
-QLSSNFGAISSVLNDILSRLD
-SNNLDSKVGGNYNYLYRLFRKS
-DGYFKIYSKHTPINLVR
-MAYRFNGIGVTQNV
-NYNYLYRLFRKSNL
-HWFVTQRNFYEPQIITT
-GGNYNYLYRLFRKSNLKPFERDIS
-GGNYNYLYRLFRK
-LNDILSRLDKVEAE
-GQTGKIADYNYKLPDDFTG
-ADYNYKLPDDFTGC
-VTQRNFYEPQIITTDNTF
-YVTQQLIRAAEIRA
-GAGAALQIPFAMQMAYRFNG
-NLDSKVGGNYNYLYR
-IAWNSNNLDSKVGGNY
-KVGGNYNYLYRLFRKSNLKP
-PFAMQMAYRFNGIGVT
-SKVGGNYNYLYRLFRKSNLKP
-NSNNLDSKVGGNYNYLY
-AGAALQIPFAMQMAYRF
-VRQIAPGQTGKIADYNYKLPDDFTG
-SKVGGNYNYLYRLF
-GAALQIPFAMQMAY
-NYNYLYRLFRKSNLKPF
-QLSSNFGAISSVL
-REFVFKNIDGYFKIYSKHTP
-KIYSKHTPINLVR
-GGNYNYLYRLFRKSNLK
-KQLSSNFGAISSVL
-QTYVTQQLIRAAE
-IADYNYKLPDDFTGCVI
-RKSNLKPFERDIS
-EFVFKNIDGYFKIYSKHT
-NSNNLDSKVGGNYNYLYRLFRKS
-VLNDILSRLDKVEAE
-GQTGKIADYNYKLP
-ISSVLNDILSRLD
-FKNIDGYFKIYSKHTPIN
-NYNYLYRLFRKSNLKP
-DFTGCVIAWNSNNLDSKVGGNYNYL
-SFVIRGDEVRQIAPGQTGKIADYNY
-YLYRLFRKSNLKP
-ISSVLNDILSRLDKVEAEV
-QIAPGQTGKIADYNYKLPDDF
-GNYNYLYRLFRKSNLKPFE
-SNNLDSKVGGNYNYLYRLF
-FVIRGDEVRQIAPGQTGKIA
-DILSRLDKVEAEV
-EVRQIAPGQTGKIADYNY
-QLSSNFGAISSVLNDILSR
-EFVFKNIDGYFKIYSKHTP
-VFKNIDGYFKIYSKH
-APGQTGKIADYNY
-IAPGQTGKIADYNYKLPDDF
-QTGKIADYNYKLPDDF
-IAPGQTGKIADYNYKLPD
-NYKLPDDFTGCVIAWNSN
-REFVFKNIDGYFKIYSKHTPI
-QTGKIADYNYKLPDDFTGC
-FVFKNIDGYFKIYSKHT
-RGDEVRQIAPGQTGKIADYNYK
-LPDDFTGCVIAWN
-WNSNNLDSKVGGNYNYLYRLFR
-FKNIDGYFKIYSKHTPI
-KLPDDFTGCVIAWNSNNLD
-DGYFKIYSKHTPINL
-FVFKNIDGYFKIYSKHTPI
-SFVIRGDEVRQIAPGQTG
-AYRFNGIGVTQNVL
-REFVFKNIDGYFKIY
-YKLPDDFTGCVIAWNSNNLDSKVG
-LQTYVTQQLIRAA
-DYNYKLPDDFTGC
-SKVGGNYNYLYRLFRKSNLK
-VLNDILSRLDKVEAEV
-DEVRQIAPGQTGKIA
-YNYKLPDDFTGCVIAW
-GCVIAWNSNNLDSKVGGNY
-GNYNYLYRLFRKSNLKPFERDIS
\ No newline at end of file
diff --git a/SupplementaryResults/SpikeHOGVAX/5064_hogvaxine.txt b/SupplementaryResults/SpikeHOGVAX/5064_hogvaxine.txt
deleted file mode 100644
index 3750d8ce00ccaad407f1fdcb93514af8ccc1807b..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SpikeHOGVAX/5064_hogvaxine.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-> MHC optimized combined peptide vaccine sequence with overlaps
-SREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVFLHVTYVPQMAYYVGYLQPRTFLLKYYVGYLQPRTFLLKSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTHWFVTQRNFYEPQIITTDNTFGAGAALQIPFAMQMAYRFNGIGVTQNVLNVKQLSSNFGAISSVLNDILSRLDKVEAEVCTFEYVSQPFLMDLEAECSLQTYVTQQLIRAAEIRASANLAA
-> MHC optimized combined peptide vaccine sequence concatenated
-TYVTQQLIRAAEIRGAISSVLNDILSRLDKVEAEVYNYKLPDDFTGCVIAWNSNNLDSKGDEVRQIAPGQTGKIADYNYKLPDDTHWFVTQRNFYEPQIILIRAAEIRASANLAATYVTQQLIRAAEIFKIYSKHTPINLVRDIYSKHTPINLVRDSFVIRGDEVRQIAPGQTGKISSNFGAISSVLNDILSRLDKVEVKQLSSNFGAISSNDILSRLDKVEAEVRNFYEPQIITTDNTFVKQLSSNFGAISSVLNDILSRLDKSSVLNDILSRLDKVEAEVGQTGKIADYNYKLPDDFTGCVIAWNTFGAGAALQIPFAMQMATGKIADYNYKLPDDFTGCVIAWNSNREFVFKNIDGYFKIYSEFVFKNIDGYFKIYSKHTPINLVRWFVTQRNFYEPQIITTDNDYNYKLPDDFTGCVIAWNSNNLDSKNLDSKVGGNYNYLYRLFRKSNLKPFVTQQLIRAAEIRASANLAADSKVGGNYNYLYRLFRKSNLKPFSKHTPINLVRDLPQGFSAYYVGYLQPRTFLLQMAYRFNGIGVTQNVLSFVIRGDEVRQIAPGQTGKIADYNQTYVTQQLIRAAEIVKQLSSNFGAISSVLNDISVLNDILSRLDKVEAEVKLPDDFTGCVIAWNSNNLDSKVGGNSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDFTGCVIAWNSNNLDSKVGGNYNYLYSFVIRGDEVRQIAPGQSKHTPINLVRDLPQGFMAYRFNGIGVTQNVLYVGYLQPRTFLLKSFVIRGDEVRQIAPGQTGKIADYVIAWNSNNLDSKVGGNYNYLYRLFSNNLDSKVGGNYNYLYRLFRKSNKNIDGYFKIYSKHTPINLVRDFVTQRNFYEPQIITTDNTDEVRQIAPGQTGKIADYNYKLPDDFFVIRGDEVRQIAPGQTGKIADYNYKGKIADYNYKLPDDFTGCVIAWNSNNNYNYLYRLFRKSNLKPFERDISADYNYKLPDDFTGCVIAWNSNNLDSPGQTGKIADYNYKLPDDFTGCVIAKIADYNYKLPDDFTGCVIAWNSNNLDYNYKLPDDFTGCVIAWNSNNLDSYYVGYLQPRTFLLVIRGDEVRQIAPGQTGKIADYNYKPFAMQMAYRFNGIGVTQNVLNIDGYFKIYSKHTPINLVRDTFGAGAALQIPFASNNLDSKVGGNYNYLYRLFRKSNLYRLFRKSNLKPFERDISQIAPGQTGKIADYNYKLPDDFTGCQIPFAMQMAYRFNGIGVTQNYVTQQLIRAAEIRTFGAGAALQIPFAMQMAYRFVKQLSSNFGAISSVSSNFGAISSVLNDILSRLDKVEATFGAGAALQIPFAMQMAYRFNLQTYVTQQLIRAAEYYVGYLQPRTFLLKVIRGDEVRQIAPGQTGKIADYNYKLVKQLSSNFGAISSVLNDILREFVFKNIDGYFKIYSKHTPINLQIPFAMQMAYRFNGIGVTQNVKQLSSNFGAISSVLNDILSRLDKVGAALQIPFAMQMAYRFNGIGVTAYYVGYLQPRTFLLKYTHWFVTQRNFYEPQIITTDFAMQMAYRFNGIGVTQNVLEVRQIAPGQTGKIADYNYKLPDDFTGDEVRQIAPGQTGKIADYNYKLPDREFVFKNIDGYFKSFVIRGDEVRQIAPSKHTPINLVRDLPQTFGAGAALQIPFAMQRLFRKSNLKPFERDISTGCVIAWNSNNLDSKVGGNYNYLYRSFVIRGDEVRQIAPGQTGKIADIPFAMQMAYRFNGIGVTQNVLTHWFVTQRNFYEPCTFEYVSQPFLMDLEGGNYNYLYRLFRKSNLKPFERDGAGAALQIPFAMQMAYRFNGIGSNFGAISSVLNDILSRLDKVEAEVVKQLSSNFGAISSVLNDILSYYVGYLQPRTFLLKYGCVIAWNSNNLDSKVGGNYNYLYRREFVFKNIDGYFKIVKQLSSNFGAISSVLNDILSRLDYNYKLPDDFTGCVIAWNSNNLDSKVFYEPQIITTDNTFYVTQQLIRAAEIRASANLCVIAWNSNNLDSKVGGNYNYLYRLMQMAYRFNGIGVTQNVLIAPGQTGKIADYNYKLPDDFTGCVIGYFKIYSKHTPINLVRDKVGGNYNYLYRLFRKSNLKPFENNLDSKVGGNYNYLYRLFRKSNLKPCTFEYVSQPFLMDLSKVGGNYNYLYRLFRKSNLKPFEAWNSNNLDSKVGGNYNYLYRLFRKTHWFVTQRNFYEPQIFEYVSQPFLMDLEYVTQQLIRAAEIRASREFVFKNIDGYFKIYSKHVKQLSSNFGAISSVLNDILSRTHWFVTQRNFYEPQIITTRQIAPGQTGKIADYNYKLPDDFTGIRAAEIRASANLAAKIADYNYKLPDDFTGCVIAWNSNNFGAISSVLNDILSRLDKVEAEVTFGAGAALQIPFAMQMAYRFNGIGIADYNYKLPDDFTGCVIAWNSNNLFRKSNLKPFERDISAGAALQIPFAMQMAYRFNGIGVTIDGYFKIYSKHTPINLVRDQIPFAMQMAYRFNGIGVTQNVLTFGAGAALQIPFAMQMAYRFNGIREFVFKNIDGYFKIYSKSFVIRGDEVRQIAAYYVGYLQPRTFLLKVKQLSSNFGAISSVLTGKIADYNYKLPDDFTGCVIAWNSYVTQQLIRAAEIRASANLAIYSKHTPINLVRDLLFRKSNLKPFERDISAMQMAYRFNGIGVTQNVLQLIRAAEIRASANLAAAPGQTGKIADYNYKLPDDFTGCVIASFVIRGDEVRQIAPGQTGGNYNYLYRLFRKSNLKPFERDIHTPINLVRDLPQGFSLNDILSRLDKVEAEVFKNIDGYFKIYSKHTPINLVRDAISSVLNDILSRLDKVEAEVVKQLSSNFGAISSVLNDFGAGAALQIPFAMQMAYRFNGIGGQTGKIADYNYKLPDDFTGCVIAWNYKLPDDFTGCVIAWNSNNLDSKVGYNYLYRLFRKSNLKPFERDISREFVFKNIDGYFKIYSKHTNSNNLDSKVGGNYNYLYRLFRKSNFKNIDGYFKIYSKHTPINLVRHWFVTQRNFYEPQIITTDNFVIRGDEVRQIAPGQTGKIADYNYVKQLSSNFGAISSVLNDILSRLVIAWNSNNLDSKVGGNYNYLYRLFRNNLDSKVGGNYNYLYRLFRKSNLKSSNFGAISSVLNDILSRLDKVEAEVREFVFKNIDGYFKIYSKHTPINLVKLPDDFTGCVIAWNSNNLDSKVGGGAGAALQIPFAMQMAYRFNGIGVAYYVGYLQPRTFLNLDSKVGGNYNYLYRLFRKSNLKPVGGNYNYLYRLFRKSNLKPFERDVIAWNSNNLDSKVGGNYNYLYRLVKQLSSNFGAISSVLNDILSRLDKVKIYSKHTPINLVRDNFYEPQIITTDNTFVFKNIDGYFKIYSKHTPINLVRSKHTPINLVRDLPQGPINLVRDLPQGFSSKHTPINLVRDLPYKLPDDFTGCVIAWNSNNLDSKVGGKHTPINLVRDLPQGFSTFEYVSQPFLMDLEIAPGQTGKIADYNYKLPDDFTGCVGAGAALQIPFAMQMAYRFNGIGVTLYRLFRKSNLKPFERDISTQRNFYEPQIITTDNTFYRFNGIGVTQNVLKVGGNYNYLYRLFRKSNLKPFERDEVRQIAPGQTGKIADYNYKLPDDQTGKIADYNYKLPDDFTGCVIAWNTQQLIRAAEIRASANLAAKVGGNYNYLYRLFRKSNLKPFERDPDDFTGCVIAWNSNNLDSKVGGNYNDGYFKIYSKHTPINLVRDWNSNNLDSKVGGNYNYLYRLFRKSYVGYLQPRTFLLKYFTGCVIAWNSNNLDSKVGGNYNYLTFGAGAALQIPFAMQMAYLDSKVGGNYNYLYRLFRKSNLKPFVRQIAPGQTGKIADYNYKLPDDFTQRNFYEPQIITTDNTFTHWFVTQRNFYEPQIITFVFKNIDGYFKIYSKHTPINLVRGKIADYNYKLPDDFTGCVIAWNSNLSSNFGAISSVLNDILSRLDKVEYVTQQLIRAAEIRASANGGNYNYLYRLFRKSNLKPFERDISRGDEVRQIAPGQTGKIADYNYKLPDLQIPFAMQMAYRFNGIGVTQVTQRNFYEPQIITTDNTFYVTQQLIRAAEIRATPINLVRDLPQGFSVGYLQPRTFLLKYNYLYRLFRKSNLKPFERDISDDFTGCVIAWNSNNLDSKVGGNYNRAAEIRASANLAADSKVGGNYNYLYRLFRKSNLKPFESFVIRGDEVRQIAPGLPDDFTGCVIAWNSNNLDSKVGGNYSFVIRGDEVRQIAPGQTGKIARGDEVRQIAPGQTGKIADYNYKLPQIAPGQTGKIADYNYKLPDDFTGCVPGQTGKIADYNYKLPDDFTGCVIAWTFGAGAALQIPFAMQMAYRFNGALQIPFAMQMAYRFNGIGVTQEVRQIAPGQTGKIADYNYKLPDDFYSKHTPINLVRDLCTFEYVSQPFLMDGAALQIPFAMQMAYRFNGIGVTQNNLDSKVGGNYNYLYRLFRKSNLVRQIAPGQTGKIADYNYKLPDDFTGTGKIADYNYKLPDDFTGCVIAWNTHWFVTQRNFYEPQIITTDNYVTQQLIRAAEIRASAYFKIYSKHTPINLVRDREFVFKNIDGYFKIYSKHTPDDFTGCVIAWNSNNLDSKVGGNYNYIAWNSNNLDSKVGGNYNYLYRLFRKQIPFAMQMAYRFNGIGVTQEFVFKNIDGYFKIYSKHTPINLVADYNYKLPDDFTGCVIAWNSNNLDYVTQQLIRAAEIRASANLAARKSNLKPFERDISQTYVTQQLIRAAESSNFGAISSVLNDILSRLDKVEAEYLYRLFRKSNLKPFERDISAALQIPFAMQMAYRFNGIGVTQTHWFVTQRNFYEPQAPGQTGKIADYNYKLPDDFTGCVIQLSSNFGAISSVLNDILSRLDKVDFTGCVIAWNSNNLDSKVGGNYNYLSFVIRGDEVRQIAPGQTGKIADYNYTFGAGAALQIPFAMQMISSVLNDILSRLDKVEAEVREFVFKNIDGYFKIYSKHTPINTFGAGAALQIPFAMQMAYRWNSNNLDSKVGGNYNYLYRLFRKSNTGCVIAWNSNNLDSKVGGNYNYLYNYKLPDDFTGCVIAWNSNNLDSKVNFGAISSVLNDILSRLDKVEAEVDILSRLDKVEAEVQQLIRAAEIRASANLAAWFVTQRNFYEPQIITTDNTDFTGCVIAWNSNNLDSKVGGNYNYAWNSNNLDSKVGGNYNYLYRLFRKSREFVFKNIDGYFKIYSKHTPIGCVIAWNSNNLDSKVGGNYNYLYRLIRGDEVRQIAPGQTGKIADYNYKLPQIAPGQTGKIADYNYKLPDDFTGFVTQRNFYEPQIITTDNTFSFVIRGDEVRQIAPGQTGTFGAGAALQIPFAMIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYIAWNSNNLDSKVGGNYNYLYRLFRVKQLSSNFGAISSVLNAYRFNGIGVTQNVLREFVFKNIDGYFKIYYKLPDDFTGCVIAWNSNNLDSKVGLPDDFTGCVIAWNSNNLDSKVGGNLQTYVTQQLIRAAVLNDILSRLDKVEAEVQLSSNFGAISSVLNDILSRLDKVEGNYNYLYRLFRKSNLKPFERDIS
\ No newline at end of file
diff --git a/SupplementaryResults/SpikeHOGVAX/639_chosen_peptides_hogvax_inc_substrings.txt b/SupplementaryResults/SpikeHOGVAX/639_chosen_peptides_hogvax_inc_substrings.txt
deleted file mode 100644
index a1311d0039a076ffe9cb53976f0d0a3679bdf1cb..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SpikeHOGVAX/639_chosen_peptides_hogvax_inc_substrings.txt
+++ /dev/null
@@ -1,149 +0,0 @@
-LQIPFAMQ
-YYVGYLQPR
-GTITDAVD
-PFAMQMAY
-VSVITPGT
-GWIFGTTLD
-VVFLHVTYV
-YNSASFSTF
-IPFAMQMA
-FCGKGYHL
-FPQSAPHGV
-HLMSFPQSA
-FCGKGYHLMS
-SETKCTLKS
-SETKCTLKSF
-ETKCTLKS
-QIPFAMQM
-IPFAMQMAYR
-EFVFKNID
-CGKGYHLMSF
-ITDAVDCA
-AMQMAYRFN
-IPIGAGICA
-SAPHGVVF
-WIFGTTLD
-EFVFKNIDGY
-IPFAMQMAY
-GGVSVITPG
-CSFGGVSVIT
-TITDAVDC
-KVGGNYNY
-SFPQSAPHG
-RGVYYPDK
-AYYVGYLQPR
-QSAPHGVVF
-IPIGAGIC
-FLHVTYVP
-FAMQMAYRF
-SFPQSAPH
-WIFGTTLDS
-ALQIPFAMQ
-TDAVDCAL
-PQSAPHGVV
-AYYVGYLQ
-YHLMSFPQS
-REFVFKNID
-SETKCTLK
-MAYRFNGIG
-KVGGNYNYLY
-SYECDIPI
-FAMQMAYRFN
-KVGGNYNYL
-AYRFNGIG
-VVFLHVTY
-DIPIGAGI
-VFLHVTYVP
-DIPIGAGICA
-GAGICASY
-LYNSASFSTF
-ITDAVDCAL
-VFKNIDGY
-NSYECDIP
-MQMAYRFN
-FVFKNIDG
-AYYVGYLQP
-SFPQSAPHGV
-SFGGVSVIT
-FPQSAPHGVV
-FEYVSQPF
-VGGNYNYL
-GGVSVITPGT
-QSAPHGVV
-LYNSASFS
-MAYRFNGI
-IGAGICASY
-IFGTTLDS
-FGTTLDSK
-GGNYNYLY
-REFVFKNI
-PQSAPHGV
-LREFVFKNI
-SFGGVSVI
-VGGNYNYLY
-GVSVITPG
