def _parse_aeolus_data(self, document, or_limit=None):
model = Model(self.graph)
rxcui_curie = "RXCUI:{}".format(document['aeolus']['rxcui'])
uni_curie = "UNII:{}".format(document['aeolus']['unii'])
model.addLabel(rxcui_curie, document['aeolus']['drug_name'])
model.addLabel(uni_curie, document['aeolus']['drug_name'])
model.addSameIndividual(rxcui_curie, uni_curie)
self.graph.addTriple(rxcui_curie,
model.annotation_properties['inchi_key'],
document['unii']['inchikey'],
object_is_literal=True)
if or_limit is not None:
outcomes = (outcome for outcome in document['aeolus']['outcomes']
if 'ror' in outcome and outcome['ror'] >= or_limit)
else:
outcomes = (outcome for outcome in document['aeolus']['outcomes'])
for outcome in outcomes:
drug2outcome_assoc = Assoc(self.graph, self.name)
meddra_curie = "MEDDRA:{}".format(outcome['code'])
model.addLabel(meddra_curie, outcome['name'])
drug2outcome_assoc.sub = rxcui_curie
drug2outcome_assoc.obj = meddra_curie
drug2outcome_assoc.rel = Assoc.object_properties[
'causes_or_contributes']
drug2outcome_assoc.description = \
"A proportional reporting ratio or odds " \
"ratio greater than or equal to {} in the " \
"AEOLUS data was the significance cut-off " \
"used for creating drug-outcome associations".format(or_limit)
drug2outcome_assoc.add_association_to_graph()
drug2outcome_assoc.add_predicate_object(
Assoc.annotation_properties['probabalistic_quantifier'],
outcome['ror'], 'Literal')
self._add_outcome_evidence(drug2outcome_assoc.assoc_id, outcome)
self._add_outcome_provenance(drug2outcome_assoc.assoc_id, outcome)
def _parse_aeolus_data(self, document, or_limit=None):
model = Model(self.graph)
rxcui_curie = "RXCUI:{}".format(document['aeolus']['rxcui'])
uni_curie = "UNII:{}".format(document['aeolus']['unii'])
model.addLabel(rxcui_curie, document['aeolus']['drug_name'])
model.addLabel(uni_curie, document['aeolus']['drug_name'])
model.addSameIndividual(rxcui_curie, uni_curie)
self.graph.addTriple(
rxcui_curie, self.globaltt['inchi_key'], document['unii']['inchikey'],
object_is_literal=True)
if or_limit is not None:
outcomes = (outcome for outcome in document['aeolus']['outcomes']
if 'ror' in outcome and outcome['ror'] >= or_limit)
else:
outcomes = (outcome for outcome in document['aeolus']['outcomes'])
for outcome in outcomes:
drug2outcome_assoc = Assoc(self.graph, self.name)
meddra_curie = "MEDDRA:{}".format(outcome['code'])
model.addLabel(meddra_curie, outcome['name'])
drug2outcome_assoc.sub = rxcui_curie
drug2outcome_assoc.obj = meddra_curie
drug2outcome_assoc.rel = self.globaltt['causes_or_contributes']
drug2outcome_assoc.description = \
"A proportional reporting ratio or odds " \
"ratio greater than or equal to {} in the " \
"AEOLUS data was the significance cut-off " \
"used for creating drug-outcome associations".format(or_limit)
drug2outcome_assoc.add_association_to_graph()
drug2outcome_assoc.add_predicate_object(
self.globaltt['probabalistic_quantifier'], outcome['ror'], 'Literal')
self._add_outcome_evidence(drug2outcome_assoc.assoc_id, outcome)
self._add_outcome_provenance(drug2outcome_assoc.assoc_id, outcome)
def make_association(self, record):
"""
contstruct the association
:param record:
:return: modeled association of genotype to mammalian phenotype
"""
# prep record
# remove description and mapp Experiment Type to apo term
experiment_type = record['Experiment Type'].split('(')[0]
experiment_type = experiment_type.split(',')
record['experiment_type'] = list()
for exp_type in experiment_type:
