def make_edges(records: list, nodes_dict: dict):
ret_list = []
for record_dict in records:
accession = record_dict['AC'][0]
curie_id = 'UniProtKB:' + accession
organism_int = record_dict['organism']
update_date = nodes_dict[curie_id]['update date']
ret_list.append(
kg2_util.make_edge(curie_id, 'NCBITaxon:' + str(organism_int),
'gene_product_has_organism_source',
UNIPROTKB_BASE_IRI, update_date))
record_xrefs = record_dict.get('DR', None)
if record_xrefs is not None:
for xref_str in record_xrefs:
hgnc_match = REGEX_HGNC.match(xref_str)
if hgnc_match is not None:
hgnc_curie = hgnc_match[1]
ret_list.append(
kg2_util.make_edge(hgnc_curie, curie_id, 'encodes',
UNIPROTKB_BASE_IRI, update_date))
gene_id_match = REGEX_NCBIGeneID.match(xref_str)
if gene_id_match is not None:
ncbi_curie = 'NCBIGene:' + gene_id_match[1]
ret_list.append(
kg2_util.make_edge(ncbi_curie, curie_id, 'encodes',
UNIPROTKB_BASE_IRI, update_date))
return ret_list
def make_kg2_graph(input_file_name: str, test_mode: bool = False):
nodes = []
edges = []
df = pd.read_csv(input_file_name)
for idx in range(len(df)):
if not df['status'].isna()[idx]:
status = df['status'][idx].lower()
else:
status = "unknown_status"
if not df['phase'].isna()[idx]:
phase = df['phase'][idx].lower().replace(" ",
"_").replace("/", "_or_")
else:
phase = "unknown_phase"
relation = "clinically_tested_" + status + "_" + phase
edge_dict = kg2_util.make_edge(
subject_id=DRUGBANK_CURIE + df['drug_id'][idx],
object_id=UMLS_CURIE + df['ind_id'][idx],
relation=REPODB_IRI + '#' +
kg2_util.convert_snake_case_to_camel_case(relation),
relation_curie=REPODB_CURIE + relation,
predicate_label=relation,
provided_by=REPODB_IRI,
update_date=None)
if not df['NCT'].isna()[idx]:
edge_dict['publications'].append(NCT_CUTRIE + df['NCT'][idx])
edge_dict['publications info'][
NCT_CUTRIE +
df['NCT'][idx]] = CLINICALTRIALS_IRI + df['NCT'][idx]
edges.append(edge_dict)
return {'nodes': nodes, 'edges': edges}
def make_rel(preds_dict: dict, subject_curie: str, object_curie: str,
predicate: str, pmid: str, pub_date: str, sentence: str,
subject_score: str, object_score: str, negated: bool):
key = subject_curie + '-' + predicate + '-' + object_curie
key_val = preds_dict.get(key, None)
publication_curie = kg2_util.CURIE_PREFIX_PMID + ':' + pmid
publication_info_dict = {
'publication date': pub_date,
'sentence': sentence,
'subject score': subject_score,
'object score': object_score
}
if key_val is None:
relation_type = predicate.lower()
if relation_type != 'xref':
relation_curie = SEMMEDDB_CURIE_PREFIX + ':' + relation_type
else:
relation_curie = 'OBO:xref'
edge_dict = kg2_util.make_edge(subject_curie, object_curie,
relation_curie, relation_type,
SEMMEDDB_CURIE_PREFIX + ':',
curr_timestamp)
edge_dict['publications'] = [publication_curie]
edge_dict['publications_info'] = {
publication_curie: publication_info_dict
}
edge_dict['negated'] = negated
preds_dict[key] = edge_dict
else:
key_val['publications_info'][publication_curie] = publication_info_dict
key_val['publications'] = key_val['publications'] + [publication_curie]
def make_edge(subject_curie_id: str, object_curie_id: str,
predicate_label: str, update_date: str):
[relation, relation_curie] = kg2_util.predicate_label_to_iri_and_curie(
predicate_label, ENSEMBL_RELATION_CURIE_PREFIX, ENSEMBL_KB_IRI)
rel = kg2_util.make_edge(subject_curie_id, object_curie_id, relation,
relation_curie, predicate_label, ENSEMBL_KB_IRI,
update_date)
return rel
def format_edge(subject_id, object_id, predicate, update_date):
relation_curie = kg2_util.predicate_label_to_curie(
predicate, DRUGCENTRAL_RELATION_CURIE_PREFIX)
if predicate == kg2_util.EDGE_LABEL_BIOLINK_SAME_AS:
return kg2_util.make_edge_biolink(subject_id, object_id, predicate,
DRUGCENTRAL_SOURCE, update_date)
else:
return kg2_util.make_edge(subject_id, object_id, relation_curie,
predicate, DRUGCENTRAL_SOURCE, update_date)
def format_edge(subject_id: str, object_id: str, predicate_label: str):
relation_curie = kg2_util.predicate_label_to_curie(
predicate_label, REACTOME_RELATION_CURIE_PREFIX)
if predicate_label == kg2_util.EDGE_LABEL_BIOLINK_SAME_AS:
return kg2_util.make_edge_biolink(subject_id, object_id,
predicate_label,
REACTOME_KB_CURIE_ID, None)
return kg2_util.make_edge(subject_id, object_id, relation_curie,
predicate_label, REACTOME_KB_CURIE_ID)
def make_edge(subject_curie_id: str, object_curie_id: str,
