def predict(self, peptides, **kwargs):
"""
Takes peptides plus their trailing C and N-terminal residues to predict
the probability that this n-mer was produced by proteasomal cleavage. It returns the score and
the peptide sequence in a AResult object. Row-IDs are the peitopes column is the prediction score.
:param peptides: A list of peptide objects or a single peptide object
:type peptides: list(:class:`~Fred2.Core.Peptide.Peptide`) or :class:`~Fred2.Core.Peptide.Peptide`
:return: Returns a :class:`Fred2.Core.Result.CleavageFragmentPredictionResult` object
:rtype: :class:`Fred2.Core.Result.CleavageFragmentPredictionResult`
"""
def __load_model(length):
allele_model = "%%s_%i" % (self.name, length)
return getattr(
__import__("Fred2.Data.pssms." + self.name + ".mat." +
allele_model,
fromlist=[allele_model]), allele_model)
if isinstance(peptides, Peptide):
pep_seqs = {str(peptides): peptides}
else:
pep_seqs = {}
for p in peptides:
if not isinstance(p, Peptide):
raise ValueError("Input is not of type Protein or Peptide")
pep_seqs[str(p)] = p
result = {self.name: {}}
for length, peps in itertools.groupby(iter(pep_seqs.keys()),
key=lambda x: len(x)):
peps = list(peps)
#dynamicaly import prediction PSSMS for alleles and predict
if length not in self.supportedLength:
warnings.warn("Peptide length of %i not supported" % length,
RuntimeWarning)
continue
#load pssm matrices
try:
pssm = __load_model(length)
except ImportError:
raise KeyError("No model found for %s with length %i" %
(self.name, length))
for p in peps:
score = sum(pssm[i][aa] for i, aa in enumerate(p)) + pssm.get(
-1, {}).get("con", 0)
pep = pep_seqs[p][self.trailingN:-self.tralingC]
result[self.name][pep] = score
if not result:
raise ValueError(
"No predictions could be made for the given input.")
df_result = CleavageFragmentPredictionResult.from_dict(result)
df_result.index = pandas.MultiIndex.from_tuples(
[tuple((i, self.name)) for i in df_result.index],
names=['Seq', 'Method'])
return df_result
def predict(self, peptides, **kwargs):
"""
Takes peptides plus their trailing C and N-terminal residues to predict
the probability that this n-mer was produced by proteasomal cleavage. It returns the score and
the peptide sequence in a AResult object. Row-IDs are the peitopes column is the prediction score.
:param peptides: A list of peptide objects or a single peptide object
:type peptides: list(:class:`~Fred2.Core.Peptide.Peptide`) or :class:`~Fred2.Core.Peptide.Peptide`
:return: Returns a :class:`Fred2.Core.Result.CleavageFragmentPredictionResult` object
:rtype: :class:`Fred2.Core.Result.CleavageFragmentPredictionResult`
"""
def __load_model(length):
allele_model = "%%s_%i"%(self.name, length)
return getattr(__import__("Fred2.Data.pssms."+self.name+".mat."+allele_model, fromlist=[allele_model]),
allele_model)
if isinstance(peptides, Peptide):
pep_seqs = {str(peptides):peptides}
else:
pep_seqs = {}
for p in peptides:
if not isinstance(p, Peptide):
raise ValueError("Input is not of type Protein or Peptide")
pep_seqs[str(p)] = p
result = {self.name:{}}
for length, peps in itertools.groupby(pep_seqs.iterkeys(), key= lambda x: len(x)):
peps = list(peps)
#dynamicaly import prediction PSSMS for alleles and predict
if length not in self.supportedLength:
warnings.warn("Peptide length of %i not supported"%length, RuntimeWarning)
continue
#load pssm matrices
try:
pssm = __load_model(length)
except ImportError:
raise KeyError("No model found for %s with length %i"%(self.name, length))
for p in peps:
score = sum(pssm[i][aa] for i, aa in enumerate(p))+pssm.get(-1, {}).get("con", 0)
pep = pep_seqs[p][self.trailingN:-self.tralingC]
result[self.name][pep] = score
if not result:
raise ValueError("No predictions could be made for the given input.")
df_result = CleavageFragmentPredictionResult.from_dict(result)
df_result.index = pandas.MultiIndex.from_tuples([tuple((i, self.name)) for i in df_result.index],
names=['Seq', 'Method'])
return df_result
def predict(self, peptides, **kwargs):
def __load_model(length):
allele_model = "%s_%i"%(self.name, length)
return getattr(__import__("Fred2.Data.pssms."+self.name+".mat."+allele_model, fromlist=[allele_model]),
allele_model)
if isinstance(peptides, Peptide):
pep_seqs = {str(peptides):peptides}
else:
if any(not isinstance(p, Peptide) for p in peptides):
raise ValueError("Input is not of type Protein or Peptide")
pep_seqs = {str(p):p for p in peptides}
result = {self.name:{}}
for length, peps in itertools.groupby(pep_seqs.iterkeys(), key=lambda x: len(x)):
peps = list(peps)
#dynamicaly import prediction PSSMS for alleles and predict
if length not in self.supportedLength:
warnings.warn("Peptide length of %i not supported"%length, RuntimeWarning)
continue
#load pssm matrices
try:
pssm = __load_model(length)
except ImportError:
raise KeyError("No model found for %s with length %i"%(self.name, length))
for p in peps:
score = pssm[0][p[0]]+pssm[1][p[1]] + sum(pssm[2][aa] for aa in p[2:-2])\
+ pssm[3][p[-2]] + pssm[4][p[-1]]
pep = pep_seqs[p][self.trailingN: -self.tralingC]
result[self.name][pep] = score
if not result:
raise ValueError("No predictions could be made for the given input.")
df_result = CleavageFragmentPredictionResult.from_dict(result)
return df_result
