def get_simple_parser():
"""
A simple flaskrest parser object that includes basic http params
"""
p = api.parser()
p.add_argument('category', action='append', help='e.g. gene, disease')
p.add_argument('prefix',
action='append',
help='ontology prefix: HP, -MONDO')
p.add_argument('boost_fx',
action='append',
help='boost function e.g. pow(edges,0.334)')
p.add_argument('boost_q',
action='append',
help='boost query e.g. category:genotype^-10')
p.add_argument(
'taxon',
action='append',
help='taxon filter, eg NCBITaxon:9606, includes inferred taxa')
#p.add_argument('engine', help='Name of engine to perform search')
p.add_argument('rows',
type=int,
required=False,
default=20,
help='number of rows')
p.add_argument('start',
type=str,
required=False,
default='0',
help='row number to start from')
p.add_argument('highlight_class',
type=str,
required=False,
help='highlight class')
return p
class EdgeResource(Resource):
parser = api.parser()
parser.add_argument('depth',
type=int,
help='How far to traverse for neighbors',
default=1)
parser.add_argument(
'direction',
choices=['INCOMING', 'OUTGOING', 'BOTH'],
help=
'Which direction to traverse (used only if relationship_type is defined)',
default='BOTH')
parser.add_argument('relationship_type',
action='append',
help='Relationship type to traverse')
parser.add_argument(
'entail',
type=inputs.boolean,
help='Include sub-properties and equivalent properties',
default=False)
@api.expect(parser)
@api.marshal_list_with(bbop_graph)
def get(self, id):
"""
Returns edges emanating from a given node.
"""
args = self.parser.parse_args()
response = sg.get_response("dynamic/cliqueLeader",
q=id,
format="json",
depth=1)
nodes = response.json()['nodes']
if not nodes:
raise NoResultFoundException(
'SciGraph dynamic/cliqueLeader yields no result for {}'.format(
id))
clique_leader = nodes[0]['id']
final_graph = BBOPGraph({'nodes': [], 'edges': []})
if args.relationship_type:
for relationship in args.relationship_type:
graph = sg.neighbors(id=clique_leader,
relationshipType=relationship,
direction=args.direction,
depth=args.depth,
entail=args.entail)
final_graph.merge(graph)
else:
graph = sg.neighbors(id=clique_leader,
depth=args.depth,
entail=args.entail)
final_graph.merge(graph)
return final_graph
def get_simple_parser():
"""
A simple flaskrest parser object that includes basic http params
"""
p = api.parser()
#p.add_argument('taxon', type=str, help='SUBJECT TAXON id, e.g. NCBITaxon:9606. Includes inferred by default')
p.add_argument('category', action='append', help='e.g. gene, disease')
#p.add_argument('subclass_of', action='append', help='restrict search to entities that are subclasses of the specified class')
#p.add_argument('engine', help='Name of engine to perform search')
p.add_argument('rows', type=int, required=False, default=20, help='number of rows')
p.add_argument('start', type=int, required=False, default=1, help='row number to start from')
return p
def get_compare_parser():
help_msg = 'A phenotype or identifier that is composed of phenotypes (eg disease, gene)'
sim_get_parser = api.parser()
sim_get_parser.add_argument('ref_id',
action='append',
help=help_msg,
default=[])
sim_get_parser.add_argument('query_id',
action='append',
help=help_msg,
default=[])
return sim_get_parser
def get_simple_parser():
"""
A simple flaskrest parser object that includes basic http params
"""
p = api.parser()
p.add_argument(
'fq',
action='append',
required=False,
help='fq string passed directly to solr, note that multiple filters '
'will be combined with an AND operator. Combining fq_string with '
'other parameters may result in unexpected behavior.')
