def get_terms(force: bool = False) -> Iterable[Term]:
"""Get terms."""
# DDB ID DDB_G ID Name UniProt ID
uniprot_mappings = multisetdict(
ensure_df(PREFIX, url=URL, force=force, name="uniprot_mappings.tsv", usecols=[1, 3]).values
)
terms = ensure_df(PREFIX, url=URL, force=force, name="gene_info.tsv")
# GENE ID (DDB_G ID) Gene Name Synonyms Gene products
for identifier, name, synonyms, products in tqdm(terms.values):
term = Term.from_triple(
prefix=PREFIX,
identifier=identifier,
name=name,
)
if products and pd.notna(products) and products != "unknown":
for synonym in products.split(","):
term.append_synonym(synonym.strip())
if synonyms and pd.notna(synonyms):
for synonym in synonyms.split(","):
term.append_synonym(Synonym(synonym.strip()))
for uniprot_id in uniprot_mappings.get(identifier, []):
if not uniprot_id or pd.isna(uniprot_id) or uniprot_id not in {"unknown", "pseudogene"}:
continue
term.append_relationship(has_gene_product, Reference.auto("uniprot", uniprot_id))
term.set_species(identifier="44689", name="Dictyostelium discoideum")
yield term
def get_terms(version: Optional[str] = None,
force: bool = False) -> Iterable[Term]: # noqa:C901
"""Get HGNC terms."""
if version is None:
version = datetime.date.today().strftime("%Y-%m-01")
unhandled_entry_keys: typing.Counter[str] = Counter()
unhandle_locus_types: DefaultDict[str, Dict[str, Term]] = defaultdict(dict)
path = ensure_path(
PREFIX,
url=DEFINITIONS_URL_FMT.format(version=version),
force=force,
version=version,
name="hgnc_complete_set.json",
)
with open(path) as file:
entries = json.load(file)["response"]["docs"]
yield from sorted(
{
Term(reference=Reference.auto("SO", so_id))
for so_id in sorted(LOCUS_TYPE_TO_SO.values()) if so_id
},
key=attrgetter("identifier"),
)
statuses = set()
for entry in tqdm(entries,
desc=f"Mapping {PREFIX}",
unit="gene",
unit_scale=True):
name, symbol, identifier = (
entry.pop("name"),
entry.pop("symbol"),
entry.pop("hgnc_id")[len("HGNC:"):],
)
status = entry.pop("status")
if status == "Approved":
is_obsolete = False
elif status not in statuses:
statuses.add(status)
logger.warning("UNHANDLED %s", status)
is_obsolete = True
else:
raise ValueError(
f"Unhandled status for hgnc:{identifier}: {status}")
term = Term(
definition=name,
reference=Reference(prefix=PREFIX,
identifier=identifier,
name=symbol),
is_obsolete=is_obsolete,
)
for uniprot_id in entry.pop("uniprot_ids", []):
term.append_relationship(
has_gene_product,
Reference.auto("uniprot", uniprot_id),
)
for ec_code in entry.pop("enzyme_id", []):
if "-" in ec_code:
continue # only add concrete annotations
term.append_relationship(
gene_product_member_of,
Reference.auto("eccode", ec_code),
)
for rna_central_ids in entry.pop("rna_central_id", []):
for rna_central_id in rna_central_ids.split(","):
term.append_relationship(
transcribes_to,
Reference(prefix="rnacentral",
identifier=rna_central_id.strip()),
)
mirbase_id = entry.pop("mirbase", None)
if mirbase_id:
term.append_relationship(
transcribes_to,
Reference.auto(
"mirbase",
mirbase_id,
),
)
snornabase_id = entry.pop("snornabase", None)
if snornabase_id:
term.append_relationship(
transcribes_to,
Reference(prefix="snornabase", identifier=snornabase_id))
for rgd_curie in entry.pop("rgd_id", []):
if not rgd_curie.startswith("RGD:"):
logger.warning(
f"hgnc:{identifier} had bad RGD CURIE: {rgd_curie}")