predicate_label: str, update_date: str):
relation = kg2_util.BIOLINK_CATEGORY_BASE_IRI + kg2_util.convert_snake_case_to_camel_case(
predicate_label)
relation_curie = kg2_util.BIOLINK_CURIE_PREFIX + ':' + predicate_label.replace(
' ', '_')
rel = kg2_util.make_edge(subject_curie_id, object_curie_id, relation,
relation_curie, predicate_label,
UNIPROTKB_BASE_IRI, update_date)
return rel
def make_edges(input_tsv: str, gene_id_dict: Dict[str, list],
pmids_dict: Dict[str, Dict[str, set]], test_mode: bool) -> list:
gene_ids_actually_used = set()
update_date = datetime.datetime.now().replace(microsecond=0).isoformat()
with open(input_tsv) as inp:
tsvin = csv.reader(inp, delimiter="\t")
edges = list()
for row in tsvin:
[
gene_id, gene_name, disease_id, disease_name, z_score, _,
source_url
] = row
gene_ids_actually_used.add(gene_id)
kg2_gene_id_list = gene_id_dict.get(gene_id, None)
if kg2_gene_id_list is None:
# print(f"Missing kg2 equivalent gene ids for {gene_id}. Skipping")
continue
if float(z_score) < 3.0:
continue
for kg2_gene_id in kg2_gene_id_list:
if pmids_dict['disease'].get(disease_id, None) is None:
# print(f"Disease id {disease_id} is not DOID. Skipping.")
continue
publications_list = list(
pmids_dict['gene'][gene_id].intersection(
pmids_dict['disease'][disease_id]))
publications_list = publications_list[:
30] # limit number of publications to 30 for size constraints
edge = kg2_util.make_edge(kg2_gene_id, disease_id,
"JensenLab:associated_with",
"associated_with",
kg2_util.CURIE_ID_JENSENLAB,
update_date)
# seems hacky, but following example in rtx_kg1_neo4j_to_kg_json.py
publication_info_dict = {
'publication date': None,
'sentence': None,
'subject score': None,
'object score': str(z_score)
}
publications_info = {edge['object']: publication_info_dict}
edge["publications"] = publications_list
edge["publications_info"] = publications_info
edges.append(edge)
if test_mode and len(gene_ids_actually_used) > 1000:
break
used_genes_missing_ids = gene_ids_actually_used - set(gene_id_dict.keys())
print(
f"Skipped {len(used_genes_missing_ids)} rows for lack of kg2 gene ids."
)
print(
f"Found {len(gene_ids_actually_used - used_genes_missing_ids)} used kg2 gene ids."
)
return edges
def make_node_and_edges(article: dict, mesh_predicate_label: str,
mesh_relation_curie: str):
nodes = []
edges = []
article_citation = article["MedlineCitation"]
pmid = kg2_util.CURIE_PREFIX_PMID + ":" + article_citation["PMID"]["#text"]
update_date = extract_date(article_citation["DateRevised"])
if pmid in pmids:
# These aren't necessary yet, but it might be someday, so I wrote
# and tested a couple of functions to extract them
#authors = get_authors(article_citation)
#journal = get_journal(article_citation)
name = article_citation["Article"]["ArticleTitle"]
if isinstance(name, dict):
try:
name = name["#text"]
except:
temp_name = name
for key in temp_name:
name = temp_name[key]["#text"]
try:
created_date = extract_date(
article_citation["Article"]["ArticleDate"])
except:
created_date = None
iri = PMID_BASE_IRI + article_citation["PMID"]["#text"]
node = kg2_util.make_node(pmid, iri, name,
BIOLINK_CATEGORY_PUBLICATION, update_date,
PMID_PROVIDED_BY_CURIE_ID)
node["creation_date"] = created_date
nodes.append(node)
try:
for mesh_topic in (
article_citation["MeshHeadingList"]["MeshHeading"]):
mesh_id = kg2_util.CURIE_PREFIX_MESH + ":" + \
mesh_topic["DescriptorName"]["@UI"]
edge = kg2_util.make_edge(pmid, mesh_id, mesh_relation_curie,
mesh_predicate_label,
PMID_PROVIDED_BY_CURIE_ID,
update_date)
edges.append(edge)
except:
mesh_id = None
return [{"nodes": nodes, "edges": edges}, update_date]
def make_kg2_graph(input_file_name: str, test_mode: bool = False):
ensembl_data = kg2_util.load_json(input_file_name)
nodes = []
edges = []
genebuild_str = ensembl_data['genebuild']
update_date = genebuild_str.split('/')[1]
gene_ctr = 0
for gene_dict in ensembl_data['genes']:
gene_ctr += 1
if test_mode and gene_ctr > 10000:
break
ensembl_gene_id = gene_dict['id']
description = gene_dict.get('description', None)
gene_symbol = gene_dict.get('name', None)
other_synonyms = []
xrefs = gene_dict.get('xrefs', None)
if xrefs is not None:
other_synonyms = list(set([xref['primary_id'] for xref in xrefs if xref['primary_id'] != ensembl_gene_id]))
node_dict = make_node(ensembl_gene_id,
description,
gene_symbol,
update_date,
