def get_complexes(self, force_contains=None):
"""Extract INDRA Complex statements from the model.
Parameters
----------
force_contains : Optional[list[str]]
A list of gene names for filtering. Only Statements in which the
gene names in the force_contains list appear will be extracted.
Default: None
"""
for obj in self.model.getObjects().toArray():
bpe = _cast_biopax_element(obj)
if not _is_complex(bpe):
continue
citations = self._get_citations(bpe)
source_id = bpe.getUri()
if not citations:
ev = Evidence(source_api='biopax',
pmid=None,
source_id=source_id)
else:
ev = [Evidence(source_api='biopax',
pmid=cit,
source_id=source_id)
for cit in citations]
members = self._get_complex_members(bpe)
if members is not None:
if len(members) > 10:
logger.info('Skipping complex with more than 10 members.')
continue
complexes = _get_combinations(members)
for c in complexes:
self.statements.append(decode_obj(Complex(c, ev),
encoding='utf-8'))
def get_complexes(self):
"""Extract INDRA Complex statements from the model."""
for obj in self.model.getObjects().toArray():
bpe = _cast_biopax_element(obj)
if not _is_complex(bpe):
continue
citations = self._get_citations(bpe)
source_id = bpe.getUri()
if not citations:
ev = Evidence(source_api='biopax',
pmid=None,
source_id=source_id)
else:
ev = [
Evidence(source_api='biopax',
pmid=cit,
source_id=source_id) for cit in citations
]
members = self._get_complex_members(bpe)
if members is not None:
if len(members) > 10:
logger.info('Skipping complex with more than 10 members.')
continue
complexes = _get_combinations(members)
for c in complexes:
self.statements.append(
decode_obj(Complex(c, ev), encoding='utf-8'))
def get_ubiquitination(self, force_contains=None):
"""Extract INDRA Ubiquitination statements from the model.
Parameters
----------
force_contains : Optional[list[str]]
A list of gene names for filtering. Only Statements in which the
gene names in the force_contains list appear will be extracted.
Default: None
"""
stmts = self._get_generic_modification('ubiq',
force_contains=force_contains)
for s in stmts:
self.statements.append(decode_obj(Ubiquitination(*s),
encoding='utf-8'))
def get_phosphorylation(self):
"""Extract INDRA Phosphorylation statements from the model."""
stmts = self._get_generic_modification('phospho')
for s in stmts:
self.statements.append(
decode_obj(Phosphorylation(*s), encoding='utf-8'))
def get_regulate_amounts(self):
"""Extract INDRA RegulateAmount statements from the model."""
pb = _bpp('PatternBox')
p = pb.controlsExpressionWithTemplateReac()
s = _bpp('Searcher')
res = s.searchPlain(self.model, p)
res_array = [_match_to_array(m) for m in res.toArray()]
stmts = []
for res in res_array:
# FIXME: for some reason labels are not accessible
# for these queries. It would be more reliable
# to get results by label instead of index.
'''
controller_er = res[p.indexOf('controller ER')]
generic_controller_er = res[p.indexOf('generic controller ER')]
controller_simple_pe = res[p.indexOf('controller simple PE')]
controller_pe = res[p.indexOf('controller PE')]
control = res[p.indexOf('Control')]
conversion = res[p.indexOf('Conversion')]
input_pe = res[p.indexOf('input PE')]
input_simple_pe = res[p.indexOf('input simple PE')]
changed_generic_er = res[p.indexOf('changed generic ER')]
output_pe = res[p.indexOf('output PE')]
output_simple_pe = res[p.indexOf('output simple PE')]
changed_er = res[p.indexOf('changed ER')]
'''
# TODO: here, res[3] is the complex physical entity
# for instance http://pathwaycommons.org/pc2/
# Complex_43c6b8330562c1b411d21e9d1185bae9
# consists of 3 components: JUN, FOS and NFAT
# where NFAT further contains 3 member physical entities.
#
# However, res[2] iterates over all 5 member physical entities
# of the complex which doesn't represent the underlying
# structure faithfully. It would be better to use res[3]
# (the complex itself) and look at components and then
# members. However, then, it would not be clear how to
# construct an INDRA Agent for the controller.
controller = self._get_agents_from_entity(res[2])
controlled_pe = res[6]
controlled = self._get_agents_from_entity(controlled_pe)
conversion = res[5]
direction = conversion.getTemplateDirection()
if direction is not None:
direction = direction.name()
if direction != 'FORWARD':
logger.warning('Unhandled conversion direction %s' %
direction)