continue
rgd_id = rgd_curie[len("RGD:"):]
term.append_relationship(
orthologous,
Reference.auto(prefix="rgd", identifier=rgd_id),
)
for mgi_curie in entry.pop("mgd_id", []):
if not mgi_curie.startswith("MGI:"):
logger.warning(
f"hgnc:{identifier} had bad MGI CURIE: {mgi_curie}")
continue
mgi_id = mgi_curie[len("MGI:"):]
if not mgi_id:
continue
term.append_relationship(
orthologous,
Reference.auto(prefix="mgi", identifier=mgi_id),
)
for xref_prefix, key in gene_xrefs:
xref_identifiers = entry.pop(key, None)
if xref_identifiers is None:
continue
if not isinstance(xref_identifiers, list):
xref_identifiers = [xref_identifiers]
for xref_identifier in xref_identifiers:
term.append_xref(
Reference(prefix=xref_prefix,
identifier=str(xref_identifier)))
for pubmed_id in entry.pop("pubmed_id", []):
term.append_provenance(
Reference(prefix="pubmed", identifier=str(pubmed_id)))
gene_group_ids = entry.pop("gene_group_id", [])
gene_groups = entry.pop("gene_group", [])
for gene_group_id, gene_group_label in zip(gene_group_ids,
gene_groups):
term.append_relationship(
member_of,
Reference(
prefix="hgnc.genegroup",
identifier=str(gene_group_id),
name=gene_group_label,
),
)
for alias_symbol in entry.pop("alias_symbol", []):
term.append_synonym(
Synonym(name=alias_symbol, type=alias_symbol_type))
for alias_name in entry.pop("alias_name", []):
term.append_synonym(Synonym(name=alias_name, type=alias_name_type))
for previous_symbol in entry.pop("previous_symbol", []):
term.append_synonym(
Synonym(name=previous_symbol, type=previous_symbol_type))
for previous_name in entry.pop("prev_name", []):
term.append_synonym(
Synonym(name=previous_name, type=previous_name_type))
for prop in ["location"]:
value = entry.pop(prop, None)
if value:
term.append_property(prop, value)
locus_type = entry.pop("locus_type")
locus_group = entry.pop("locus_group")
so_id = LOCUS_TYPE_TO_SO.get(locus_type)
if so_id:
term.append_parent(Reference.auto("SO", so_id))
else:
term.append_parent(Reference.auto("SO", "0000704")) # gene
unhandle_locus_types[locus_type][identifier] = term
term.append_property("locus_type", locus_type)
term.append_property("locus_group", locus_group)
term.set_species(identifier="9606", name="H**o sapiens")
for key in entry:
unhandled_entry_keys[key] += 1
yield term
with open(prefix_directory_join(PREFIX, name="unhandled.json"),
"w") as file:
json.dump(
{
k: {hgnc_id: term.name
for hgnc_id, term in v.items()}
for k, v in unhandle_locus_types.items()
},
file,
indent=2,
)
with open(prefix_directory_join(PREFIX, name="unhandled.md"), "w") as file:
for k, v in sorted(unhandle_locus_types.items()):
t = tabulate(
[(
hgnc_id,
term.name,
term.is_obsolete,
term.link,
", ".join(p.link for p in term.provenance if p.link),
) for hgnc_id, term in sorted(v.items())],
headers=["hgnc_id", "name", "obsolete", "link", "provenance"],
tablefmt="github",
)
print(f"## {k} ({len(v)})", file=file) # noqa: T201
print(t, "\n", file=file) # noqa: T201
unhandle_locus_type_counter = Counter(
{locus_type: len(d)
for locus_type, d in unhandle_locus_types.items()})
logger.warning("Unhandled locus types:\n%s",
tabulate(unhandle_locus_type_counter.most_common()))
logger.warning("Unhandled keys:\n%s",
tabulate(unhandled_entry_keys.most_common()))
def get_terms(force: bool = False,
version: Optional[str] = None) -> Iterable[Term]:
"""Get terms."""