p.add_argument('category', action='append', help='e.g. gene, disease')
p.add_argument('prefix',
action='append',
help='ontology prefix: HP, -MONDO')
p.add_argument('boost_fx',
action='append',
help='boost function e.g. pow(edges,0.334)')
p.add_argument('boost_q',
action='append',
help='boost query e.g. category:genotype^-10')
p.add_argument(
'taxon',
action='append',
help='taxon filter, eg NCBITaxon:9606, includes inferred taxa')
p.add_argument('rows',
type=int,
required=False,
default=20,
help='number of rows')
p.add_argument('start',
type=str,
required=False,
default='0',
help='row number to start from')
p.add_argument('highlight_class',
type=str,
required=False,
help='highlight class')
p.add_argument(
'min_match',
type=str,
help='minimum should match parameter, see solr docs for details')
p.add_argument('minimal_tokenizer',
type=inputs.boolean,
default=False,
help='set to true to use the minimal tokenizer, '
'good for variants and genotypes')
return p
def get_layperson_parser():
"""
A simple flaskrest parser object that includes basic http params
"""
p = api.parser()
p.add_argument('rows', type=int, required=False, default=10, help='number of rows')
p.add_argument('start', type=str, required=False, default='0', help='row number to start from')
p.add_argument('phenotype_group', type=str, required=False, help='phenotype group id')
p.add_argument('phenotype_group_label', type=str, required=False, help='phenotype group label')
p.add_argument('anatomical_system', type=str, required=False, help='anatomical system id')
p.add_argument('anatomical_system_label', type=str, required=False, help='anatomical system label')
p.add_argument('highlight_class', type=str, required=False, help='highlight class')
return p
def get_search_parser():
sim_search_parser = api.parser()
sim_search_parser.add_argument(
'id',
action='append',
help=
'A phenotype or identifier that is composed of phenotypes (eg disease, gene)',
default=[])
sim_search_parser.add_argument('limit',
type=int,
required=False,
default=100,
help='number of rows')
sim_search_parser.add_argument('taxon',
type=int,
required=False,
help='ncbi taxon id')
return sim_search_parser
import logging
from flask import request
from flask_restplus import Resource
from biolink.datamodel.serializers import association
from biolink.api.restplus import api
from ontobio.sparql.sparql_ontol_utils import batch_fetch_labels
import pysolr
log = logging.getLogger(__name__)
parser = api.parser()
parser.add_argument('id', action='append', help='List of ids')
class OntolLabelerResource(Resource):
@api.expect(parser)
def get(self):
"""
Fetches a map from CURIEs/IDs to labels
"""
args = parser.parse_args()
return batch_fetch_labels(args.id)
class EdgeResource(Resource):
parser = api.parser()
parser.add_argument('depth',
type=int,
help='How far to traverse for neighbors',
default=1)
parser.add_argument(
'direction',
choices=['INCOMING', 'OUTGOING', 'BOTH'],
help=
'Which direction to traverse (used only if relationship_type is defined)',
default='BOTH')
parser.add_argument('relationship_type',
action='append',
help='Relationship type to traverse')
parser.add_argument(
'entail',
type=inputs.boolean,
help='Include sub-properties and equivalent properties',
default=False)
parser.add_argument('graph',
choices=['data', 'ontology'],
help='Which monarch graph to query',
default='data')
@api.expect(parser)
@api.marshal_list_with(bbop_graph)
def get(self, id):
"""
Returns edges emanating from a given node.
"""
args = self.parser.parse_args()
if args['graph'] == 'data':
scigraph = sg_data
elif args['graph'] == 'ontology':
scigraph = sg_ont
else:
raise UnhandledException('{} not supported graph'.format(
args['graph']))
node = scigraph.get_clique_leader(id)
if not node:
raise NoResultFoundException(
'SciGraph dynamic/cliqueLeader yields no result for {}'.format(
id))
clique_leader = node.id
final_graph = BBOPGraph({'nodes': [], 'edges': []})
if args.relationship_type:
for relationship in args.relationship_type:
graph = scigraph.neighbors(id=clique_leader,
relationshipType=relationship,
direction=args.direction,
depth=args.depth,
entail=args.entail)
final_graph.merge(graph)
else:
graph = scigraph.neighbors(id=clique_leader,
depth=args.depth,
entail=args.entail)
final_graph.merge(graph)
return final_graph.as_dict()
from flask_restplus import Resource
from flask import request
from ontobio.sim.annotation_scorer import AnnotationScorer
from ontobio.sim.api.owlsim2 import OwlSim2Api
from biolink.api.restplus import api
from biolink.datamodel.sim_serializers import sufficiency_input, sufficiency_output
annotation_scorer = AnnotationScorer(OwlSim2Api())
score_get = api.parser()
score_get.add_argument('id',
action='append',
help='Phenotype identifier (eg HP:0004935)')
score_get.add_argument('absent_id',
default=[],
action='append',
help='absent phenotype (eg HP:0002828)')
class AnnotationScore(Resource):
@api.expect(sufficiency_input)
@api.marshal_with(sufficiency_output)
def post(self):
"""
Get annotation score
"""
data = request.json
phenotypes = [
feature['id'] for feature in data['features']
if feature['isPresent'] is True
]
from flask_restplus import Resource, inputs
from flask import request
from ontobio.sim.api.owlsim2 import OwlSim2Api
from ontobio.sim.phenosim_engine import PhenoSimEngine
from ontobio.vocabulary.similarity import SimAlgorithm
from biolink.api.restplus import api
from biolink.api.sim.endpoints.owlsim import get_owlsim_api
from biolink.datamodel.sim_serializers import sim_result, compare_input
metrics = [matcher.value for matcher in OwlSim2Api.matchers()]
# Common args
sim_parser = api.parser()
sim_parser.add_argument(
'is_feature_set',
type=inputs.boolean,
help='set to true if *all* input ids are phenotypic features, else set to false',
default=True
)
sim_parser.add_argument(
'metric', type=str, choices=metrics,
default='phenodigm', help='Metric for computing similarity')
def get_compare_parser():
help_msg = 'A phenotype or identifier that is composed of phenotypes (eg disease, gene)'
sim_get_parser = sim_parser.copy()
sim_get_parser.add_argument(
from biolink.datamodel.serializers import node, named_object, bio_object, association_results, association, publication, gene, substance, genotype, allele, search_result
#import biolink.datamodel.serializers
from biolink.api.restplus import api
from ontobio.golr.golr_associations import search_associations, search_associations_go, select_distinct_subjects
from scigraph.scigraph_util import SciGraph
from biowikidata.wd_sparql import doid_to_wikidata, resolve_to_wikidata, condition_to_drug
from ontobio.vocabulary.relations import HomologyTypes
from ..closure_bins import create_closure_bin
import pysolr
log = logging.getLogger(__name__)
ns = api.namespace('bioentity', description='Retrieval of domain entities plus associations')
core_parser = api.parser()
core_parser.add_argument('rows', type=int, required=False, default=100, help='number of rows')
core_parser.add_argument('start', type=int, required=False, default=1, help='row to start at')
core_parser.add_argument('unselect_evidence', type=bool, help='If set, excludes evidence objects in response')
core_parser.add_argument('exclude_automatic_assertions', default=False, type=bool, help='If set, excludes associations that involve IEAs (ECO:0000501)')
core_parser.add_argument('fetch_objects', type=bool, default=True, help='If true, returns a distinct set of association.objects (typically ontology terms). This appears at the top level of the results payload')
core_parser.add_argument('use_compact_associations', type=bool, default=False, help='If true, returns results in compact associations format')
core_parser.add_argument('slim', action='append', help='Map objects up (slim) to a higher level category. Value can be ontology class ID or subset ID')
core_parser.add_argument('evidence', help="""Object id, e.g. ECO:0000501 (for IEA; Includes inferred by default)
or a specific publication or other supporting ibject, e.g. ZFIN:ZDB-PUB-060503-2.