continue
# Sometimes interaction type is annotated as
# term=='TRANSCRIPTION'. Other times this is not
# annotated.
int_type = conversion.getInteractionType().toArray()
if int_type:
for it in int_type:
for term in it.getTerm().toArray():
pass
control = res[4]
control_type = control.getControlType()
if control_type:
control_type = control_type.name()
citations = BiopaxProcessor._get_citations(control)
source_id = control.getUri()
if not citations:
citations = [None]
ev = [
Evidence(source_api='biopax', pmid=cit, source_id=source_id)
for cit in citations
]
for subj, obj in itertools.product(_listify(controller),
_listify(controlled)):
subj_act = ActivityCondition('transcription', True)
subj.activity = subj_act
if control_type == 'ACTIVATION':
st = IncreaseAmount(subj, obj, evidence=ev)
elif control_type == 'INHIBITION':
st = DecreaseAmount(subj, obj, evidence=ev)
else:
logger.warning('Unhandled control type %s' % control_type)
continue
st_dec = decode_obj(st, encoding='utf-8')
self.statements.append(st_dec)
def get_activity_modification(self):
"""Extract INDRA ActiveForm statements from the model."""
mcc = _bpp('constraint.ModificationChangeConstraint')
mcct = _bpp('constraint.ModificationChangeConstraint$Type')
mod_filter = 'residue modification, active'
for is_active in [True, False]:
p = self._construct_modification_pattern()
if is_active:
rel = mcct.GAIN
else:
rel = mcct.LOSS
p.add(mcc(rel, mod_filter), "input simple PE", "output simple PE")
s = _bpp('Searcher')
res = s.searchPlain(self.model, p)
res_array = [_match_to_array(m) for m in res.toArray()]
for r in res_array:
reaction = r[p.indexOf('Conversion')]
citations = self._get_citations(reaction)
activity = 'activity'
input_spe = r[p.indexOf('input simple PE')]
output_spe = r[p.indexOf('output simple PE')]
# Get the modifications
mod_in =\
BiopaxProcessor._get_entity_mods(input_spe)
mod_out =\
BiopaxProcessor._get_entity_mods(output_spe)
mod_shared = set(mod_in).intersection(set(mod_out))
gained_mods = set(mod_out).difference(set(mod_in))
# Here we get the evidence for the BiochemicalReaction
source_id = reaction.getUri()
citations = BiopaxProcessor._get_citations(reaction)
if not citations:
ev = Evidence(source_api='biopax',
pmid=None,
source_id=source_id)
else:
ev = [
Evidence(source_api='biopax',
pmid=cit,
source_id=source_id) for cit in citations
]
monomers = self._get_agents_from_entity(output_spe)
for monomer in _listify(monomers):
static_mods =\
set(monomer.mods).difference(gained_mods)
mods = [
m for m in gained_mods
if m[0] not in ['active', 'inactive']
]
mcs = [ModCondition(m[0], m[1], m[2], True) for m in mods]
# NOTE: with the ActiveForm representation we cannot
# separate static_mods and gained_mods. We assume here
# that the static_mods are inconsequential and therefore
# are not mentioned as an Agent condition, following
# don't care don't write semantics. Therefore only the
# gained_mods are listed in the ActiveForm as Agent
# conditions.
monomer.mods = mcs
if mods:
stmt = ActiveForm(monomer,
activity,
is_active,
evidence=ev)
self.statements.append(
decode_obj(stmt, encoding='utf-8'))
def get_ubiquitination(self):
"""Extract INDRA Ubiquitination statements from the model."""
stmts = self._get_generic_modification('ubiq')
for s in stmts:
self.statements.append(
decode_obj(Ubiquitination(*s), encoding='utf-8'))
def get_glycosylation(self):
"""Extract INDRA Glycosylation statements from the model."""
stmts = self._get_generic_modification('glycosyl')
for s in stmts:
self.statements.append(
decode_obj(Glycosylation(*s), encoding='utf-8'))
def get_regulate_amounts(self):
pb = _bpp('PatternBox')
p = pb.controlsExpressionWithTemplateReac()
s = _bpp('Searcher')
res = s.searchPlain(self.model, p)
res_array = [_match_to_array(m) for m in res.toArray()]
stmts = []
for res in res_array:
# FIXME: for some reason labels are not accessible
# for these queries. It would be more reliable
# to get results by label instead of index.
'''
controller_er = res[p.indexOf('controller ER')]
generic_controller_er = res[p.indexOf('generic controller ER')]
controller_simple_pe = res[p.indexOf('controller simple PE')]
controller_pe = res[p.indexOf('controller PE')]
control = res[p.indexOf('Control')]
conversion = res[p.indexOf('Conversion')]
input_pe = res[p.indexOf('input PE')]
input_simple_pe = res[p.indexOf('input simple PE')]
changed_generic_er = res[p.indexOf('changed generic ER')]
output_pe = res[p.indexOf('output PE')]
output_simple_pe = res[p.indexOf('output simple PE')]
changed_er = res[p.indexOf('changed ER')]
'''
