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Remove code duplication
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@susannasiebert
susannasiebert committed Apr 28, 2021
1 parent 29446c3 commit 7acd5a1
Showing 1 changed file with 66 additions and 82 deletions.
148 changes: 66 additions & 82 deletions tools/pvacfuse/run.py
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Original file line number Diff line number Diff line change
Expand Up @@ -117,89 +117,73 @@ def main(args_input = sys.argv[1:]):
'run_post_processor' : False
}

if len(class_i_prediction_algorithms) > 0 and len(class_i_alleles) > 0:
if args.iedb_install_directory:
iedb_mhc_i_executable = os.path.join(args.iedb_install_directory, 'mhc_i', 'src', 'predict_binding.py')
if not os.path.exists(iedb_mhc_i_executable):
sys.exit("IEDB MHC I executable path doesn't exist %s" % iedb_mhc_i_executable)
else:
iedb_mhc_i_executable = None

print("Executing MHC Class I predictions")

output_dir = os.path.join(base_output_dir, 'MHC_Class_I')
os.makedirs(output_dir, exist_ok=True)

output_files = []
for epitope_length in args.class_i_epitope_length:
(input_file, per_epitope_output_dir) = generate_fasta(args, output_dir, epitope_length)

class_i_arguments = shared_arguments.copy()
class_i_arguments['input_file'] = input_file
class_i_arguments['alleles'] = class_i_alleles
class_i_arguments['iedb_executable'] = iedb_mhc_i_executable
class_i_arguments['epitope_lengths'] = [epitope_length]
class_i_arguments['prediction_algorithms'] = class_i_prediction_algorithms
class_i_arguments['output_dir'] = per_epitope_output_dir
class_i_arguments['netmhc_stab'] = args.netmhc_stab
pipeline = PvacbindPipeline(**class_i_arguments)
pipeline.execute()
intermediate_output_file = os.path.join(per_epitope_output_dir, "{}.all_epitopes.tsv".format(args.sample_name))
output_file = os.path.join(per_epitope_output_dir, "{}.all_epitopes.final.tsv".format(args.sample_name))
append_columns(intermediate_output_file, "{}.tsv".format(input_file), output_file)
output_files.append(output_file)
if epitope_length == max(args.class_i_epitope_length):
fasta_file = os.path.join(output_dir, "{}.fasta".format(args.sample_name))
shutil.copy(input_file, fasta_file)
all_epitopes_file = os.path.join(output_dir, "{}.all_epitopes.tsv".format(args.sample_name))
filtered_file = os.path.join(output_dir, "{}.filtered.tsv".format(args.sample_name))
create_combined_reports(output_files, all_epitopes_file, filtered_file, True, args)
elif len(class_i_prediction_algorithms) == 0:
print("No MHC class I prediction algorithms chosen. Skipping MHC class I predictions.")
elif len(class_i_alleles) == 0:
print("No MHC class I alleles chosen. Skipping MHC class I predictions.")

if len(class_ii_prediction_algorithms) > 0 and len(class_ii_alleles) > 0:
if args.iedb_install_directory:
iedb_mhc_ii_executable = os.path.join(args.iedb_install_directory, 'mhc_ii', 'mhc_II_binding.py')
if not os.path.exists(iedb_mhc_ii_executable):
sys.exit("IEDB MHC II executable path doesn't exist %s" % iedb_mhc_ii_executable)
else:
iedb_mhc_ii_executable = None

print("Executing MHC Class II predictions")

output_dir = os.path.join(base_output_dir, 'MHC_Class_II')
os.makedirs(output_dir, exist_ok=True)
if args.iedb_install_directory:
iedb_mhc_i_executable = os.path.join(args.iedb_install_directory, 'mhc_i', 'src', 'predict_binding.py')
if not os.path.exists(iedb_mhc_i_executable):
sys.exit("IEDB MHC I executable path doesn't exist %s" % iedb_mhc_i_executable)
iedb_mhc_ii_executable = os.path.join(args.iedb_install_directory, 'mhc_ii', 'mhc_II_binding.py')
if not os.path.exists(iedb_mhc_ii_executable):
sys.exit("IEDB MHC II executable path doesn't exist %s" % iedb_mhc_ii_executable)
else:
iedb_mhc_i_executable = None
iedb_mhc_ii_executable = None

all_params = {
'I': {
'iedb_executable': iedb_mhc_i_executable,
'prediction_algorithms': class_i_prediction_algorithms,
'alleles': class_i_alleles,
'epitope_lengths': args.class_i_epitope_length,
'netmhc_stab': args.netmhc_stab
},
'II': {
'iedb_executable': iedb_mhc_ii_executable,
'prediction_algorithms': class_ii_prediction_algorithms,
'alleles': class_ii_alleles,
'epitope_lengths': args.class_ii_epitope_length,
'netmhc_stab': False
}
}