-QIPFAMQMA
-PFAMQMAYR
-CGKGYHLMS
-NSASFSTF
-QMAYRFNGI
-IGAGICAS
-APHGVVFL
-CSFGGVSV
-VFLHVTYV
-AYRFNGIGV
-LREFVFKNID
-MQMAYRFNG
-IFGTTLDSK
-TFEYVSQP
-PIGAGICAS
-GWIFGTTL
-EFVFKNIDG
-YNSASFST
-GKGYHLMS
-TITDAVDCA
-CSFGGVSVI
-ALQIPFAM
-GVVFLHVTY
-ETKCTLKSF
-FVFKNIDGY
-IPIGAGICAS
-CGKGYHLM
-FAMQMAYR
-GVVFLHVTYV
-VVFLHVTYVP
-YHLMSFPQ
-YHLMSFPQSA
-LMSFPQSA
-AMQMAYRF
-KGYHLMSF
-YECDIPIG
-GYHLMSFP
-MAYRFNGIGV
-QMAYRFNG
-WIFGTTLDSK
-FPQSAPHG
-YRFNGIGV
-YYVGYLQP
-LREFVFKN
-GKGYHLMSF
-PIGAGICA
-GGVSVITP
-SYECDIPIG
-GVVFLHVT
-LYNSASFST
-TITDAVDCAL
-MQMAYRFNGI
-REFVFKNIDG
-PQSAPHGVVF
-FGGVSVIT
-HLMSFPQS
-SAPHGVVFL
-GVSVITPGT
-TKCTLKSF
-KGYHLMSFP
-DIPIGAGIC
-FCGKGYHLM
-YVGYLQPR
-GKGYHLMSFP
-LQIPFAMQM
\ No newline at end of file
diff --git a/SupplementaryResults/SpikeHOGVAX/639_hogvaxine.txt b/SupplementaryResults/SpikeHOGVAX/639_hogvaxine.txt
deleted file mode 100644
index 52b6664b020521691805b89284cfe10f8f2c7112..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SpikeHOGVAX/639_hogvaxine.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-> MHC optimized combined peptide vaccine sequence with overlaps
-CSFGGVSVITPGTITDAVDCALQIPFAMQMAYRFNGIGVLREFVFKNIDGYSETKCTLKSFGWIFGTTLDSKVGGNYNYLYNSASFSTFEYVSQPFQMAYYVGYLQPRGVYYPDKVGGVSNSYECDIPIGAGICASYGSFCGKGYHLMSFPQSAPHGVVFLHVTYVP
-> MHC optimized combined peptide vaccine sequence concatenated
-LQIPFAMQYYVGYLQPRGTITDAVDVSVITPGTGWIFGTTLDYNSASFSTFVVFLHVTYVIPFAMQMAFCGKGYHLFPQSAPHGVHLMSFPQSAFCGKGYHLMSSETKCTLKSSETKCTLKSFQIPFAMQMIPFAMQMAYRCGKGYHLMSFAMQMAYRFNIPIGAGICASAPHGVVFWIFGTTLDEFVFKNIDGYIPFAMQMAYGGVSVITPGCSFGGVSVITTITDAVDCKVGGNYNYSFPQSAPHGRGVYYPDKQSAPHGVVFAYYVGYLQPRFLHVTYVPFAMQMAYRFSFPQSAPHWIFGTTLDSALQIPFAMQTDAVDCALPQSAPHGVVAYYVGYLQYHLMSFPQSREFVFKNIDSETKCTLKMAYRFNGIGKVGGNYNYLYSYECDIPIFAMQMAYRFNKVGGNYNYLDIPIGAGIVFLHVTYVPDIPIGAGICAGAGICASYLYNSASFSTFITDAVDCALVFKNIDGYNSYECDIPMQMAYRFNAYYVGYLQPSFPQSAPHGVSFGGVSVITFPQSAPHGVVFEYVSQPFGGVSVITPGTLYNSASFSMAYRFNGIIGAGICASYFGTTLDSKLREFVFKNIVGGNYNYLYQIPFAMQMAKGYHLMSFPPFAMQMAYRCGKGYHLMSNSASFSTFQMAYRFNGIIGAGICASAPHGVVFLCSFGGVSVAYRFNGIGVLREFVFKNIDMQMAYRFNGIFGTTLDSKTFEYVSQPPIGAGICASGWIFGTTLEFVFKNIDGTITDAVDCACSFGGVSVIALQIPFAMGVVFLHVTYETKCTLKSFFVFKNIDGYIPIGAGICASFAMQMAYRGVVFLHVTYVVVFLHVTYVPYHLMSFPQYHLMSFPQSALMSFPQSAYECDIPIGGYHLMSFPMAYRFNGIGVWIFGTTLDSKYRFNGIGVLREFVFKNGKGYHLMSFGGVSVITPSYECDIPIGGVVFLHVTLYNSASFSTTITDAVDCALMQMAYRFNGIREFVFKNIDGPQSAPHGVVFFGGVSVITSAPHGVVFLGVSVITPGTTKCTLKSFGGNYNYLYDIPIGAGICFCGKGYHLMYVGYLQPRGKGYHLMSFPLQIPFAMQM
\ No newline at end of file
diff --git a/SupplementaryResults/SpikeHOGVAX/evalvax-unlinked.csv b/SupplementaryResults/SpikeHOGVAX/evalvax-unlinked.csv
deleted file mode 100644
index 3cb6a15409718c7c545adf4ee2ecc3154201708e..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SpikeHOGVAX/evalvax-unlinked.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-,Method,Population Coverage,Number of Peptides
-0,MHCI OptiVax-Unlinked,0.8801156847533053,19
-1,MHCI HOGVAX,0.8880697526406931,149
-2,MHCII OptiVax-Unlinked,0.9300214630615633,19
-3,MHCII HOGVAX,0.9635200707398426,1180
diff --git a/SupplementaryResults/SpikeHOGVAX/hit_hist_mhc1_HOGVAX.csv b/SupplementaryResults/SpikeHOGVAX/hit_hist_mhc1_HOGVAX.csv
deleted file mode 100644
index 7c54c358afe1696e895079f7e67ac59e2d9b7cc8..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SpikeHOGVAX/hit_hist_mhc1_HOGVAX.csv
+++ /dev/null
@@ -1,25 +0,0 @@
-,count,prob
-0,0,0.007030629790698364
-1,1,0.09880267262651987
-2,2,0.15424160000050577
-3,3,0.08782338080736832
-4,4,0.07163626140696201
-5,5,0.050602919468027044
-6,6,0.06229827290754035
-7,7,0.15390189321482584
-8,8,0.15638662266255707
-9,9,0.0537167936368702
-10,10,0.03257029884066984
-11,11,0.02158472366325711
-12,12,0.00914772287058042
-13,13,0.028466808508070776
-14,14,0.010439043136865745
-15,15,0.0003849245364235028
-16,16,0.0008694677688031713
-17,17,8.968388640060034e-05
-18,18,6.280267050899451e-06
-19,19,0.0
-20,20,0.0
-21,21,0.0
-22,22,0.0
-23,23,0.0
diff --git a/SupplementaryResults/SpikeHOGVAX/hit_hist_mhc1_OptiVax.csv b/SupplementaryResults/SpikeHOGVAX/hit_hist_mhc1_OptiVax.csv
deleted file mode 100644
index e1f01c8d5dd7008c09ed22d1b6f85587d5be2f7b..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SpikeHOGVAX/hit_hist_mhc1_OptiVax.csv
+++ /dev/null
@@ -1,18 +0,0 @@
-,count,prob
-0,0,0.007030629790698364
-1,1,0.09981921824344599
-2,2,0.19522998737147698
-3,3,0.2159382834976773
-4,4,0.12366444237212021
-5,5,0.10957217111596876
-6,6,0.13568448854836912
-7,7,0.07521343267647497
-8,8,0.02979855361319842
-9,9,0.005347831358350252
-10,10,0.002411099335967603
-11,11,0.00028468262008907196
-12,12,5.179456197033931e-06
-13,13,0.0
-14,14,0.0
-15,15,0.0
-16,16,0.0
diff --git a/SupplementaryResults/SpikeHOGVAX/hit_hist_mhc2_HOGVAX.csv b/SupplementaryResults/SpikeHOGVAX/hit_hist_mhc2_HOGVAX.csv
deleted file mode 100644
index 096e0419874e651b4c9444afd19f34e2d1f8c15d..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SpikeHOGVAX/hit_hist_mhc2_HOGVAX.csv
+++ /dev/null
@@ -1,382 +0,0 @@
-,count,prob
-0,0,0.013812362127136992
-1,1,0.03156721339012784
-2,2,0.0075657579130143145
-3,3,0.08008606314725554
-4,4,0.05006940589536811
-5,5,0.003924254587825054
-6,6,0.0274314300327875
-7,7,0.040334991749930375
-8,8,0.019342749209270108
-9,9,0.005574762083767487
-10,10,0.05291500704764909
-11,11,0.0010187400788622143
-12,12,0.001684689666272161
-13,13,0.004038640266188352
-14,14,0.010010846549613659
-15,15,0.0005999307884991537
-16,16,0.005964800880795699
-17,17,0.0005414043763454203
-18,18,0.004689676012848896
-19,19,0.004488899795545237
-20,20,0.0034966202596388124
-21,21,0.008000153481536458
-22,22,0.0014282673390164987
-23,23,6.17046919609258e-05
-24,24,0.0006606472962574384
-25,25,0.02774499006384859
-26,26,0.01831515285477123
-27,27,0.0013235781725837667
-28,28,0.03715647968242838
-29,29,0.0013779729701729328
-30,30,0.0007830148574479167
-31,31,0.004041404803198397
-32,32,0.024584582098681747
-33,33,0.01444611061014333
-34,34,0.012562145081527566
-35,35,0.009193118327019327
-36,36,0.01794569368556729
-37,37,0.002092359343904703
-38,38,0.004047400885582364
-39,39,0.0055643677840130825
-40,40,0.014504843628222786
-41,41,0.014537441100195924
-42,42,0.00455966098072028
-43,43,0.01769151962398271
-44,44,0.01714274596132365
-45,45,0.0009475493794987135
-46,46,0.004724586341904466
-47,47,0.008582019808657634
-48,48,0.0037165513360969613
-49,49,0.0007430311568893726
-50,50,0.007984137881169338
-51,51,0.0019492476816664734
-52,52,0.0007942933600113221
-53,53,0.0008225661071175776
-54,54,0.002258966616529544
-55,55,0.0003740464262916227
-56,56,0.0016005144753605705
-57,57,0.003160416983247458
-58,58,0.008046747796795116
-59,59,0.002096337764095481
-60,60,0.0006192655033079523
-61,61,0.021454150754820815
-62,62,0.01633609423389789
-63,63,0.003665080419264355
-64,64,0.027219619310032105
-65,65,0.014397614222901006
-66,66,0.008248861678653484
-67,67,0.0013760387884644949
-68,68,0.021504742564324563
-69,69,0.002806745938613908
-70,70,0.001747803482498743
-71,71,0.0008583986551044845
-72,72,0.0064347050933437925
-73,73,0.005300536831456461
-74,74,0.004847424386104442
-75,75,0.004100800505682233
-76,76,0.0029477252803543695
-77,77,0.0004670998426011276
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\ No newline at end of file
diff --git a/SupplementaryResults/SpikeHOGVAX/min_hit_dictmhc1_HOGVAX.csv b/SupplementaryResults/SpikeHOGVAX/min_hit_dictmhc1_HOGVAX.csv
deleted file mode 100644
index 0f7d19946f522663821617169e1c14a0bad5ef58..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SpikeHOGVAX/min_hit_dictmhc1_HOGVAX.csv
+++ /dev/null
@@ -1,41 +0,0 @@
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diff --git a/SupplementaryResults/SpikeHOGVAX/min_hit_dictmhc1_OptiVax.csv b/SupplementaryResults/SpikeHOGVAX/min_hit_dictmhc1_OptiVax.csv
deleted file mode 100644
index 636bf69d2abda318f97eb1d9c0712f82dbf3a256..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SpikeHOGVAX/min_hit_dictmhc1_OptiVax.csv
+++ /dev/null
@@ -1,41 +0,0 @@
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diff --git a/SupplementaryResults/SpikeHOGVAX/min_hit_dictmhc2_HOGVAX.csv b/SupplementaryResults/SpikeHOGVAX/min_hit_dictmhc2_HOGVAX.csv
deleted file mode 100644
index f7274c4e62564cafbe0b293979d2ccf35b99ed2d..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SpikeHOGVAX/min_hit_dictmhc2_HOGVAX.csv
+++ /dev/null
@@ -1,41 +0,0 @@
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diff --git a/SupplementaryResults/SpikeHOGVAX/min_hit_dictmhc2_OptiVax.csv b/SupplementaryResults/SpikeHOGVAX/min_hit_dictmhc2_OptiVax.csv
deleted file mode 100644
index 06f7445ac091ae6daca75c54ce64d4263249e8be..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SpikeHOGVAX/min_hit_dictmhc2_OptiVax.csv
+++ /dev/null
@@ -1,41 +0,0 @@
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diff --git a/SupplementaryResults/SpikeHOGVAX/min_hit_number_bar_mhc1_HOGVAX.html b/SupplementaryResults/SpikeHOGVAX/min_hit_number_bar_mhc1_HOGVAX.html
deleted file mode 100644
index ae5587c7327c092709c4706e4cda69df86c5fc42..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SpikeHOGVAX/min_hit_number_bar_mhc1_HOGVAX.html
+++ /dev/null
@@ -1,35 +0,0 @@
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\ No newline at end of file
