def rnaplfold_to_eden(iterable,
max_num_edges=1,
window_size=150,
max_bp_span=100,
hard_threshold=0.5,
avg_bp_prob_cutoff=0.2,
no_lonely_bps=True,
nesting=True):
"""Fold RNA sequence with RNAfold."""
assert (is_iterable(iterable)), 'Not iterable'
for header, seq in iterable:
try:
assert (header), 'Empty header'
assert (seq), 'Empty seq'
sequence = (header, seq)
graph = _rnaplfold_to_eden(sequence, max_num_edges, window_size,
max_bp_span, hard_threshold,
avg_bp_prob_cutoff, no_lonely_bps,
nesting)
except Exception as e:
logger.debug(e.__doc__)
logger.debug(e.message)
logger.debug('Error in: %s' % seq)
graph = sequence_fold((header, seq))
yield graph
def annotate(self, iterable):
assert(is_iterable(iterable)), 'Not iterable'
graphs = mp_pre_process(iterable,
pre_processor=self.pre_processor,
pre_processor_args=self.pre_processor_args,
n_blocks=self.pre_processor_n_blocks,
block_size=self.pre_processor_block_size,
n_jobs=self.pre_processor_n_jobs)
return self.vectorizer.annotate(graphs, self.estimator)
def annotate(self, iterable):
assert (is_iterable(iterable)), 'Not iterable'
graphs = mp_pre_process(iterable,
pre_processor=self.pre_processor,
pre_processor_args=self.pre_processor_args,
n_blocks=self.pre_processor_n_blocks,
block_size=self.pre_processor_block_size,
n_jobs=self.pre_processor_n_jobs)
return self.vectorizer.annotate(graphs, self.estimator)
def rnafold_to_eden(iterable=None, **options):
assert(is_iterable(iterable)), 'Not iterable'
for header, seq in iterable:
try:
G = string_to_networkx(header, seq, **options)
except Exception as e:
print e.__doc__
print e.message
print 'Error in: %s' % seq
G = seq_to_networkx(header, seq, **options)
yield G
def rnafold_to_eden(iterable=None, **options):
assert (is_iterable(iterable)), 'Not iterable'
for header, seq in iterable:
try:
G = string_to_networkx(header, seq, **options)
except Exception as e:
print e.__doc__
print e.message
print 'Error in: %s' % seq
G = seq_to_networkx(header, seq, **options)
yield G
def rnasubopt_to_eden(iterable, **options):
assert(is_iterable(iterable)), 'Not iterable'
for header, seq, const in iterable:
try:
for G in string_to_networkx(header, seq, const, **options):
yield G
except Exception as e:
print e.__doc__
print e.message
print 'Error in: %s' % seq
G = seq_to_networkx(header, seq, **options)
yield G
def rnashapes_struct_to_eden(iterable, **options):
assert(is_iterable(iterable)), 'Not iterable'
for header, seq in iterable:
try:
for G in string_to_networkx(header, seq, **options):
yield G
except Exception as e:
print e.__doc__
print e.message
print 'Error in: %s %s' % (header, seq)
G = seq_to_networkx(header, seq, **options)
yield G
def rnashapes_struct_to_eden(iterable, **options):
assert(is_iterable(iterable)), 'Not iterable'
for header, seq in iterable:
try:
for G in string_to_networkx(header, seq, **options):
yield G
except Exception as e:
print e.__doc__
print e.message
print 'Error in: %s %s' % (header, seq)
graph = seq_to_networkx(header, seq, **options)
yield graph
def test_fasta_to_sequence_no_normalize(self):
"""Test default test_fasta_to_sequence with default parameters. -> moved to garden doctest"""
fa_fn = "test/test_fasta_to_sequence.fa"
seq = fasta_to_sequence(fa_fn, normalize=False)
assert (is_iterable(seq))
(header, sequence) = seq.next()
# sequence should correspond to the unmodified fasta string
assert (
sequence ==
"gtggcgtactcacggccaCCTTAGGACTCCGCGGACTTTATGCCCACCAAAAAAACGAGCCGTTTCTACGCGTCCTCCGTCGCCTgtgtcgataaagcaa"
)
def test_fasta_to_sequence_normalized(self):
