A script to generate the peptide_table.tsv for Modifying peptides

Author: evelyn-schmidt

scripts/modify_peptides.py

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+import argparse
+from itertools import product, chain
+import pandas as pd
+
+
+# ---- PARSE ARGUMENTS -------------------------------------------------------
+# Parses command line arguments
+# Enables user help
+def parse_arguments():
+    # Parse command line arugments
+    parser = argparse.ArgumentParser(description='Create peptide_table.tsv needed for modifying peptide sequences for pVACbind')
+
+    parser.add_argument('-n',
+                        help='The maximum number of mofifying peptides to add to the begining or end')
+    parser.add_argument('-m',
+                        help='A csv file containing the name/indentifer for the sequence which does NOT have to be unique')
+    return(parser.parse_args())
+
+def generate_modifed_peptides(n, name, base_sequence):
+    characters = ['K', 'R']
+    
+    # Generate all possible combinations up to length n
+    all_combinations = chain.from_iterable(product(characters, repeat=i) for i in range(1, n + 1))
+    possible_modifications = set(''.join(combination) for combination in all_combinations)
+    
+    peptide_table = []
+
+    for modification in possible_modifications:
+
+        Nterm_dict = {}
+        Cterm_dict = {}
+
+        Nterm_sequence_name = name + "." + "n-term" + "-" + modification
+        Cterm_sequence_name = name + "." + "c-term" + "-" + modification
+
+        Nterm_sequence = modification + base_sequence
+        Cterm_sequence = base_sequence + modification 
+
+        Nterm_parsed_sequence = modification + '|' + base_sequence
+        Cterm_parsed_sequence = base_sequence + '|' + modification
+
+        Nterm_dict.update({'sequence_name': Nterm_sequence_name, 'sequence': Nterm_sequence, 'parsed_sequence': Nterm_parsed_sequence})
+        Cterm_dict.update({'sequence_name': Cterm_sequence_name, 'sequence': Cterm_sequence, 'parsed_sequence': Cterm_parsed_sequence})
+
+        peptide_table.append(Nterm_dict)
+        peptide_table.append(Cterm_dict)
+
+    return(peptide_table)
+
+
+def assign_unique_numbers(df, column_name):
+    counts = df[column_name].value_counts()
+    duplicated_values = counts[counts > 1].index
+
+    for value in duplicated_values:
+        indices = df.index[df[column_name] == value]
+        for i, index in enumerate(indices, start=1):
+            df.at[index, column_name] = f"{value}.{i}"
+
+    return df
+
+def main():
+    args = parse_arguments()
+
+    max_length = args.n
+
+    peptides = pd.read_csv(args.m, names=["Name", "Sequence"], header=None)
+    peptides = peptides[1:]
+    peptides = assign_unique_numbers(peptides, "Name")
+
+    max_length = 3
+    list = []
+
+    for index, row in peptides.iterrows():
+        sequences_list = []
+        
+        name = row['Name']
+        base_sequence = row['Sequence']
+        sequences_list  = generate_modifed_peptides(max_length, name, base_sequence)
+
+        list = list + sequences_list
+
+    df = pd.DataFrame(list) 
+
+    df.to_csv('peptide_table.tsv', sep="\t", index=False, header=None)
+
+if __name__ == "__main__":
+    main()