def main():
SCRIPT_PATH = sys.path[0]
# Change the path to WEKA to the appropriate location on your computer
WEKA_PATH = SCRIPT_PATH + '/weka-3-8-1/weka.jar'
PEPSTATS_PATH = SCRIPT_PATH + '/EMBOSS-6.5.7/emboss/'
# -----------------------------------------------------------------------------------------------------------
# Check that the path to the WEKA software exists
path_exists = os.access(WEKA_PATH, os.F_OK)
if not path_exists:
print()
print("Path to WEKA software does not exist!")
print(
"Check the installation and the given path to the WEKA software %s in ApoplastP.py (line 40)."
% WEKA_PATH)
print()
sys.exit(1)
# -----------------------------------------------------------------------------------------------------------
# Check that the path to the EMBOSS software exists for pepstats
path_exists = os.access(PEPSTATS_PATH, os.F_OK)
if not path_exists:
print()
print("Path to EMBOSS software does not exist!")
print(
"Check the installation and the given path to the EMBOSS software %s in ApoplastP.py (line 41)."
% PEPSTATS_PATH)
print()
sys.exit(1)
# -----------------------------------------------------------------------------------------------------------
commandline = sys.argv[1:]
# -----------------------------------------------------------------------------------------------------------
if commandline:
FASTA_FILE, short_format, output_file, apoplast_output, nonapoplast_output, prob_threshold = functions.scan_arguments(
commandline)
# If no FASTA file was provided with the -i option
if not FASTA_FILE:
print()
print('Please specify a FASTA input file using the -i option!')
functions.usage()
else:
functions.usage()
# -----------------------------------------------------------------------------------------------------------
# Temporary folder name identifier that will be used to store results
RESULTS_PATH = tempfile.mkdtemp() + '/'
# -----------------------------------------------------------------------------------------------------------
# Check if FASTA file exists
try:
open(FASTA_FILE, 'r')
except OSError as e:
print("Unable to open FASTA file:",
FASTA_FILE) #Does not exist OR no read permissions
print("I/O error({0}): {1}".format(e.errno, e.strerror))
sys.exit(1)
# -----------------------------------------------------------------------------------------------------------
# Try to create folder where results will be stored
try:
os.mkdir(RESULTS_PATH)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
# -----------------------------------------------------------------------------------------------------------
# Extract the identifiers and sequences from input FASTA file
ORIGINAL_IDENTIFIERS, SEQUENCES = functions.get_seqs_ids_fasta(FASTA_FILE)
SEQUENCES = [seq.upper() for seq in SEQUENCES]
# -----------------------------------------------------------------------------------------------------------
print('-----------------')
print()
print("ApoplastP is running for", len(ORIGINAL_IDENTIFIERS),
"proteins given in FASTA file", FASTA_FILE)
print()
print('-----------------')
# -----------------------------------------------------------------------------------------------------------
# Replace ambiguous amino acids for ProtParam
ORIGINAL_SEQUENCES = SEQUENCES
SEQUENCES = functions.filterX(SEQUENCES)
# -----------------------------------------------------------------------------------------------------------
# Write new FASTA file with short identifiers because pepstats can't handle long names
f_output = RESULTS_PATH + 'short_ids.fasta'
SHORT_IDENTIFIERS = functions.write_FASTA_short_ids(
f_output, ORIGINAL_IDENTIFIERS, SEQUENCES)
# -----------------------------------------------------------------------------------------------------------
# Call pepstats
ProcessExe = PEPSTATS_PATH + 'pepstats'
ParamList = [
ProcessExe, '-sequence', f_output, '-outfile',
RESULTS_PATH + 'pepstats.out'
]
try:
Process = subprocess.Popen(ParamList, shell=False)
sts = Process.wait()
cstdout, cstderr = Process.communicate()
if Process.returncode:
raise Exception("Calling pepstats returned %s" %
Process.returncode)
if cstdout:
pass
elif cstderr:
sys.exit()
except:
e = sys.exc_info()[1]
print("Error calling pepstats: %s" % e)
sys.exit(1)
# -----------------------------------------------------------------------------------------------------------
# Parse pepstats file
pepstats_dic = functions.pepstats(SHORT_IDENTIFIERS, SEQUENCES,