"""Test default test_fasta_to_sequence with default parameters. -> moved to garden doctest"""
fa_fn = "test/test_fasta_to_sequence.fa"
seq = fasta_to_sequence(fa_fn, normalize=True)
assert (is_iterable(seq))
(header, sequence) = seq.next()
# sequence should be uppercased and all Ts should be replaced by Us
assert (
sequence ==
"GUGGCGUACUCACGGCCACCUUAGGACUCCGCGGACUUUAUGCCCACCAAAAAAACGAGCCGUUUCUACGCGUCCUCCGUCGCCUGUGUCGAUAAAGCAA"
)
def rnasubopt_to_eden(iterable, **options):
assert (is_iterable(iterable)), 'Not iterable'
for header, seq in iterable:
try:
for graph in string_to_networkx(header, seq, **options):
yield graph
except Exception as e:
print e.__doc__
print e.message
print 'Error in: %s' % seq
graph = seq_to_networkx(header, seq, **options)
yield graph
def sequence_to_eden(iterable, **options):
"""Convert sequence tuples to EDeN graphs."""
no_header = options.get('no_header', False)
assert (is_iterable(iterable)), 'Not iterable'
if no_header is True:
for seq in iterable:
graph = seq_to_networkx('NONE', seq, **options)
yield graph
else:
for header, seq in iterable:
graph = seq_to_networkx(header, seq, **options)
yield graph
def sequence_to_eden(iterable, **options):
"""Convert sequence tuples to EDeN graphs."""
no_header = options.get('no_header', False)
assert(is_iterable(iterable)), 'Not iterable'
if no_header is True:
for seq in iterable:
graph = seq_to_networkx('NONE', seq, **options)
yield graph
else:
for header, seq in iterable:
graph = seq_to_networkx(header, seq, **options)
yield graph
def sample_parameters_uniformly_at_random(parameters_priors):
"""Sample parameters in parameters dictionaries uniformly at random."""
if parameters_priors:
parameters = {}
for param in parameters_priors:
if is_iterable(parameters_priors[param]):
value = random.choice(parameters_priors[param])
else:
value = parameters_priors[param]
parameters[param] = value
return parameters
else:
return None
def sample_parameters_uniformly_at_random(parameters_priors):
"""Sample parameters in parameters dictionaries uniformly at random."""
if parameters_priors:
parameters = {}
for param in parameters_priors:
if is_iterable(parameters_priors[param]):
value = random.choice(parameters_priors[param])
else:
value = parameters_priors[param]
parameters[param] = value
return parameters
else:
return None
def _data_matrix(self, iterable, fit_vectorizer=False):
assert(is_iterable(iterable)), 'Not iterable'
graphs = mp_pre_process(iterable,
pre_processor=self.pre_processor,
pre_processor_args=self.pre_processor_args,
n_blocks=self.pre_processor_n_blocks,
block_size=self.pre_processor_block_size,
n_jobs=self.pre_processor_n_jobs)
graphs, graphs_ = tee(graphs)
self.vectorizer.set_params(**self.vectorizer_args)
if fit_vectorizer:
self.vectorizer.fit(graphs_)
X = vectorize(graphs, vectorizer=self.vectorizer, n_jobs=self.n_jobs, n_blocks=self.n_blocks)
return X
def test_fasta_to_sequence_default(self):
"""Test test_fasta_to_sequence with default parameters. -> moved to garden doctest"""
fa_fn = "test/test_fasta_to_sequence.fa"
seq = fasta_to_sequence(fa_fn)
assert (is_iterable(seq))
(header, sequence) = seq.next()
# header should contain the fasta header with '>' removed
assert (header == "ID0")
# sequence should be uppercased and all Ts should be replaced by Us
assert (
sequence ==
"GUGGCGUACUCACGGCCACCUUAGGACUCCGCGGACUUUAUGCCCACCAAAAAAACGAGCCGUUUCUACGCGUCCUCCGUCGCCUGUGUCGAUAAAGCAA"
)
def rnaplfold_to_eden(iterable, **options):
assert(is_iterable(iterable)), 'Not iterable'
for header, seq in iterable:
try:
graph = string_to_networkx(header, seq, **options)
except Exception as e:
print
print '-' * 80
# print e.__doc__
print e.message
print 'Error in: %s %s' % (header, seq)
print 'Reverting to path graph from sequence'
graph = seq_to_networkx(header, seq, **options)
yield graph
def rnaplfold_to_eden(iterable, **options):
assert (is_iterable(iterable)), 'Not iterable'
for header, seq in iterable:
try:
graph = string_to_networkx(header, seq, **options)
except Exception as e:
print
print '-' * 80
# print e.__doc__
print e.message
print 'Error in: %s %s' % (header, seq)
print 'Reverting to path graph from sequence'
graph = seq_to_networkx(header, seq, **options)
yield graph
def rnafold_to_eden(iterable=None, **options):
'''
Parameters
----------
iterable: over (header_string, sequence_string)
options
Returns
-------
nx.graph generator
'''
assert (is_iterable(iterable)), 'Not iterable'
for header, seq in iterable:
try:
graph = string_to_networkx(header, seq, **options)
except Exception as e:
print e.__doc__
print e.message
print 'Error in: %s' % seq
graph = seq_to_networkx(header, seq, **options)
yield graph
def rnafold_to_eden(iterable=None, **options):
"""Fold RNA seq with RNAfold.
Parameters
----------
iterable: over (header_string, sequence_string)
options
Returns
-------
nx.graph generator
"""
assert (is_iterable(iterable)), 'Not iterable'
for header, seq in iterable:
try:
graph = _string_to_networkx(header, seq, **options)
except Exception as e:
logger.debug(e.__doc__)
logger.debug(e.message)
logger.debug('Error in: %s' % seq)
graph = sequence_fold(header, seq, **options)
yield graph
def rnafold_to_eden(iterable=None, **options):
'''
Parameters
----------
iterable: over (header_string, sequence_string)
options
Returns
-------
nx.graph generator
'''
assert (is_iterable(iterable)), 'Not iterable'
for header, seq in iterable:
try:
graph = string_to_networkx(header, seq, **options)
except Exception as e:
print e.__doc__
print e.message
print 'Error in: %s' % seq
graph = seq_to_networkx(header, seq, **options)
yield graph
def rnashapes_to_eden(iterable, **options):
"""Transforms sequences to graphs that encode secondary structure information
according to the RNAShapes algorithm.
Parameters
----------
sequences : iterable
iterable pairs of header and sequence strings
rnashapes_version : int (default 2)
The version of RNAshapes that is in the path.
2 e.g. RNAshapes version 2.1.6
3 e.g. RNAshapes version 3.3.0
shape_type : int (default 5)
Is the level of abstraction or dissimilarity which defines a different shape.
In general, helical regions are depicted by a pair of opening and closing brackets
and unpaired regions are represented as a single underscore. The differences of the
shape types are due to whether a structural element (bulge loop, internal loop, multiloop,
hairpin loop, stacking region and external loop) contributes to the shape representation:
Five types are implemented.
1 Most accurate - all loops and all unpaired [_[_[]]_[_[]_]]_
2 Nesting pattern for all loop types and unpaired regions in external loop
and multiloop [[_[]][_[]_]]
3 Nesting pattern for all loop types but no unpaired regions [[[]][[]]]
4 Helix nesting pattern in external loop and multiloop [[][[]]]
5 Most abstract - helix nesting pattern and no unpaired regions [[][]]
energy_range : float (default 10)
Sets the energy range as percentage value of the minimum free energy.
For example, when relative deviation is specified as 5.0, and the minimum free energy
is -10.0 kcal/mol, the energy range is set to -9.5 to -10.0 kcal/mol.