other_synonyms)
nodes.append(node_dict)
ensembl_gene_curie_id = node_dict['id']
taxon_id_int = gene_dict.get('taxon_id', None)
assert taxon_id_int == 9606, "unexpected taxon ID"
edges.append(kg2_util.make_edge(ensembl_gene_curie_id,
'NCBITaxon:' + str(taxon_id_int),
'gene_found_in_organism',
ENSEMBL_KB_IRI,
update_date))
hgnc_list = gene_dict.get('HGNC', None)
if hgnc_list is not None:
for hgnc_curie in hgnc_list:
edges.append(kg2_util.make_edge(ensembl_gene_curie_id,
hgnc_curie,
'xref',
ENSEMBL_KB_IRI,
update_date))
return {'nodes': nodes,
'edges': edges}
def make_edge(subject_id: str,
object_id: str,
predicate_label: str,
update_date: str = None,
publications: list = None):
relation_curie = kg2_util.CURIE_PREFIX_CHEMBL_MECHANISM + ':' + predicate_label
edge = kg2_util.make_edge(subject_id, object_id, relation_curie,
predicate_label, CHEMBL_KB_CURIE_ID, update_date)
edge['publications'] = [] if publications is None else publications
edge['publications_info'] = {}
return edge
def make_kg2_graph(input_file_name: str, test_mode: bool = False):
ensembl_data = kg2_util.load_json(input_file_name)
nodes = []
edges = []
genebuild_str = ensembl_data['genebuild']
update_date = genebuild_str.split('/')[1]
gene_ctr = 0
ontology_curie_id = ENSEMBL_KB_CURIE_ID
ens_kp_node = kg2_util.make_node(ontology_curie_id, ENSEMBL_KB_URI,
'Ensembl Genes',
kg2_util.BIOLINK_CATEGORY_DATA_FILE,
update_date, ontology_curie_id)
nodes.append(ens_kp_node)
for gene_dict in ensembl_data['genes']:
gene_ctr += 1
if test_mode and gene_ctr > 10000:
break
ensembl_gene_id = gene_dict['id']
description = gene_dict.get('description', None)
gene_symbol = gene_dict.get('name', None)
other_synonyms = []
xrefs = gene_dict.get('xrefs', None)
if xrefs is not None:
other_synonyms = list(
set([
xref['primary_id'] for xref in xrefs
if xref['primary_id'] != ensembl_gene_id
]))
node_dict = make_node(ensembl_gene_id, description, gene_symbol,
update_date, other_synonyms)
nodes.append(node_dict)
ensembl_gene_curie_id = node_dict['id']
taxon_id_int = gene_dict.get('taxon_id', None)
assert taxon_id_int == 9606, "unexpected taxon ID"
edges.append(
kg2_util.make_edge_biolink(
ensembl_gene_curie_id,
kg2_util.CURIE_PREFIX_NCBI_TAXON + ':' + str(taxon_id_int),
kg2_util.EDGE_LABEL_BIOLINK_IN_TAXON, ENSEMBL_KB_CURIE_ID,
update_date))
hgnc_list = gene_dict.get('HGNC', None)
if hgnc_list is not None:
for hgnc_curie in hgnc_list:
edges.append(
kg2_util.make_edge(ensembl_gene_curie_id, hgnc_curie,
kg2_util.CURIE_ID_OWL_SAME_AS,
kg2_util.EDGE_LABEL_OWL_SAME_AS,
ENSEMBL_KB_CURIE_ID, update_date))
return {'nodes': nodes, 'edges': edges}
def format_edge(subject_id: str,
object_id: str,
predicate_label: str,
description: str,
publications: list = None):
relation_curie = kg2_util.predicate_label_to_curie(
predicate_label, DRUGBANK_RELATION_CURIE_PREFIX)
edge = kg2_util.make_edge(subject_id, object_id, relation_curie,
predicate_label, DRUGBANK_KB_CURIE_ID, None)
if description is not None:
edge["publications_info"] = {"sentence": description}
if publications is not None:
edge["publications"] = publications
return edge
def make_hmdb_edge(subject_id: str, object_id: str, subject_prefix: str,
object_prefix: str, predicate_label: str, update_date: str,
publications_info: dict):
relation_curie = kg2_util.predicate_label_to_curie(predicate_label,
CURIE_PREFIX_HMDB)
subject = subject_prefix + ":" + subject_id
object = object_id
if object_prefix is not None:
object = object_prefix + ":" + object_id
if predicate_label == kg2_util.EDGE_LABEL_BIOLINK_SAME_AS:
edge = kg2_util.make_edge_biolink(subject, object, predicate_label,
HMDB_PROVIDED_BY_CURIE_ID,
update_date)
else:
edge = kg2_util.make_edge(subject, object, relation_curie,
predicate_label, HMDB_PROVIDED_BY_CURIE_ID,
update_date)
edge["publications_info"] = publications_info
return edge
def make_kg2_graph(input_file_name: str, test_mode: bool = False):
update_date = os.path.getmtime(input_file_name)
nodes = [
kg2_util.make_node(id=REPODB_CURIE + ':',
iri=REPODB_IRI,
name='repoDB drug repositioning database',
category_label=kg2_util.BIOLINK_CATEGORY_DATA_FILE,
update_date=update_date,
provided_by=REPODB_CURIE + ':')
]
edges = []
df = pd.read_csv(input_file_name)
for idx in range(len(df)):
if not df['status'].isna()[idx]:
status = df['status'][idx].lower()
else:
status = "unknown_status"
if not df['phase'].isna()[idx]:
phase = df['phase'][idx].lower().replace(" ",
"_").replace("/", "_or_")
else:
phase = "unknown_phase"
relation = "clinically_tested_" + status + "_" + phase
edge_dict = kg2_util.make_edge(
subject_id=DRUGBANK_CURIE + ':' + df['drug_id'][idx],
object_id=UMLS_CURIE + ':' + df['ind_id'][idx],
relation_curie=REPODB_CURIE + ':' + relation,
relation_label=relation,
provided_by=REPODB_CURIE + ':',
update_date=None)
if not df['NCT'].isna()[idx]:
edge_dict['publications'].append(NCT_CURIE + df['NCT'][idx])
edge_dict['publications_info'][
NCT_CURIE +
df['NCT'][idx]] = CLINICALTRIALS_IRI + df['NCT'][idx]
edges.append(edge_dict)
return {'nodes': nodes, 'edges': edges}
def make_rel(preds_dict: dict, subject_curie: str, object_curie: str,
predicate: str, pmid: str, pub_date: str, sentence: str,
subject_score: str, object_score: str, negated: bool):
key = subject_curie + '-' + predicate + '-' + object_curie
key_val = preds_dict.get(key, None)
publication_curie = 'PMID:' + pmid
publication_info_dict = {
'publication date': pub_date,
'sentence': sentence,
'subject score': subject_score,
'object score': object_score
}
if key_val is None:
relation_type = predicate.lower()
if relation_type != 'xref':
relation_iri = kg2_util.convert_snake_case_to_camel_case(
relation_type.replace(' ', '_'))
relation_iri = relation_iri[0].lower() + relation_iri[1:]
relation_iri = SEMMEDDB_IRI + '#' + relation_iri
relation_curie = 'SEMMEDDB:' + relation_type
else:
relation_curie = 'OBO:xref'
relation_iri = prefixcommons.expand_uri(relation_curie)
edge_dict = kg2_util.make_edge(subject_curie, object_curie,
relation_iri, relation_curie,
relation_type, SEMMEDDB_IRI,
curr_timestamp)
edge_dict['publications'] = [publication_curie]
edge_dict['publications info'] = {
publication_curie: publication_info_dict
}
edge_dict['negated'] = negated
preds_dict[key] = edge_dict
else:
key_val['publications info'][publication_curie] = publication_info_dict
key_val['publications'] = key_val['publications'] + [publication_curie]
def make_xref(subject: str, object: str, update_date: str):
edge_dict = kg2_util.make_edge(subject, object, kg2_util.IRI_OWL_SAME_AS,
kg2_util.CURIE_OWL_SAME_AS, 'equivalent_to',
UNICHEM_KB_IRI, update_date)
return edge_dict
def make_edge(intact_row):
if row.startswith('#'):
return None
data = row.split('\t')
# last data element is 'Identification method participant B'
[subject_id, # ID(s) interactor A
object_id, # ID(s) interactor B,
_, # Alt. ID(s) interactor A,
_, # Alt. ID(s) interactor B,
subject_name, # Alias(es) interactor A,
object_name, # Alias(es) interactor B,
_, # Interaction detection method(s),
_, # Publication 1st author(s),
publications, # Publication Identifier(s),
subject_taxon, # Taxid interactor A,
object_taxon, # Taxid interactor B,
predicate, # Interaction type(s),
_, # Source database(s),
_, # Interaction identifier(s),
confidence, # Confidence value(s),
_, # Expansion method(s),
_, # Biological role(s) interactor A,
_, # Biological role(s) interactor B,
_, # Experimental role(s) interactor A,
_, # Experimental role(s) interactor B,
_, # Type(s) interactor A,
_, # Type(s) interactor B,
_, # Xref(s) interactor A,
_, # Xref(s) interactor B,
_, # Interaction Xref(s),
_, # Annotation(s) interactor A,
_, # Annotation(s) interactor B,
_, # Interaction annotation(s),
taxon, # Host organism(s),
_, # Interaction parameter(s),
created_date, # Creation date,
update_date, # Update date,
_, # Checksum(s) interactor A,
_, # Checksum(s) interactor B,
_, # Interaction Checksum(s),
_, # Negative,
_, # Feature(s) interactor A,
_, # Feature(s) interactor B,
_, # Stoichiometry(s) interactor A,
_, # Stoichiometry(s) interactor B,
_, # Identification method participant A
_] = data
if subject_taxon == HUMAN_TAXON and object_taxon == HUMAN_TAXON:
publications = [format_pmid(publication)
for publication in publications.split('|')
if publication.startswith('pubmed')]
confidence = [score.replace('intact-miscore:', '')
for score in confidence.split('|')
if confidence.startswith('intact-miscore:')]
if len(confidence) < 1:
confidence = None
else:
confidence = confidence[0]
relation_label = format_rel_label(predicate)
relation = predicate.split('"')[1]
subject_id = subject_id.replace('uniprotkb',
kg2_util.CURIE_PREFIX_UNIPROT)
object_id = object_id.replace('uniprotkb',
kg2_util.CURIE_PREFIX_UNIPROT)
created_date = format_date(created_date)