RESULTS_PATH + 'pepstats.out')
# -----------------------------------------------------------------------------------------------------------
# Write the WEKA arff file for classification of the input FASTA file
weka_input = RESULTS_PATH + 'weka.arff'
functions.write_weka_input(weka_input, SHORT_IDENTIFIERS, SEQUENCES,
pepstats_dic)
# -----------------------------------------------------------------------------------------------------------
# Call WEKA Random Forest model for classification of input FASTA file
# -----------------------------------------------------------------------------------------------------------
ParamList = [
'java', '-cp', WEKA_PATH, 'weka.classifiers.trees.RandomForest', '-l',
SCRIPT_PATH + '/RATIO4_55_MODEL.model', '-T',
RESULTS_PATH + 'weka.arff', '-p', 'first-last'
]
with open(RESULTS_PATH + 'APOPLAST_Predictions.txt', 'wb') as out:
try:
Process = subprocess.Popen(ParamList, shell=False, stdout=out)
sts = Process.wait()
cstdout, cstderr = Process.communicate()
if Process.returncode:
raise Exception("Calling WEKA returned %s" %
Process.returncode)
if cstdout:
pass
elif cstderr:
sys.exit(1)
except:
e = sys.exc_info()[1]
print("Error calling WEKA: %s" % e)
sys.exit(1)
# -----------------------------------------------------------------------------------------------------------
# Parse the WEKA output file
file_input = RESULTS_PATH + 'APOPLAST_Predictions.txt'
predicted_apoplast, predictions = functions.parse_weka_output(
file_input, ORIGINAL_IDENTIFIERS, ORIGINAL_SEQUENCES, prob_threshold)
# -----------------------------------------------------------------------------------------------------------
# If user wants the stdout output directed to a specified file
if output_file:
with open(output_file, 'wb') as out:
# Short format: output predictions for all proteins as tab-delimited table
if short_format:
out.writelines(functions.short_output(predictions))
# If the user wants to see the long format, output additional information and stats
else:
out.writelines(functions.short_output(predictions))
out.writelines(
functions.long_output(ORIGINAL_IDENTIFIERS,
predicted_apoplast))
print('ApoplastP results were saved to output file:', output_file)
else:
# Short format: output predictions for all proteins as tab-delimited table to stdout
if short_format:
print(functions.short_output(predictions))
# If the user wants to see the long format, output additional information and stats
else:
print(functions.short_output(predictions))
print(
functions.long_output(ORIGINAL_IDENTIFIERS,
predicted_apoplast))
# -----------------------------------------------------------------------------------------------------------
# If the user additionally wants to save the predicted apoplastic proteins in a provided FASTA file
if apoplast_output:
with open(apoplast_output, 'w') as f_output:
for apoplast, prob, sequence in predicted_apoplast:
f_output.writelines('>' + apoplast +
' | Apoplast probability: ' + str(prob) +
'\n')
f_output.writelines(sequence + '\n')
if nonapoplast_output:
with open(nonapoplast_output, 'w') as f_output:
for apoplast, pred, prob, sequence in predictions:
if pred == 'Non-apoplastic':
f_output.writelines('>' + apoplast +
' | Non-apoplast probability: ' +
str(prob) + '\n')
f_output.writelines(sequence + '\n')
# -----------------------------------------------------------------------------------------------------------
# Clean up and delete temporary folder that was created
shutil.rmtree(RESULTS_PATH)
# -----------------------------------------------------------------------------------------------------------
try:
sys.stdout.close()
except:
pass
try:
sys.stderr.close()
except:
pass
def main():
# -----------------------------------------------------------------------------------------------------------
SCRIPT_PATH = sys.path[0]
# Change the path to WEKA to the appropriate location on your computer
WEKA_PATH = SCRIPT_PATH + '/weka-3-6-12/weka.jar'
PEPSTATS_PATH = SCRIPT_PATH + '/EMBOSS-6.5.7/emboss/'
# -----------------------------------------------------------------------------------------------------------
# Check that the path to the WEKA software exists
path_exists = os.access(WEKA_PATH, os.F_OK)
if not path_exists:
print()
print("Path to WEKA software does not exist!")
