added missing files

workflow/scripts/predict_affinities.py

@@ -0,0 +1,176 @@
+import sys
+import subprocess
+
+"""
+Usage: python predict_affinities.py <peptides> <alleles> <epitope_len>
+"""
+
+
+def main():
+
+    peptides = []
+    #generate fasta
+    wt_fasta = open('workflow/scripts/wt.fa','w')
+    mt_fasta = open('workflow/scripts/mt.fa','w')
+
+    wt_seqnum = 1
+    mt_seqnum = 1
+
+    # laod peptides
+    fh = open(sys.argv[1], 'r')
+    next(fh)
+    for line in fh:
+        entries = line.rstrip().split('\t')
+
+        if '$' in entries[11]:
+            wt_seq = entries[11].split('$')[0]
+        else:
+            wt_seq = entries[11]
+
+        if len(wt_seq) >= 9:
+            wt_fasta.write(f'>{wt_seqnum}\n{wt_seq}\n')
+            entries.append(wt_seqnum)
+            wt_seqnum += 1
+        else:
+            entries.append(0)
+
+        mt_seq = entries[12]
+        if len(mt_seq) >= 9:
+            mt_fasta.write(f'>{mt_seqnum}\n{mt_seq}\n')
+            entries.append(mt_seqnum)
+            mt_seqnum += 1
+        else:
+            entries.append(0)
+
+
+        peptides.append(entries)
+
+    fh.close()
+    wt_fasta.close()
+    mt_fasta.close()
+
+    # parse alleles from input
+    alleles = []
+    al_handler = open(sys.argv[2], 'r')
+    for al in al_handler:
+        alleles.append(al.rstrip())
+
+    # parse epitope length
+    epilens = sys.argv[3].split(',')
+
+    wt_affinities = calc_peptide_binding(alleles, 'workflow/scripts/wt.fa', epilens, 'wt')
+    mt_affinities = calc_peptide_binding(alleles, 'workflow/scripts/mt.fa', epilens, 'mt')
+
+    outputfile = open(sys.argv[4], 'w')
+    print_header(outputfile)
+
+    for entry in peptides:
+        final_result = {}
+        final_result['chrom'] = entry[0]
+        final_result['start'] = int(entry[1])
+        final_result['end'] = int(entry[2])
+        final_result['ref'] = entry[3]
+        final_result['alt'] = entry[4]
+        final_result['gene_name'] = entry[5]
+        final_result['gene_id'] = entry[6]
+        final_result['transcript_id'] = entry[7]
+        final_result['source'] = entry[8]
+        final_result['group'] = entry[9]
+        final_result['variant_type'] = entry[10]
+        final_result['wt_subseq'] = entry[11]
+        final_result['mt_subseq'] = entry[12]
+        final_result['new_var_start_pos'] = int(entry[13])
+        final_result['wt_seqnum'] = entry[14]
+        final_result['mt_seqnum'] = entry[15]
+
+        wt = None
+        # search for epitopes with high binding affinity
+        if final_result['wt_seqnum'] in wt_affinities:
+            wt = wt_affinities[final_result['wt_seqnum']]
+        else:
+            final_result['wt_epitope_ic50'] = 'NA'
+            final_result['wt_epitope_rank'] = 'NA'
+
+        if final_result['mt_seqnum'] in mt_affinities:
+            mt = mt_affinities[final_result['mt_seqnum']]
+        else:
+            continue
+
+        for epitope in mt.keys():
+            # determine by mhc_i / convert to 0-based
+            start_pos_in_subseq = int(mt[epitope][1]-1)
+            end_pos_in_subseq = int(mt[epitope][2]-1)
+
+            # check if mutation is part of the subsequence (within or upstream)
+            if final_result['new_var_start_pos'] >= end_pos_in_subseq:
+                continue
+            elif final_result['new_var_start_pos'] <= start_pos_in_subseq:
+                final_result['mutation_position'] = 0
+            else:
+                final_result['mutation_position'] = final_result['new_var_start_pos'] - start_pos_in_subseq
+
+            final_result['mt_epitope_seq'] = epitope
+            final_result['allele'] = mt[epitope][0]
+            final_result['mt_epitope_ic50'] = mt[epitope][3]
