def iter_terms() -> Iterable[Term]:
"""Iterate over terms for KEGG Genome."""
errors = 0
for kegg_genome in iter_kegg_genomes():
xrefs = []
if kegg_genome.taxonomy_id is not None:
taxonomy_name = pyobo.get_name('ncbitaxon', kegg_genome.taxonomy_id)
if taxonomy_name is None:
errors += 1
tqdm.write(f'could not find name for taxonomy:{kegg_genome.taxonomy_id}')
xrefs.append(Reference(
prefix='ncbitaxon',
identifier=kegg_genome.taxonomy_id,
name=taxonomy_name,
))
term = Term(
reference=Reference(
prefix='kegg.genome',
identifier=kegg_genome.identifier,
name=kegg_genome.name,
),
xrefs=xrefs,
)
yield term
logger.info('[%s] unable to find %d taxonomy names in NCBI', KEGG_GENOME_PREFIX, errors)
def _get_term_from_tree(tree: ElementTree.ElementTree) -> Term:
name = _find_text(tree, "name")
description = _find_text(tree, "description")
if description:
description = description.strip().replace("\n", " ")
identifier = _find_text(tree, "identifier")
if identifier is None:
raise ValueError
term = Term(
reference=Reference(PREFIX, identifier, name),
definition=description,
)
for experiment in tree.findall("experiments"):
experiment_name = _find_text(experiment, "name")
experiment_identifier = _find_text(experiment, "identifier")
if experiment_identifier is None:
continue
term.append_relationship(
has_part,
Reference(
"gwascentral.experiment",
identifier=experiment_identifier,
name=experiment_name,
),
)
return term
def _iter_genome_terms(
*,
list_pathway_path: str,
link_pathway_path: str,
kegg_genome: KEGGGenome,
) -> Iterable[Term]:
terms = {}
with open(list_pathway_path) as file:
list_pathway_lines = [line.strip() for line in file]
for line in list_pathway_lines:
line = line.strip()
pathway_id, name = [part.strip() for part in line.split("\t")]
pathway_id = pathway_id[len("path:"):]
terms[pathway_id] = term = Term.from_triple(
prefix=KEGG_PATHWAY_PREFIX,
identifier=pathway_id,
name=name,
)
# Annotate species information
kegg_genome.annotate_term(term)
# Annotate the non-species specific code
_start = min(i for i, e in enumerate(pathway_id) if e.isnumeric())
pathway_code = pathway_id[_start:]
term.append_relationship(
species_specific,
Reference(prefix=KEGG_PATHWAY_PREFIX,
identifier=f"map{pathway_code}"),
)
for pathway_id, protein_ids in _get_link_pathway_map(
link_pathway_path).items():
term = terms.get(pathway_id)
if term is None:
tqdm.write(
f"could not find kegg.pathway:{pathway_id} for {kegg_genome.name}"
)
continue
for protein_id in protein_ids:
term.append_relationship(
has_part,
Reference(
prefix=KEGG_GENES_PREFIX,
identifier=protein_id,
),
)
yield from terms.values()
def _get_xref_df(version: str) -> Mapping[str, List[Reference]]:
base_url = f"https://raw.githubusercontent.com/sorgerlab/famplex/{version}"
xrefs_url = f"{base_url}/equivalences.csv"
xrefs_df = ensure_df(PREFIX,
url=xrefs_url,
version=version,
header=None,
sep=",",
dtype=str)
# Normalize nextprot families
ns_remapping = {
"NXP": "nextprot.family",
}
xrefs_df[0] = xrefs_df[0].map(lambda s: ns_remapping.get(s, s))
xrefs_df[1] = [
xref_identifier
if xref_prefix != "nextprot.family" else xref_identifier[len("FA:"):]
for xref_prefix, xref_identifier in xrefs_df[[0, 1]].values
]
xrefs_df[0] = xrefs_df[0].map(normalize_prefix)
xrefs_df = xrefs_df[xrefs_df[0].notna()]
xrefs_df = xrefs_df[xrefs_df[0] != "bel"]
return multidict(
(identifier, Reference(xref_prefix, xref_identifier))
for xref_prefix, xref_identifier, identifier in xrefs_df.values)
def iter_terms(version: str) -> Iterable[Term]:
"""Iterate over terms for KEGG Genome."""
errors = 0
for kegg_genome in iter_kegg_genomes(version=version, desc="KEGG Genomes"):
if kegg_genome.identifier in SKIP:
continue
term = Term.from_triple(
prefix=KEGG_GENOME_PREFIX,
identifier=kegg_genome.identifier,
name=kegg_genome.name,
)
if kegg_genome.taxonomy_id is not None:
taxonomy_name = pyobo.get_name("ncbitaxon",
kegg_genome.taxonomy_id)
if taxonomy_name is None:
errors += 1
logger.debug(
f"[{KEGG_GENOME_PREFIX}] could not find name for taxonomy:{kegg_genome.taxonomy_id}"
)
term.append_xref(
Reference(
prefix="ncbitaxon",
identifier=kegg_genome.taxonomy_id,
name=taxonomy_name,
))
yield term
logger.info("[%s] unable to find %d taxonomy names in NCBI",
KEGG_GENOME_PREFIX, errors)
def iter_terms(version: str, force: bool = False) -> Iterable[Term]:
"""Iterate over terms from GWAS Central Phenotype."""