# TODO: here, res[3] is the complex physical entity
# for instance http://pathwaycommons.org/pc2/
# Complex_43c6b8330562c1b411d21e9d1185bae9
# consists of 3 components: JUN, FOS and NFAT
# where NFAT further contains 3 member physical entities.
#
# However, res[2] iterates over all 5 member physical entities
# of the complex which doesn't represent the underlying
# structure faithfully. It would be better to use res[3]
# (the complex itself) and look at components and then
# members. However, then, it would not be clear how to
# construct an INDRA Agent for the controller.
controller = self._get_agents_from_entity(res[2])
controlled_pe = res[6]
controlled = self._get_agents_from_entity(controlled_pe)
conversion = res[5]
direction = conversion.getTemplateDirection()
if direction is not None:
direction = direction.name()
if direction != 'FORWARD':
logger.warning('Unhandled conversion direction %s' %
direction)
continue
# Sometimes interaction type is annotated as
# term=='TRANSCRIPTION'. Other times this is not
# annotated.
int_type = conversion.getInteractionType().toArray()
if int_type:
for it in int_type:
for term in it.getTerm().toArray():
pass
control = res[4]
control_type = control.getControlType()
if control_type:
control_type = control_type.name()
citations = BiopaxProcessor._get_citations(control)
source_id = control.getUri()
if not citations:
citations = [None]
ev = [Evidence(source_api='biopax', pmid=cit,
source_id=source_id)
for cit in citations]
for subj, obj in itertools.product(_listify(controller),
_listify(controlled)):
subj_act = ActivityCondition('transcription', True)
subj.activity = subj_act
if control_type == 'ACTIVATION':
st = IncreaseAmount(subj, obj, evidence=ev)
elif control_type == 'INHIBITION':
st = DecreaseAmount(subj, obj, evidence=ev)
else:
logger.warning('Unhandled control type %s' % control_type)
continue
st_dec = decode_obj(st, encoding='utf-8')
self.statements.append(st_dec)
def get_activity_modification(self, force_contains=None):
"""Extract INDRA ActiveForm statements from the model.
Parameters
----------
force_contains : Optional[list[str]]
A list of gene names for filtering. Only Statements in which the
gene names in the force_contains list appear will be extracted.
Default: None
"""
mcc = _bpp('constraint.ModificationChangeConstraint')
mcct = _bpp('constraint.ModificationChangeConstraint$Type')
mod_filter = 'residue modification, active'
for is_active in [True, False]:
p = self._construct_modification_pattern()
if is_active:
rel = mcct.GAIN
else:
rel = mcct.LOSS
p.add(mcc(rel, mod_filter),
"input simple PE", "output simple PE")
s = _bpp('Searcher')
res = s.searchPlain(self.model, p)
res_array = [_match_to_array(m) for m in res.toArray()]
for r in res_array:
reaction = r[p.indexOf('Conversion')]
citations = self._get_citations(reaction)
activity = 'activity'
input_spe = r[p.indexOf('input simple PE')]
output_spe = r[p.indexOf('output simple PE')]
# Get the modifications
mod_in =\
BiopaxProcessor._get_entity_mods(input_spe)
mod_out =\
BiopaxProcessor._get_entity_mods(output_spe)
mod_shared = set(mod_in).intersection(set(mod_out))
gained_mods = set(mod_out).difference(set(mod_in))
# Here we get the evidence for the BiochemicalReaction
source_id = reaction.getUri()
citations = BiopaxProcessor._get_citations(reaction)
if not citations:
ev = Evidence(source_api='biopax',
pmid=None,
source_id=source_id)
else:
ev = [Evidence(source_api='biopax',
pmid=cit,
source_id=source_id)
for cit in citations]
monomers = self._get_agents_from_entity(output_spe)
for monomer in _listify(monomers):
if force_contains is not None:
if monomer not in force_contains:
continue
static_mods =\
set(monomer.mods).difference(gained_mods)
mods = [m for m in gained_mods
if m.mod_type not in ['active', 'inactive']]
# NOTE: with the ActiveForm representation we cannot
# separate static_mods and gained_mods. We assume here
# that the static_mods are inconsequential and therefore
# are not mentioned as an Agent condition, following
# don't care don't write semantics. Therefore only the
# gained_mods are listed in the ActiveForm as Agent
# conditions.
monomer.mods = mods
if mods:
stmt = ActiveForm(monomer, activity, is_active,
evidence=ev)
self.statements.append(decode_obj(stmt,
encoding='utf-8'))