+            final_result['mt_epitope_rank'] = mt[epitope][4]
+
+            # search for corresponding WT
+            startpos_epitope_subseq = final_result['mt_epitope_seq'].find(epitope)
+            final_result['wt_epitope_seq'] = final_result['wt_subseq'][startpos_epitope_subseq:len(epitope)]
+
+            if wt is not None:
+                if final_result['wt_epitope_seq'] in wt.keys():
+                    final_result['wt_epitope_ic50'] = wt[final_result['wt_epitope_seq']][3]
+                    final_result['wt_epitope_rank'] = wt[final_result['wt_epitope_seq']][4]
+                else:
+                    final_result['wt_epitope_ic50'] = 'NA'
+                    final_result['wt_epitope_rank'] = 'NA'
+
+            print_row(final_result, outputfile)
+
+
+def print_header(outputfile):
+    outputfile.write(f"chrom\tstart\tend\tref\talt\tgene_name\tgene_id")
+    outputfile.write(f"\ttranscript_id\tsource\tgroup\tvariant_type")
+    outputfile.write(f"\tallele\twt_epitope_seq\twt_epitope_ic50\twt_epitope_rank")
+    outputfile.write(f"\tmt_epitope_seq\tmt_epitope_ic50\tmt_epitope_rank\tmutation_position\n")
+
+def print_row(row, outputfile):
+    outputfile.write(f"{row['chrom']}\t{row['start']}\t{row['end']}\t{row['ref']}\t{row['alt']}\t{row['gene_name']}\t{row['gene_id']}")
+    outputfile.write(f"\t{row['transcript_id']}\t{row['source']}\t{row['group']}\t{row['variant_type']}")
+    outputfile.write(f"\t{row['allele']}\t{row['wt_epitope_seq']}\t{row['wt_epitope_ic50']}\t{row['wt_epitope_rank']}")
+    outputfile.write(f"\t{row['mt_epitope_seq']}\t{row['mt_epitope_ic50']}\t{row['mt_epitope_rank']}\t{row['mutation_position']}\n")
+
+def calc_peptide_binding(alleles, fa_file, epilens, wt_mt):
+    binding_affinity = {}
+    for allele in alleles:
+        for epilen in epilens:
+            print(f"allele: {allele}, epilen: {epilen}")
+            call = ['python', 
+                'workflow/scripts/mhc_i/src/predict_binding.py', 
+                'netmhcpan', 
+                allele, 
+                str(epilen),
+                fa_file]
+            result = subprocess.run(call,
+                stdout = subprocess.PIPE,
+                universal_newlines = True 
+            )
+            predictions = result.stdout
+            line_by_line = predictions.rstrip().split('\n')
+            for line in line_by_line[1:]:
+                entries = line.split('\t')
+                if wt_mt == 'mt':
+                    if float(entries[8]) >= 500:
+                        continue
+
+                if int(entries[1]) not in binding_affinity:
+                    binding_affinity[int(entries[1])] = {}
+
+                if entries[5] not in binding_affinity[int(entries[1])]:
+                    # allele, start, end, ic50, rank
+                    binding_affinity[int(entries[1])][entries[5]] = (entries[0], int(entries[2]), int(entries[3]), float(entries[8]), float(entries[9]))
+
+    return binding_affinity
+
+main()

workflow/scripts/prep_scanexitron_config.py

@@ -0,0 +1,33 @@
+import sys
+import os
+
+def main():
+    template = """
+    [fasta]
+
+    hg38={hg38_path}
+    hg19=
+
+    [annotation]
+
+    hg38={hg38_annotation_path}
+    hg19=
+
+    [cds]
+    
+    hg38={hg38_cds_path}
+    hg19=
+    """
+    parameters = {
+            'hg38_path': str(os.path.abspath(sys.argv[1])),
+            'hg38_annotation_path': str(os.path.abspath(sys.argv[2])),
+            'hg38_cds_path': str(os.path.abspath(sys.argv[3]))
+    }
+
+    filled_template = template.format(**parameters)
+    output_path = sys.argv[4]
+
+    with open(output_path, 'w') as output_file:
+        output_file.write(filled_template)
+
+main()