def buildModel( num_bins = 3, annotate_terminals = None ):
"""build evolutionary trace like model.
This model is parameterized by the number of bins. Each
bin corresponds to a column evolving at a differnent rate.
X chains {X: 0..num_bins-1} are created, in which transitions are parameterized as
KX * PAB, where PAB is the rate for going from A to B (amino acid alphabet),
and KX is rate constant for chain X.
If initial_model is given, initial frequencies and rate estimates
are taken from this model. Otherwise, uniform frequencies are chosen.
Note, that the naming scheme in the initial model has to follow the
one chosen here.
"""
model = XGram.Model.Model()
grammar = XGram.Model.Grammar()
alphabet = XGram.Model.Alphabet()
for x in range(num_bins):
chain = XGram.Model.Chain()
generator_chain = Chain( \
alphabet = Bio.Alphabet.IUPAC.IUPACProtein.letters.lower(),
generator_initial_states = InitialStatesParametric( is_const = True),
generator_transitions = TransitionsScaledConst( rate = "K%i" % x, is_const = True)
)
generator_chain.buildGrammar( chain )
chain.setTerminals( ("T%i" % x,) )
grammar.addChain( chain )
# add annotaions
generator_grammar = GrammarLinearSequenceMultipleChains( annotate_terminals = annotate_terminals )
generator_grammar.buildGrammar( grammar )
grammar.setName( "XGramProteinSites" )
generator_alphabet = AlphabetProtein()
generator_alphabet.buildGrammar(alphabet)
model.setAlphabet( alphabet )
model.setGrammar( grammar )
return model
def buildModel(num_bins=3, annotate_terminals=None):
"""build evolutionary trace like model.
This model is parameterized by the number of bins. Each
bin corresponds to a column evolving at a differnent rate.
X chains {X: 0..num_bins-1} are created, in which transitions are parameterized as
KX * PAB, where PAB is the rate for going from A to B (amino acid alphabet),
and KX is rate constant for chain X.
If initial_model is given, initial frequencies and rate estimates
are taken from this model. Otherwise, uniform frequencies are chosen.
Note, that the naming scheme in the initial model has to follow the
one chosen here.
"""
model = XGram.Model.Model()
grammar = XGram.Model.Grammar()
alphabet = XGram.Model.Alphabet()
for x in range(num_bins):
chain = XGram.Model.Chain()
generator_chain = Chain( \
alphabet = Bio.Alphabet.IUPAC.IUPACProtein.letters.lower(),
generator_initial_states = InitialStatesParametric( is_const = True),
generator_transitions = TransitionsScaledConst( rate = "K%i" % x, is_const = True)
)
generator_chain.buildGrammar(chain)
chain.setTerminals(("T%i" % x, ))
grammar.addChain(chain)
# add annotaions
generator_grammar = GrammarLinearSequenceMultipleChains(
annotate_terminals=annotate_terminals)
generator_grammar.buildGrammar(grammar)
grammar.setName("XGramProteinSites")
generator_alphabet = AlphabetProtein()
generator_alphabet.buildGrammar(alphabet)
model.setAlphabet(alphabet)
model.setGrammar(grammar)
return model
def buildGrammar(self, chain):
"""output the grammar."""
Chain.buildGrammar( self, chain )
chain.setUpdatePolicy( "parametric" )
def buildGrammar(self, chain):
"""output the grammar."""
Chain.buildGrammar(self, chain)
chain.setUpdatePolicy("parametric")