for n in trange(1, 11000, desc=f"{PREFIX} download"):
try:
path = ensure_path(
PREFIX,
"phenotype",
version=version,
url=
f"https://www.gwascentral.org/phenotype/HGVPM{n}?format=json",
name=f"HGVPM{n}.json",
force=force,
)
except OSError as e:
tqdm.write(f"{n}: {e}")
continue
with open(path) as file:
j = json.load(file)
description = j.get("description")
if description is not None:
description = description.strip().replace("\n", " ")
term = Term(
reference=Reference(PREFIX, j["identifier"], j["name"]),
definition=description,
)
yield term
def test_extract_definition(self):
"""Test extracting a definition."""
expected_text = "Test Text."
for s, expected_references in [
(f'"{expected_text}"', []),
(f'"{expected_text}" []', []),
(f'"{expected_text}" [PMID:1234]', [Reference('pubmed', '1234')]),
(f'"{expected_text}" [PMID:1234, PMID:1235]',
[Reference('pubmed', '1234'),
Reference('pubmed', '1235')]),
]:
with self.subTest(s=s):
actual_text, actual_references = _extract_definition(
s, prefix='chebi', identifier='XXX')
self.assertEqual(expected_text, actual_text)
self.assertEqual(expected_references, actual_references)
def iter_terms(version: str, force: bool = False) -> Iterable[Term]:
"""Iterate over DrugCentral terms."""
url = f"https://unmtid-shinyapps.net/download/DrugCentral/{version}/structures.smiles.tsv"
df = ensure_df(PREFIX, url=url, version=version, force=force)
for smiles, inchi, inchi_key, drugcentral_id, drugcentral_name, cas in df.values:
if pd.isna(smiles) or pd.isna(inchi) or pd.isna(inchi_key):
logger.warning("missing data for drugcentral:%s", drugcentral_id)
continue
term = Term.from_triple(prefix=PREFIX,
identifier=drugcentral_id,
name=drugcentral_name)
term.append_xref(Reference(prefix="inchikey", identifier=inchi_key))
term.append_property("smiles", smiles)
term.append_property("inchi", inchi)
if pd.notna(cas):
term.append_xref(Reference(prefix="cas", identifier=cas))
yield term
def test_extract_definition_with_escapes(self):
"""Test extracting a definition with escapes in it."""
expected_text = '''The canonical 3' splice site has the sequence "AG".'''
s = '''"The canonical 3' splice site has the sequence \\"AG\\"." [PMID:1234]'''
actual_text, actual_references = _extract_definition(s,
strict=True,
prefix='chebi',
identifier='XXX')
self.assertEqual(expected_text, actual_text)
self.assertEqual([Reference('pubmed', '1234')], actual_references)
def test_extract_definition_with_escapes(self):
"""Test extracting a definition with escapes in it."""
expected_text = """The canonical 3' splice site has the sequence "AG"."""
s = """"The canonical 3' splice site has the sequence \\"AG\\"." [PMID:1234]"""
actual_text, actual_references = _extract_definition(s,
strict=True,
prefix="chebi",
identifier="XXX")
self.assertEqual(expected_text, actual_text)
self.assertEqual([Reference("pubmed", "1234")], actual_references)
def test_extract_definition(self):
"""Test extracting a definition."""
expected_text = "Test Text."
for s, expected_references in [
(f'"{expected_text}"', []),
(f'"{expected_text}" []', []),
(f'"{expected_text}" [PMID:1234]', [Reference("pubmed", "1234")]),
(
f'"{expected_text}" [PMID:1234, PMID:1235]',
[Reference("pubmed", "1234"),
Reference("pubmed", "1235")],
),
]:
with self.subTest(s=s):
actual_text, actual_references = _extract_definition(
s, prefix="chebi", identifier="XXX")
self.assertEqual(expected_text, actual_text)
self.assertEqual(expected_references, actual_references)
def get_terms() -> Iterable[Term]:
"""Get ComplexPortal terms."""