alt_ids_df = ensure_df(
PREFIX,
url=ALTS_URL,
name="alts.tsv",
force=force,
header=None,
names=["alt", "zfin_id"],
version=version,
)
primary_to_alt_ids = defaultdict(set)
for alt_id, zfin_id in alt_ids_df.values:
primary_to_alt_ids[zfin_id].add(alt_id)
human_orthologs = multisetdict(
ensure_df(PREFIX,
url=HUMAN_ORTHOLOGS,
force=force,
header=None,
usecols=[0, 7],
version=version).values)
mouse_orthologs = multisetdict(
ensure_df(PREFIX,
url=MOUSE_ORTHOLOGS,
force=force,
header=None,
usecols=[0, 5],
version=version).values)
fly_orthologs = multisetdict(
ensure_df(PREFIX,
url=FLY_ORTHOLOGS,
force=force,
header=None,
usecols=[0, 5],
version=version).values)
entrez_mappings = dict(
ensure_df(PREFIX,
url=ENTREZ_MAPPINGS,
force=force,
header=None,
usecols=[0, 3],
version=version).values)
uniprot_mappings = multidict(
ensure_df(PREFIX,
url=UNIPROT_MAPPINGS,
force=force,
header=None,
usecols=[0, 3],
version=version).values)
df = ensure_df(
PREFIX,
url=URL,
name="markers.tsv",
force=force,
header=None,
names=MARKERS_COLUMNS,
version=version,
)
df["sequence_ontology_id"] = df["sequence_ontology_id"].map(
lambda x: x[len("SO:"):])
so = {
sequence_ontology_id: Reference.auto(prefix="SO",
identifier=sequence_ontology_id)
for sequence_ontology_id in df["sequence_ontology_id"].unique()
}
for _, reference in sorted(so.items()):
yield Term(reference=reference)
for identifier, name, definition, _entity_type, sequence_ontology_id in tqdm(
df.values):
term = Term.from_triple(
prefix=PREFIX,
identifier=identifier,
name=name,
definition=definition if definition != name else None,
)
term.set_species(identifier="7955", name="Danio rerio")
term.append_parent(so[sequence_ontology_id])
# Entity type is redundant of identifier
# term.append_property("type", entity_type)
for alt_id in primary_to_alt_ids[identifier]:
term.append_alt(alt_id)
entrez_id = entrez_mappings.get(identifier)
if entrez_id:
term.append_xref(Reference("ncbigene", entrez_id))
for uniprot_id in uniprot_mappings.get(identifier, []):
term.append_relationship(has_gene_product,
Reference.auto("uniprot", uniprot_id))
for hgnc_id in human_orthologs.get(identifier, []):
term.append_relationship(orthologous,
Reference.auto("hgnc", hgnc_id))
for mgi_curie in mouse_orthologs.get(identifier, []):
mouse_ortholog = Reference.from_curie(mgi_curie, auto=True)
if mouse_ortholog:
term.append_relationship(orthologous, mouse_ortholog)
for flybase_id in fly_orthologs.get(identifier, []):
term.append_relationship(orthologous,
Reference("flybase", flybase_id))
yield term
def get_terms(force: bool = False,
version: Optional[str] = None) -> Iterable[Term]:
"""Get RGD terms."""
df = ensure_df(
PREFIX,
url=GENES_URL,
sep="\t",
header=0,
comment="#",
dtype=str,
force=force,
version=version,
quoting=3,
error_bad_lines=False,
)
for _, row in tqdm(df.iterrows(),
total=len(df.index),
desc=f"Mapping {PREFIX}",
unit_scale=True):
if pd.notna(row["NAME"]):
definition = row["NAME"]
elif pd.notna(row["GENE_DESC"]):
definition = row["GENE_DESC"]
else:
definition = None
term = Term(
reference=Reference(prefix=PREFIX,
identifier=row["GENE_RGD_ID"],
name=row["SYMBOL"]),
definition=definition,
)
old_names = row["OLD_NAME"]
if old_names and pd.notna(old_names):
for old_name in old_names.split(";"):
term.append_synonym(Synonym(name=old_name, type=old_name_type))
old_symbols = row["OLD_SYMBOL"]
if old_symbols and pd.notna(old_symbols):
for old_symbol in old_symbols.split(";"):
term.append_synonym(
Synonym(name=old_symbol, type=old_symbol_type))
for prefix, key in namespace_to_column:
xref_ids = str(row[key])
if xref_ids and pd.notna(xref_ids):
for xref_id in xref_ids.split(";"):
if xref_id == "nan":
continue
if prefix == "uniprot":
term.append_relationship(
has_gene_product,
Reference.auto(prefix=prefix, identifier=xref_id))
elif prefix == "ensembl":
if xref_id.startswith("ENSMUSG") or xref_id.startswith(
"ENSRNOG"):
# second one is reverse strand
term.append_xref(
Reference(prefix=prefix, identifier=xref_id))
elif xref_id.startswith("ENSMUST"):
term.append_relationship(
transcribes_to,
Reference(prefix=prefix, identifier=xref_id))
elif xref_id.startswith("ENSMUSP"):
term.append_relationship(
has_gene_product,
Reference(prefix=prefix, identifier=xref_id))
else:
logger.warning("[%s] unhandled xref ensembl:%s",
PREFIX, xref_id)
else:
term.append_xref(
Reference(prefix=prefix, identifier=xref_id))
pubmed_ids = row["CURATED_REF_PUBMED_ID"]
if pubmed_ids and pd.notna(pubmed_ids):
for pubmed_id in str(pubmed_ids).split(";"):
term.append_provenance(
Reference(prefix="pubmed", identifier=pubmed_id))
term.set_species(identifier="10116", name="Rattus norvegicus")
yield term