output_files = []
for epitope_length in args.class_ii_epitope_length:
(input_file, per_epitope_output_dir) = generate_fasta(args, output_dir, epitope_length)

class_ii_arguments = shared_arguments.copy()
class_ii_arguments['input_file'] = input_file
class_ii_arguments['alleles'] = class_ii_alleles
class_ii_arguments['iedb_executable'] = iedb_mhc_ii_executable
class_ii_arguments['epitope_lengths'] = [epitope_length]
class_ii_arguments['prediction_algorithms'] = class_ii_prediction_algorithms
class_ii_arguments['output_dir'] = per_epitope_output_dir
class_ii_arguments['netmhc_stab'] = False
pipeline = PvacbindPipeline(**class_ii_arguments)
pipeline.execute()
intermediate_output_file = os.path.join(per_epitope_output_dir, "{}.all_epitopes.tsv".format(args.sample_name))
output_file = os.path.join(per_epitope_output_dir, "{}.all_epitopes.final.tsv".format(args.sample_name))
append_columns(intermediate_output_file, "{}.tsv".format(input_file), output_file)
output_files.append(output_file)
if epitope_length == max(args.class_ii_epitope_length):
fasta_file = os.path.join(output_dir, "{}.fasta".format(args.sample_name))
shutil.copy(input_file, fasta_file)
all_epitopes_file = os.path.join(output_dir, "{}.all_epitopes.tsv".format(args.sample_name))
filtered_file = os.path.join(output_dir, "{}.filtered.tsv".format(args.sample_name))
create_combined_reports(output_files, all_epitopes_file, filtered_file, True, args)
elif len(class_ii_prediction_algorithms) == 0:
print("No MHC class II prediction algorithms chosen. Skipping MHC class II predictions.")
elif len(class_ii_alleles) == 0:
print("No MHC class II alleles chosen. Skipping MHC class II predictions.")
for (mhc_class, params) in all_params.items():
prediction_algorithms = params['prediction_algorithms']
alleles = params['alleles']
epitope_lengths = params['epitope_lengths']

if len(prediction_algorithms) > 0 and len(alleles) > 0:
print("Executing MHC Class {} predictions".format(mhc_class))

output_dir = os.path.join(base_output_dir, 'MHC_Class_{}'.format(mhc_class))
os.makedirs(output_dir, exist_ok=True)

output_files = []
for epitope_length in epitope_lengths:
(input_file, per_epitope_output_dir) = generate_fasta(args, output_dir, epitope_length)

class_i_arguments = shared_arguments.copy()
class_i_arguments['input_file'] = input_file
class_i_arguments['alleles'] = alleles
class_i_arguments['iedb_executable'] = iedb_mhc_i_executable
class_i_arguments['epitope_lengths'] = [epitope_length]
class_i_arguments['prediction_algorithms'] = prediction_algorithms
class_i_arguments['output_dir'] = per_epitope_output_dir
class_i_arguments['netmhc_stab'] = params['netmhc_stab']
pipeline = PvacbindPipeline(**class_i_arguments)
pipeline.execute()
intermediate_output_file = os.path.join(per_epitope_output_dir, "{}.all_epitopes.tsv".format(args.sample_name))
output_file = os.path.join(per_epitope_output_dir, "{}.all_epitopes.final.tsv".format(args.sample_name))
append_columns(intermediate_output_file, "{}.tsv".format(input_file), output_file)
output_files.append(output_file)
if epitope_length == max(epitope_lengths):
fasta_file = os.path.join(output_dir, "{}.fasta".format(args.sample_name))
shutil.copy(input_file, fasta_file)
all_epitopes_file = os.path.join(output_dir, "{}.all_epitopes.tsv".format(args.sample_name))
filtered_file = os.path.join(output_dir, "{}.filtered.tsv".format(args.sample_name))
create_combined_reports(output_files, all_epitopes_file, filtered_file, True, args)
elif len(prediction_algorithms) == 0:
print("No MHC class {} prediction algorithms chosen. Skipping MHC class I predictions.".format(mhc_class))
elif len(alleles) == 0:
print("No MHC class{} alleles chosen. Skipping MHC class I predictions.".format(mhc_class))

if len(class_i_prediction_algorithms) > 0 and len(class_i_alleles) > 0 and len(class_ii_prediction_algorithms) > 0 and len(class_ii_alleles) > 0:
print("Creating combined reports")
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