def get_mappings() -> Iterable[PredictionTuple]:
"""Iterate high-confidence lexical mappings between MeSH and UniProt human proteins."""
url = get_script_url(__file__)
mapping_type = "lexical"
match_type = "skos:exactMatch"
confidence = 0.999
for mesh_name, mesh_id in mesh_client.mesh_name_to_id.items():
match = MESH_PROTEIN_RE.match(mesh_name)
if not match:
continue
gene_name = match.groups()[0]
hgnc_id = hgnc_client.get_hgnc_id(gene_name)
if not hgnc_id:
continue
uniprot_id = hgnc_client.get_uniprot_id(hgnc_id)
if not uniprot_id or "," in uniprot_id:
continue
yield PredictionTuple(
"mesh",
mesh_id,
mesh_name,
match_type,
"uniprot",
uniprot_id,
gene_name,
mapping_type,
confidence,
url,
)
def main():
"""Make species specific groundings from reactome to wikipathways."""
target_prefixes = ["reactome", "wikipathways", "pw"]
provenance = get_script_url(__file__)
for prefix in ["reactome", "wikipathways"]:
append_gilda_predictions(prefix,
target_prefixes,
provenance=provenance,
relation="speciesSpecific")
def main():
"""Generate CCLE mappings."""
provenance = get_script_url(__file__)
prefix = "ccle.cell"
targets = ["depmap", "efo", "cellosaurus", "cl", "bto"]
custom_filter = get_custom_filter(prefix, targets)
append_gilda_predictions(
prefix,
targets,
provenance=provenance,
relation="skos:exactMatch",
custom_filter=custom_filter,
identifiers_are_names=True,
)
def iter_gilda_prediction_tuples(prefix: str,
relation: str) -> Iterable[PredictionTuple]:
"""Iterate over prediction tuples for a given prefix."""
provenance = get_script_url(__file__)
id_name_mapping = pyobo.get_id_name_mapping(prefix)
for identifier, name in tqdm(id_name_mapping.items(),
desc=f'Mapping {prefix}'):
for scored_match in gilda.ground(name):
yield PredictionTuple(
prefix,
identifier,
name,
relation,
scored_match.term.db.lower(),
scored_match.term.id,
scored_match.term.entry_name,
'lexical',
scored_match.score,
provenance,
)
def iterate_kegg_matches() -> Iterable[PredictionTuple]:
"""Iterate over predictions from KEGG Pathways to GO and MeSH."""
provenance = get_script_url(__file__)
id_name_mapping = ensure_list_pathways()
for identifier, name in tqdm(id_name_mapping.items(),
desc='Mapping KEGG Pathways'):
for scored_match in gilda.ground(name):
if scored_match.term.db.lower() not in {'go', 'mesh'}:
continue
yield (
'kegg.pathway',
identifier,
name,
'skos:exactMatch',
scored_match.term.db.lower(),
scored_match.term.id,
scored_match.term.entry_name,
'lexical',
scored_match.score,
provenance,
)
def get_mappings() -> Iterable[PredictionTuple]:
"""Iterate lexical mappings from Gilda."""
url = get_script_url(__file__)
mapping_type = "lexical"
match_type = "skos:exactMatch"
confidence = 0.95
primary_mappings = get_primary_mappings()
with open(GILDA_MAPPINGS, "r") as fh:
for _, mesh_id, mesh_name, db_ns, db_id, db_name in csv.reader(fh, delimiter="\t"):
if ("mesh", mesh_id, db_ns, db_id) in primary_mappings:
continue
yield PredictionTuple(
"mesh",
mesh_id,
mesh_name,
match_type,
db_ns_mappings[db_ns],
db_id,
db_name,
mapping_type,
confidence,
url,
)
def main():
"""Generate MONDO mappings."""
provenance = get_script_url(__file__)
append_gilda_predictions("mondo", ["doid", "efo"],
provenance=provenance,
relation="skos:exactMatch")