update_date = format_date(update_date)
edge = kg2_util.make_edge(subject_id,
object_id,
relation,
relation_label,
INTACT_KB_CURIE_ID,
update_date)
edge['publications'] = publications
return edge
return None
for line in file_arr:
if line[0].startswith("!") is False:
predicate_label = line[2]
subject_curie = kg2_util.CURIE_PREFIX_UNIPROT + ":" + line[1]
object_curie = line[3]
publications = [line[4]]
eco_code = line[5]
source = line[6].split("|")
update_date = line[8]
evidence = line[10]
negated = False
if "NOT|" in predicate_label:
negated = True
predicate_label = predicate_label.replace("NOT|", "")
relation_curie = CURIE_PREFIX_GO + ":" + predicate_label
edge = kg2_util.make_edge(subject_curie, object_curie,
relation_curie, predicate_label,
GO_PROVIDED_BY_CURIE_ID, update_date)
edge["negated"] = negated
edge["publications"] = publications
edges.append(edge)
kg2_util.save_json({
"nodes": [],
"edges": edges
}, args.outputFile, args.test)
print("Ending at", date())
def make_kg2_graph(input_file_name: str, test_mode: bool = False):
nodes = []
edges = []
gene_ctr = 0
update_date = os.path.getmtime(input_file_name)
ontology_curie_id = NCBI_KB_CURIE_ID
ens_kp_node = kg2_util.make_node(ontology_curie_id, NCBI_KB_URL,
'NCBI Genes',
kg2_util.BIOLINK_CATEGORY_DATA_FILE,
update_date, ontology_curie_id)
nodes.append(ens_kp_node)
with open(input_file_name, 'r') as input_file:
for line in input_file:
if line.startswith('#'):
continue
gene_ctr += 1
if test_mode and gene_ctr > 10000:
break
fields = line.rstrip("\n").split("\t")
fields = [(field if field.strip() != '-' else None)
for field in fields]
[
taxon_id_str, ncbi_gene_id, gene_symbol, locus_tag,
synonyms_str, db_xrefs, chromosome, map_location, description,
type_of_gene, symbol_auth, full_name_auth, nomenc_status,
other_desig, modify_date, feature_type
] = fields
taxon_id_int = int(taxon_id_str)
if taxon_id_int != kg2_util.NCBI_TAXON_ID_HUMAN:
# skip neanderthal- and denisovan-specific genes
continue
node_synonyms = list()
if synonyms_str is not None:
node_synonyms += synonyms_str.split('|')
if other_desig is not None:
node_synonyms += other_desig.split('|')
if symbol_auth is not None and symbol_auth != gene_symbol:
node_synonyms = [symbol_auth] + node_synonyms
node_synonyms = list(set(node_synonyms))
full_name = full_name_auth
if full_name is None:
full_name = description
if type_of_gene != "unknown" or (db_xrefs is None) or (not db_xrefs.startswith("MIM:")) or \
nomenc_status is not None:
category_label = kg2_util.BIOLINK_CATEGORY_GENE
else:
full_name = 'Genetic locus associated with ' + full_name
category_label = kg2_util.BIOLINK_CATEGORY_GENOMIC_ENTITY
if full_name.startswith('microRNA'):
category_label = kg2_util.BIOLINK_CATEGORY_MICRORNA
node_dict = make_node(ncbi_gene_id, full_name, gene_symbol,
modify_date, category_label, node_synonyms)
node_curie_id = node_dict['id']
type_str = 'Type:' + type_of_gene
node_description = ''
if description is not None and description != full_name_auth:
node_description = description + '; '
node_description += type_str
if nomenc_status is not None:
nomenc_tag = 'official'
else:
nomenc_tag = 'unofficial'
if map_location is not None:
node_description += '; Locus:' + map_location
node_description += '; NameStatus:' + nomenc_tag
node_dict['description'] = node_description
nodes.append(node_dict)
org_curie = kg2_util.CURIE_PREFIX_NCBI_TAXON + ':' + taxon_id_str
predicate_label = 'in_taxon'
edge_dict = kg2_util.make_edge_biolink(node_curie_id, org_curie,
predicate_label,
NCBI_KB_CURIE_ID,
modify_date)
edges.append(edge_dict)
if db_xrefs is not None:
xrefs_list = db_xrefs.split('|')
for xref_curie in xrefs_list:
if xref_curie.startswith('HGNC:HGNC:'):
xref_curie = kg2_util.CURIE_PREFIX_HGNC + ':' + xref_curie.replace(
'HGNC:', '')
elif xref_curie.startswith('Ensembl:'):
xref_curie = xref_curie.upper()
elif xref_curie.startswith('MIM:'):
xref_curie = kg2_util.CURIE_PREFIX_OMIM + ':' + xref_curie.replace(
'MIM:', '')
elif xref_curie.startswith('miRBase:'):
xref_curie = kg2_util.CURIE_PREFIX_MIRBASE + ':' + xref_curie.replace(
'miRBase:', '')
edges.append(
kg2_util.make_edge(node_curie_id, xref_curie,
kg2_util.CURIE_ID_OWL_SAME_AS,
kg2_util.EDGE_LABEL_OWL_SAME_AS,
NCBI_KB_CURIE_ID, modify_date))
return {'nodes': nodes, 'edges': edges}
def get_rels_dict(nodes: dict, owl_file_information_dict_list: list,