diff --git a/SupplementaryResults/SpikeHOGVAX/min_hit_number_bar_mhc1_OptiVax.html b/SupplementaryResults/SpikeHOGVAX/min_hit_number_bar_mhc1_OptiVax.html
deleted file mode 100644
index 9c48c7e6ac3061a7479e360a164982192fc9b29d..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SpikeHOGVAX/min_hit_number_bar_mhc1_OptiVax.html
+++ /dev/null
@@ -1,35 +0,0 @@
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\ No newline at end of file
diff --git a/SupplementaryResults/SpikeHOGVAX/min_hit_number_bar_mhc2_HOGVAX.html b/SupplementaryResults/SpikeHOGVAX/min_hit_number_bar_mhc2_HOGVAX.html
deleted file mode 100644
index cc6ea91779f7f910cbef458096c34c79b322e143..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SpikeHOGVAX/min_hit_number_bar_mhc2_HOGVAX.html
+++ /dev/null
@@ -1,35 +0,0 @@
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deleted file mode 100644
index db80971c6a2fe626d5d258491a5b3643dbe667b2..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SpikeHOGVAX/min_hit_number_bar_mhc2_OptiVax.html
+++ /dev/null
@@ -1,35 +0,0 @@
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-<html>
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diff --git a/SupplementaryResults/SpikeHOGVAX/unlinked_pop_coverage_bar.html b/SupplementaryResults/SpikeHOGVAX/unlinked_pop_coverage_bar.html
deleted file mode 100644
index 16c659e1afb3db313bfb4dcbaf0c70f97370635f..0000000000000000000000000000000000000000
--- a/SupplementaryResults/SpikeHOGVAX/unlinked_pop_coverage_bar.html
+++ /dev/null
@@ -1,35 +0,0 @@
-<!DOCTYPE html>
-<html>
-<head>
- <style>
- .error {
- color: red;
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- </style>
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diff --git a/Thesis.pdf b/Thesis.pdf
deleted file mode 100644
index 9bdbdd5bac984e602f2197ee2a162a6aa069027d..0000000000000000000000000000000000000000
Binary files a/Thesis.pdf and /dev/null differ
diff --git a/VaccineFormulations/mhc1_allele/4512_chosen_peptides_hog.txt b/VaccineFormulations/mhc1_allele/4512_chosen_peptides_hog.txt
deleted file mode 100644
index 3764c9da023401576e4ea63d258a4bfc7c662250..0000000000000000000000000000000000000000
--- a/VaccineFormulations/mhc1_allele/4512_chosen_peptides_hog.txt
+++ /dev/null
@@ -1,87 +0,0 @@
-PYFFTLLLQ
-AQFAPSASA
-TQMNLKYA
-NYMPYFFT
-SQMEIDFL
-QFHQKLLK
-TISLAGSYKD
-ISLAGSYKD
-ISDYDYYRY
-RQFHQKLLK
-GEAANFCAL
-MPYFFTLLL
-GEAANFCA
-YRARAGEA
-ETISLAGSY
-QMNLKYAI
-HSYFTSDY
-MARKTLNSL
-SLAGSYKDW
-LHSYFTSD
-YFFTLLLQ
-TQMNLKYAI
-EAANFCAL
-FAPSASAF
-LHSYFTSDY
-MADQAMTQ
-PFNKWGKA
-MEIDFLEL
-KSYELQTPF
-ADQAMTQM
-ETISLAGS
-ISDYDYYR
-EIDFLELA
-ISLAGSYKDW
-FPFNKWGK
-WFLAYILF
-LAYILFTR
-QFAPSASAF
-HSYFTSDYY
-LAGSYKDW
-WFLAYILFTR
-YELQTPFE
-YMPYFFTLL
-YMPYFFTL
-QEILGTVS
-AEWFLAYILF
-LVAEWFLA
-ETISLAGSYK
-SYELQTPFE
-RQFHQKLL
-SQMEIDFLEL
-FEYVSQPF
-EILGTVSW
-YASAVVLL
-MARKTLNS
-MEIDFLELA
-SQMEIDFLE
-TISLAGSYK
-QMEIDFLEL
-EWFLAYILF
-FLAYILFTR
-YMPYFFTLLL
-FPFNKWGKA
-WFLAYILFT
-SYFTSDYY
-KSYELQTPFE
-MPYFFTLLLQ
-ASAVVLLI
-QEILGTVSW
-IPFAMQMAY
-YRARAGEAA
-RKTLNSLE
-AEWFLAYIL
-IPFAMQMA
-PFAMQMAY
-LVAEWFLAY
-KSYELQTP
-ARKTLNSL
-MADQAMTQM
-YASAVVLLI
-SDYDYYRY
-NYMPYFFTL
-AEWFLAYI
-AQFAPSAS
-AQFAPSASAF
-ARKTLNSLE
-VAEWFLAY
\ No newline at end of file
diff --git a/VaccineFormulations/mhc1_allele/4512_chosen_peptides_hogvax_inc_substrings.txt b/VaccineFormulations/mhc1_allele/4512_chosen_peptides_hogvax_inc_substrings.txt
deleted file mode 100644
index 1e805d423e7d52723a962cd4ca8492f8a2f4db5d..0000000000000000000000000000000000000000
--- a/VaccineFormulations/mhc1_allele/4512_chosen_peptides_hogvax_inc_substrings.txt
+++ /dev/null
@@ -1,97 +0,0 @@
-PYFFTLLLQ
-AQFAPSASA
-TQMNLKYA
-NYMPYFFT
-QMEIDFLE
-SQMEIDFL
-QFHQKLLK
-TISLAGSYKD
-ISLAGSYKD
-ISDYDYYRY
-RQFHQKLLK
-GEAANFCAL
-MPYFFTLLL
-GEAANFCA
-MPYFFTLL
-YRARAGEA
-ETISLAGSY
-QMNLKYAI
-HSYFTSDY
-MARKTLNSL
-SLAGSYKDW
-LHSYFTSD
-YFFTLLLQ
-QFAPSASA
-TQMNLKYAI
-EAANFCAL
-FAPSASAF
-LHSYFTSDY
-MADQAMTQ
-PFNKWGKA
-MEIDFLEL
-KSYELQTPF
-ADQAMTQM
-ETISLAGS
-SLAGSYKD
-ISDYDYYR
-EIDFLELA
-ISLAGSYKDW
-FPFNKWGK
-WFLAYILF
-LAYILFTR
-PYFFTLLL
-QFAPSASAF
-HSYFTSDYY
-LAGSYKDW
-WFLAYILFTR
-YELQTPFE
-YMPYFFTLL
-YMPYFFTL
-QEILGTVS
-AEWFLAYILF
-LVAEWFLA
-ETISLAGSYK
-SYELQTPFE
-RQFHQKLL
-SQMEIDFLEL
-FEYVSQPF
-EILGTVSW
-YASAVVLL
-MARKTLNS
-MEIDFLELA
-SQMEIDFLE
-TISLAGSYK
-QMEIDFLEL
-EWFLAYILF
-FLAYILFTR
-YMPYFFTLLL
-FPFNKWGKA
-WFLAYILFT
-SYFTSDYY
-TISLAGSY
-KSYELQTPFE
-MPYFFTLLLQ
-EWFLAYIL
-FLAYILFT
-ASAVVLLI
-QEILGTVSW
-IPFAMQMAY
-YRARAGEAA
-RKTLNSLE
-AEWFLAYIL
-IPFAMQMA
-PFAMQMAY
-LVAEWFLAY
-KSYELQTP
-ARKTLNSL
-MADQAMTQM
-YASAVVLLI
-SDYDYYRY
-NYMPYFFTL
-SYELQTPF
-AEWFLAYI
-AQFAPSAS
-AQFAPSASAF
-ISLAGSYK
-ARKTLNSLE
-VAEWFLAY
\ No newline at end of file
diff --git a/VaccineFormulations/mhc1_allele/4512_hogvaxine.txt b/VaccineFormulations/mhc1_allele/4512_hogvaxine.txt
deleted file mode 100644
index 6fe0965e3083796f1128d088fa73193ca158d841..0000000000000000000000000000000000000000
--- a/VaccineFormulations/mhc1_allele/4512_hogvaxine.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-> MHC optimized combined peptide vaccine sequence with overlaps
-PLHSYFTSDYYRARAGEAANFCALVAEWFLAYILFTRQFHQKLLKSYELQTPFEYVSQPFPFNKWGKASQMEIDFLELAQFAPSASAFMADQAMTQMNLKYAIPFAMQMAYASAVVLLISDYDYYRYNYMPYFFTLLLQEILGTVSWMARKTLNSLETISLAGSYKDW
-> MHC optimized combined peptide vaccine sequence concatenated
-PYFFTLLLQAQFAPSASATQMNLKYANYMPYFFTSQMEIDFLQFHQKLLKTISLAGSYKDISLAGSYKDISDYDYYRYRQFHQKLLKGEAANFCALMPYFFTLLLGEAANFCAYRARAGEAETISLAGSYQMNLKYAIHSYFTSDYMARKTLNSLSLAGSYKDWLHSYFTSDYFFTLLLQTQMNLKYAIEAANFCALFAPSASAFLHSYFTSDYMADQAMTQPFNKWGKAMEIDFLELKSYELQTPFADQAMTQMETISLAGSISDYDYYREIDFLELAISLAGSYKDWFPFNKWGKWFLAYILFLAYILFTRQFAPSASAFHSYFTSDYYLAGSYKDWWFLAYILFTRYELQTPFEYMPYFFTLLYMPYFFTLQEILGTVSAEWFLAYILFLVAEWFLAETISLAGSYKSYELQTPFERQFHQKLLSQMEIDFLELFEYVSQPFEILGTVSWYASAVVLLMARKTLNSMEIDFLELASQMEIDFLETISLAGSYKQMEIDFLELEWFLAYILFFLAYILFTRYMPYFFTLLLFPFNKWGKAWFLAYILFTSYFTSDYYKSYELQTPFEMPYFFTLLLQASAVVLLIQEILGTVSWIPFAMQMAYYRARAGEAARKTLNSLEAEWFLAYILIPFAMQMAPFAMQMAYLVAEWFLAYKSYELQTPARKTLNSLMADQAMTQMYASAVVLLISDYDYYRYNYMPYFFTLAEWFLAYIAQFAPSASAQFAPSASAFARKTLNSLEVAEWFLAY
\ No newline at end of file
diff --git a/VaccineFormulations/mhc1_haplotype/4519_chosen_peptides_hog.txt b/VaccineFormulations/mhc1_haplotype/4519_chosen_peptides_hog.txt
deleted file mode 100644
index b5b880b195ffdf5d9c019e283389a8099ddc4174..0000000000000000000000000000000000000000
--- a/VaccineFormulations/mhc1_haplotype/4519_chosen_peptides_hog.txt
+++ /dev/null
@@ -1,103 +0,0 @@
-DQAMTQMY
-AYFNMVYM
-TDLEGNFYG
-STMTNRQFHQ
-LQFTSLEIP
-VMYASAVV
-ETISLAGSYK
-TVAYFNMVYM
-LQFTSLEIPR
-AYFNMVYMP
-HSYFTSDY
-TVAYFNMVY
-MADQAMTQM
-IPFAMQMAY
-ELPTGVHAGT
-MYASAVVLL
-IETISLAG
-SLAGSYKDW
-FAMQMAYR
-MPYFFTLLL
-TISLAGSYKD
-ETISLAGSY
-ASAVVLLI
-HAGTDLEG
-ELPTGVHA
-MARKTLNSL
-IPFAMQMAYR
-STMTNRQF
-LPTGVHAGT
-YDKLQFTS
-GTDLEGNFYG
-ELPTGVHAG
-CTFEYVSQ
-TNRQFHQKLL
-HSYFTSDYY
-DLEGNFYG
-MARKTLNSLE
-PTGVHAGTD
-FTSLEIPR
-SQMEIDFLE
-PYFFTLLL
-YDKLQFTSL
-TMTNRQFHQK
-GTDLEGNFY
-YASAVVLLI
-IPFAMQMA
-ISLAGSYKDW
-EIDFLELA
-MADQAMTQ
-VAYFNMVYM
-SETKCTLKS
-VVLHSYFT
-MYASAVVLLI
-QFHQKLLK
-LAGSYKDW
-SETKCTLKSF
-AGTDLEGN
-KLQFTSLEIP
-DKLQFTSLEI
-LPTGVHAGTD
-TFEYVSQPF
-MEIDFLEL
-CTFEYVSQPF
-MEIDFLELA
-QFTSLEIPR
-GVHAGTDL
-SETKCTLK
-NRQFHQKLL
-YFNMVYMP
-ADQAMTQM
-SYFTSDYY
-DKLQFTSLE
-MARKTLNS
-AGTDLEGNF
-CTFEYVSQP
-ETKCTLKSF
-TNRQFHQK
-MYASAVVL
-STMTNRQFH
-YDKLQFTSLE
-YMPYFFTL
-QMEIDFLEL
-FEYVSQPF
-KLQFTSLEI
-PFAMQMAYR
-TKCTLKSF
-ARKTLNSLE
-AGTDLEGNFY
-TVAYFNMV
-YMPYFFTLL
-TISLAGSYK
-ISLAGSYKD
-QMEIDFLE
-RKTLNSLE
-TGVHAGTD
-TMTNRQFHQ
-SQMEIDFL
-TNRQFHQKL
-KDCVVLHS
-RQFHQKLL
-MPYFFTLL
-MTNRQFHQK
-ETISLAGS
\ No newline at end of file
diff --git a/VaccineFormulations/mhc1_haplotype/4519_chosen_peptides_hogvax_inc_substrings.txt b/VaccineFormulations/mhc1_haplotype/4519_chosen_peptides_hogvax_inc_substrings.txt
deleted file mode 100644
index 014f136fdc868c4b0fddd04ca0c6ccb0c3a9686b..0000000000000000000000000000000000000000
--- a/VaccineFormulations/mhc1_haplotype/4519_chosen_peptides_hogvax_inc_substrings.txt
+++ /dev/null
@@ -1,123 +0,0 @@
-DQAMTQMY
-DKLQFTSL
-AYFNMVYM
-TDLEGNFYG
-SLAGSYKD
-STMTNRQFHQ
-LQFTSLEIP
-VMYASAVV
-ETISLAGSYK
-LQFTSLEIPR
-TVAYFNMVYM
-AYFNMVYMP
-HSYFTSDY
-TVAYFNMVY
-MADQAMTQM
-IPFAMQMAY
-ELPTGVHAGT
-MYASAVVLL
-IETISLAG
-SLAGSYKDW
-FAMQMAYR
-QFTSLEIP
-MPYFFTLLL
-TISLAGSYKD
-ETISLAGSY
-ASAVVLLI
-HAGTDLEG
-ELPTGVHA
-MARKTLNSL
-IPFAMQMAYR
-STMTNRQF
-LPTGVHAGT
-YDKLQFTS
-GTDLEGNFYG
-ELPTGVHAG
-CTFEYVSQ
-TNRQFHQKLL
-HSYFTSDYY
-DLEGNFYG
-MARKTLNSLE
-PTGVHAGTD
-FTSLEIPR
-SQMEIDFLE
-PYFFTLLL
-YDKLQFTSL
-PFAMQMAY
-TMTNRQFHQK
-GTDLEGNFY
-YASAVVLLI
-IPFAMQMA
-ISLAGSYKDW
-EIDFLELA
-MADQAMTQ
-VAYFNMVYM
-SETKCTLKS
-VVLHSYFT
-MYASAVVLLI
-LQFTSLEI
-TISLAGSY
-QFHQKLLK
-LAGSYKDW
-LPTGVHAG
-PTGVHAGT
-SETKCTLKSF
-LPTGVHAGTD
-AGTDLEGN
-KLQFTSLEIP
-DKLQFTSLEI
-TFEYVSQPF
-MEIDFLEL
-TFEYVSQP
-CTFEYVSQPF
-NRQFHQKL
-MEIDFLELA
-ETKCTLKS
-QFTSLEIPR
-GVHAGTDL
-SETKCTLK
-NRQFHQKLL
-YFNMVYMP
-ADQAMTQM
-SYFTSDYY
-MTNRQFHQ
-DKLQFTSLE
-MARKTLNS
-AGTDLEGNF
-CTFEYVSQP
-ETKCTLKSF
-ARKTLNSL
-TNRQFHQK
-MYASAVVL
-STMTNRQFH
-YDKLQFTSLE
-YMPYFFTL
-QMEIDFLEL
-VAYFNMVY
-FEYVSQPF
-KLQFTSLEI
-YASAVVLL
-PFAMQMAYR
-TKCTLKSF
-TDLEGNFY
-ARKTLNSLE
-AGTDLEGNFY
-TVAYFNMV
-YMPYFFTLL
-TISLAGSYK
-ISLAGSYKD
-GTDLEGNF