Relative deviation must be a positive floating point number; by default it is set to to 10 %.
max_num : int (default 3)
Is the maximum number of structures that are generated.
split_components : bool (default False)
If True each structure is yielded as an independent graph. Otherwise all structures
are part of the same graph that has therefore several disconnectd components.
example: transform a simple sequence using RNAshapes version 3+
>>> graphs = rnashapes_to_eden([("ID", "CCCCCGGGGG")], rnashapes_version=3)
>>> g = graphs.next()
>>> # extract sequence from graph nodes
>>> "".join([ value["label"] for (key, value) in g.nodes(data=True)])
'CCCCCGGGGG'
>>> # get vertice types
>>> [(start, end, g.edge[start][end]["type"]) for start, end in g.edges()]
[(0, 8, 'basepair'), (0, 1, 'backbone'), (1, 2, 'backbone'), (1, 7, 'basepair'), (2, 3, 'backbone'), (2, 6, 'basepair'), (3, 4, 'backbone'), (4, 5, 'backbone'), (5, 6, 'backbone'), (6, 7, 'backbone'), (7, 8, 'backbone'), (8, 9, 'backbone')]
example: transform a simple sequence using RNAshapes version 3+, splitting components
>>> graphs = rnashapes_to_eden([("ID", "CCCCCGGGGG")], split_components=True, rnashapes_version=3)
>>> g = graphs.next()
>>> # extract sequence from graph nodes
>>> "".join([ value["label"] for (key, value) in g.nodes(data=True)])
'CCCCCGGGGG'
>>> # get dotbracket structure annotation
>>> g.graph["structure"]
'(((...))).'
>>> # get vertice types
>>> [ (start, end, g.edge[start][end]["type"]) for start, end in g.edges()]
[(0, 8, 'basepair'), (0, 1, 'backbone'), (1, 2, 'backbone'), (1, 7, 'basepair'), (2, 3, 'backbone'), (2, 6, 'basepair'), (3, 4, 'backbone'), (4, 5, 'backbone'), (5, 6, 'backbone'), (6, 7, 'backbone'), (7, 8, 'backbone'), (8, 9, 'backbone')]
test max_num parameter with RNAshapes version 3+
>>> seq = "CGUCGUCGCAUCGUACGCAUGACUCAGCAUCAGACUACGUACGCAUACGUCAGCAUCAGUCAGCAUCAGCAUGCAUCACUAGCAUGCACCCCCGGGGGCACAUCGUACGUACGCUCAGUACACUGCAUGACUACGU"
>>> graphs = rnashapes_to_eden([("ID", seq)], split_components=True, max_num=2, rnashapes_version=3)
>>> g = graphs.next()
>>> # get dotbracket structure annotations
>>> len([g.graph["structure"] for g in graphs])
2
"""
assert(is_iterable(iterable)), 'Not iterable'
for header, seq in iterable:
try:
for graph in string_to_networkx(header, seq, **options):
yield graph
except Exception as e:
print e.__doc__
print e.message
print 'Error in: %s' % seq
graph = seq_to_networkx(header, seq, **options)
yield graph
def rnashapes_to_eden(iterable, **options):
"""Transform sequences to graphs with RNAShapes.
Parameters
----------
sequences : iterable
iterable pairs of header and sequence strings
rnashapes_version : int (default 2)
The version of RNAshapes that is in the path.
2 e.g. RNAshapes version 2.1.6
3 e.g. RNAshapes version 3.3.0
shape_type : int (default 5)
Is the level of abstraction or dissimilarity which defines a different
shape.
In general, helical regions are depicted by a pair of opening and
closing brackets and unpaired regions are represented as a single
underscore. The differences of the shape types are due to whether a
structural element (bulge loop, internal loop, multiloop, hairpin loop,
stacking region and external loop) contributes to the shape
representation:
Five types are implemented.
1 Most accurate - all loops and all unpaired [_[_[]]_[_[]_]]_
2 Nesting pattern for all loop types and unpaired regions in external
loop and multiloop [[_[]][_[]_]]
3 Nesting pattern for all loop types but no unpaired
regions [[[]][[]]]
4 Helix nesting pattern in external loop and multiloop [[][[]]]
5 Most abstract - helix nesting pattern and no unpaired
regions [[][]]
energy_range : float (default 10)
Sets the energy range as percentage value of the minimum free energy.
