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 test_peptide_cleavage_prediction_mixed_input(self):
for m in CleavageSitePredictorFactory.available_methods():
if m != "NetChop":
mo = CleavageSitePredictorFactory(m)
mo.predict(self.seqs)
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 main():
model = argparse.ArgumentParser(
description='Commandline tool for string-of-beads epitope assembly',
)
model.add_argument('-i',
'--input',
type=str,
required=True,
help='Path to the input file'
)
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('-w',
'--weight',
type=float,
default=0.0,
help="Specifies how strong unwanted cleavage sites should be punished [0,1], \
where 0 means they will be ignored, and 1 the sum of all unwanted cleave sites is \
subtracted from the cleave site between two epitopes"
)
model.add_argument('-a',
'--approximate',
action="store_true",
help="Flag whether epitope ordering should be approximated"
)
model.add_argument('-s',
'--solver',
type=str,
default="cbc",
help="The ILP solver to be used by Pyomo (must be in PATH)"
)
model.add_argument('-so',
'--solver_options',
type=str,
default="",
help="Solver specific options (will not be checked for correctness)"
)
model.add_argument('-o',
'--output',
type=str,
required=True,
help='Path to the output file'
)
args = model.parse_args()
try:
peptides = read_lines(args.input)
except Exception as e:
print e
sys.stderr.write("Input file could not be read. Please check the existence and input format.")
return -1
try:
if args.version == "":
predictor = CleavageSitePredictorFactory(args.method)
else:
predictor = CleavageSitePredictorFactory(args.method, version=args.version)
except ValueError as e:
sys.stderr.write(str(e))
return -1
try:
assembler = EpitopeAssembly(peptides, predictor, solver=args.solver, weight=args.weight)
except Exception as e:
sys.stderr.write(str(e))
return -1
try:
if args.approximate:
assembly = assembler.approximate()
else:
options = {}
for opt in args.solver_options.split():
name,value = opt.partition("=")[::2]
try:
options[name] = float(value)
except Exception:
options[name] = value
assembly = assembler.solve(options=options)
except Exception as e:
sys.stderr.write(str(e))
return -1
try:
with open(args.output, "w") as f:
f.write(">assembled_polypeptide\n"+"".join(str(p) for p in assembly))
except IOError as e:
sys.stderr.write(str(e))
return -1
return 0
def test_CleavageSite_prediction_available_methods(self):
print CleavageSitePredictorFactory.available_methods()
def test_CleavageSite_prediction_available_methods(self):
print CleavageSitePredictorFactory.available_methods()
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