-ISLAGSYK
-QMEIDFLE
-RKTLNSLE
-TGVHAGTD
-TMTNRQFHQ
-SQMEIDFL
-TNRQFHQKL
-KDCVVLHS
-RQFHQKLL
-MPYFFTLL
-MTNRQFHQK
-ETISLAGS
-TMTNRQFH
-KLQFTSLE
\ No newline at end of file
diff --git a/VaccineFormulations/mhc1_haplotype/4519_hogvaxine.txt b/VaccineFormulations/mhc1_haplotype/4519_hogvaxine.txt
deleted file mode 100644
index 79ea3e6df9fbdfe303882953c0666a564e88ee29..0000000000000000000000000000000000000000
--- a/VaccineFormulations/mhc1_haplotype/4519_hogvaxine.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-> MHC optimized combined peptide vaccine sequence with overlaps
-MARKTLNSLEKMADQAMTQMYDKLQFTSLEIPRSQMEIDFLELAITVAYFNMVYMPYFFTLLLNIPFAMQMAYRSETKCTLKSFNKWGKIELPTGVHAGTDLEGNFYGSFCTFEYVSQPFSTMTNRQFHQKLLKDCVMYASAVVLLIETISLAGSYKDWKDCVVLHSYFTSDYY
-> MHC optimized combined peptide vaccine sequence concatenated
-DQAMTQMYAYFNMVYMTDLEGNFYGSTMTNRQFHQLQFTSLEIPVMYASAVVETISLAGSYKTVAYFNMVYMLQFTSLEIPRAYFNMVYMPHSYFTSDYTVAYFNMVYMADQAMTQMIPFAMQMAYELPTGVHAGTMYASAVVLLIETISLAGSLAGSYKDWFAMQMAYRMPYFFTLLLTISLAGSYKDETISLAGSYASAVVLLIHAGTDLEGELPTGVHAMARKTLNSLIPFAMQMAYRSTMTNRQFLPTGVHAGTYDKLQFTSGTDLEGNFYGELPTGVHAGCTFEYVSQTNRQFHQKLLHSYFTSDYYDLEGNFYGMARKTLNSLEPTGVHAGTDFTSLEIPRSQMEIDFLEPYFFTLLLYDKLQFTSLTMTNRQFHQKGTDLEGNFYYASAVVLLIIPFAMQMAISLAGSYKDWEIDFLELAMADQAMTQVAYFNMVYMSETKCTLKSVVLHSYFTMYASAVVLLIQFHQKLLKLAGSYKDWSETKCTLKSFAGTDLEGNKLQFTSLEIPDKLQFTSLEILPTGVHAGTDTFEYVSQPFMEIDFLELCTFEYVSQPFMEIDFLELAQFTSLEIPRGVHAGTDLSETKCTLKNRQFHQKLLYFNMVYMPADQAMTQMSYFTSDYYDKLQFTSLEMARKTLNSAGTDLEGNFCTFEYVSQPETKCTLKSFTNRQFHQKMYASAVVLSTMTNRQFHYDKLQFTSLEYMPYFFTLQMEIDFLELFEYVSQPFKLQFTSLEIPFAMQMAYRTKCTLKSFARKTLNSLEAGTDLEGNFYTVAYFNMVYMPYFFTLLTISLAGSYKISLAGSYKDQMEIDFLERKTLNSLETGVHAGTDTMTNRQFHQSQMEIDFLTNRQFHQKLKDCVVLHSRQFHQKLLMPYFFTLLMTNRQFHQKETISLAGS
\ No newline at end of file
diff --git a/VaccineFormulations/mhc2_allele/37435_chosen_peptides_hog.txt b/VaccineFormulations/mhc2_allele/37435_chosen_peptides_hog.txt
deleted file mode 100644
index fc290b466727cdbfcad8e4e03914fc347ebb871b..0000000000000000000000000000000000000000
--- a/VaccineFormulations/mhc2_allele/37435_chosen_peptides_hog.txt
+++ /dev/null
@@ -1,278 +0,0 @@
-THHWLLLTILTSLLVLVQS
-MMGFKMNYQVNGYPNMFITR
-MNYQVNGYPNMFITR
-WLTNIFGTVYEKLK
-SYFIASFRLFARTRSMWSFNPETN
-ILTSLLVLVQSTQW
-TFCAGSTFISDEVARDLSLQFKR
-VARDLSLQFKRPI
-TFISDEVARDLSLQFKRP
-LSYFIASFRLFARTRSMWSFNP
-CDTFCAGSTFISDEVARDLSL
-VGLMWLSYFIASFR
-TQQLIRAAEIRASANL
-TFCAGSTFISDEVARDLSLQFKRP
-MTYRRLISMMGFKMNYQVNGY
-FIASFRLFARTRSMWSFNPETNIL
-RRLISMMGFKMNYQVNGYPNMF
-RLFARTRSMWSFNPETNILLNVPL
-IPTITQMNLKYAISAKNRART
-IPTITQMNLKYAIS
-MTYRRLISMMGFKMNYQVNGYPN
-GSTFISDEVARDLSLQFKRP
-LMWLSYFIASFRLFA
-HFYWFFSNYLKRRV
-THHWLLLTILTSLLVLVQ
-FNMVYMPASWVMRIM
-TIKGTHHWLLLTI
-TQQLIRAAEIRASANLAA
-TVLSFCAFAVDAAKAY
-TKHFYWFFSNYLKRRV
-THHWLLLTILTSLLVLVQST
-FISDEVARDLSLQFKRP
-VKRTIKGTHHWLLL
-IICISTKHFYWFFS
-STVLSFCAFAVDAA
-YVTQQLIRAAEIRAS
-FCAFAVDAAKAYKDYL
-FYWFFSNYLKRRV
-DMTYRRLISMMGFKMNYQVNG
-CDTFCAGSTFISDEVARDLSLQ
-GIAIAMACLVGLMWLSY
-RTRSMWSFNPETNILLNVPLHGTI
-QMNLKYAISAKNRARTVAGVSIC
-RAAEIRASANLAA
-CDTFCAGSTFISDEV
-LTILTSLLVLVQSTQW
-TVAYFNMVYMPASW
-GIPKDMTYRRLISM
-ISAKNRARTVAGVSICST
-PKDMTYRRLISMMGFKMNYQ
-IPKDMTYRRLISMMGFKM
-ISDEVARDLSLQFKRP
-GLMWLSYFIASFR
-AMACLVGLMWLSYFIAS
-NYQVNGYPNMFITR
-IICISTKHFYWFF
-SYFIASFRLFARTRSMWSFNP
-TITQMNLKYAISAKNRARTVA
-LVGLMWLSYFIASF
-CDTFCAGSTFISDEVAR
-KGTHHWLLLTILTS
-NMVYMPASWVMRIM
-WLSYFIASFRLFARTRS
-KNRARTVAGVSICST
-GIAIAMACLVGLMWLSYFI
-ITQMNLKYAISAKNRARTVAG
-IAIAMACLVGLMWLSYFI
-YAISAKNRARTVAGVSICST
-ATVAYFNMVYMPA
-LFARTRSMWSFNPETNILLNVPLHG
-MNLKYAISAKNRARTVAGVSICST
-CAGSTFISDEVARDLSLQFKRP
-KDMTYRRLISMMGFKMNYQVNG
-CDTFCAGSTFISDEVARD
-THHWLLLTILTSLLVLVQSTQW
-IPKDMTYRRLISMMGFKMNY
-YVTQQLIRAAEIR
-SMMGFKMNYQVNGYPNMFITR
-STVLSFCAFAVDAAKAY
-IPTITQMNLKYAISAKNR
-AYFNMVYMPASWVMRI
-TKHFYWFFSNYLK
-STVLSFCAFAVDAAK
-CDTFCAGSTFISDEVARDLSLQF
-ISTKHFYWFFSNYL
-RARTVAGVSICST
-KHFYWFFSNYLKRRV
-TNILLNVPLHGTI
-MTYRRLISMMGFKMNYQVNGYP
-TRSMWSFNPETNILLNVPLHGTI
-ASFRLFARTRSMWSFNPETNILLN
-IPTITQMNLKYAISAKNRAR
-FRLFARTRSMWSFNPETNILLNVPL
-GIPKDMTYRRLIS
-ITQMNLKYAISAKNRARTVAGVSIC
-STKHFYWFFSNYLK
-TYRRLISMMGFKMNYQVNGYPN
-ACLVGLMWLSYFIAS
-CDTFCAGSTFISDEVARDLSLQFK
-GTHHWLLLTILTS
-IASFRLFARTRSMWSFNPETNILLN
-LIRAAEIRASANLAA
-THHWLLLTILTSL
-NPETNILLNVPLHGTI
-QWLTNIFGTVYEK
-ITQMNLKYAISAKNRARTVAGVSI
-IAMACLVGLMWLSYFI
-STFISDEVARDLSLQFKRP
-TFCAGSTFISDEVARDLSLQFK
-ICISTKHFYWFFSNY
-GIAIAMACLVGLMW
-VGLMWLSYFIASF
-VKRTIKGTHHWLLLT
-IICISTKHFYWFFSNY
-KYAISAKNRARTVAGVSICST
-WLLLTILTSLLVLVQSTQW
-DTFCAGSTFISDEVARDLSLQFK
-SDEVARDLSLQFKRPI
-EVARDLSLQFKRPI
-THHWLLLTILTSLLVL
-AISAKNRARTVAGVSICST
-GIPKDMTYRRLISMMG
-VKRTIKGTHHWLL
-CDTFCAGSTFISD
-SYFIASFRLFARTRSMWSFNPETNI
-FARTRSMWSFNPETNILLNVPLHG
-FARTRSMWSFNPETNILLNVPLHGT
-ATVAYFNMVYMPASW
-FRLFARTRSMWSFNPETNILLNVP
-MGFKMNYQVNGYPNMFITR
-MWSFNPETNILLNVPLHGTI
-ARTRSMWSFNPETNILLNVPLHGT
-GIAIAMACLVGLMWL
-VAYFNMVYMPASWV
-HWLLLTILTSLLVLVQSTQW
-CAFAVDAAKAYKDYL
-IPTITQMNLKYAI
-IPTITQMNLKYAISA
-AGSTFISDEVARDLSLQFKRP
-IPTITQMNLKYAISAKNRA
-HHWLLLTILTSLLVLVQSTQW
-KRTIKGTHHWLLLTI
-TILTSLLVLVQSTQW
-CDTFCAGSTFISDE
-WSFNPETNILLNVPLHGTI
-LLTILTSLLVLVQSTQW
-VLSFCAFAVDAAKAYKDYL
-CDTFCAGSTFISDEVARDL
-QLIRAAEIRASANLAA
-QMNLKYAISAKNRARTVAGVSICS
-YFIASFRLFARTRSMWSFNPETNIL
-AKNRARTVAGVSICST
-KMNYQVNGYPNMFITR
-MVYMPASWVMRIM
-VAYFNMVYMPASWVMRI
-KGTHHWLLLTILT
-THHWLLLTILTSLLVLV
-YFNMVYMPASWVMRIM
-AFAVDAAKAYKDYL
-RSMWSFNPETNILLNVPLHGTI
-CLVGLMWLSYFIAS
-LTSLLVLVQSTQW
-ARTRSMWSFNPETNILLNVPLHGTI
-RLFARTRSMWSFNPETNILLNVPLH
-GIAIAMACLVGLMWLSYF
-IPTITQMNLKYAISAKNRARTV
-TKHFYWFFSNYLKR
-IPKDMTYRRLISMMGFKMN
-SMWSFNPETNILLNVPLHGTI
-THHWLLLTILTSLLV
-YFIASFRLFARTRSMWSFNPETNI
-SYFIASFRLFARTRSMWSFNPE
-WLSYFIASFRLFARTRSMW
-FNPETNILLNVPLHGTI
-LSYFIASFRLFARTRSMWSFN
-LFARTRSMWSFNPETNILLNVPLH
-SFRLFARTRSMWSFNPETNILLNV
-TQQLIRAAEIRASANLA
-WLSYFIASFRLFARTRSMWS
-GIPKDMTYRRLISMMGF
-QWLTNIFGTVYEKL
-RRLISMMGFKMNYQVNGYPNMFIT
-WLSYFIASFRLFARTRSM
-IPTITQMNLKYAISAKNRARTVA
-WLSYFIASFRLFAR
-GLMWLSYFIASFRL
-PETNILLNVPLHGTI
-GIPKDMTYRRLISMM
-QQLIRAAEIRASANLAA
-VAYFNMVYMPASW
-TVLSFCAFAVDAAKAYK
-YVTQQLIRAAEIRA
-IASFRLFARTRSMWSFNPETNILL
-SYFIASFRLFARTRSMWSFNPET
-RRLISMMGFKMNYQVNGYPNMFI
-CDTFCAGSTFISDEVA
-YVTQQLIRAAEIRASAN
-IICISTKHFYWFFSN
-NRARTVAGVSICST
-ITQMNLKYAISAKNRARTVAGV
-QMNLKYAISAKNRARTVAGVSICST
-ASFRLFARTRSMWSFNPETNILLNV
-VTQQLIRAAEIRASANL
-ITQMNLKYAISAKNRARTVA
-SFNPETNILLNVPLHGTI
-MACLVGLMWLSYFIAS
-IAMACLVGLMWLSYFIA
-TKHFYWFFSNYLKRR
-YVTQQLIRAAEIRASANL
-TYRRLISMMGFKMNYQVNGYPNMF
-WLSYFIASFRLFARTRSMWSF
-FKMNYQVNGYPNMFITR
-LKYAISAKNRARTVAGVSICST
-SFRLFARTRSMWSFNPETNILLNVP
-SFCAFAVDAAKAYKDYL
-ISMMGFKMNYQVNGYPNMFITR
-TQMNLKYAISAKNRARTVAGVSIC
-VKRTIKGTHHWLLLTI
-GLMWLSYFIASFRLF
-CDTFCAGSTFISDEVARDLS
-PKDMTYRRLISMMGFKMNYQV
-YVTQQLIRAAEIRASA
-IPTITQMNLKYAISAK
-GIAIAMACLVGLM
-FIASFRLFARTRSMWSFNPETNILL
-LLLTILTSLLVLVQSTQW
-FAVDAAKAYKDYL
-MWLSYFIASFRLFA
-LTNIFGTVYEKLK
-WLSYFIASFRLFA
-LSFCAFAVDAAKAYKDYL
-YQVNGYPNMFITR
-DMTYRRLISMMGFKMNYQVNGY
-IAMACLVGLMWLSYFIAS
-FCAGSTFISDEVARDLSLQFKRP
-VAYFNMVYMPASWVM
-CISTKHFYWFFSNY
-STVLSFCAFAVDA
-GIAIAMACLVGLMWLS
-IPKDMTYRRLISMMGFK
-IPKDMTYRRLISMMGFKMNYQ
-WLSYFIASFRLFART
-QWLTNIFGTVYEKLK
-TVLSFCAFAVDAAKAYKD
-TVLSFCAFAVDAAKAYKDY
-AIAMACLVGLMWLSYFI
-ATVAYFNMVYMPAS
-LVGLMWLSYFIAS
-SAKNRARTVAGVSICST
-PKDMTYRRLISMMGFKMNYQVNG
-YFNMVYMPASWVMRI
-RLISMMGFKMNYQVNGYPNMFITR
-LSYFIASFRLFARTRSMWSF
-DEVARDLSLQFKRPI
-TVLSFCAFAVDAAKAYKDYL
-RRLISMMGFKMNYQVNGYPNMFITR
-NLKYAISAKNRARTVAGVSICST
-STKHFYWFFSNYL
-THHWLLLTILTSLLVLVQSTQ
-TYRRLISMMGFKMNYQVNGYPNM
-WLSYFIASFRLFARTR
-ISTKHFYWFFSNY
-RTIKGTHHWLLLTI
-YRRLISMMGFKMNYQVNGYPNMF
-SDEVARDLSLQFKRP
-LISMMGFKMNYQVNGYPNMFITR
-PKDMTYRRLISMMGFKMNYQVN
-THHWLLLTILTSLL
-PTITQMNLKYAISAKNRARTVA
-IRAAEIRASANLAA
-ETNILLNVPLHGTI
-ITQMNLKYAISAKNRARTVAGVS
-STVLSFCAFAVDAAKA
-VAYFNMVYMPASWVMR
-GIPKDMTYRRLISMMGFK
-GFKMNYQVNGYPNMFITR
-GLMWLSYFIASFRLFA
-IPTITQMNLKYAISAKN
\ No newline at end of file
diff --git a/VaccineFormulations/mhc2_allele/37435_chosen_peptides_hogvax_inc_substrings.txt b/VaccineFormulations/mhc2_allele/37435_chosen_peptides_hogvax_inc_substrings.txt
deleted file mode 100644
index bf2219bb52437a942a9891e22c61a909d672ca01..0000000000000000000000000000000000000000
--- a/VaccineFormulations/mhc2_allele/37435_chosen_peptides_hogvax_inc_substrings.txt
+++ /dev/null
@@ -1,907 +0,0 @@
-PTITQMNLKYAISAKNRA
-MNYQVNGYPNMFITR
-WLTNIFGTVYEKLK
-TYRRLISMMGFKMNY
-GSTFISDEVARDLSL
-VARDLSLQFKRPI
-FISDEVARDLSLQ
-CDTFCAGSTFISDEVARDLSL
-RLFARTRSMWSFNPETN
-KYAISAKNRARTVAG
-RSMWSFNPETNILLNV
-GSTFISDEVARDLSLQFK
-RRLISMMGFKMNY
-YAISAKNRARTVAGVSICS
-MTYRRLISMMGFKMNYQVNGYPN
-LIRAAEIRASANL
-GSTFISDEVARDLSLQFKRP
-YRRLISMMGFKMNYQVNGYP
-LKYAISAKNRARTVAG
-THHWLLLTILTSLLVLVQ
-STFISDEVARDLS
-FNMVYMPASWVMRIM
-PKDMTYRRLISMMGF
-SFRLFARTRSMWSFNPETN
-TVLSFCAFAVDAAK
-IICISTKHFYWFFS
-RLISMMGFKMNYQVNGYPNMFI
-YFIASFRLFARTRSMWSFNPET
-TQMNLKYAISAKNRARTV
-IAMACLVGLMWLSYF
-YRRLISMMGFKMNYQVN
-TFISDEVARDLSL
-ASFRLFARTRSMWSFN
-HWLLLTILTSLLVLV
-TILTSLLVLVQSTQ
-CDTFCAGSTFISDEVARDLSLQ
-RTRSMWSFNPETNILLNVPLHGTI
-FRLFARTRSMWSFNPET
-AMACLVGLMWLSYF
-TVAYFNMVYMPASW
-IAMACLVGLMWLSY
-HHWLLLTILTSLLVLVQSTQ
-IPKDMTYRRLISMMGFKM
-GLMWLSYFIASFR
-LISMMGFKMNYQVNGYPNMF
-KYAISAKNRARTVA
-IASFRLFARTRSMWSF
-IICISTKHFYWFF
-SYFIASFRLFARTRSMWSFNP
-YAISAKNRARTVA
-CDTFCAGSTFISDEVAR
-HFYWFFSNYLKRR
-MWSFNPETNILLNVPLHGT
-MACLVGLMWLSYFI
-HWLLLTILTSLLVLVQSTQ
-WLSYFIASFRLFARTRS
-DTFCAGSTFISDEVARDLS
-YRRLISMMGFKMN
-YAISAKNRARTVAGVSI