uri_to_curie_shortener: callable,
map_of_node_ontology_ids_to_curie_ids: dict):
rels_dict = dict()
for owl_file_information_dict in owl_file_information_dict_list:
ontology = owl_file_information_dict['ontology']
ontology_id = owl_file_information_dict['id']
ont_graph = ontology.get_graph()
ontology_curie_id = map_of_node_ontology_ids_to_curie_ids[ontology_id]
for (object_id, subject_id,
predicate_dict) in ont_graph.edges(data=True):
assert type(predicate_dict) == dict
ontology_node = nodes.get(ontology_curie_id, None)
if ontology_node is not None:
ontology_update_date = ontology_node['update date']
if subject_id == OWL_BASE_CLASS or object_id == OWL_BASE_CLASS:
continue
if subject_id.startswith(MYSTERIOUS_BASE_NODE_ID_TO_FILTER) or \
object_id.startswith(MYSTERIOUS_BASE_NODE_ID_TO_FILTER):
continue
# subject_id and object_id are IDs from the original ontology objects; these may not
# always be the node curie IDs (e.g., for SNOMED terms). Need to map them
subject_curie_id = map_of_node_ontology_ids_to_curie_ids.get(
subject_id, None)
if subject_curie_id is None:
kg2_util.log_message(
message="ontology node ID has no curie ID in the map",
ontology_name=ontology.id,
node_curie_id=subject_id,
output_stream=sys.stderr)
continue
object_curie_id = map_of_node_ontology_ids_to_curie_ids.get(
object_id, None)
if object_curie_id is None:
kg2_util.log_message(
message="ontology node ID has no curie ID in the map",
ontology_name=ontology.id,
node_curie_id=object_id,
output_stream=sys.stderr)
continue
predicate_label = None
edge_pred_string = predicate_dict['pred']
if subject_curie_id.startswith(
'TUI:') and object_curie_id.startswith(
'TUI:') and edge_pred_string == 'subClassOf':
continue
if not edge_pred_string.startswith(
'http:') and not edge_pred_string.startswith('https'):
# edge_pred_string is not a URI; this is the most common case
if ':' not in edge_pred_string:
# edge_pred_string is not a CURIE; this is the most common subcase
if edge_pred_string != 'subClassOf':
predicate_curie = 'owl:' + edge_pred_string
else:
predicate_curie = 'rdfs:subClassOf'
predicate_label = kg2_util.convert_camel_case_to_snake_case(
edge_pred_string)
else:
# edge_pred_string is a CURIE
predicate_curie = edge_pred_string
predicate_node = nodes.get(predicate_curie, None)
if predicate_node is not None:
predicate_label = predicate_node['name']
else:
# predicate has no node object defined; just pull the label out of the CURIE
if edge_pred_string.startswith('OBO:'):
test_curie = edge_pred_string.replace('OBO:',
'').replace(
'_', ':')
predicate_node = nodes.get(test_curie, None)
if predicate_node is None:
predicate_label = edge_pred_string.split(
':')[1].split('#')[-1]
else:
predicate_curie = test_curie
else:
predicate_label = edge_pred_string
predicate_iri = prefixcommons.expand_uri(predicate_curie)
predicate_curie_new = uri_to_curie_shortener(predicate_iri)
if predicate_curie_new is not None:
predicate_curie = predicate_curie_new
else:
predicate_iri = edge_pred_string
predicate_curie = uri_to_curie_shortener(predicate_iri)
if predicate_curie is None:
kg2_util.log_message(message="predicate IRI has no CURIE: " +
predicate_iri,
ontology_name=ontology.id,
output_stream=sys.stderr)
continue
if subject_curie_id == object_curie_id and predicate_label == 'xref':
continue
if predicate_curie == 'UMLS:hasSTY':
subject_node = nodes[subject_curie_id]
object_node = nodes[object_curie_id]
subject_description = subject_node['description']
if subject_description is None:
subject_description = ''
subject_node['description'] = '; '.join(
list(
filter(None, [
subject_description,
'UMLS Semantic Type: ' + object_node['id']
])))
continue
rel_key = make_rel_key(subject_curie_id, predicate_curie,
object_curie_id, ontology_curie_id)
if predicate_label is None and ':' in predicate_curie:
pred_node = nodes.get(predicate_curie, None)
if pred_node is not None:
predicate_label = pred_node['name']
if predicate_label[0].isupper():
predicate_label = predicate_label[0].lower(
) + predicate_label[1:]
assert predicate_label is not None
predicate_label = predicate_label.replace(' ', '_')
# Only tested on Food and Efo ontologies
predicate_label = kg2_util.convert_camel_case_to_snake_case(
predicate_label)
if rels_dict.get(rel_key, None) is None:
edge = kg2_util.make_edge(subject_curie_id, object_curie_id,
predicate_iri, predicate_curie,
