def main(epitope_list=None,
alignments_dir=None,
alignment_score_threshold=None,
slope_parameter=None,
output_file=None):
# Compute MHC amplitudes for all neoantigens
a_val_by_index = {}
peptide_by_index = {}
sample_by_index = {}
with open(epitope_list) as f:
for data in csv.DictReader(f, delimiter='\t'):
index = data['id']
sample = data['sample']
mtpeptide = data['epitope']
kdwt = data['wt_score']
kdmt = data['mt_score']
kdmt = float(kdmt)
if kdwt == 'nan':
kdwt = 1000.
kdwt = float(kdwt)
index = int(index)
peptide_by_index[index] = mtpeptide.upper()
a_val_by_index[index] = kdwt / kdmt
sample_by_index[index] = sample
# Compute TCR-recognition probabilities for all neoantigens
aligner = Aligner()
for sname in set(sample_by_index.values()):
xml_path = join(alignments_dir, f'neoantigens_{sname}_iedb.xml')
aligner.read_all_blast_alignments(xml_path)
aligner.compute_rval(alignment_score_threshold, slope_parameter)
# Compute qualities for all epitopes and write the result
with open(output_file, 'w') as out:
header = [
'Sample', 'NeoantigenID', 'MT.Peptide.Form', 'NeoantigenQuality',
'NeoantigenAlignment', 'IEDB_EpitopeAlignment', 'AlignmentScore',
'IEDB_Epitope'
]
out.write('\t'.join(header) + '\n')
for index, peptide in peptide_by_index.items():
a_val = a_val_by_index[index]
[r_val, species, alignment] = aligner.get_rval(index)
neo_alignment = alignment[0]
epitope_alignment = alignment[1]
score = alignment[2]
quality = a_val * r_val
res = [
sample_by_index[index], index, peptide, quality, neo_alignment,
epitope_alignment, score, species
]
out.write('\t'.join(map(str, res)) + '\n')