print("Check the installation and the given path to the WEKA software %s in EffectorP.py (line 47)." % WEKA_PATH)
print()
sys.exit(1)
# -----------------------------------------------------------------------------------------------------------
# Check that the path to the EMBOSS software exists for pepstats
path_exists = os.access(PEPSTATS_PATH, os.F_OK)
if not path_exists:
print()
print("Path to EMBOSS software does not exist!")
print("Check the installation and the given path to the EMBOSS software %s in EffectorP.py (line 48)." % PEPSTATS_PATH)
print()
sys.exit(1)
# -----------------------------------------------------------------------------------------------------------
commandline = sys.argv[1:]
# -----------------------------------------------------------------------------------------------------------
if commandline:
FASTA_FILE, short_format, output_file, effector_output = functions.scan_arguments(commandline)
# If no FASTA file was provided with the -i option
if not FASTA_FILE:
print()
print('Please specify a FASTA input file using the -i option!')
functions.usage()
else:
functions.usage()
# -----------------------------------------------------------------------------------------------------------
# Temporary folder name identifier that will be used to store results
FOLDER_IDENTIFIER = str(uuid.uuid4())
# Path to temporary results folder
if not os.path.exists(SCRIPT_PATH + '/tmp/'):
os.makedirs(SCRIPT_PATH + '/tmp/')
RESULTS_PATH = SCRIPT_PATH + '/tmp/' + FOLDER_IDENTIFIER + '/'
# -----------------------------------------------------------------------------------------------------------
# Check if FASTA file exists
try:
open(FASTA_FILE, 'r')
except OSError as e:
print("Unable to open FASTA file:", FASTA_FILE) # Does not exist OR no read permissions
print("I/O error({0}): {1}".format(e.errno, e.strerror))
sys.exit(1)
# -----------------------------------------------------------------------------------------------------------
# Try to create folder where results will be stored
try:
os.mkdir(RESULTS_PATH)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
# -----------------------------------------------------------------------------------------------------------
# Extract the identifiers and sequences from input FASTA file
ORIGINAL_IDENTIFIERS, SEQUENCES = functions.get_seqs_ids_fasta(FASTA_FILE)
# -----------------------------------------------------------------------------------------------------------
print('-----------------')
print()
print("EffectorP is running for", len(ORIGINAL_IDENTIFIERS), "proteins given in FASTA file", FASTA_FILE)
print()
# -----------------------------------------------------------------------------------------------------------
# Write new FASTA file with short identifiers because pepstats can't handle long names
f_output = RESULTS_PATH + FOLDER_IDENTIFIER + '_short_ids.fasta'
SHORT_IDENTIFIERS = functions.write_FASTA_short_ids(f_output, ORIGINAL_IDENTIFIERS, SEQUENCES)
# -----------------------------------------------------------------------------------------------------------
# Call pepstats
print('Call pepstats...')
ProcessExe = PEPSTATS_PATH + 'pepstats'
ParamList = [ProcessExe, '-sequence', RESULTS_PATH + FOLDER_IDENTIFIER + '_short_ids.fasta',
'-outfile', RESULTS_PATH + FOLDER_IDENTIFIER + '.pepstats']
try:
Process = subprocess.Popen(ParamList, shell=False)
sts = Process.wait()
cstdout, cstderr = Process.communicate()
if Process.returncode:
raise Exception("Calling pepstats returned %s" % Process.returncode)
if cstdout:
pass
elif cstderr:
sys.exit()
except:
e = sys.exc_info()[1]
print("Error calling pepstats: %s" % e)
sys.exit()
print('Done.')