-MNYQVNGYPNMFI
-IAIAMACLVGLMWLSYFI
-HHWLLLTILTSLLVLVQS
-CAGSTFISDEVARDLSLQFKR
-TQMNLKYAISAKNRARTVAG
-FCAGSTFISDEVARDLSLQFKR
-SMMGFKMNYQVNGYPNMFITR
-TRSMWSFNPETNILLNVPLHGT
-FCAGSTFISDEVARDLSLQF
-SMWSFNPETNILLNVPLHGT
-LKYAISAKNRARTVAGVS
-TQMNLKYAISAKNRARTVAGV
-TQMNLKYAISAKNRARTVA
-IPTITQMNLKYAISAKNR
-FNPETNILLNVPLH
-HHWLLLTILTSLLV
-MGFKMNYQVNGYP
-TRSMWSFNPETNILLNVP
-TFCAGSTFISDEV
-FARTRSMWSFNPETNILLNVP
-RSMWSFNPETNILLNVP
-PKDMTYRRLISMMGFK
-TITQMNLKYAISAKNRAR
-KDMTYRRLISMMG
-IPTITQMNLKYAISAKNRAR
-ITQMNLKYAISAKNRARTVAGVSIC
-TYRRLISMMGFKMN
-LLTILTSLLVLVQS
-SFRLFARTRSMWSFNP
-HWLLLTILTSLLV
-RLFARTRSMWSFNPETNIL
-CDTFCAGSTFISDEVARDLSLQFK
-ITQMNLKYAISAKNRART
-LIRAAEIRASANLAA
-IASFRLFARTRSMWS
-RTRSMWSFNPETNILLNVP
-FARTRSMWSFNPETNILLNV
-PKDMTYRRLISMMG
-GIAIAMACLVGLMW
-VKRTIKGTHHWLLLT
-TITQMNLKYAISA
-DMTYRRLISMMGFKMN
-KDMTYRRLISMMGFKMNYQV
-ISDEVARDLSLQF
-TVLSFCAFAVDAA
-RTRSMWSFNPETNILLN
-RRLISMMGFKMNYQVN
-CAGSTFISDEVARDLSLQF
-SYFIASFRLFARTRSMWSFNPETNI
-MTYRRLISMMGFKM
-MACLVGLMWLSYF
-FARTRSMWSFNPETNILLNVPLHGT
-RLFARTRSMWSFNP
-LFARTRSMWSFNPE
-MNYQVNGYPNMFIT
-MGFKMNYQVNGYPNMFITR
-MTYRRLISMMGFKMNYQ
-RLFARTRSMWSFNPET
-DMTYRRLISMMGFK
-FCAGSTFISDEVAR
-ARTRSMWSFNPETNILLNVPLHGT
-GIAIAMACLVGLMWL
-TYRRLISMMGFKMNYQV
-MGFKMNYQVNGYPN
-AISAKNRARTVAGVSI
-IPTITQMNLKYAISAKNRA
-TILTSLLVLVQSTQW
-HHWLLLTILTSLLVLVQSTQW
-QMNLKYAISAKNRARTV
-DTFCAGSTFISDEVA
-WSFNPETNILLNVPLHGTI
-NLKYAISAKNRARTVAGVSIC
-CDTFCAGSTFISDEVARDL
-TRSMWSFNPETNILLNVPL
-LISMMGFKMNYQVNGYPNMFI
-QMNLKYAISAKNRA
-THHWLLLTILTSLLVLV
-MWSFNPETNILLNVPLH
-AMACLVGLMWLSYFI
-SYFIASFRLFARTRS
-DMTYRRLISMMGFKMNYQVN
-PETNILLNVPLHG
-YRRLISMMGFKMNYQVNGY
-SFRLFARTRSMWSF
-TITQMNLKYAISAKN
-ISMMGFKMNYQVNGYPNMFI
-NPETNILLNVPLHG
-LTSLLVLVQSTQW
-ACLVGLMWLSYFI
-TKHFYWFFSNYLKR
-IPKDMTYRRLISMMGFKMN
-FIASFRLFARTRSMWSFN
-FRLFARTRSMWSFNP
-ISAKNRARTVAGVS
-KYAISAKNRARTVAGVS
-QMNLKYAISAKNRARTVAGVS
-KMNYQVNGYPNMFIT
-KYAISAKNRARTVAGVSI
-WLLLTILTSLLVLVQ
-FIASFRLFARTRSMW
-HHWLLLTILTSLLVL
-RTRSMWSFNPETNIL
-KHFYWFFSNYLKR
-VLSFCAFAVDAAK
-HWLLLTILTSLLVL
-WLSYFIASFRLFAR
-IAMACLVGLMWLS
-PKDMTYRRLISMMGFKMN
-VAYFNMVYMPASW
-NLKYAISAKNRARTVA
-RRLISMMGFKMNYQVNGY
-TVLSFCAFAVDAAKAYK
-RLFARTRSMWSFNPETNILLNVP
-IASFRLFARTRSMWSFNPETNILL
-RRLISMMGFKMNYQVNGYPNMFI
-QLIRAAEIRASANLA
-LLTILTSLLVLVQ
-ITQMNLKYAISAKNRARTVAGV
-YFIASFRLFARTRSMWSFN
-VTQQLIRAAEIRASANL
-ITQMNLKYAISAKNRARTVA
-DMTYRRLISMMGFKMNYQ
-KYAISAKNRARTVAGVSICS
-FCAFAVDAAKAYK
-SMMGFKMNYQVNGYPNMF
-TYRRLISMMGFKMNYQVNGYPNMF
-FARTRSMWSFNPET
-YAISAKNRARTVAGVSIC
-ASFRLFARTRSMWS
-LTILTSLLVLVQST
-WLSYFIASFRLFARTRSMWSF
-FKMNYQVNGYPNMFITR
-DEVARDLSLQFKR
-ICISTKHFYWFFS
-FARTRSMWSFNPETNILLNVPLH
-SMMGFKMNYQVNGYPNM
-QMNLKYAISAKNRART
-LLLTILTSLLVLVQST
-TFCAGSTFISDEVAR
-SFNPETNILLNVPLHGT
-WSFNPETNILLNVP
-YVTQQLIRAAEIRASA
-SAKNRARTVAGVSI
-AKNRARTVAGVSI
-TITQMNLKYAISAKNRART
-WSFNPETNILLNV
-FIASFRLFARTRSMWSFNPETNILL
-KDMTYRRLISMMGFK
-DMTYRRLISMMGFKMNYQVNGY
-IAMACLVGLMWLSYFIAS
-STFISDEVARDLSLQF
-VAYFNMVYMPASWVM
-NLKYAISAKNRART
-FCAGSTFISDEVARDLSLQFK
-IPKDMTYRRLISMMGFK
-QWLTNIFGTVYEKLK
-AGSTFISDEVARDLSL
-TVLSFCAFAVDAAKAYKDY
-NMVYMPASWVMRI
-FCAFAVDAAKAYKDY
-PKDMTYRRLISMMGFKMNY
-LVGLMWLSYFIAS
-ITQMNLKYAISAKN
-QMNLKYAISAKNRARTVAGVSI
-HWLLLTILTSLLVLVQ
-ISDEVARDLSLQFK
-TVLSFCAFAVDAAKA
-CAFAVDAAKAYKD
-SMWSFNPETNILLNVPLH
-RLISMMGFKMNYQVNGYPN
-MGFKMNYQVNGYPNMF
-DTFCAGSTFISDEVARDLSL
-THHWLLLTILTSLLVLVQS
-MMGFKMNYQVNGYPNMFITR
-FISDEVARDLSLQFKR
-LISMMGFKMNYQVNGYPN
-FARTRSMWSFNPETNILL
-SMWSFNPETNILLNVPL
-VGLMWLSYFIASFR
-LFARTRSMWSFNPETNILLNVP
-RSMWSFNPETNILLNVPLHG
-LSYFIASFRLFARTRSMWSFNP
-LSYFIASFRLFARTRSMW
-RSMWSFNPETNIL
-MTYRRLISMMGFK
-IASFRLFARTRSMWSFNPETNIL
-STFISDEVARDLSLQFK
-CISTKHFYWFFSN
-RLFARTRSMWSFNPETNILLNVPL
-RRLISMMGFKMNYQVNG
-ISMMGFKMNYQVNG
-IPTITQMNLKYAIS
-SDEVARDLSLQFKR
-LKYAISAKNRARTVAGVSIC
-LMWLSYFIASFRLFA
-SMMGFKMNYQVNGY
-RRLISMMGFKMNYQVNGYPN
-LMWLSYFIASFRL
-TQQLIRAAEIRASANLAA
-TFCAGSTFISDEVARDLS
-RLFARTRSMWSFN
-VKRTIKGTHHWLLL
-DTFCAGSTFISDE
-LLTILTSLLVLVQSTQ
-RLFARTRSMWSFNPETNILLNV
-YVTQQLIRAAEIRAS
-DTFCAGSTFISDEVARDLSLQF
-GSTFISDEVARDL
-FYWFFSNYLKRRV
-TYRRLISMMGFKMNYQVNG
-FIASFRLFARTRSMWSFNPETNI
-GFKMNYQVNGYPNMFI
-CAGSTFISDEVARDLS
-ASFRLFARTRSMWSFNPE
-ITQMNLKYAISAKNR
-GSTFISDEVARDLS
-RTRSMWSFNPETNILL
-ITQMNLKYAISAK
-KDMTYRRLISMMGFKMNYQ
-TFCAGSTFISDEVARDLSL
-GIPKDMTYRRLISM
-ISAKNRARTVAGVSICST
-FRLFARTRSMWSFN
-SMWSFNPETNILLNV
-MNLKYAISAKNRARTVAGVS
-SMMGFKMNYQVNGYPNMFIT
-NYQVNGYPNMFITR
-TFISDEVARDLSLQ
-SYFIASFRLFART
-MTYRRLISMMGFKMNYQVN
-FNMVYMPASWVMR
-PKDMTYRRLISMM
-ATVAYFNMVYMPA
-MNLKYAISAKNRARTVAGVSICST
-YAISAKNRARTVAGVSICST
-CDTFCAGSTFISDEVARD
-CAGSTFISDEVARDLSLQFKRP
-THHWLLLTILTSLLVLVQSTQW
-LFARTRSMWSFNPETN
-DTFCAGSTFISDEVARD
-WLLLTILTSLLVLVQS
-RTRSMWSFNPETNILLNVPLHG
-SMWSFNPETNILL
-CDTFCAGSTFISDEVARDLSLQF
-ISTKHFYWFFSNYL
-YRRLISMMGFKMNYQV
-LSFCAFAVDAAKAY
-TRSMWSFNPETNILLNVPLHG
-ITQMNLKYAISAKNRAR
-YAISAKNRARTVAGV
-KHFYWFFSNYLKRRV
-ISMMGFKMNYQVNGYPN
-TNILLNVPLHGTI
-TRSMWSFNPETNILLNVPLHGTI
-ISMMGFKMNYQVNGYPNM
-ACLVGLMWLSYFIAS
-FARTRSMWSFNPETNIL
-AIAMACLVGLMWLSY
-FCAGSTFISDEVA
-AGSTFISDEVARDLSLQFK
-RSMWSFNPETNILL
-SFNPETNILLNVPLH
-FIASFRLFARTRSMWSFNPET
-VTQQLIRAAEIRASAN
-THHWLLLTILTSL
-AIAMACLVGLMWLSYF
-ITQMNLKYAISAKNRARTVAGVSI
-LISMMGFKMNYQVNGYPNM
-VGLMWLSYFIASF
-KYAISAKNRARTVAGVSICST
-SDEVARDLSLQFKRPI
-FRLFARTRSMWSFNPE
-KNRARTVAGVSIC
-GIPKDMTYRRLISMMG
-MNLKYAISAKNRA
-IPKDMTYRRLISMMGF
-MMGFKMNYQVNGY
-QQLIRAAEIRASAN
-FARTRSMWSFNPETNILLNVPLHG
-FRLFARTRSMWSFNPETNILLNVP
-MWSFNPETNILLNVPLHGTI
-RLISMMGFKMNYQVNGYPNMF
-LSYFIASFRLFARTRSMWS
-AYFNMVYMPASWV
-PKDMTYRRLISMMGFKM
-LLTILTSLLVLVQSTQW
-LFARTRSMWSFNPET
-YFIASFRLFARTRSMWSFNPETNIL
-KMNYQVNGYPNMFITR
-LFARTRSMWSFNPETNILL
-AFAVDAAKAYKDYL
-WSFNPETNILLNVPLH
-IASFRLFARTRSMWSFNPET
-SFRLFARTRSMWSFNPETNI
-ETNILLNVPLHGT
-RSMWSFNPETNILLNVPLHGTI
-ARTRSMWSFNPETNILLNVPLHGTI
-GIAIAMACLVGLMWLSYF
-KDMTYRRLISMMGF
-TITQMNLKYAISAKNRA
-AISAKNRARTVAG
-FNPETNILLNVPLHGTI
-MTYRRLISMMGFKMNYQVNG
-WLSYFIASFRLFARTRSMWS
-GIPKDMTYRRLISMMGF
-RRLISMMGFKMNYQVNGYPNMFIT
-MNLKYAISAKNRARTVAGVSI
-SYFIASFRLFARTR
-WSFNPETNILLNVPLHGT
-NPETNILLNVPLHGT
-YAISAKNRARTVAGVS
-GIPKDMTYRRLISMM
-YFIASFRLFARTRSMWSF
-ASFRLFARTRSMW
-ARTRSMWSFNPETNILLNVPL
-HWLLLTILTSLLVLVQST
-ITQMNLKYAISAKNRARTV
-GFKMNYQVNGYPNM
-YVTQQLIRAAEIRA
-RSMWSFNPETNILLNVPLHGT
-AGSTFISDEVARDLSLQ
-AISAKNRARTVAGV
-KDMTYRRLISMMGFKM
-LKYAISAKNRART
-QMNLKYAISAKNRARTVAGVSICST
-VTQQLIRAAEIRAS
-FCAGSTFISDEVARDLSL
-NPETNILLNVPLH
-NRARTVAGVSICS
-IAMACLVGLMWLSYFIA
-TITQMNLKYAISAKNRARTV
-ACLVGLMWLSYFIA
-QMNLKYAISAKNRARTVAGV
-SFRLFARTRSMWSFNPE
-WLLLTILTSLLVLVQSTQ
-RLISMMGFKMNYQVNGYPNM
-YFIASFRLFARTRSMWSFNP
-YFIASFRLFARTR
-TQMNLKYAISAKNRA
-RRLISMMGFKMNYQVNGYPNM
-ISMMGFKMNYQVNGYPNMFITR
-VKRTIKGTHHWLLLTI
-GLMWLSYFIASFRLF
-YFNMVYMPASWVMR
-RLISMMGFKMNYQVNGY
-AGSTFISDEVARDLS
-ISMMGFKMNYQVN
-ASFRLFARTRSMWSFNPETNI
-RRLISMMGFKMNYQVNGYP
-PTITQMNLKYAISA
-QMNLKYAISAKNR
-LKYAISAKNRARTVAGV
-NLKYAISAKNRARTV
-CAGSTFISDEVAR
-GIAIAMACLVGLMWLS
-ASFRLFARTRSMWSF
-MMGFKMNYQVNGYPNM
-DTFCAGSTFISDEVAR
-ATVAYFNMVYMPAS
-ILTSLLVLVQSTQ
-KNRARTVAGVSICS
-PETNILLNVPLHGT
-SFNPETNILLNVP
-SAKNRARTVAGVSICST
-YFNMVYMPASWVMRI
-RLISMMGFKMNYQVNGYPNMFITR
-TVLSFCAFAVDAAKAYKDYL
-DTFCAGSTFISDEV
-DEVARDLSLQFKRPI
-TYRRLISMMGFKMNYQVNGYPNM
-WLSYFIASFRLFARTR
-YFIASFRLFARTRSMWSFNPE
-STKHFYWFFSNYL
-TFCAGSTFISDEVARDLSLQF
-RTRSMWSFNPETNI
-LLLTILTSLLVLVQSTQ
-ASFRLFARTRSMWSFNPETNILL
-SFCAFAVDAAKAYK
-ARTRSMWSFNPET
-KYAISAKNRARTVAGV
-RTIKGTHHWLLLTI
-EVARDLSLQFKRP
-PTITQMNLKYAISAKNRARTVA
-STVLSFCAFAVDAAKA
-RSMWSFNPETNILLNVPL
-TYRRLISMMGFKMNYQVNGY
-SYFIASFRLFARTRSMWSFNPE
-AISAKNRARTVAGVSIC
-FARTRSMWSFNPETN
-MNLKYAISAKNRARTV
-LKYAISAKNRARTV
-VLSFCAFAVDAAKAYK
-SYFIASFRLFARTRSMWSFNPETN
-ILTSLLVLVQSTQW
-TFCAGSTFISDEVARDLSLQFKR
-MTYRRLISMMGFKMN
-TFISDEVARDLSLQFKRP
-RTRSMWSFNPETNILLNVPLH
-FNPETNILLNVPLHG
-FNPETNILLNVPL
-TFCAGSTFISDEVARDLSLQFKRP
-MTYRRLISMMGFKMNYQVNGY
-RTRSMWSFNPETN
-LSYFIASFRLFART
-FCAGSTFISDEVARDLS
-TYRRLISMMGFKMNYQVN
-TYRRLISMMGFKM
-WLLLTILTSLLVLV
-LLTILTSLLVLVQST
-TQMNLKYAISAKNRAR
-SFRLFARTRSMWSFNPETNILL
-LSYFIASFRLFARTRS
-HFYWFFSNYLKRRV
-KMNYQVNGYPNMF
-FIASFRLFARTRSMWSF
-ARTRSMWSFNPETNILLNVPLHG
-TIKGTHHWLLLTI
-MGFKMNYQVNGYPNMFIT
-TKHFYWFFSNYLKRRV
-QLIRAAEIRASAN
-RTRSMWSFNPETNILLNVPL
-NYQVNGYPNMFIT
-FRLFARTRSMWSFNPETNILLN
-STVLSFCAFAVDAA
-RSMWSFNPETNILLN
-TRSMWSFNPETNILLNVPLH
-PTITQMNLKYAISAK
-AYFNMVYMPASWVMR
-MWSFNPETNILLNVP
-RLISMMGFKMNYQVNGYPNMFIT
-WSFNPETNILLNVPL
-AFAVDAAKAYKDY
-AISAKNRARTVAGVSICS
-DMTYRRLISMMGFKMNYQVNG
-SYFIASFRLFARTRSMWSFN
-ISAKNRARTVAGVSIC
-TQMNLKYAISAKNR
-QMNLKYAISAKNRARTVA
-HHWLLLTILTSLLVLVQST
-LTILTSLLVLVQSTQW
-LLLTILTSLLVLVQ
-RLFARTRSMWSFNPETNILL
-LSYFIASFRLFAR
-LFARTRSMWSFNP
-GFKMNYQVNGYPNMF
-LSFCAFAVDAAKAYKD
-PKDMTYRRLISMMGFKMNYQ
-KMNYQVNGYPNMFI
-RRLISMMGFKMNYQ
-KDMTYRRLISMMGFKMNY
-TITQMNLKYAISAKNRARTVA
-LVGLMWLSYFIASF
-VLSFCAFAVDAAKAYKDY
-MNLKYAISAKNRARTVAGVSICS
-NMVYMPASWVMRIM
-IASFRLFARTRSMWSFNPE
-KNRARTVAGVSICST
-GIAIAMACLVGLMWLSYFI
-ITQMNLKYAISAKNRARTVAG
-LFARTRSMWSFNPETNILLNVPLHG
-DMTYRRLISMMGFKMNYQV
-IPKDMTYRRLISMMGFKMNY
-ITQMNLKYAISAKNRA
-CAFAVDAAKAYKDY
-YFIASFRLFARTRSMWS
-STFISDEVARDLSLQFKR
-MNLKYAISAKNRARTVAGV
-STVLSFCAFAVDAAKAY
-YFIASFRLFARTRSMW
-AYFNMVYMPASWVMRI
-TKHFYWFFSNYLK
-GSTFISDEVARDLSLQ
-TRSMWSFNPETNILL
-LTILTSLLVLVQSTQ
-CAGSTFISDEVARDL
-STVLSFCAFAVDAAK
-RLISMMGFKMNYQV
-SMMGFKMNYQVNG
-SMWSFNPETNILLN
-RARTVAGVSICST
-THHWLLLTILTSLLV
-MTYRRLISMMGFKMNYQVNGYP
-STKHFYWFFSNYLK
-FRLFARTRSMWSFNPETNILLNVPL
-FCAGSTFISDEVARDL
-AIAMACLVGLMWLS