""")
scigraph = SciGraph('https://scigraph-data.monarchinitiative.org/scigraph/')
homol_rel = HomologyTypes.Homolog.value
from ontobio.sparql.sparql_ontol_utils import run_sparql_on, EOntology, transform, transformArray
from ontobio.golr.golr_query import GolrSearchQuery, run_solr_on, ESOLR, ESOLRDoc, replace
from ontobio.ontol_factory import OntologyFactory
from biolink.ontology.ontology_manager import get_ontology
from ontobio.io.ontol_renderers import OboJsonGraphRenderer
import json
### Some query parameters & parsers
IS_A = "isa"
IS_A_PART_OF = "isa_partof"
REGULATES = "regulates"
related_params = api.parser()
related_params.add_argument(
'relationship_type',
choices=[IS_A, IS_A_PART_OF, REGULATES],
default=IS_A_PART_OF,
help="relationship type ('{}', '{}' or '{}')".format(
IS_A, IS_A_PART_OF, REGULATES))
TOPOLOGY = "topology_graph"
REGULATES_TRANSITIVITY = "regulates_transitivity_graph"
NEIGHBORHOOD_GRAPH = "neighborhood_graph"
NEIGHBORHOOD_LIMITED_GRAPH = "neighborhood_limited_graph"
graph_params = api.parser()
graph_params.add_argument('graph_type',
choices=[
TOPOLOGY, REGULATES_TRANSITIVITY,
import logging
from flask import request
from flask_restplus import Resource
from biolink.datamodel.serializers import association_results, association, gene, drug, genotype, allele, search_result
#import biolink.datamodel.serializers
from biolink.api.restplus import api
from biolink.util.golr_associations import search_associations
import pysolr
log = logging.getLogger(__name__)
ns = api.namespace('bio',
description='Retrieval of domain objects plus associations')
core_parser = api.parser()
core_parser.add_argument('exclude_evidence',
type=bool,
help='If set, excludes evidence objects in response')
@ns.route('/gene/<id>')
@api.doc(params={'id': 'id, e.g. NCBIGene:84570'})
class GeneObject(Resource):
@api.expect(core_parser)
@api.marshal_list_with(gene)
def get(self, id):
"""
TODO Returns gene object
"""
return {'foo': 'bar'}
from ontobio.golr.golr_query import GolrSearchQuery, run_solr_on, run_solr_text_on, ESOLR, ESOLRDoc, replace
from ontobio.ontol_factory import OntologyFactory
from biolink.ontology.ontology_manager import get_ontology
from ontobio.io.ontol_renderers import OboJsonGraphRenderer
from biolink.api.entityset.endpoints.slimmer import gene_to_uniprot_from_mygene, uniprot_to_gene_from_mygene
import json
### Some query parameters & parsers
IS_A = "isa"
IS_A_PART_OF = "isa_partof"
REGULATES = "regulates"
related_params = api.parser()
related_params.add_argument(
'relationship_type',
choices=[IS_A, IS_A_PART_OF, REGULATES],
default=IS_A_PART_OF,
help="relationship type ('{}', '{}' or '{}')".format(
IS_A, IS_A_PART_OF, REGULATES))
TOPOLOGY = "topology_graph"
REGULATES_TRANSITIVITY = "regulates_transitivity_graph"
NEIGHBORHOOD_GRAPH = "neighborhood_graph"
NEIGHBORHOOD_LIMITED_GRAPH = "neighborhood_limited_graph"
graph_params = api.parser()
graph_params.add_argument('graph_type',
choices=[
TOPOLOGY, REGULATES_TRANSITIVITY,