print()
# -----------------------------------------------------------------------------------------------------------
# Parse pepstats file
print('Scan pepstats file')
pepstats_dic = functions.pepstats(SHORT_IDENTIFIERS, SEQUENCES, RESULTS_PATH + FOLDER_IDENTIFIER + '.pepstats')
print('Done.')
print()
# -----------------------------------------------------------------------------------------------------------
# Write the WEKA arff file for classification of the input FASTA file
weka_input = RESULTS_PATH + FOLDER_IDENTIFIER + '.arff'
functions.write_weka_input(weka_input, SHORT_IDENTIFIERS, pepstats_dic)
# -----------------------------------------------------------------------------------------------------------
# Call WEKA Naive Bayes model for classification of input FASTA file
print('Start classification with EffectorP...')
ParamList = ['java', '-cp', WEKA_PATH, 'weka.classifiers.bayes.NaiveBayes', '-l', SCRIPT_PATH + '/trainingdata_samegenomes_iteration15_ratio3_bayes.model',
'-T', RESULTS_PATH + FOLDER_IDENTIFIER + '.arff', '-p', 'first-last']
with open(RESULTS_PATH + FOLDER_IDENTIFIER + '_Predictions.txt', 'wb') as out:
try:
Process = subprocess.Popen(ParamList, shell=False, stdout=out)
sts = Process.wait()
cstdout, cstderr = Process.communicate()
if Process.returncode:
raise Exception("Calling WEKA returned %s"%Process.returncode)
if cstdout:
pass
elif cstderr:
sys.exit(1)
except:
e = sys.exc_info()[1]
print("Error calling WEKA: %s" % e)
sys.exit(1)
print('Done.')
print()
print('-----------------')
# -----------------------------------------------------------------------------------------------------------
# Parse the WEKA output file
file_input = RESULTS_PATH + FOLDER_IDENTIFIER + '_Predictions.txt'
predicted_effectors, predictions = functions.parse_weka_output(file_input, ORIGINAL_IDENTIFIERS, SEQUENCES)
# -----------------------------------------------------------------------------------------------------------
# If user wants the stdout output directed to a specified file
if output_file:
with open(output_file, 'wb') as out:
# Short format: output predictions for all proteins as tab-delimited table
if short_format:
out.writelines(functions.short_output(predictions))
# If the user wants to see the long format, output additional information and stats
else:
out.writelines(functions.short_output(predictions))
out.writelines(functions.long_output(ORIGINAL_IDENTIFIERS, predicted_effectors))
print('EffectorP results were saved to output file:', output_file)
else:
# Short format: output predictions for all proteins as tab-delimited table to stdout
if short_format:
print(functions.short_output(predictions))
# If the user wants to see the long format, output additional information and stats
else:
print(functions.short_output(predictions))
print(functions.long_output(ORIGINAL_IDENTIFIERS, predicted_effectors))
# -----------------------------------------------------------------------------------------------------------
# If the user additionally wants to save the predicted effectors in a provided FASTA file
if effector_output:
with open(effector_output, 'w') as f_output:
for effector, prob, sequence in predicted_effectors:
f_output.writelines('>' + effector + ' | Effector probability: ' + str(prob) + '\n')
f_output.writelines(sequence + '\n')
# -----------------------------------------------------------------------------------------------------------
# Clean up and delete temporary folder that was created
shutil.rmtree(RESULTS_PATH)
# -----------------------------------------------------------------------------------------------------------
return
if Process.returncode:
raise Exception("Calling WEKA returned %s" %
Process.returncode)
if cstdout:
pass
elif cstderr:
sys.exit()
except:
e = sys.exc_info()[1]
print "Error calling WEKA: %s" % e
sys.exit()
#--------------------------------------------------------------
# Parse the WEKA output file
file_input = RESULTS_PATH + 'Predictions.txt'
predicted_effectors, predicted_noneffectors, predictions = functions.parse_weka_output(
file_input, ORIGINAL_IDENTIFIERS, SEQUENCES)