-AGSTFISDEVARDL
-FIASFRLFARTRSMWSFNPETN
-TRSMWSFNPETNIL
-TFCAGSTFISDEVARDLSLQ
-KYAISAKNRARTVAGVSIC
-GTHHWLLLTILTS
-NPETNILLNVPLHGTI
-QWLTNIFGTVYEK
-QQLIRAAEIRASA
-ARTRSMWSFNPETNILLNVPLH
-IAMACLVGLMWLSYFI
-STFISDEVARDLSLQFKRP
-ASFRLFARTRSMWSFNPET
-LFARTRSMWSFNPETNI
-FCAGSTFISDEVARDLSLQ
-IICISTKHFYWFFSNY
-DTFCAGSTFISDEVARDLSLQFK
-IASFRLFARTRSMWSFN
-SYFIASFRLFARTRSMWS
-HWLLLTILTSLLVLVQS
-KYAISAKNRARTV
-FRLFARTRSMWSFNPETNI
-AISAKNRARTVAGVSICST
-AISAKNRARTVAGVS
-MTYRRLISMMGFKMNY
-VKRTIKGTHHWLL
-FARTRSMWSFNPETNILLNVPL
-KRTIKGTHHWLLL
-CDTFCAGSTFISD
-IPKDMTYRRLISM
-PTITQMNLKYAISAKNRARTV
-TRSMWSFNPETNI
-ARTRSMWSFNPETNILLNVP
-ATVAYFNMVYMPASW
-VLSFCAFAVDAAKAY
-YRRLISMMGFKMNYQVNGYPNM
-LKYAISAKNRARTVAGVSI
-SFRLFARTRSMWSFNPETNILLN
-VAYFNMVYMPASWV
-YFIASFRLFARTRSMWSFNPETN
-IPTITQMNLKYAI
-IPTITQMNLKYAISA
-HHWLLLTILTSLLVLVQ
-AGSTFISDEVARDLSLQFKRP
-VLSFCAFAVDAAKAYKDYL
-MTYRRLISMMGFKMNYQV
-ISAKNRARTVAGV
-RTRSMWSFNPETNILLNV
-MGFKMNYQVNGYPNM
-DTFCAGSTFISDEVARDL
-PTITQMNLKYAISAKN
-AKNRARTVAGVSICST
-YFNMVYMPASWVMRIM
-FISDEVARDLSLQFK
-IRAAEIRASANLA
-AIAMACLVGLMWL
-FIASFRLFARTRSMWS
-SMMGFKMNYQVNGYPNMFI
-TFCAGSTFISDEVARDL
-RLFARTRSMWSFNPETNILLNVPLH
-RLFARTRSMWSFNPETNILLN
-MMGFKMNYQVNGYPNMF
-IAIAMACLVGLMWLS
-LFARTRSMWSFNPETNIL
-FARTRSMWSFNPETNI
-MNLKYAISAKNRAR
-YFIASFRLFARTRSMWSFNPETNI
-SYFIASFRLFARTRSM
-ARTRSMWSFNPETNILL
-WLSYFIASFRLFARTRSMW
-MMGFKMNYQVNGYPNMFIT
-GSTFISDEVARDLSLQFKR
-LKYAISAKNRARTVAGVSICS
-LFARTRSMWSFNPETNILLNVPLH
-TQQLIRAAEIRASANLA
-SAKNRARTVAGVSICS
-FCAFAVDAAKAYKD
-QMNLKYAISAKNRAR
-RTRSMWSFNPETNILLNVPLHGT
-GLMWLSYFIASFRL
-LSYFIASFRLFARTR
-YRRLISMMGFKMNYQVNG
-IASFRLFARTRSMWSFNPETNI
-SYFIASFRLFARTRSMWSFNPET
-MMGFKMNYQVNGYP
-LSFCAFAVDAAKAYK
-CDTFCAGSTFISDEVA
-IICISTKHFYWFFSN
-FIASFRLFARTRSMWSFNPE
-NRARTVAGVSICST
-WLLLTILTSLLVLVQST
-ASFRLFARTRSMWSFNPETNILLNV
-SFNPETNILLNVPLHGTI
-LFARTRSMWSFNPETNILLNVPL
-TKHFYWFFSNYLKRR
-MWSFNPETNILLN
-ASFRLFARTRSMWSFNPETNIL
-AGSTFISDEVARDLSLQF
-CAGSTFISDEVARD
-VTQQLIRAAEIRA
-FKMNYQVNGYPNM
-ICISTKHFYWFFSN
-AMACLVGLMWLSYFIA
-TQMNLKYAISAKN
-IPTITQMNLKYAISAK
-LLLTILTSLLVLVQSTQW
-MWLSYFIASFRLFA
-LTNIFGTVYEKLK
-WLSYFIASFRLFA
-KDMTYRRLISMMGFKMN
-MNLKYAISAKNRARTVAGVSIC
-FCAGSTFISDEVARDLSLQFKRP
-WSFNPETNILLNVPLHG
-LKYAISAKNRARTVA
-STVLSFCAFAVDA
-WLLLTILTSLLVL
-TQQLIRAAEIRASAN
-IPKDMTYRRLISMMGFKMNYQ
-WLSYFIASFRLFART
-TITQMNLKYAISAKNR
-DTFCAGSTFISDEVARDLSLQ
-TVLSFCAFAVDAAKAYKD
-NLKYAISAKNRARTVAGVSI
-LLLTILTSLLVLVQS
-PKDMTYRRLISMMGFKMNYQVNG
-YVTQQLIRAAEIRASAN
-AGSTFISDEVARDLSLQFKR
-LFARTRSMWSFNPETNILLNV
-LSYFIASFRLFARTRSMWSF
-SAKNRARTVAGVS
-RRLISMMGFKMNYQVNGYPNMFITR
-NLKYAISAKNRARTVAGVSICST
-THHWLLLTILTSLLVLVQSTQ
-ARTRSMWSFNPETNILLN
-IPKDMTYRRLISMM
-YRRLISMMGFKMNYQVNGYPNMF
-TQMNLKYAISAKNRART
-LISMMGFKMNYQVNGYPNMFITR
-PKDMTYRRLISMMGFKMNYQVN
-VAYFNMVYMPASWVMR
-IRAAEIRASANLAA
-ETNILLNVPLHGTI
-FIASFRLFARTRSMWSFNP
-KDMTYRRLISMMGFKMNYQVN
-TQMNLKYAISAKNRARTVAGVSI
-IAIAMACLVGLMWL
-GFKMNYQVNGYPNMFITR
-IPTITQMNLKYAISAKN
-MMGFKMNYQVNGYPN
-LISMMGFKMNYQVNGYPNMFIT
-LTILTSLLVLVQS
-CLVGLMWLSYFIA
-FRLFARTRSMWSFNPETNIL
-IASFRLFARTRSM
-MGFKMNYQVNGYPNMFI
-SFCAFAVDAAKAYKD
-TQQLIRAAEIRASANL
-FIASFRLFARTRSMWSFNPETNIL
-QQLIRAAEIRASANL
-RRLISMMGFKMNYQVNGYPNMF
-ARTRSMWSFNPETNIL
-RLISMMGFKMNYQ
-SFRLFARTRSMWS
-IPTITQMNLKYAISAKNRART
-ISAKNRARTVAGVSI
-GFKMNYQVNGYPNMFIT
-RLISMMGFKMNYQVNG
-SFNPETNILLNVPL
-DMTYRRLISMMGFKM
-QLIRAAEIRASANL
-PTITQMNLKYAIS
-TQMNLKYAISAKNRARTVAGVS
-TVLSFCAFAVDAAKAY
-THHWLLLTILTSLLVLVQST
-FISDEVARDLSLQFKRP
-GSTFISDEVARDLSLQF
-LIRAAEIRASANLA
-SFRLFARTRSMWSFNPET
-FCAFAVDAAKAYKDYL
-CAGSTFISDEVARDLSL
-AGSTFISDEVARD
-GIAIAMACLVGLMWLSY
-QMNLKYAISAKNRARTVAGVSIC
-SDEVARDLSLQFK
-RAAEIRASANLAA
-CDTFCAGSTFISDEV
-LLLTILTSLLVLV
-IASFRLFARTRSMWSFNP
-ISDEVARDLSLQFKRP
-AMACLVGLMWLSYFIAS
-MMGFKMNYQVNGYPNMFI
-YAISAKNRARTVAG
-TFCAGSTFISDEVARD
-KGTHHWLLLTILTS
-TFCAGSTFISDEVA
-RLFARTRSMWSFNPETNI
-SFCAFAVDAAKAYKDY
-FRLFARTRSMWSF
-LFARTRSMWSFNPETNILLN
-KDMTYRRLISMMGFKMNYQVNG
-YVTQQLIRAAEIR
-HHWLLLTILTSLL
-LISMMGFKMNYQVN
-DMTYRRLISMMGF
-DEVARDLSLQFKRP
-DMTYRRLISMMGFKMNY
-ISAKNRARTVAGVSICS
-AYFNMVYMPASWVM
-YRRLISMMGFKMNYQ
-MWSFNPETNILLNVPL
-RSMWSFNPETNILLNVPLH
-IAIAMACLVGLMW
-FIASFRLFARTRSM
-YRRLISMMGFKMNYQVNGYPN
-SMMGFKMNYQVNGYPN
-ASFRLFARTRSMWSFNPETNILLN
-GIPKDMTYRRLIS
-FKMNYQVNGYPNMFI
-TYRRLISMMGFKMNYQVNGYPN
-ISMMGFKMNYQVNGYPNMFIT
-SFNPETNILLNVPLHG
-KHFYWFFSNYLKRR
-FRLFARTRSMWSFNPETNILLNV
-VLSFCAFAVDAAKAYKD
-IASFRLFARTRSMWSFNPETNILLN
-LISMMGFKMNYQVNG
-HHWLLLTILTSLLVLV
-SMWSFNPETNILLNVPLHG
-TVAYFNMVYMPAS
-ARTRSMWSFNPETNILLNV
-TFCAGSTFISDEVARDLSLQFK
-ICISTKHFYWFFSNY
-LISMMGFKMNYQVNGY
-WLLLTILTSLLVLVQSTQW
-VTQQLIRAAEIRASA
-ISMMGFKMNYQVNGYPNMF
-SFRLFARTRSMWSFN
-EVARDLSLQFKRPI
-CAGSTFISDEVARDLSLQ
-THHWLLLTILTSLLVL
-SFRLFARTRSMWSFNPETNIL
-YFNMVYMPASWVM
-TYRRLISMMGFKMNYQ
-MACLVGLMWLSYFIA
-FIASFRLFARTRS
-FARTRSMWSFNPETNILLN
-ISMMGFKMNYQVNGYP
-FNPETNILLNVPLHGT
-GFKMNYQVNGYPN
-HWLLLTILTSLLVLVQSTQW
-CAFAVDAAKAYKDYL
-KRTIKGTHHWLLLTI
-LMWLSYFIASFRLF
-CDTFCAGSTFISDE
-RRLISMMGFKMNYQV
-KRTIKGTHHWLLLT
-LSYFIASFRLFARTRSM
-AKNRARTVAGVSICS
-QLIRAAEIRASANLAA
-QMNLKYAISAKNRARTVAGVSICS
-MNLKYAISAKNRART
-FISDEVARDLSLQF
-VAYFNMVYMPASWVMRI
-MVYMPASWVMRIM
-KGTHHWLLLTILT
-MNLKYAISAKNRARTVA
-LSFCAFAVDAAKAYKDY
-QQLIRAAEIRASANLA
-YRRLISMMGFKMNY
-STFISDEVARDLSL
-NLKYAISAKNRARTVAGVSICS
-TITQMNLKYAISAK
-MNLKYAISAKNRARTVAG
-ASFRLFARTRSMWSFNP
-CLVGLMWLSYFIAS
-ISMMGFKMNYQVNGY
-SFCAFAVDAAKAY
-IPTITQMNLKYAISAKNRARTV
-SMWSFNPETNILLNVPLHGTI
-RLISMMGFKMNYQVN
-QMNLKYAISAKNRARTVAG
-TFISDEVARDLSLQFK
-IASFRLFARTRSMWSFNPETN
-VLSFCAFAVDAAKA
-STFISDEVARDLSLQ
-AMACLVGLMWLSY
-LSYFIASFRLFARTRSMWSFN
-SFRLFARTRSMWSFNPETNILLNV
-TFISDEVARDLSLQFKR
-IPKDMTYRRLISMMG
-SMWSFNPETNILLNVP
-QWLTNIFGTVYEKL
-TFISDEVARDLSLQF
-WLSYFIASFRLFARTRSM
-IPTITQMNLKYAISAKNRARTVA
-PTITQMNLKYAISAKNR
-PETNILLNVPLHGTI
-FARTRSMWSFNPE
-QQLIRAAEIRASANLAA
-PTITQMNLKYAISAKNRAR
-ARTRSMWSFNPETNI
-NLKYAISAKNRARTVAG
-WLTNIFGTVYEKL
-TQQLIRAAEIRAS
-MWSFNPETNILLNV
-YFIASFRLFARTRS
-RLISMMGFKMNYQVNGYP
-TYRRLISMMGFKMNYQVNGYP
-MACLVGLMWLSYFIAS
-SYFIASFRLFARTRSMWSF
-NLKYAISAKNRAR
-MWLSYFIASFRLF
-YVTQQLIRAAEIRASANL
-NLKYAISAKNRARTVAGVS
-ISDEVARDLSLQFKR
-CAGSTFISDEVARDLSLQFK
-FCAGSTFISDEVARD
-IAIAMACLVGLMWLSY
-LISMMGFKMNYQVNGYP
-LKYAISAKNRARTVAGVSICST
-ARTRSMWSFNPETN
-SFRLFARTRSMWSFNPETNILLNVP
-SFCAFAVDAAKAYKDYL
-FKMNYQVNGYPNMFIT
-TQMNLKYAISAKNRARTVAGVSIC
-LISMMGFKMNYQV
-SMMGFKMNYQVNGYP
-CDTFCAGSTFISDEVARDLS
-PTITQMNLKYAISAKNRART
-PKDMTYRRLISMMGFKMNYQV
-ASFRLFARTRSMWSFNPETN
-GIAIAMACLVGLM
-IASFRLFARTRSMW
-FNMVYMPASWVMRI
-FAVDAAKAYKDYL
-YQVNGYPNMFITR
-TQQLIRAAEIRASA
-LSFCAFAVDAAKA
-LSFCAFAVDAAKAYKDYL
-SAKNRARTVAGVSIC
-YFIASFRLFARTRSM
-CISTKHFYWFFSNY
-RLFARTRSMWSFNPE
-RTIKGTHHWLLLT
-TRSMWSFNPETNILLN
-TILTSLLVLVQST
-AIAMACLVGLMWLSYFI
-FRLFARTRSMWSFNPETN
-TRSMWSFNPETNILLNV
-IAIAMACLVGLMWLSYF
-FRLFARTRSMWSFNPETNILL
-NLKYAISAKNRARTVAGV
-ISTKHFYWFFSNY
-FKMNYQVNGYPNMF
-SYFIASFRLFARTRSMW
-MWSFNPETNILLNVPLHG
-SDEVARDLSLQFKRP
-THHWLLLTILTSLL
-ITQMNLKYAISAKNRARTVAGVS
-AKNRARTVAGVSIC
-GIPKDMTYRRLISMMGFK
-GLMWLSYFIASFRLFA
\ No newline at end of file
diff --git a/VaccineFormulations/mhc2_allele/37435_hogvaxine.txt b/VaccineFormulations/mhc2_allele/37435_hogvaxine.txt
deleted file mode 100644
index e2e5bfa539badc91914f3bdcbd83e1e34c5d145d..0000000000000000000000000000000000000000
--- a/VaccineFormulations/mhc2_allele/37435_hogvaxine.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-> MHC optimized combined peptide vaccine sequence with overlaps
-GCDTFCAGSTFISDEVARDLSLQFKRPINLGIAIAMACLVGLMWLSYFIASFRLFARTRSMWSFNPETNILLNVPLHGTIAYIICISTKHFYWFFSNYLKRRVFAYTKRNVIPTITQMNLKYAISAKNRARTVAGVSICSTVLSFCAFAVDAAKAYKDYLGIPKDMTYRRLISMMGFKMNYQVNGYPNMFITRYVTQQLIRAAEIRASANLAATVAYFNMVYMPASWVMRIMAVKRTIKGTHHWLLLTILTSLLVLVQSTQWLTNIFGTVYEKLK
-> MHC optimized combined peptide vaccine sequence concatenated
-THHWLLLTILTSLLVLVQSMMGFKMNYQVNGYPNMFITRMNYQVNGYPNMFITRWLTNIFGTVYEKLKSYFIASFRLFARTRSMWSFNPETNILTSLLVLVQSTQWTFCAGSTFISDEVARDLSLQFKRVARDLSLQFKRPITFISDEVARDLSLQFKRPLSYFIASFRLFARTRSMWSFNPCDTFCAGSTFISDEVARDLSLVGLMWLSYFIASFRTQQLIRAAEIRASANLTFCAGSTFISDEVARDLSLQFKRPMTYRRLISMMGFKMNYQVNGYFIASFRLFARTRSMWSFNPETNILRRLISMMGFKMNYQVNGYPNMFRLFARTRSMWSFNPETNILLNVPLIPTITQMNLKYAISAKNRARTIPTITQMNLKYAISMTYRRLISMMGFKMNYQVNGYPNGSTFISDEVARDLSLQFKRPLMWLSYFIASFRLFAHFYWFFSNYLKRRVTHHWLLLTILTSLLVLVQFNMVYMPASWVMRIMTIKGTHHWLLLTITQQLIRAAEIRASANLAATVLSFCAFAVDAAKAYTKHFYWFFSNYLKRRVTHHWLLLTILTSLLVLVQSTFISDEVARDLSLQFKRPVKRTIKGTHHWLLLIICISTKHFYWFFSSTVLSFCAFAVDAAYVTQQLIRAAEIRASFCAFAVDAAKAYKDYLFYWFFSNYLKRRVDMTYRRLISMMGFKMNYQVNGCDTFCAGSTFISDEVARDLSLQGIAIAMACLVGLMWLSYRTRSMWSFNPETNILLNVPLHGTIQMNLKYAISAKNRARTVAGVSICRAAEIRASANLAACDTFCAGSTFISDEVLTILTSLLVLVQSTQWTVAYFNMVYMPASWGIPKDMTYRRLISMISAKNRARTVAGVSICSTPKDMTYRRLISMMGFKMNYQIPKDMTYRRLISMMGFKMISDEVARDLSLQFKRPGLMWLSYFIASFRAMACLVGLMWLSYFIASNYQVNGYPNMFITRIICISTKHFYWFFSYFIASFRLFARTRSMWSFNPTITQMNLKYAISAKNRARTVALVGLMWLSYFIASFCDTFCAGSTFISDEVARKGTHHWLLLTILTSNMVYMPASWVMRIMWLSYFIASFRLFARTRSKNRARTVAGVSICSTGIAIAMACLVGLMWLSYFIITQMNLKYAISAKNRARTVAGIAIAMACLVGLMWLSYFIYAISAKNRARTVAGVSICSTATVAYFNMVYMPALFARTRSMWSFNPETNILLNVPLHGMNLKYAISAKNRARTVAGVSICSTCAGSTFISDEVARDLSLQFKRPKDMTYRRLISMMGFKMNYQVNGCDTFCAGSTFISDEVARDTHHWLLLTILTSLLVLVQSTQWIPKDMTYRRLISMMGFKMNYYVTQQLIRAAEIRSMMGFKMNYQVNGYPNMFITRSTVLSFCAFAVDAAKAYIPTITQMNLKYAISAKNRAYFNMVYMPASWVMRITKHFYWFFSNYLKSTVLSFCAFAVDAAKCDTFCAGSTFISDEVARDLSLQFISTKHFYWFFSNYLRARTVAGVSICSTKHFYWFFSNYLKRRVTNILLNVPLHGTIMTYRRLISMMGFKMNYQVNGYPTRSMWSFNPETNILLNVPLHGTIASFRLFARTRSMWSFNPETNILLNIPTITQMNLKYAISAKNRARFRLFARTRSMWSFNPETNILLNVPLGIPKDMTYRRLISITQMNLKYAISAKNRARTVAGVSICSTKHFYWFFSNYLKTYRRLISMMGFKMNYQVNGYPNACLVGLMWLSYFIASCDTFCAGSTFISDEVARDLSLQFKGTHHWLLLTILTSIASFRLFARTRSMWSFNPETNILLNLIRAAEIRASANLAATHHWLLLTILTSLNPETNILLNVPLHGTIQWLTNIFGTVYEKITQMNLKYAISAKNRARTVAGVSIIAMACLVGLMWLSYFISTFISDEVARDLSLQFKRPTFCAGSTFISDEVARDLSLQFKICISTKHFYWFFSNYGIAIAMACLVGLMWVGLMWLSYFIASFVKRTIKGTHHWLLLTIICISTKHFYWFFSNYKYAISAKNRARTVAGVSICSTWLLLTILTSLLVLVQSTQWDTFCAGSTFISDEVARDLSLQFKSDEVARDLSLQFKRPIEVARDLSLQFKRPITHHWLLLTILTSLLVLAISAKNRARTVAGVSICSTGIPKDMTYRRLISMMGVKRTIKGTHHWLLCDTFCAGSTFISDSYFIASFRLFARTRSMWSFNPETNIFARTRSMWSFNPETNILLNVPLHGFARTRSMWSFNPETNILLNVPLHGTATVAYFNMVYMPASWFRLFARTRSMWSFNPETNILLNVPMGFKMNYQVNGYPNMFITRMWSFNPETNILLNVPLHGTIARTRSMWSFNPETNILLNVPLHGTGIAIAMACLVGLMWLVAYFNMVYMPASWVHWLLLTILTSLLVLVQSTQWCAFAVDAAKAYKDYLIPTITQMNLKYAIIPTITQMNLKYAISAAGSTFISDEVARDLSLQFKRPIPTITQMNLKYAISAKNRAHHWLLLTILTSLLVLVQSTQWKRTIKGTHHWLLLTITILTSLLVLVQSTQWCDTFCAGSTFISDEWSFNPETNILLNVPLHGTILLTILTSLLVLVQSTQWVLSFCAFAVDAAKAYKDYLCDTFCAGSTFISDEVARDLQLIRAAEIRASANLAAQMNLKYAISAKNRARTVAGVSICSYFIASFRLFARTRSMWSFNPETNILAKNRARTVAGVSICSTKMNYQVNGYPNMFITRMVYMPASWVMRIMVAYFNMVYMPASWVMRIKGTHHWLLLTILTTHHWLLLTILTSLLVLVYFNMVYMPASWVMRIMAFAVDAAKAYKDYLRSMWSFNPETNILLNVPLHGTICLVGLMWLSYFIASLTSLLVLVQSTQWARTRSMWSFNPETNILLNVPLHGTIRLFARTRSMWSFNPETNILLNVPLHGIAIAMACLVGLMWLSYFIPTITQMNLKYAISAKNRARTVTKHFYWFFSNYLKRIPKDMTYRRLISMMGFKMNSMWSFNPETNILLNVPLHGTITHHWLLLTILTSLLVYFIASFRLFARTRSMWSFNPETNISYFIASFRLFARTRSMWSFNPEWLSYFIASFRLFARTRSMWFNPETNILLNVPLHGTILSYFIASFRLFARTRSMWSFNLFARTRSMWSFNPETNILLNVPLHSFRLFARTRSMWSFNPETNILLNVTQQLIRAAEIRASANLAWLSYFIASFRLFARTRSMWSGIPKDMTYRRLISMMGFQWLTNIFGTVYEKLRRLISMMGFKMNYQVNGYPNMFITWLSYFIASFRLFARTRSMIPTITQMNLKYAISAKNRARTVAWLSYFIASFRLFARGLMWLSYFIASFRLPETNILLNVPLHGTIGIPKDMTYRRLISMMQQLIRAAEIRASANLAAVAYFNMVYMPASWTVLSFCAFAVDAAKAYKYVTQQLIRAAEIRAIASFRLFARTRSMWSFNPETNILLSYFIASFRLFARTRSMWSFNPETRRLISMMGFKMNYQVNGYPNMFICDTFCAGSTFISDEVAYVTQQLIRAAEIRASANIICISTKHFYWFFSNNRARTVAGVSICSTITQMNLKYAISAKNRARTVAGVQMNLKYAISAKNRARTVAGVSICSTASFRLFARTRSMWSFNPETNILLNVVTQQLIRAAEIRASANLITQMNLKYAISAKNRARTVASFNPETNILLNVPLHGTIMACLVGLMWLSYFIASIAMACLVGLMWLSYFIATKHFYWFFSNYLKRRYVTQQLIRAAEIRASANLTYRRLISMMGFKMNYQVNGYPNMFWLSYFIASFRLFARTRSMWSFFKMNYQVNGYPNMFITRLKYAISAKNRARTVAGVSICSTSFRLFARTRSMWSFNPETNILLNVPSFCAFAVDAAKAYKDYLISMMGFKMNYQVNGYPNMFITRTQMNLKYAISAKNRARTVAGVSICVKRTIKGTHHWLLLTIGLMWLSYFIASFRLFCDTFCAGSTFISDEVARDLSPKDMTYRRLISMMGFKMNYQVYVTQQLIRAAEIRASAIPTITQMNLKYAISAKGIAIAMACLVGLMFIASFRLFARTRSMWSFNPETNILLLLLTILTSLLVLVQSTQWFAVDAAKAYKDYLMWLSYFIASFRLFALTNIFGTVYEKLKWLSYFIASFRLFALSFCAFAVDAAKAYKDYLYQVNGYPNMFITRDMTYRRLISMMGFKMNYQVNGYIAMACLVGLMWLSYFIASFCAGSTFISDEVARDLSLQFKRPVAYFNMVYMPASWVMCISTKHFYWFFSNYSTVLSFCAFAVDAGIAIAMACLVGLMWLSIPKDMTYRRLISMMGFKIPKDMTYRRLISMMGFKMNYQWLSYFIASFRLFARTQWLTNIFGTVYEKLKTVLSFCAFAVDAAKAYKDTVLSFCAFAVDAAKAYKDYAIAMACLVGLMWLSYFIATVAYFNMVYMPASLVGLMWLSYFIASSAKNRARTVAGVSICSTPKDMTYRRLISMMGFKMNYQVNGYFNMVYMPASWVMRIRLISMMGFKMNYQVNGYPNMFITRLSYFIASFRLFARTRSMWSFDEVARDLSLQFKRPITVLSFCAFAVDAAKAYKDYLRRLISMMGFKMNYQVNGYPNMFITRNLKYAISAKNRARTVAGVSICSTSTKHFYWFFSNYLTHHWLLLTILTSLLVLVQSTQTYRRLISMMGFKMNYQVNGYPNMWLSYFIASFRLFARTRISTKHFYWFFSNYRTIKGTHHWLLLTIYRRLISMMGFKMNYQVNGYPNMFSDEVARDLSLQFKRPLISMMGFKMNYQVNGYPNMFITRPKDMTYRRLISMMGFKMNYQVNTHHWLLLTILTSLLPTITQMNLKYAISAKNRARTVAIRAAEIRASANLAAETNILLNVPLHGTIITQMNLKYAISAKNRARTVAGVSSTVLSFCAFAVDAAKAVAYFNMVYMPASWVMRGIPKDMTYRRLISMMGFKGFKMNYQVNGYPNMFITRGLMWLSYFIASFRLFAIPTITQMNLKYAISAKN
\ No newline at end of file
diff --git a/VaccineFormulations/mhc2_haplotype/37435_chosen_peptides_hog.txt b/VaccineFormulations/mhc2_haplotype/37435_chosen_peptides_hog.txt
deleted file mode 100644
index 99ae97ea81c63ee2b1ee95a8d4e8483966174b0d..0000000000000000000000000000000000000000
--- a/VaccineFormulations/mhc2_haplotype/37435_chosen_peptides_hog.txt
+++ /dev/null
@@ -1,147 +0,0 @@
-VGGQIVTCAKEIK
-TYRRLISMMGFKMN
-TFISDEVARDLSLQFKRP
-TFISDEVARDLSLQFKR
-TFISDEVARDLSLQFKRPI
-LISMMGFKMNYQVN
-TQMNLKYAISAKNRAR
-HHWLLLTILTSLL
-RLFARTRSMWSFNPETNILLNVPL
-TILTSLLVLVQSTQW
-YRRLISMMGFKMNYQV
-STFISDEVARDLS
-TYRRLISMMGFKMNY
-RTIKGTHHWLLLT
-MWSFNPETNILLNVPLHGTI
-VGGQIVTCAKEIKE
-FIASFRLFARTRSMWSFNPETNIL
-GQIVTCAKEIKESV
-MMGFKMNYQVNGYPNMF
-IASFRLFARTRSMWSFNPETNILLN
-KGTHHWLLLTILT
-QMNLKYAISAKNRARTVAG
-YFIASFRLFARTRSMWSFNPETNIL
-ARTRSMWSFNPETNILLNVPLHG
-HHWLLLTILTSLLV
-LISMMGFKMNYQVNGYP
-QWLTNIFGTVYEK
-VTQQLIRAAEIRAS
-LFARTRSMWSFNPETNILLNVPLHG
-SFRLFARTRSMWSFNPETNILLNV
-IASFRLFARTRSMWSFNPETNILL
-ISMMGFKMNYQVNGYPNMF
-TQMNLKYAISAKNRARTVA
-HHWLLLTILTSLLVLVQSTQW
-GGQIVTCAKEIKESV
-GFKMNYQVNGYPNMF
-VTQQLIRAAEIRASA
-RLFARTRSMWSFNPETNILLNVP
-TQMNLKYAISAKNR
-LISMMGFKMNYQVNGYPNMF
-FRLFARTRSMWSFNPETNILLNVP
-HHWLLLTILTSLLVLVQS
-FARTRSMWSFNPETNILLNVPLHG
-QWLTNIFGTVYEKLK
-STFISDEVARDLSLQF
-YAISAKNRARTVAG
-FISDEVARDLSLQFKRPI
-ISDEVARDLSLQFKRPI
-GGQIVTCAKEIKES
-TQMNLKYAISAKNRARTV
-NPETNILLNVPLHGTI
-LFARTRSMWSFNPETNILLNVPLH
-LISMMGFKMNYQV
-KMNYQVNGYPNMF
-TQMNLKYAISAKN
-FKMNYQVNGYPNMF
-SYFIASFRLFARTRSMWSFN
-LISMMGFKMNYQVNGYPN
-ETNILLNVPLHGTI
-AISMWALIISVTS
-AISAKNRARTVAG
-TIKGTHHWLLLTI
-SYFIASFRLFART
-THHWLLLTILTSL
-STFISDEVARDLSLQFKR
-VKRTIKGTHHWLLL
-WSFNPETNILLNVPLHGTI
-LLLTILTSLLVLVQSTQW
-STFISDEVARDLSLQFK
-QLIRAAEIRASAN
-WLLLTILTSLLVLVQSTQW
-SYFIASFRLFARTRSMWS
-SMWSFNPETNILLNVPLHGT
-RRLISMMGFKMNYQV
-SYFIASFRLFARTRSMWSFNPETN
-STFISDEVARDLSL
-RLISMMGFKMNYQV
-LISMMGFKMNYQVNGYPNM
-TQMNLKYAISAKNRA
-PETNILLNVPLHGTI
-LTNIFGTVYEKLK
-HHWLLLTILTSLLVLVQST
-HHWLLLTILTSLLVL
-HHWLLLTILTSLLVLV
-HHWLLLTILTSLLVLVQ
-KRTIKGTHHWLLLT
-FNPETNILLNVPLHGTI
-ASFRLFARTRSMWSFNPETNILLN
-VKRTIKGTHHWLL
-TRSMWSFNPETNILLNVPLHGT
-EVARDLSLQFKRPI
-WLTNIFGTVYEKLK
-SYFIASFRLFARTRSM
-VKRTIKGTHHWLLLT
-RTIKGTHHWLLLTI
-IASFRLFARTRSMWSFNPETNIL
-SYFIASFRLFARTRS
-TQMNLKYAISAKNRARTVAG
-VARDLSLQFKRPI
-MNLKYAISAKNRARTVAG
-SMMGFKMNYQVNGYPNMF
-ARTRSMWSFNPETNILLNVPLHGT
-SYFIASFRLFARTR
-SFNPETNILLNVPLHGTI
-DEVARDLSLQFKRPI
-YFIASFRLFARTRSMWSFNPETNI
-TQQLIRAAEIRASAN
-IKGTHHWLLLTIL
-TQMNLKYAISAKNRART
-SYFIASFRLFARTRSMWSFNPET
-LISMMGFKMNYQVNG
-HWLLLTILTSLLVLVQSTQW
-KGTHHWLLLTILTS
-LTILTSLLVLVQSTQW
-FRLFARTRSMWSFNPETNILLNV
-SYFIASFRLFARTRSMWSFNPE
-KYAISAKNRARTVAG
-RTRSMWSFNPETNILLNVPLHGT
-TYRRLISMMGFKMNYQV
-TNILLNVPLHGTI
-MGFKMNYQVNGYPNMF
-GTHHWLLLTILTS
-LISMMGFKMNYQVNGY
-NLKYAISAKNRARTVAG
-SDEVARDLSLQFKRPI
-VTQQLIRAAEIRA
-ILTSLLVLVQSTQW
-RLFARTRSMWSFNPETNILLNVPLH
-TYRRLISMMGFKMNYQ
-SYFIASFRLFARTRSMWSFNP
-LLTILTSLLVLVQSTQW
-QWLTNIFGTVYEKL
-SYFIASFRLFARTRSMW
-ASFRLFARTRSMWSFNPETNILLNV
-QQLIRAAEIRASAN
-YFIASFRLFARTRSMWSFNPETN
-VGGQIVTCAKEIKES
-SYFIASFRLFARTRSMWSF
-TQQLIRAAEIRASA
-QIVTCAKEIKESV
-STFISDEVARDLSLQ
-LTSLLVLVQSTQW
-MWSFNPETNILLNVPLHGT
-TYRRLISMMGFKM
-LKYAISAKNRARTVAG
-RSMWSFNPETNILLNVPLHGT
-HHWLLLTILTSLLVLVQSTQ
\ No newline at end of file
diff --git a/VaccineFormulations/mhc2_haplotype/37435_chosen_peptides_hogvax_inc_substrings.txt b/VaccineFormulations/mhc2_haplotype/37435_chosen_peptides_hogvax_inc_substrings.txt
deleted file mode 100644
index e389477853ce2e139630fbefbcd2ed89fbf35500..0000000000000000000000000000000000000000
--- a/VaccineFormulations/mhc2_haplotype/37435_chosen_peptides_hogvax_inc_substrings.txt
+++ /dev/null
@@ -1,414 +0,0 @@
-VGGQIVTCAKEIK
-RSMWSFNPETNILLNVP
-LISMMGFKMNYQVN
-SFRLFARTRSMWSFNPETNILL
-WLLLTILTSLLVLVQST
-TILTSLLVLVQSTQW
-YRRLISMMGFKMNYQV
-RSMWSFNPETNILLNVPL
-TYRRLISMMGFKMNY
-RTIKGTHHWLLLT
-QMNLKYAISAKNRARTVAG
-SFRLFARTRSMWSFNPETNIL
-RTRSMWSFNPETNI
-LKYAISAKNRARTVA
-HHWLLLTILTSLLV
-QWLTNIFGTVYEK
-MMGFKMNYQVNGYPN
-SFRLFARTRSMWSFNPETNILLNV
-ASFRLFARTRSMWSFNPE
-YFIASFRLFARTRSMWS
-VTQQLIRAAEIRASA
-HHWLLLTILTSLLVLVQSTQW
-FNPETNILLNVPLHGT
-SMWSFNPETNILLNV
-RLISMMGFKMNYQ
-MWSFNPETNILLN
-PETNILLNVPLHGT
-ISDEVARDLSLQFKRPI
-TQMNLKYAISAKNRARTV
-ISMMGFKMNYQVN
-KMNYQVNGYPNMF
-TQMNLKYAISAKN
-MWSFNPETNILLNVPLH
-LISMMGFKMNYQVNGYPN
-LLTILTSLLVLVQST
-ETNILLNVPLHGTI
-FIASFRLFARTRS
-ISMMGFKMNYQVNGY
-FRLFARTRSMWSFNPETNILLN
-TIKGTHHWLLLTI
-FNPETNILLNVPL
-VKRTIKGTHHWLLL
-STFISDEVARDLSLQFK
-ARTRSMWSFNPETNIL
-QLIRAAEIRASAN
-WLLLTILTSLLVLVQSTQW
-MNLKYAISAKNRART
-LKYAISAKNRART
-HWLLLTILTSLLV
-FARTRSMWSFNPETNILLNVP
-FRLFARTRSMWSFNPE
-ARTRSMWSFNPETNILLNV
-QQLIRAAEIRASA
-LISMMGFKMNYQVNGYPNM
-MWSFNPETNILLNVPL
-RLFARTRSMWSFNPETN
-SFNPETNILLNVPLH
-EVARDLSLQFKRP
-SMWSFNPETNILLNVP
-WSFNPETNILLNVP
-WSFNPETNILLNVPLHGT
-VKRTIKGTHHWLL
-EVARDLSLQFKRPI
-MGFKMNYQVNGYPNM
-SFNPETNILLNVP
-FISDEVARDLSLQFKRP
-FIASFRLFARTRSMWSFNPET
-SYFIASFRLFARTRS
-RTRSMWSFNPETNIL
-LLTILTSLLVLVQ
-RTRSMWSFNPETNILL
-QMNLKYAISAKNRA
-LTILTSLLVLVQST
-LISMMGFKMNYQVNG
-KGTHHWLLLTILTS
-IASFRLFARTRSMWSF
-LLLTILTSLLVLVQSTQ
-ISMMGFKMNYQVNG
-ARTRSMWSFNPETN
-MNLKYAISAKNRARTV
-SYFIASFRLFARTRSMWSFNPE
-LKYAISAKNRARTV
-RTRSMWSFNPETNILLNVPLHGT
-YFIASFRLFARTRS
-RLFARTRSMWSFNPETNILLNV
-MGFKMNYQVNGYPNMF
-GTHHWLLLTILTS
-LLLTILTSLLVLVQST
-SDEVARDLSLQFKRPI
-FARTRSMWSFNPETN
-NLKYAISAKNRAR
-SYFIASFRLFARTRSMW
-LFARTRSMWSFNPETNILL
-QQLIRAAEIRASAN
-FIASFRLFARTRSMWSFNPETNI
-YFIASFRLFARTRSMWSFNPE
-GFKMNYQVNGYPN
-TQQLIRAAEIRASA
-QIVTCAKEIKESV
-TYRRLISMMGFKM
-SDEVARDLSLQFK
-LKYAISAKNRARTVAG
-FRLFARTRSMWSFNPETNI
-FIASFRLFARTRSMWSFNPETN
-QMNLKYAISAKNRARTVA
-TILTSLLVLVQST
-WLLLTILTSLLVL
-TFISDEVARDLSLQFKR
-TFISDEVARDLSLQFKRPI
-LLLTILTSLLVLV
-TQMNLKYAISAKNRAR
-MNLKYAISAKNRA
-LFARTRSMWSFNPETNILLN
-ASFRLFARTRSMWSFNPET
-RLFARTRSMWSFNPET
-RLFARTRSMWSFNPE
-FARTRSMWSFNPETNILLNVPLH
-FNPETNILLNVPLH
-SMMGFKMNYQVNGYPN
-MMGFKMNYQVNGYPNMF