predicate_label, ontology_id,
ontology_update_date)
rels_dict[rel_key] = edge
for node_id, node_dict in nodes.items():
xrefs = node_dict['xrefs']
if xrefs is not None:
for xref_node_id in xrefs:
if xref_node_id in nodes and node_id != xref_node_id:
provided_by = nodes[node_id]['provided by']
key = make_rel_key(node_id, CURIE_OBO_XREF,
xref_node_id, provided_by)
if rels_dict.get(key, None) is None:
edge = kg2_util.make_edge(node_id, xref_node_id,
IRI_OBO_XREF,
CURIE_OBO_XREF, 'xref',
provided_by,
ontology_update_date)
rels_dict[key] = edge
return rels_dict
def make_xref(subject: str, object: str, update_date: str):
edge_dict = kg2_util.make_edge(subject, object,
kg2_util.CURIE_ID_OWL_SAME_AS,
kg2_util.EDGE_LABEL_OWL_SAME_AS,
UNICHEM_KB_CURIE, update_date)
return edge_dict
def make_kg2_graph(input_file_name: str, test_mode: bool = False):
nodes = []
edges = []
line_ctr = 0
update_date = None
with open(input_file_name, 'r') as input_file:
for line in input_file:
line = line.rstrip("\n")
if line.startswith('#'):
update_date = line.replace('#', '')
continue
if line.startswith('gene_name\t'):
continue
line_ctr += 1
if test_mode and line_ctr > 10000:
break
fields = line.split("\t")
[gene_name,
gene_claim_name,
entrez_id,
interaction_claim_source,
interaction_types,
drug_claim_name,
drug_claim_primary_name,
drug_name,
drug_concept_id,
_, #12.5.2020 new field in tsv: interaction group score
PMIDs] = fields
if entrez_id != "":
object_curie_id = kg2_util.CURIE_PREFIX_NCBI_GENE + ':' + entrez_id
if drug_concept_id != "":
if "chembl" in drug_concept_id:
_, chembl_id = drug_concept_id.split(":")
subject_curie_id = kg2_util.CURIE_PREFIX_CHEMBL_COMPOUND + ':' + chembl_id
else:
print(f"DGIDB: Skipping row with drug concept id {drug_concept_id}", file=sys.stderr)
continue #skipping over wikidata nodes, see #1185
else:
if drug_claim_name != "":
node_pubs_list = []
subject_curie_id = None
if interaction_claim_source == INTERACTION_CLAIM_SOURCE_GTPI:
subject_curie_id = GTPI_CURIE_PREFIX + ':' + drug_claim_name
pmid_match = RE_PMID.match(drug_claim_primary_name)
if pmid_match is not None:
node_pubs_list = [pmid_match[2].replace(' ', '').strip()]
node_name = pmid_match[1].strip()
else:
node_name = drug_claim_primary_name
node_iri = GTPI_BASE_URL + drug_claim_name
provided_by = GTPI_KB_CURIE
elif interaction_claim_source == INTERACTION_CLAIM_SOURCE_TTD:
subject_curie_id = TTD_CURIE_PREFIX + ':' + drug_claim_name
node_name = drug_claim_primary_name
node_iri = TTD_IRI_BASE + drug_claim_name
provided_by = TTD_KB_CURIE
if subject_curie_id is not None:
node_dict = kg2_util.make_node(subject_curie_id,
node_iri,
node_name,
kg2_util.BIOLINK_CATEGORY_CHEMICAL_SUBSTANCE,
update_date,
provided_by)
node_dict['publications'] = node_pubs_list
nodes.append(node_dict)
if subject_curie_id is None:
print("DGIDB: no controlled ID was provided for this drug: " + drug_claim_primary_name +
"; source DB: " + interaction_claim_source, file=sys.stderr)
continue
if interaction_types == "":
print("DGIDB: interaction type was empty. Setting to 'affects'.", file=sys.stderr)
interaction_types = "affects"
pmids_list = []
if PMIDs.strip() != "":
pmids_list = [(kg2_util.CURIE_PREFIX_PMID + ':' + pmid.strip()) for pmid in PMIDs.split(',')]
interaction_list = interaction_types.split(',')
for interaction in interaction_list:
interaction = interaction.replace(' ', '_')
edge_dict = kg2_util.make_edge(subject_curie_id,
object_curie_id,
DGIDB_CURIE_PREFIX + ':' + interaction,
interaction,
DGIDB_KB_CURIE,
update_date)
edge_dict['publications'] = pmids_list
edges.append(edge_dict)
return {'nodes': nodes,
'edges': edges}
def make_kg2_graph(input_file_name: str, test_mode: bool = False):
nodes = []
edges = []
gene_ctr = 0
with open(input_file_name, 'r') as input_file:
for line in input_file:
if line.startswith('#'):
continue
gene_ctr += 1
if test_mode and gene_ctr > 10000:
break
fields = line.rstrip("\n").split("\t")
fields = [(field if field.strip() != '-' else None) for field in fields]
[taxon_id_str,
ncbi_gene_id,
gene_symbol,
locus_tag,
synonyms_str,
db_xrefs,
chromosome,
map_location,
description,
type_of_gene,
symbol_auth,
full_name_auth,
nomenc_status,
other_desig,
modify_date,
feature_type] = fields
taxon_id_int = int(taxon_id_str)
if taxon_id_int != kg2_util.NCBI_TAXON_ID_HUMAN:
# skip neanderthal- and denisovan-specific genes
continue
