def test_path_option_and_optionl_parameters(self):
netchop = CleavageSitePredictorFactory("NetChop")
exe = netchop.command.split()[0]
for try_path in os.environ["PATH"].split(os.pathsep):
try_path = try_path.strip('"')
exe_try = os.path.join(try_path, exe).strip()
if os.path.isfile(exe_try) and os.access(exe_try, os.X_OK):
print exe_try
netchop.predict(self.seqs, path=exe_try, options="-v 1")
def test_wrong_input(self):
with self.assertRaises(ValueError):
mo = CleavageSitePredictorFactory("NetChop")
mo.predict(self.seqs[0])
def test_peptide_cleavage_prediction_mixed_input(self):
mo = CleavageSitePredictorFactory("NetChop")
mo.predict(self.seqs)
def test_peptide_cleavage_prediction_single_input(self):
mo = CleavageSitePredictorFactory("NetChop")
mo.predict(self.seqs[0])
mo.predict(self.seqs[1])
def test_peptide_cleavage_prediction_single_input(self):
for m in CleavageSitePredictorFactory.available_methods():
if m.lower() != "netchop":
mo = CleavageSitePredictorFactory(m)
mo.predict(self.seqs[0])
mo.predict(self.seqs[1])
def main():
#Specify CTD interface
# Every CTD Model has to have at least a name and a version, plus any of the optional attributes below them.
model = argparse.ArgumentParser(
description='Commandline tool for cleavage site prediction', )
model.add_argument(
'-m',
'--method',
type=str,
choices=CleavageSitePredictorFactory.available_methods().keys(),
default="pcm",
help='The name of the prediction method')
model.add_argument('-v',
'--version',
type=str,
default="",
help='The version of the prediction method')
model.add_argument('-i',
'--input',
type=str,
required=True,
help='Path to the input file (in fasta format)')
model.add_argument('-l',
'--length',
type=int,
default=0,
help='The length of peptides')
model.add_argument(
'-op',
'--options',
type=str,
default="",
help="Additional options that get directly past to the tool")
model.add_argument('-o',
'--output',
type=str,
required=True,
help='Path to the output file')
args = model.parse_args()
#fasta protein
peptides = read_fasta(args.input, in_type=Protein)
if args.version == "":
predictor = CleavageSitePredictorFactory(args.method)
result = predictor.predict(peptides, options=args.method)
else:
predictor = CleavageSitePredictorFactory(args.method,
version=args.version)
result = predictor.predict(peptides, options=args.method)
#if length is specified, than generate compact output
if int(args.length) > 0:
length = int(args.length)
with open(args.output, "w") as f:
f.write("Sequence\tMethod\tScore\tProtein ID\tPosition\n")
for seq_id in set(result.index.get_level_values(0)):
seq = "".join(result.ix[seq_id]["Seq"])
for start in xrange(len(seq) - (length - 1)):
pep_seq = seq[start:(start + length)]
score = result.loc[(seq_id, start + (length - 1)),
predictor.name]
f.write(pep_seq + "\t" + predictor.name + "\t" +
"%.3f" % score + "\t" + seq_id + "\t" +
str(start) + "\n")
else:
result.to_csv(args.output, float_format="%.3f", sep="\t")
return 0
def test_peptide_cleavage_prediction_mixed_input(self):
for m in CleavageSitePredictorFactory.available_methods():
if m != "NetChop":
mo = CleavageSitePredictorFactory(m)
mo.predict(self.seqs)
def test_peptide_cleavage_prediction_single_input(self):
for m in CleavageSitePredictorFactory.available_methods():
if m.lower() != "netchop":
mo = CleavageSitePredictorFactory(m)
mo.predict(self.seqs[0])
mo.predict(self.seqs[1])
def main():
#Specify CTD interface
# Every CTD Model has to have at least a name and a version, plus any of the optional attributes below them.
model = argparse.ArgumentParser(
description='Commandline tool for cleavage site prediction',
)
model.add_argument('-m',
'--method',
type=str,
choices=CleavageSitePredictorFactory.available_methods().keys(),
default="pcm",
help='The name of the prediction method'
)
model.add_argument('-v',
'--version',
type=str,
default="",
help='The version of the prediction method'
)
model.add_argument('-i',
'--input',
type=str,
required=True,
help='Path to the input file (in fasta format)'
)
model.add_argument('-l',
'--length',
type=int,
default=0,
help='The length of peptides'
)
model.add_argument('-op',
'--options',
type=str,
default="",
help="Additional options that get directly past to the tool"
)
model.add_argument('-o',
'--output',
type=str,
required=True,
help='Path to the output file'
)
args = model.parse_args()
#fasta protein
peptides = read_fasta(args.input, in_type=Protein)
if args.version == "":
predictor = CleavageSitePredictorFactory(args.method)
result = predictor.predict(peptides, options=args.method)
else:
predictor = CleavageSitePredictorFactory(args.method, version=args.version)
result = predictor.predict(peptides, options=args.method)
#if length is specified, than generate compact output
if int(args.length) > 0:
length = int(args.length)
with open(args.output, "w") as f:
f.write("Sequence\tMethod\tScore\tProtein ID\tPosition\n")
for seq_id in set(result.index.get_level_values(0)):
seq = "".join(result.ix[seq_id]["Seq"])
for start in xrange(len(seq)-(length-1)):
pep_seq = seq[start:(start+length)]
score = result.loc[(seq_id, start+(length-1)), predictor.name]
f.write(pep_seq+"\t"+predictor.name+"\t"+"%.3f"%score+"\t"+seq_id+"\t"+str(start)+"\n")
else:
result.to_csv(args.output, float_format="%.3f", sep="\t")
return 0
