def main(args_list=None):
if not args_list:
args_list = sys.argv[1:]
args = parse_args(parser, args_list)
print(args)
if args.hits_txt and len(args.hits_txt) > 0:
dataset = Dataset.from_peptides_text_files(args.hits_txt)
dataset = dataset.assemble_contigs()
else:
dataset = Dataset.from_csv(args.dataset_csv)
dataframes = []
for allele, allele_dataset in dataset.groupby_allele():
print("-- %s" % allele)
df = allele_dataset.to_dataframe()
netmhciipan = NetMHCIIpan([allele])
# pad peptides with alanines in case they're shorter than 9mer
peptides = ensure_peptide_lengths(df.peptide)
n_peptides = len(peptides)
binding_predictions = netmhciipan.predict_peptides(peptides)
ic50_pred = np.array([x.affinity for x in binding_predictions])
percentile_rank_pred = np.array(
[x.percentile_rank for x in binding_predictions])
assert len(ic50_pred) == n_peptides
assert len(percentile_rank_pred) == n_peptides
df["netmhciipan_ic50"] = ic50_pred
df["netmhciipan_percentile_rank"] = percentile_rank_pred
dataframes.append(df)
combined_df = pd.concat(dataframes)
combined_df.to_csv(args.output_csv, index=False)
def test_class2_8mer_fails():
ii_pan_predictor = NetMHCIIpan(alleles=["HLA-DRB1*01:01"])
with assert_raises(ValueError):
ii_pan_predictor.predict_peptides(["A" * 8])
def test_class2_9mer_success():
ii_pan_predictor = NetMHCIIpan(alleles=["HLA-DRB1*01:01"])
predictions = ii_pan_predictor.predict_peptides(["A" * 9])
eq_(len(predictions), 1)