for index, (ident, prediction, prob, seq) in enumerate(predictions):
short_ident = 'seq' + str(index)
if short_ident in ensembl_votes:
previous_predictions = ensembl_votes[short_ident]
ensembl_votes[short_ident] = previous_predictions + [
(prediction, prob)
]
else:
ensembl_votes[short_ident] = [(prediction, prob)]
#--------------------------------------------------------------
for model in functions.models_J48:
#--------------------------------------------------------------
def main():
# -----------------------------------------------------------------------------------------------------------
SCRIPT_PATH = sys.path[0]
# Change the path to WEKA to the appropriate location on your computer
WEKA_PATH = SCRIPT_PATH + '/weka-3-8-1/weka.jar'
PEPSTATS_PATH = SCRIPT_PATH + '/EMBOSS-6.5.7/emboss/'
# -----------------------------------------------------------------------------------------------------------
# Check that the path to the WEKA software exists
path_exists = os.access(WEKA_PATH, os.F_OK)
if not path_exists:
print()
print("Path to WEKA software does not exist!")
print("Check the installation and the given path to the WEKA software %s in EffectorP.py (line 47)." % WEKA_PATH)
print()
sys.exit(1)
# -----------------------------------------------------------------------------------------------------------
# Check that the path to the EMBOSS software exists for pepstats
path_exists = os.access(PEPSTATS_PATH, os.F_OK)
if not path_exists:
print()
print("Path to EMBOSS software does not exist!")
print("Check the installation and the given path to the EMBOSS software %s in EffectorP.py (line 48)." % PEPSTATS_PATH)
print()
sys.exit(1)
# -----------------------------------------------------------------------------------------------------------
commandline = sys.argv[1:]
# -----------------------------------------------------------------------------------------------------------
if commandline:
FASTA_FILE, short_format, output_file, effector_output, noneffector_output = functions.scan_arguments(commandline)
# If no FASTA file was provided with the -i option
if not FASTA_FILE:
print()
print('Please specify a FASTA input file using the -i option!')
functions.usage()
else:
functions.usage()
# -----------------------------------------------------------------------------------------------------------
# Temporary folder name identifier that will be used to store results
RESULTS_PATH = tempfile.mkdtemp() + '/'
# -----------------------------------------------------------------------------------------------------------
# Check if FASTA file exists
try:
open(FASTA_FILE, 'r')
except OSError as e:
print("Unable to open FASTA file:", FASTA_FILE) #Does not exist OR no read permissions
print("I/O error({0}): {1}".format(e.errno, e.strerror))
sys.exit(1)
# -----------------------------------------------------------------------------------------------------------
# Try to create folder where results will be stored
try:
os.mkdir(RESULTS_PATH)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
# -----------------------------------------------------------------------------------------------------------
# Extract the identifiers and sequences from input FASTA file
ORIGINAL_IDENTIFIERS, SEQUENCES = functions.get_seqs_ids_fasta(FASTA_FILE)
SEQUENCES = [seq.upper() for seq in SEQUENCES]
# -----------------------------------------------------------------------------------------------------------
print('-----------------')
print()
print("EffectorP 2.0 is running for", len(ORIGINAL_IDENTIFIERS), "proteins given in FASTA file", FASTA_FILE)
print()
# -----------------------------------------------------------------------------------------------------------
# Write new FASTA file with short identifiers because pepstats can't handle long names
f_output = RESULTS_PATH + 'short_ids.fasta'
SHORT_IDENTIFIERS = functions.write_FASTA_short_ids(f_output, ORIGINAL_IDENTIFIERS, SEQUENCES)
# -----------------------------------------------------------------------------------------------------------
# Call pepstats
print('Call pepstats...')