-FRLFARTRSMWSFNPETNILL
-ETNILLNVPLHGT
-RTRSMWSFNPETNILLN
-KGTHHWLLLTILT
-SFNPETNILLNVPLHGT
-YFIASFRLFARTRSMWSFNPETNIL
-ARTRSMWSFNPETNILLNVPLH
-LFARTRSMWSFNPET
-FARTRSMWSFNPET
-VTQQLIRAAEIRAS
-FRLFARTRSMWSFN
-IASFRLFARTRSMWSFNPETNILL
-NLKYAISAKNRARTV
-LTILTSLLVLVQSTQ
-MNLKYAISAKNRAR
-TQQLIRAAEIRAS
-FRLFARTRSMWSFNPET
-SFRLFARTRSMWSFNPETN
-FIASFRLFARTRSMWSF
-FRLFARTRSMWSFNPETNILLNVP
-HHWLLLTILTSLLVLVQS
-NLKYAISAKNRARTVA
-QWLTNIFGTVYEKLK
-LFARTRSMWSFNPETNI
-YAISAKNRARTVAG
-FISDEVARDLSLQFKRPI
-GGQIVTCAKEIKES
-SYFIASFRLFARTRSMWSFN
-TILTSLLVLVQSTQ
-ASFRLFARTRSMWSFNPETNILL
-ASFRLFARTRSMWSFNP
-AISMWALIISVTS
-AISAKNRARTVAG
-RLFARTRSMWSFNPETNIL
-ISMMGFKMNYQVNGYP
-SFRLFARTRSMWSFNPE
-LLLTILTSLLVLVQSTQW
-TFISDEVARDLSL
-IASFRLFARTRSMWSFNPET
-HWLLLTILTSLLVLVQSTQ
-SMWSFNPETNILLNVPLHGT
-TRSMWSFNPETNILLNVP
-LTNIFGTVYEKLK
-ISMMGFKMNYQVNGYPNM
-HHWLLLTILTSLLVL
-RSMWSFNPETNILLNVPLHG
-FNPETNILLNVPLHG
-KYAISAKNRARTV
-HWLLLTILTSLLVL
-FIASFRLFARTRSMWSFNPE
-KRTIKGTHHWLLLT
-LFARTRSMWSFNP
-FARTRSMWSFNPETNILLNVPL
-ASFRLFARTRSMWSFNPETNILLN
-TRSMWSFNPETNILLNVPLHGT
-VKRTIKGTHHWLLLT
-RSMWSFNPETNILLN
-QMNLKYAISAKNRARTV
-FKMNYQVNGYPNM
-RTIKGTHHWLLLTI
-LFARTRSMWSFNPETNILLNV
-SMMGFKMNYQVNGYPNMF
-ARTRSMWSFNPETNILLNVPLHGT
-WLLLTILTSLLVLVQ
-RTRSMWSFNPETNILLNV
-SFRLFARTRSMWSFNP
-SFNPETNILLNVPLHG
-FRLFARTRSMWSFNPETNILLNV
-KYAISAKNRARTVAG
-SFRLFARTRSMWSF
-MNLKYAISAKNRARTVA
-LISMMGFKMNYQVNGY
-RSMWSFNPETNILLNV
-ILTSLLVLVQSTQW
-RLFARTRSMWSFNPETNILLNVPLH
-FISDEVARDLSLQF
-ISMMGFKMNYQVNGYPN
-QMNLKYAISAKNR
-HWLLLTILTSLLVLVQS
-WLLLTILTSLLVLV
-ASFRLFARTRSMWSFN
-VGGQIVTCAKEIKES
-QMNLKYAISAKNRAR
-SYFIASFRLFARTRSMWSF
-FRLFARTRSMWSF
-WLTNIFGTVYEKL
-MGFKMNYQVNGYPN
-FRLFARTRSMWSFNPETN
-TYRRLISMMGFKMN
-HHWLLLTILTSLL
-RLFARTRSMWSFNPETNILLNVPL
-STFISDEVARDLS
-RLFARTRSMWSFNPETNI
-PETNILLNVPLHG
-FARTRSMWSFNPETNILL
-FIASFRLFARTRSMWSFNPETNIL
-LLLTILTSLLVLVQS
-MGFKMNYQVNGYP
-HWLLLTILTSLLVLV
-TRSMWSFNPETNIL
-IASFRLFARTRSMWSFNPETNILLN
-IASFRLFARTRSMWSFNP
-LTILTSLLVLVQS
-LISMMGFKMNYQVNGYP
-FIASFRLFARTRSMWSFNP
-LFARTRSMWSFNPETNILLNVPLHG
-FIASFRLFARTRSMW
-IASFRLFARTRSMWSFNPE
-TRSMWSFNPETNILLNVPLH
-SFRLFARTRSMWSFNPETNILLN
-RLFARTRSMWSFNPETNILLNVP
-TQMNLKYAISAKNR
-DEVARDLSLQFKR
-TRSMWSFNPETNILLN
-LISMMGFKMNYQVNGYPNMF
-YFIASFRLFARTRSMWSFNP
-FARTRSMWSFNPETNILLNVPLHG
-LLTILTSLLVLVQSTQ
-SFRLFARTRSMWSFNPETNI
-LFARTRSMWSFNPETNILLNVPL
-LFARTRSMWSFNPETNILLNVPLH
-ASFRLFARTRSMWS
-ARTRSMWSFNPETNILL
-YRRLISMMGFKMNYQ
-YRRLISMMGFKMN
-WLLLTILTSLLVLVQS
-GGQIVTCAKEIKE
-SYFIASFRLFART
-RRLISMMGFKMNY
-TRSMWSFNPETNILLNVPL
-MWSFNPETNILLNVP
-YFIASFRLFARTRSM
-WSFNPETNILLNVPLH
-ISDEVARDLSLQFKRP
-IASFRLFARTRSM
-GFKMNYQVNGYPNM
-NLKYAISAKNRART
-NPETNILLNVPLHGT
-TFISDEVARDLSLQ
-RRLISMMGFKMNYQV
-TQMNLKYAISAKNRA
-PETNILLNVPLHGTI
-HHWLLLTILTSLLVLV
-LFARTRSMWSFNPETNILLNVP
-FIASFRLFARTRSM
-HHWLLLTILTSLLVLVQ
-LFARTRSMWSFNPE
-SFRLFARTRSMWS
-FARTRSMWSFNPE
-YFIASFRLFARTRSMWSF
-ISDEVARDLSLQFKR
-ISDEVARDLSLQFK
-SYFIASFRLFARTRSM
-VARDLSLQFKRPI
-IASFRLFARTRSMWSFNPETN
-TRSMWSFNPETNILLNVPLHG
-SDEVARDLSLQFKRP
-SYFIASFRLFARTR
-YFIASFRLFARTRSMWSFNPETNI
-TQMNLKYAISAKNRART
-SYFIASFRLFARTRSMWSFNPET
-HWLLLTILTSLLVLVQ
-TYRRLISMMGFKMNYQV
-FARTRSMWSFNPETNILLNV
-TNILLNVPLHGTI
-TFISDEVARDLSLQF
-FARTRSMWSFNPETNI
-RTRSMWSFNPETNILLNVPLHG
-ASFRLFARTRSMW
-NLKYAISAKNRARTVAG
-RLFARTRSMWSFN
-TYRRLISMMGFKMNYQ
-SYFIASFRLFARTRSMWSFNP
-LLTILTSLLVLVQSTQW
-SFNPETNILLNVPL
-WSFNPETNILLNVPLHG
-QWLTNIFGTVYEKL
-MWSFNPETNILLNVPLHG
-SMWSFNPETNILLNVPL
-ASFRLFARTRSMWSFNPETNILLNV
-YRRLISMMGFKMNY
-FARTRSMWSFNPETNILLN
-RSMWSFNPETNIL
-ARTRSMWSFNPET
-LTSLLVLVQSTQW
-ARTRSMWSFNPETNILLN
-RSMWSFNPETNILLNVPLHGT
-FIASFRLFARTRSMWS
-IASFRLFARTRSMWSFNPETNI
-HHWLLLTILTSLLVLVQSTQ
-SMWSFNPETNILLNVPLH
-RTRSMWSFNPETNILLNVP
-TFISDEVARDLSLQFKRP
-RTRSMWSFNPETN
-IASFRLFARTRSMWSFN
-GQIVTCAKEIKES
-RTRSMWSFNPETNILLNVPL
-MWSFNPETNILLNVPLHGTI
-VGGQIVTCAKEIKE
-GQIVTCAKEIKESV
-ASFRLFARTRSMWSFNPETNI
-ARTRSMWSFNPETNILLNVPL
-RTRSMWSFNPETNILLNVPLH
-RLFARTRSMWSFNPETNILLN
-IASFRLFARTRSMWS
-ARTRSMWSFNPETNILLNVPLHG
-SMMGFKMNYQVNGY
-LFARTRSMWSFNPETN
-ISMMGFKMNYQVNGYPNMF
-SFRLFARTRSMWSFNPET
-MMGFKMNYQVNGY
-KRTIKGTHHWLLL
-YAISAKNRARTVA
-TQMNLKYAISAKNRARTVA
-GGQIVTCAKEIKESV
-GFKMNYQVNGYPNMF
-FISDEVARDLSLQFKR
-RLFARTRSMWSFNPETNILL
-IASFRLFARTRSMW
-TRSMWSFNPETNILLNV
-STFISDEVARDLSLQF
-ARTRSMWSFNPETNI
-SFRLFARTRSMWSFN
-FRLFARTRSMWSFNPETNIL
-NPETNILLNVPLHGTI
-LISMMGFKMNYQV
-SMWSFNPETNILLNVPLHG
-YFIASFRLFARTRSMW
-ASFRLFARTRSMWSFNPETNIL
-MMGFKMNYQVNGYPNM
-FKMNYQVNGYPNMF
-QMNLKYAISAKNRART
-DEVARDLSLQFKRP
-RSMWSFNPETNILLNVPLH
-THHWLLLTILTSL
-STFISDEVARDLSLQFKR
-FIASFRLFARTRSMWSFN
-WSFNPETNILLNVPLHGTI
-TRSMWSFNPETNILL
-FRLFARTRSMWSFNP
-YFIASFRLFARTRSMWSFNPET
-SMMGFKMNYQVNGYP
-SYFIASFRLFARTRSMWS
-SMMGFKMNYQVNG
-SYFIASFRLFARTRSMWSFNPETN
-FISDEVARDLSLQFK
-STFISDEVARDLSL
-HWLLLTILTSLLVLVQST
-RLISMMGFKMNYQV
-RSMWSFNPETNILL
-FISDEVARDLSLQ
-WSFNPETNILLNV
-ASFRLFARTRSMWSF
-TRSMWSFNPETNI
-SDEVARDLSLQFKR
-HHWLLLTILTSLLVLVQST
-RLFARTRSMWSFNP
-MWSFNPETNILLNV
-LLTILTSLLVLVQS
-RRLISMMGFKMNYQ
-FNPETNILLNVPLHGTI
-ISDEVARDLSLQF
-MMGFKMNYQVNGYP
-LFARTRSMWSFNPETNIL
-ASFRLFARTRSMWSFNPETN
-WLTNIFGTVYEKLK
-WLLLTILTSLLVLVQSTQ
-FARTRSMWSFNPETNIL
-IASFRLFARTRSMWSFNPETNIL
-NPETNILLNVPLH
-TQMNLKYAISAKNRARTVAG
-MNLKYAISAKNRARTVAG
-SMMGFKMNYQVNGYPNM
-SFNPETNILLNVPLHGTI
-DEVARDLSLQFKRPI
-TQQLIRAAEIRASAN
-KYAISAKNRARTVA
-IKGTHHWLLLTIL
-ILTSLLVLVQSTQ
-HWLLLTILTSLLVLVQSTQW
-LTILTSLLVLVQSTQW
-TFISDEVARDLSLQFK
-YFIASFRLFARTR
-ARTRSMWSFNPETNILLNVP
-YFIASFRLFARTRSMWSFN
-VTQQLIRAAEIRA
-WSFNPETNILLNVPL
-YFIASFRLFARTRSMWSFNPETN
-LLLTILTSLLVLVQ
-SMWSFNPETNILLN
-STFISDEVARDLSLQ
-SMWSFNPETNILL
-MWSFNPETNILLNVPLHGT
-NPETNILLNVPLHG
\ No newline at end of file
diff --git a/VaccineFormulations/mhc2_haplotype/37435_hogvaxine.txt b/VaccineFormulations/mhc2_haplotype/37435_hogvaxine.txt
deleted file mode 100644
index bc18d1be225491e850f29c1fcc461ca05fa4eda3..0000000000000000000000000000000000000000
--- a/VaccineFormulations/mhc2_haplotype/37435_hogvaxine.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-> MHC optimized combined peptide vaccine sequence with overlaps
-AISMWALIISVTSTQMNLKYAISAKNRARTVAGAAISMWALIISVTSLAIDAYAYLASASVYSVTYVTQQLIRAAEIRASANYQKVGGQIVTCAKEIKESVTYRRLISMMGFKMNYQVNGYPNMFDAYVNTFSSTFISDEVARDLSLQFKRPIHFYSKWYIRVCTNYQPPQTRAGAAISMWALIISVTSLAIDAYAYLQPRTFLLKYNSYFIASFRLFARTRSMWSFNPETNILLNVPLHGTILRTTNGDSEVVLQVKRTIKGTHHWLLLTILTSLLVLVQSTQWLTNIFGTVYEKLK
-> MHC optimized combined peptide vaccine sequence concatenated
-VGGQIVTCAKEIKTYRRLISMMGFKMNTFISDEVARDLSLQFKRPTFISDEVARDLSLQFKRTFISDEVARDLSLQFKRPILISMMGFKMNYQVNTQMNLKYAISAKNRARHHWLLLTILTSLLRLFARTRSMWSFNPETNILLNVPLTILTSLLVLVQSTQWYRRLISMMGFKMNYQVSTFISDEVARDLSTYRRLISMMGFKMNYRTIKGTHHWLLLTMWSFNPETNILLNVPLHGTIVGGQIVTCAKEIKEFIASFRLFARTRSMWSFNPETNILGQIVTCAKEIKESVMMGFKMNYQVNGYPNMFIASFRLFARTRSMWSFNPETNILLNKGTHHWLLLTILTQMNLKYAISAKNRARTVAGYFIASFRLFARTRSMWSFNPETNILARTRSMWSFNPETNILLNVPLHGHHWLLLTILTSLLVLISMMGFKMNYQVNGYPQWLTNIFGTVYEKVTQQLIRAAEIRASLFARTRSMWSFNPETNILLNVPLHGSFRLFARTRSMWSFNPETNILLNVIASFRLFARTRSMWSFNPETNILLISMMGFKMNYQVNGYPNMFTQMNLKYAISAKNRARTVAHHWLLLTILTSLLVLVQSTQWGGQIVTCAKEIKESVGFKMNYQVNGYPNMFVTQQLIRAAEIRASARLFARTRSMWSFNPETNILLNVPTQMNLKYAISAKNRLISMMGFKMNYQVNGYPNMFFRLFARTRSMWSFNPETNILLNVPHHWLLLTILTSLLVLVQSFARTRSMWSFNPETNILLNVPLHGQWLTNIFGTVYEKLKSTFISDEVARDLSLQFYAISAKNRARTVAGFISDEVARDLSLQFKRPIISDEVARDLSLQFKRPIGGQIVTCAKEIKESTQMNLKYAISAKNRARTVNPETNILLNVPLHGTILFARTRSMWSFNPETNILLNVPLHLISMMGFKMNYQVKMNYQVNGYPNMFTQMNLKYAISAKNFKMNYQVNGYPNMFSYFIASFRLFARTRSMWSFNLISMMGFKMNYQVNGYPNETNILLNVPLHGTIAISMWALIISVTSAISAKNRARTVAGTIKGTHHWLLLTISYFIASFRLFARTTHHWLLLTILTSLSTFISDEVARDLSLQFKRVKRTIKGTHHWLLLWSFNPETNILLNVPLHGTILLLTILTSLLVLVQSTQWSTFISDEVARDLSLQFKQLIRAAEIRASANWLLLTILTSLLVLVQSTQWSYFIASFRLFARTRSMWSSMWSFNPETNILLNVPLHGTRRLISMMGFKMNYQVSYFIASFRLFARTRSMWSFNPETNSTFISDEVARDLSLRLISMMGFKMNYQVLISMMGFKMNYQVNGYPNMTQMNLKYAISAKNRAPETNILLNVPLHGTILTNIFGTVYEKLKHHWLLLTILTSLLVLVQSTHHWLLLTILTSLLVLHHWLLLTILTSLLVLVHHWLLLTILTSLLVLVQKRTIKGTHHWLLLTFNPETNILLNVPLHGTIASFRLFARTRSMWSFNPETNILLNVKRTIKGTHHWLLTRSMWSFNPETNILLNVPLHGTEVARDLSLQFKRPIWLTNIFGTVYEKLKSYFIASFRLFARTRSMVKRTIKGTHHWLLLTRTIKGTHHWLLLTIIASFRLFARTRSMWSFNPETNILSYFIASFRLFARTRSTQMNLKYAISAKNRARTVAGVARDLSLQFKRPIMNLKYAISAKNRARTVAGSMMGFKMNYQVNGYPNMFARTRSMWSFNPETNILLNVPLHGTSYFIASFRLFARTRSFNPETNILLNVPLHGTIDEVARDLSLQFKRPIYFIASFRLFARTRSMWSFNPETNITQQLIRAAEIRASANIKGTHHWLLLTILTQMNLKYAISAKNRARTSYFIASFRLFARTRSMWSFNPETLISMMGFKMNYQVNGHWLLLTILTSLLVLVQSTQWKGTHHWLLLTILTSLTILTSLLVLVQSTQWFRLFARTRSMWSFNPETNILLNVSYFIASFRLFARTRSMWSFNPEKYAISAKNRARTVAGRTRSMWSFNPETNILLNVPLHGTTYRRLISMMGFKMNYQVTNILLNVPLHGTIMGFKMNYQVNGYPNMFGTHHWLLLTILTSLISMMGFKMNYQVNGYNLKYAISAKNRARTVAGSDEVARDLSLQFKRPIVTQQLIRAAEIRAILTSLLVLVQSTQWRLFARTRSMWSFNPETNILLNVPLHTYRRLISMMGFKMNYQSYFIASFRLFARTRSMWSFNPLLTILTSLLVLVQSTQWQWLTNIFGTVYEKLSYFIASFRLFARTRSMWASFRLFARTRSMWSFNPETNILLNVQQLIRAAEIRASANYFIASFRLFARTRSMWSFNPETNVGGQIVTCAKEIKESSYFIASFRLFARTRSMWSFTQQLIRAAEIRASAQIVTCAKEIKESVSTFISDEVARDLSLQLTSLLVLVQSTQWMWSFNPETNILLNVPLHGTTYRRLISMMGFKMLKYAISAKNRARTVAGRSMWSFNPETNILLNVPLHGTHHWLLLTILTSLLVLVQSTQ
\ No newline at end of file
diff --git a/download_data.sh b/download_data.sh
deleted file mode 100644
index 650c16e81c70409fdd195455c7ae3575607adf2c..0000000000000000000000000000000000000000
--- a/download_data.sh
+++ /dev/null
@@ -1,4 +0,0 @@
-#!/bin/sh
-
-wget "https://uni-duesseldorf.sciebo.de/s/jRTn9Vpff78kLqi/download?path=/&files=" -O download.zip
-unzip download.zip