node_synonyms = list()
if synonyms_str is not None:
node_synonyms += synonyms_str.split('|')
if other_desig is not None:
node_synonyms += other_desig.split('|')
if symbol_auth is not None and symbol_auth != gene_symbol:
node_synonyms = [symbol_auth] + node_synonyms
node_synonyms = list(set(node_synonyms))
full_name = full_name_auth
if full_name is None:
full_name = description
node_dict = make_node(ncbi_gene_id,
full_name,
gene_symbol,
modify_date,
node_synonyms)
node_curie_id = node_dict['id']
type_str = 'Type:'+type_of_gene
node_description = ''
if description is not None and description != full_name_auth:
node_description = description + '; '
node_description += type_str
if map_location is not None:
node_description += '; Locus:' + map_location
if nomenc_status is not None:
nomenc_tag = 'official'
else:
nomenc_tag = 'unofficial'
node_description += '; NameStatus:' + nomenc_tag
node_dict['description'] = node_description
nodes.append(node_dict)
org_curie = kg2_util.CURIE_PREFIX_NCBI_TAXON + ':' + taxon_id_str
predicate_label = 'gene_found_in_organism'
[relation, relation_curie] = kg2_util.predicate_label_to_iri_and_curie(predicate_label,
NCBI_RELATION_CURIE_PREFIX,
NCBI_BASE_IRI)
edge_dict = kg2_util.make_edge(node_curie_id,
org_curie,
relation,
relation_curie,
predicate_label,
NCBI_BASE_IRI,
modify_date)
edges.append(edge_dict)
if db_xrefs is not None:
xrefs_list = db_xrefs.split('|')
for xref_curie in xrefs_list:
if xref_curie.startswith('HGNC:HGNC:'):
xref_curie = 'HGNC:' + xref_curie.replace('HGNC:', '')
elif xref_curie.startswith('Ensembl:'):
xref_curie = xref_curie.upper()
elif xref_curie.startswith('MIM:'):
xref_curie = 'OMIM:' + xref_curie.replace('MIM:', '')
edges.append(kg2_util.make_edge(node_curie_id,
xref_curie,
kg2_util.IRI_OWL_SAME_AS,
kg2_util.CURIE_OWL_SAME_AS,
'equivalent_to',
NCBI_BASE_IRI,
modify_date))
return {'nodes': nodes,
'edges': edges}
def make_kg2_graph(input_file_name: str, test_mode: bool = False):
nodes = []
edges = []
line_ctr = 0
update_date = None
with open(input_file_name, 'r') as input_file:
for line in input_file:
line = line.rstrip("\n")
if line.startswith('#'):
update_date = line.replace('#', '')
continue
if line.startswith('gene_name\t'):
continue
line_ctr += 1
if test_mode and line_ctr > 10000:
break
fields = line.split("\t")
[gene_name,
gene_claim_name,
entrez_id,
interaction_claim_source,
interaction_types,
drug_claim_name,
drug_claim_primary_name,
drug_name,
drug_chembl_id,
PMIDs] = fields
if entrez_id != "":
object_curie_id = 'NCBIGene:' + entrez_id
if drug_chembl_id != "":
subject_curie_id = 'CHEMBL.COMPOUND:' + drug_chembl_id
else:
if drug_claim_name != "":
node_pubs_list = []
subject_curie_id = None
if interaction_claim_source == "GuideToPharmacologyInteractions":
subject_curie_id = GTPI_CURIE_PREFIX + ':' + drug_claim_name
pmid_match = RE_PMID.match(drug_claim_primary_name)
if pmid_match is not None:
node_pubs_list = [pmid_match[2].replace(' ', '').strip()]
node_name = pmid_match[1].strip()
else:
node_name = drug_claim_primary_name
node_iri = GTPI_IRI_BASE + GTPI_LIGAND_SUFFIX + drug_claim_name
provided_by = GTPI_IRI_BASE
elif interaction_claim_source == "TTD":
subject_curie_id = TTD_CURIE_PREFIX + ':' + drug_claim_name
node_name = drug_claim_primary_name
node_iri = TTD_IRI_BASE + drug_claim_name
provided_by = TTD_IRI_BASE
if subject_curie_id is not None:
node_dict = kg2_util.make_node(subject_curie_id,
node_iri,
node_name,
'chemical_substance',
update_date,
provided_by)
node_dict['publications'] = node_pubs_list
nodes.append(node_dict)
if subject_curie_id is None:
print("DGIDB: no controlled ID was provided for this drug: " + drug_claim_primary_name + "; source DB: " + interaction_claim_source, file=sys.stderr)
continue
if interaction_types == "":
interaction_types = "affects"
pmids_list = []
if PMIDs.strip() != "":
pmids_list = [('PMID:' + pmid.strip()) for pmid in PMIDs.split(',')]
interaction_list = interaction_types.split(',')
for interaction in interaction_list:
interaction = interaction.replace(' ', '_')
edge_dict = kg2_util.make_edge(subject_curie_id,
object_curie_id,
DGIDB_BASE_IRI + '/' +
kg2_util.convert_snake_case_to_camel_case(interaction),
DGIDB_CURIE_PREFIX + ':' + interaction,
interaction,
DGIDB_BASE_IRI,
update_date)
edge_dict['publications'] = pmids_list
edges.append(edge_dict)
return {'nodes': nodes,
'edges': edges}