For example, when relative deviation is specified as 5.0, and the
minimum free energy
is -10.0 kcal/mol, the energy range is set to -9.5 to -10.0 kcal/mol.
Relative deviation must be a positive floating point number;
by default it is set to to 10 %.
max_num : int (default 3)
Is the maximum number of structures that are generated.
split_components : bool (default False)
If True each structure is yielded as an independent graph.
Otherwise all structures are part of the same graph that has
therefore several disconnectd components.
example: transform a simple sequence using RNAshapes version 3+
>>> graphs = rnashapes_to_eden([("ID", "CCCCCGGGGG")], rnashapes_version=3)
>>> g = graphs.next()
>>> # extract sequence from graph nodes
>>> "".join([ value["label"] for (key, value) in g.nodes(data=True)])
'CCCCCGGGGG'
>>> # get vertice types
>>> [(start, end, g.edge[start][end]["type"]) for start, end in g.edges()]
[(0, 8, 'basepair'), (0, 1, 'backbone'), (1, 2, 'backbone'), (1, 7, 'basepair'), (2, 3, 'backbone'), (2, 6, 'basepair'), (3, 4, 'backbone'), (4, 5, 'backbone'), (5, 6, 'backbone'), (6, 7, 'backbone'), (7, 8, 'backbone'), (8, 9, 'backbone')]
example: transform a simple sequence using RNAshapes version 3+, splitting components
>>> graphs = rnashapes_to_eden([("ID", "CCCCCGGGGG")], split_components=True, rnashapes_version=3)
>>> g = graphs.next()
>>> # extract sequence from graph nodes
>>> "".join([ value["label"] for (key, value) in g.nodes(data=True)])
'CCCCCGGGGG'
>>> # get dotbracket structure annotation
>>> g.graph["structure"]
'(((...))).'
>>> # get vertice types
>>> [ (start, end, g.edge[start][end]["type"]) for start, end in g.edges()]
[(0, 8, 'basepair'), (0, 1, 'backbone'), (1, 2, 'backbone'), (1, 7, 'basepair'), (2, 3, 'backbone'), (2, 6, 'basepair'), (3, 4, 'backbone'), (4, 5, 'backbone'), (5, 6, 'backbone'), (6, 7, 'backbone'), (7, 8, 'backbone'), (8, 9, 'backbone')]
test max_num parameter with RNAshapes version 3+
>>> seq = "CGUCGUCGCAUCGUACGCAUGACUCAGCAUCAGACUACGUACGCAUACGUCAGCAUCAGUCAGCAUCAGCAUGCAUCACUAGCAUGCACCCCCGGGGGCACAUCGUACGUACGCUCAGUACACUGCAUGACUACGU"
>>> graphs = rnashapes_to_eden([("ID", seq)], split_components=True, max_num=2, rnashapes_version=3)
>>> g = graphs.next()
>>> # get dotbracket structure annotations
>>> len([g.graph["structure"] for g in graphs])
2
"""
assert (is_iterable(iterable)), 'Not iterable'
for header, seq in iterable:
try:
for graph in _string_to_networkx(header, seq, **options):
yield graph
except Exception as e:
logger.debug(e.__doc__)
logger.debug(e.message)
logger.debug('Error in: %s' % seq)
graph = sequence_fold(header, seq)
yield graph
def sequence_to_eden(iterable, **options):
"""Convert sequence tuples to EDeN graphs."""
assert(is_iterable(iterable)), 'Not iterable'
for header, seq in iterable:
graph = seq_to_networkx(header, seq, **options)
yield graph
def sequence_to_eden(iterable, **options):
"""Convert sequence tuples to EDeN graphs."""
assert (is_iterable(iterable)), 'Not iterable'
for header, seq in iterable:
graph = seq_to_networkx(header, seq, **options)
yield graph