exp_type = exp_type.lstrip().rstrip()
record['experiment_type'].append(
{
'id': self.apo_term_id[exp_type],
'term': exp_type,
})
sgd_phenotype = record['Phenotype']
pheno_obj = {
'entity': {
'term': None,
'apo_id': None
},
'quality': {
'term': None,
'apo_id': None
},
'has_quality': False # False = phenotype was descriptive and don't bother looking for a quality
}
phenotype = record['Phenotype']
if ':' in phenotype:
pheno_obj['has_quality'] = True
ent_qual = sgd_phenotype.split(': ')
entity = ent_qual[0]
quality = ent_qual[1]
pheno_obj['entity']['term'] = entity
pheno_obj['entity']['apo_id'] = self.apo_term_id[entity]
pheno_obj['quality']['term'] = quality
pheno_obj['quality']['apo_id'] = self.apo_term_id[quality]
else:
pheno_obj['entity']['term'] = phenotype
pheno_obj['entity']['apo_id'] = self.apo_term_id[phenotype]
record['pheno_obj'] = pheno_obj
# begin modeling
model = Model(self.graph)
# define the triple
gene = 'SGD:{}'.format(record['SGDID'])
relation = Model.object_properties['has_phenotype'] # has phenotype
if record['pheno_obj']['has_quality']:
pheno_label = '{0}:{1}'.format(
record['pheno_obj']['entity']['term'],
record['pheno_obj']['quality']['term'])
pheno_id = 'MONARCH:{0}{1}'.format(
record['pheno_obj']['entity']['apo_id'].replace(':', '_'),
record['pheno_obj']['quality']['apo_id'].replace(':', '_')
)
g2p_assoc = Assoc(self.graph, self.name, sub=gene, obj=pheno_id, pred=relation)
else:
pheno_label = record['pheno_obj']['entity']['term']
pheno_id = record['pheno_obj']['entity']['apo_id']
g2p_assoc = Assoc(self.graph, self.name, sub=gene, obj=pheno_id, pred=relation)
assoc_id = g2p_assoc.make_association_id(definedby='yeastgenome.org', subject=gene, predicate=relation,
object=pheno_id)
g2p_assoc.set_association_id(assoc_id=assoc_id)
# add to graph to mint assoc id
g2p_assoc.add_association_to_graph()
model.addLabel(subject_id=gene, label=record['Gene Name'])
# add the association triple
model.addTriple(subject_id=gene, predicate_id=relation, obj=pheno_id)
# make pheno subclass of UPHENO:0001001
model.addTriple(subject_id=pheno_id, predicate_id=Model.object_properties['subclass_of'], obj='UPHENO:0001001')
# label nodes
# pheno label
model.addLabel(subject_id=pheno_id, label=pheno_label)
g2p_assoc.description = self._make_description(record)
# add the references
references = record['Reference']
references = references.replace(' ', '')
references = references.split('|')
# created RGDRef prefix in curie map to route to proper reference URL in RGD
if len(references) > 0:
# make first ref in list the source
g2p_assoc.add_source(identifier=references[0])
ref_model = Reference(
self.graph, references[0],
Reference.ref_types['publication']
)
ref_model.addRefToGraph()
if len(references) > 1:
# create equivalent source for any other refs in list
for ref in references[1:]:
model.addSameIndividual(sub=references[0], obj=ref)
# add experiment type as evidence
for exp_type in record['experiment_type']:
g2p_assoc.add_evidence(exp_type['id'])
model.addLabel(subject_id=exp_type['id'], label=exp_type['term'])
try:
g2p_assoc.add_association_to_graph()
except Exception as e:
print(e)
return
def make_association(self, record):
"""
contstruct the association
:param record:
:return: modeled association of genotype to mammalian??? phenotype
"""
# prep record
# remove description and mapp Experiment Type to apo term
experiment_type = record['Experiment Type'].split('(')[0]
experiment_type = experiment_type.split(',')
record['experiment_type'] = list()
for exp_type in experiment_type:
exp_type = exp_type.lstrip().rstrip()
record['experiment_type'].append({
'id': self.apo_term_id[exp_type],
'term': exp_type,
})
sgd_phenotype = record['Phenotype']
pheno_obj = {
'entity': {
'term': None,
'apo_id': None
},
'quality': {
'term': None,
'apo_id': None
},
'has_quality':
False # descriptive and don't bother looking for a quality
}
phenotype = record['Phenotype']
if ':' in phenotype:
pheno_obj['has_quality'] = True
ent_qual = sgd_phenotype.split(': ')
entity = ent_qual[0]
quality = ent_qual[1]
pheno_obj['entity']['term'] = entity
pheno_obj['entity']['apo_id'] = self.apo_term_id[entity]
pheno_obj['quality']['term'] = quality
pheno_obj['quality']['apo_id'] = self.apo_term_id[quality]
else:
pheno_obj['entity']['term'] = phenotype
pheno_obj['entity']['apo_id'] = self.apo_term_id[phenotype]
record['pheno_obj'] = pheno_obj
# begin modeling
model = Model(self.graph)
# define the triple
gene = 'SGD:{}'.format(record['SGDID'])
relation = self.globaltt['has phenotype']
if record['pheno_obj']['has_quality']:
pheno_label = '{0}:{1}'.format(
record['pheno_obj']['entity']['term'],
record['pheno_obj']['quality']['term'])
pheno_id = 'MONARCH:{0}{1}'.format(
record['pheno_obj']['entity']['apo_id'].replace(':', '_'),
record['pheno_obj']['quality']['apo_id'].replace(':', '_'))
g2p_assoc = Assoc(self.graph,
self.name,
sub=gene,
obj=pheno_id,
pred=relation)
else:
pheno_label = record['pheno_obj']['entity']['term']
pheno_id = record['pheno_obj']['entity']['apo_id']
g2p_assoc = Assoc(self.graph,
self.name,
sub=gene,
obj=pheno_id,
pred=relation)
assoc_id = g2p_assoc.make_association_id('yeastgenome.org', gene,
relation, pheno_id)
g2p_assoc.set_association_id(assoc_id=assoc_id)
# add to graph to mint assoc id
g2p_assoc.add_association_to_graph()
model.addLabel(subject_id=gene, label=record['Gene Name'])
# add the association triple
model.addTriple(subject_id=gene, predicate_id=relation, obj=pheno_id)
model.addTriple(subject_id=pheno_id,
predicate_id=self.globaltt['subclass_of'],
obj=self.globaltt['Phenotype'])
# label nodes
# pheno label
model.addLabel(subject_id=pheno_id, label=pheno_label)
g2p_assoc.description = self._make_description(record)
# add the references
references = record['Reference']
references = references.replace(' ', '')
references = references.split('|')
# created Ref prefix in curie map to route to proper reference URL in SGD
if len(references) > 0:
# make first ref in list the source
g2p_assoc.add_source(identifier=references[0])
ref_model = Reference(self.graph, references[0],
self.globaltt['publication'])
ref_model.addRefToGraph()
if len(references) > 1:
# create equivalent source for any other refs in list
for ref in references[1:]:
model.addSameIndividual(sub=references[0], obj=ref)
# add experiment type as evidence
for exp_type in record['experiment_type']:
g2p_assoc.add_evidence(exp_type['id'])
model.addLabel(subject_id=exp_type['id'], label=exp_type['term'])
try:
g2p_assoc.add_association_to_graph()
except Exception as e:
print(e)
return
def make_association(self, record):
"""
contstruct the association
:param record:
:return: modeled association of genotype to mammalian phenotype
"""
# prep record
# remove description and mapp Experiment Type to apo term
experiment_type = record['Experiment Type'].split('(')[0]
experiment_type = experiment_type.split(',')
record['experiment_type'] = list()
for exp_type in experiment_type:
exp_type = exp_type.lstrip().rstrip()
record['experiment_type'].append({
'id': self.apo_term_id[exp_type],
'term': exp_type,