ProcessExe = PEPSTATS_PATH + 'pepstats'
ParamList = [ProcessExe, '-sequence', RESULTS_PATH + 'short_ids.fasta',
'-outfile', RESULTS_PATH + 'pepstats.out']
try:
Process = subprocess.Popen(ParamList, shell=False)
sts = Process.wait()
cstdout, cstderr = Process.communicate()
if Process.returncode:
raise Exception("Calling pepstats returned %s"%Process.returncode)
if cstdout:
pass
elif cstderr:
sys.exit()
except:
e = sys.exc_info()[1]
print("Error calling pepstats: %s" % e)
sys.exit()
print('Done.')
print()
# -----------------------------------------------------------------------------------------------------------
# Parse pepstats file
print('Scan pepstats file')
pepstats_dic = functions.pepstats(SHORT_IDENTIFIERS, SEQUENCES, RESULTS_PATH + 'pepstats.out')
print('Done.')
print()
# -----------------------------------------------------------------------------------------------------------
# Write the WEKA arff file for classification of the input FASTA file
weka_input = RESULTS_PATH + 'weka.arff'
functions.write_weka_input(weka_input, SHORT_IDENTIFIERS, pepstats_dic)
# -----------------------------------------------------------------------------------------------------------
# Ensembl averaging approach, use seq0,seq1,seq2... as keys in case there are duplicate FASTA identifiers
ensembl_votes = {}
# -----------------------------------------------------------------------------------------------------------
# Call WEKA models for classification of input FASTA file
# -----------------------------------------------------------------------------------------------------------
models = functions.models_bayes + functions.models_J48
# -----------------------------------------------------------------------------------------------------------
print('Ensemble classification')
for model in functions.models_bayes:
#--------------------------------------------------------------
ParamList = ['java', '-cp', WEKA_PATH, 'weka.classifiers.bayes.NaiveBayes', '-l', SCRIPT_PATH + model, '-T', RESULTS_PATH + 'weka.arff', '-p', 'first-last']
with open(RESULTS_PATH + 'Predictions.txt', 'wb') as out:
try:
Process = subprocess.Popen(ParamList, shell=False, stdout=out)
sts = Process.wait()
cstdout, cstderr = Process.communicate()
if Process.returncode:
raise Exception("Calling WEKA returned %s"%Process.returncode)
if cstdout:
pass
elif cstderr:
sys.exit()
except:
e = sys.exc_info()[1]
print("Error calling WEKA: %s" % e)
sys.exit(1)
#--------------------------------------------------------------
# Parse the WEKA output file
file_input = RESULTS_PATH + 'Predictions.txt'
predicted_effectors, predicted_noneffectors, predictions = functions.parse_weka_output(file_input, ORIGINAL_IDENTIFIERS, SEQUENCES)
for index, (ident, prediction, prob, seq) in enumerate(predictions):
short_ident = 'seq' + str(index)
if short_ident in ensembl_votes:
previous_predictions = ensembl_votes[short_ident]
ensembl_votes[short_ident] = previous_predictions + [(prediction, prob)]
else:
ensembl_votes[short_ident] = [(prediction, prob)]
#--------------------------------------------------------------
for model in functions.models_J48:
#--------------------------------------------------------------
ParamList = ['java', '-cp', WEKA_PATH, 'weka.classifiers.trees.J48', '-l', SCRIPT_PATH + model, '-T', RESULTS_PATH + 'weka.arff', '-p', 'first-last']
with open(RESULTS_PATH + 'Predictions.txt', 'wb') as out:
try:
Process = subprocess.Popen(ParamList, shell=False, stdout=out)
sts = Process.wait()
cstdout, cstderr = Process.communicate()
if Process.returncode:
raise Exception("Calling WEKA returned %s"%Process.returncode)
if cstdout:
pass
elif cstderr:
sys.exit()
except:
e = sys.exc_info()[1]
print("Error calling WEKA: %s" % e)
sys.exit(1)
#--------------------------------------------------------------
# Parse the WEKA output file
file_input = RESULTS_PATH + 'Predictions.txt'
predicted_effectors, predicted_noneffectors, predictions = functions.parse_weka_output(file_input, ORIGINAL_IDENTIFIERS, SEQUENCES)
for index, (ident, prediction, prob, seq) in enumerate(predictions):
short_ident = 'seq' + str(index)
if short_ident in ensembl_votes:
previous_predictions = ensembl_votes[short_ident]
ensembl_votes[short_ident] = previous_predictions + [(prediction, prob)]
else:
ensembl_votes[short_ident] = [(prediction, prob)]
print('Done.')
