def test_normalize_predicates():
test_set = {
"CTD:affects^expression": "CTD:affects_expression",
"CTD:affects^metabolic processing": "CTD:affects_metabolic_processing",
"CTD:affects^ADP-ribosylation": "CTD:affects_ADP-ribosylation",
"GAMMA:other/unknown": "GAMMA:other_unknown",
"CTD:affects^N-linked glycosylation":
"CTD:affects_N-linked_glycosylation"
}
for unformatted, formatted in test_set.items():
result = Text.normalize_predicate(unformatted)
assert result == formatted
def standardize_relationship(relationship):
predicate_id = Text.normalize_predicate(relationship.identifier)
key = urllib.parse.quote_plus(predicate_id)
url = f'https://edgenormalization-sri.renci.org/resolve?key={key}'
response = requests.get(url)
if response.status_code == 200:
response = response.json()
r = response.get(relationship.identifier, None)
if r is not None:
return LabeledID(identifier=r['identifier'], label=r['label'])
else:
logger.debug(
f'Error calling {url} to standardize predicate {relationship.identifier} -- {response.status_code}'
)
# every fail condition will be handled here, defaulting to related to ???
# @TODO maybe this should be handled differently
return LabeledID(identifier="GAMMA:Unmapped_Relation",
label="Unmapped_Relation")
def get_gene_by_drug(self, input_node):
drugbank_ids = input_node.get_synonyms_by_prefix('DRUGBANK')
response = []
for drugbank_id in drugbank_ids:
drugbank_id = Text.un_curie(drugbank_id)
url = f'{self.url}chem/{drugbank_id}?fields=drugbank.enzymes,drugbank.targets,drugbank.carriers,drugbank.transporters'
logger.debug(url)
results = self.query(url)
if 'drugbank' in results:
# maybe gene are everywhere they are enzymes they are
genes = []
if 'enzymes' in results['drugbank']:
# maybe we don't need this filter ... ???
logger.debug('found enzymes')
genes += list(
filter(
lambda x:
('organism' in x) and (x['organism'] == 'Humans'),
results['drugbank']['enzymes']))
if 'transporters' in results['drugbank']:
logger.debug('found transporters')
genes += list(
filter(
lambda x:
('organism' in x) and (x['organism'] == 'Humans'),
results['drugbank']['transporters']))
if 'carriers' in results['drugbank']:
logger.debug('found some carriers')
genes += list(
filter(
lambda x:
('organism' in x) and (x['organism'] == 'Humans'),
results['drugbank']['carriers']))
if 'targets' in results['drugbank']:
logger.debug('found targets')
genes += list(
filter(
lambda x:
('organism' in x) and (x['organism'] == 'Humans'),
results['drugbank']['targets']))
for gene in genes:
# Actions relate what the drug does to the enzyme ... ?./>
# so I think we can treat actions as relationship types
# eg : Alfuzosin (DB00346) is a substrate for CYP34A (Uniprokb:P08684) which implies its metabolized by that enzyme ....
# we might have (A drug) that (inhibits) a gene and the action here is inhibitor.
# So I think its safe to generalize the actions are what the drug is to the enzyme. Or how the enzyme acts to the drug.
# so more like (Drug) - is a/an (action) for -> (Enzyme/gene), but some <- so list contains direction
#action_to_predicate_map = {
# 'substrate': (LabeledID(identifier='CTD:increases_degradation_of', label= 'substrate'), True), #(label, direction where true means reverse)
# 'inhibitor': (LabeledID(identifier= 'CTD:decreases_activity_of', label = "inhibitor"), False),
# 'inducer': (LabeledID(identifier = 'CTD:increases_activity_of', label="inducer"), False),
# 'antagonist': (LabeledID(identifier= 'CTD:decreases_activity_of', label = "antagonist"), False),
# 'weak inhibitor': (LabeledID(identifier= 'CTD:decreases_activity_of', label = "weak_inhibitor"), False),
# 'partial antagonist': (LabeledID(identifier= 'CTD:decreases_activity_of', label = "partial_antagonist"), False),
# 'blocker': (LabeledID(identifier= 'CTD:decreases_activity_of', label = "blocker"), False),
# 'inverse agonist': (LabeledID(identifier= 'CTD:decreases_activity_of', label = "inverse_agonist"), False),
# 'binder': (LabeledID(identifier='CTD:molecularly_interacts_with', label= 'binder'), False),
# 'activator': (LabeledID(identifier = 'CTD:increases_activity_of', label="activator"), False),
# 'agonist': (LabeledID(identifier = 'CTD:increases_activity_of', label="agonist"), False),
# 'partial agonist': (LabeledID(identifier = 'CTD:increases_activity_of', label="partial_agonist"), False),
# 'potentiator': (LabeledID(identifier = 'CTD:increases_activity_of', label="potentiator"), False),
# 'carrier': (LabeledID(identifier = 'CTD:increases_transport_of', label="potentiator"), True),
# 'product of': (LabeledID(identifier= 'CTD:increases_synthesis_of', label = "product_of"), False),
# 'inhibition of synthesis': (LabeledID(identifier= 'CTD:decreases_synthesis_of', label = "inhibition_of_synthesis"), False),
# 'inactivator': (LabeledID(identifier= 'CTD:decreases_activity_of', label = "inactivator"), False),
#}
reverse = ['substrate', 'carrier']
#Some genes are more like gene familes, and we don't want them
if 'actions' in gene and 'uniprot' in gene:
actions = gene['actions'] if type(
gene['actions']) == type(
[]) else [gene['actions']]
# create the gene node
if 'gene_name' in gene:
nm = gene['gene_name']
else:
nm = ''
gene_node = KNode(f"UNIPROTKB:{gene['uniprot']}",
name=nm,
type=node_types.GENE)
publications = [f'PMID:{x}' for x in gene['pmids']
] if 'pmids' in gene else []
for action in actions:
if action in reverse:
direction = True
else:
direction = False
#predicate,direction = action_to_predicate_map.get(action, (LabeledID(identifier= 'CTD:interacts_with', label=action),False))
rel = Text.snakify(action)
rel = Text.normalize_predicate(rel)
predicate = LabeledID(identifier=f'GAMMA:{rel}',
label=rel)
source_node = input_node
target_node = gene_node
if direction: # swap input and target nodes
source_node = gene_node
target_node = input_node
if predicate:
edge = self.create_edge(
source_node,
target_node,
'mychem.get_gene_by_drug',
source_node.id,
predicate,
publications=publications)
response.append((edge, gene_node))
return response