})
sgd_phenotype = record['Phenotype']
pheno_obj = {
'entity': {
'term': None,
'apo_id': None
},
'quality': {
'term': None,
'apo_id': None
},
'has_quality':
False # False = phenotype was descriptive and don't bother looking for a quality
}
phenotype = record['Phenotype']
if ':' in phenotype:
pheno_obj['has_quality'] = True
ent_qual = sgd_phenotype.split(': ')
entity = ent_qual[0]
quality = ent_qual[1]
pheno_obj['entity']['term'] = entity
pheno_obj['entity']['apo_id'] = self.apo_term_id[entity]
pheno_obj['quality']['term'] = quality
pheno_obj['quality']['apo_id'] = self.apo_term_id[quality]
else:
pheno_obj['entity']['term'] = phenotype
pheno_obj['entity']['apo_id'] = self.apo_term_id[phenotype]
record['pheno_obj'] = pheno_obj
# begin modeling
model = Model(self.graph)
# define the triple
gene = 'SGD:{}'.format(record['SGDID'])
relation = Model.object_properties['has_phenotype'] # has phenotype
if record['pheno_obj']['has_quality']:
pheno_label = '{0}:{1}'.format(
record['pheno_obj']['entity']['term'],
record['pheno_obj']['quality']['term'])
pheno_id = 'MONARCH:{0}{1}'.format(
record['pheno_obj']['entity']['apo_id'].replace(':', '_'),
record['pheno_obj']['quality']['apo_id'].replace(':', '_'))
g2p_assoc = Assoc(self.graph,
self.name,
sub=gene,
obj=pheno_id,
pred=relation)
else:
pheno_label = record['pheno_obj']['entity']['term']
pheno_id = record['pheno_obj']['entity']['apo_id']
g2p_assoc = Assoc(self.graph,
self.name,
sub=gene,
obj=pheno_id,
pred=relation)
assoc_id = g2p_assoc.make_association_id(
definedby='yeastgenome.org',
subject=gene,
predicate=relation,
object=pheno_id)
g2p_assoc.set_association_id(assoc_id=assoc_id)
# add to graph to mint assoc id
g2p_assoc.add_association_to_graph()
model.addLabel(subject_id=gene, label=record['Gene Name'])
# add the association triple
model.addTriple(subject_id=gene, predicate_id=relation, obj=pheno_id)
# make pheno subclass of UPHENO:0001001
model.addTriple(subject_id=pheno_id,
predicate_id=Model.object_properties['subclass_of'],
obj='UPHENO:0001001')
# label nodes
# pheno label
model.addLabel(subject_id=pheno_id, label=pheno_label)
# add the descripiton: all the unmodeled data in a '|' delimited list
description = [
'genomic_background: {}'.format(record['Strain Background']),
'allele: {}'.format(record['Allele']),
'chemical: {}'.format(record['Chemical']),
'condition: {}'.format(record['Condition']),
'details: {}'.format(record['Details']),
'feature_name: {}'.format(record['Feature Name']),
'gene_name: {}'.format(record['Gene Name']),
'mutant_type: {}'.format(record['Mutant Type']),
'reporter: {}'.format(record['Reporter']),
]
g2p_assoc.description = " | ".join(description)
# add the references
references = record['Reference']
references = references.replace(' ', '')
references = references.split('|')
# created RGDRef prefix in curie map to route to proper reference URL in RGD
if len(references) > 0:
# make first ref in list the source
g2p_assoc.add_source(identifier=references[0])
ref_model = Reference(self.graph, references[0],
Reference.ref_types['publication'])
ref_model.addRefToGraph()
if len(references) > 1:
# create equivalent source for any other refs in list
for ref in references[1:]:
model.addSameIndividual(sub=references[0], obj=ref)
# add experiment type as evidence
for exp_type in record['experiment_type']:
g2p_assoc.add_evidence(exp_type['id'])
model.addLabel(subject_id=exp_type['id'], label=exp_type['term'])
try:
g2p_assoc.add_association_to_graph()
except Exception as e:
print(e)
return