print()
#--------------------------------------------------------------
# Soft voting
#--------------------------------------------------------------
ensemble_predictions, predicted_effectors, predicted_noneffectors, predicted_weakeffectors = [], [], [], []
#--------------------------------------------------------------
for index, (ident, prediction, prob, seq) in enumerate(predictions):
short_ident = 'seq' + str(index)
yes_prob, no_prob = [], []
for vote, prob in ensembl_votes[short_ident]:
if vote == 'Non-effector':
no_prob.append(prob)
yes_prob.append(1.0 - prob)
if vote == 'Effector':
yes_prob.append(prob)
no_prob.append(1.0 - prob)
yes_prob = sum(yes_prob)/float(len(models))
no_prob = sum(no_prob)/float(len(models))
yes_prob, no_prob = round(yes_prob,3), round(no_prob,3)
if yes_prob > no_prob:
if yes_prob > 0.55:
prediction = 'Effector'
prob = round(yes_prob,3)
predicted_effectors.append((ident, prob, seq))
else:
prediction = 'Unlikely effector'
prob = round(yes_prob,3)
predicted_weakeffectors.append((ident, prob, seq))
else:
prediction = 'Non-effector'
prob = round(no_prob,3)
predicted_noneffectors.append((ident, prob, seq))
ensemble_predictions.append((ident, prediction, prob, seq))
#--------------------------------------------------------------
# If user wants the stdout output directed to a specified file
if output_file:
with open(output_file, 'wb') as out:
# Short format: output predictions for all proteins as tab-delimited table
if short_format:
out.writelines(functions.short_output(ensemble_predictions))
# If the user wants to see the long format, output additional information and stats
else:
out.writelines(functions.short_output(ensemble_predictions))
out.writelines(functions.long_output(ORIGINAL_IDENTIFIERS, predicted_effectors))
print('EffectorP results were saved to output file:', output_file)
else:
# Short format: output predictions for all proteins as tab-delimited table to stdout
if short_format:
print(functions.short_output(ensemble_predictions))
# If the user wants to see the long format, output additional information and stats
else:
print(functions.short_output(ensemble_predictions))
print(functions.long_output(ORIGINAL_IDENTIFIERS, predicted_effectors))
# -----------------------------------------------------------------------------------------------------------
# If the user additionally wants to save the predicted effectors in a provided FASTA file
if effector_output:
with open(effector_output, 'w') as f_output:
for effector, prob, sequence in predicted_effectors:
f_output.writelines('>' + effector + ' | Effector probability: ' + str(prob) + '\n')
f_output.writelines(sequence + '\n')
if noneffector_output:
with open(noneffector_output, 'w') as f_output:
for effector, prob, sequence in predicted_noneffectors:
f_output.writelines('>' + effector + ' | Non-effector probability: ' + str(prob) + '\n')
f_output.writelines(sequence + '\n')
for effector, prob, sequence in predicted_weakeffectors:
f_output.writelines('>' + effector + ' | Unlikely effector probability: ' + str(prob) + '\n')
f_output.writelines(sequence + '\n')
# -----------------------------------------------------------------------------------------------------------
# -----------------------------------------------------------------------------------------------------------
# Clean up and delete temporary folder that was created
shutil.rmtree(RESULTS_PATH)
# -----------------------------------------------------------------------------------------------------------
try:
sys.stdout.close()
except:
pass
try:
sys.stderr.close()
except:
pass