df = get_df()
df['aliases'] = df['aliases'].map(lambda s: s.split('|')
if pd.notna(s) else [])
df['members'] = df['members'].map(_parse_members)
df['xrefs'] = df['xrefs'].map(_parse_xrefs)
taxnomy_id_to_name = get_id_name_mapping('ncbitaxon')
df['taxonomy_name'] = df['taxonomy_id'].map(taxnomy_id_to_name.get)
slim_df = df[[
'complexportal_id',
'name',
'definition',
'aliases',
'xrefs',
'taxonomy_id',
'taxonomy_name',
'members',
]]
it = tqdm(slim_df.values,
total=len(slim_df.index),
desc=f'mapping {PREFIX}')
unhandled_xref_type = set()
for complexportal_id, name, definition, aliases, xrefs, taxonomy_id, taxonomy_name, members in it:
synonyms = [Synonym(name=alias) for alias in aliases]
_xrefs = []
provenance = []
for reference, note in xrefs:
if note == 'identity':
_xrefs.append(reference)
elif note == 'see-also' and reference.prefix == 'pubmed':
provenance.append(reference)
elif (note, reference.prefix) not in unhandled_xref_type:
logger.debug(
f'unhandled xref type: {note} / {reference.prefix}')
unhandled_xref_type.add((note, reference.prefix))
term = Term(
reference=Reference(prefix=PREFIX,
identifier=complexportal_id,
name=name),
definition=definition.strip(),
synonyms=synonyms,
xrefs=_xrefs,
provenance=provenance,
)
term.set_species(identifier=taxonomy_id, name=taxonomy_name)
for reference, _count in members:
term.append_relationship(has_part, reference)
yield term
def test_extract_synonym(self):
"""Test extracting synonym strings."""
iupac_name = SynonymTypeDef(id="IUPAC_NAME", name="IUPAC NAME")
synoynym_typedefs = {
"IUPAC_NAME": iupac_name,
}
for synonym, s in [
(
Synonym(
name="LTEC I",
specificity="EXACT",
type=iupac_name,
provenance=[Reference("orphanet", "93938")],
),
'"LTEC I" EXACT IUPAC_NAME [Orphanet:93938]',
),
(
Synonym(name="LTEC I",
specificity="EXACT",
provenance=[Reference("orphanet", "93938")]),
'"LTEC I" EXACT [Orphanet:93938]',
),
(
Synonym(name="LTEC I",
specificity="EXACT",
provenance=[Reference("orphanet", "93938")]),
'"LTEC I" [Orphanet:93938]',
),
(
Synonym(name="LTEC I", specificity="EXACT"),
'"LTEC I" []',
),
]:
with self.subTest(s=s):
self.assertEqual(
synonym,
_extract_synonym(s,
synoynym_typedefs,
prefix="chebi",
identifier="XXX"),
)
def _parse_xrefs(s) -> List[Tuple[Reference, str]]:
if pd.isna(s):
return []
rv = []
for xref in s.split('|'):
entity_id, note = xref.split('(')
note = note.rstrip(')')
prefix, identifier = entity_id.split(':', 1)
rv.append((Reference(prefix=prefix, identifier=identifier), note))
return rv
def iter_terms(version: str) -> Iterable[Term]:
"""Iterate over DrugCentral terms."""
df = ensure_df(PREFIX, url=URL, version=version)
for smiles, inchi, inchi_key, drugcentral_id, drugcentral_name, cas in df.values:
if pd.isna(smiles) or pd.isna(inchi) or pd.isna(inchi_key):
logger.warning("missing data for drugcentral:%s", drugcentral_id)
continue
xrefs = [
Reference(prefix="smiles", identifier=smiles),
Reference(prefix="inchi", identifier=inchi),
Reference(prefix="inchikey", identifier=inchi_key),
]
if pd.notna(cas):
xrefs.append(Reference(prefix="cas", identifier=cas))
yield Term(
reference=Reference(prefix=PREFIX, identifier=drugcentral_id, name=drugcentral_name),
xrefs=xrefs,
)
def test_extract_synonym(self):
"""Test extracting synonym strings."""
iupac_name = SynonymTypeDef(id='IUPAC_NAME', name='IUPAC NAME')
synoynym_typedefs = {
'IUPAC_NAME': iupac_name,
}
for synonym, s in [
(
Synonym(
name='LTEC I',
specificity='EXACT',
type=iupac_name,
provenance=[Reference('orphanet', '93938')],
),
'"LTEC I" EXACT IUPAC_NAME [Orphanet:93938]',
),
(
Synonym(name='LTEC I',
specificity='EXACT',
provenance=[Reference('orphanet', '93938')]),
'"LTEC I" EXACT [Orphanet:93938]',
),
(
Synonym(name='LTEC I',
specificity='EXACT',
provenance=[Reference('orphanet', '93938')]),
'"LTEC I" [Orphanet:93938]',
),
(
Synonym(name='LTEC I', specificity='EXACT'),
'"LTEC I" []',
),
]:
with self.subTest(s=s):
self.assertEqual(
synonym,
_extract_synonym(s,
synoynym_typedefs,
prefix='chebi',
identifier='XXX'))
def _get_term_from_tree(tree: ElementTree.ElementTree) -> Term:
name = tree.find('name').text
description = tree.find('description').text
if description:
description = description.strip().replace('\n', ' ')
identifier = tree.find('identifier').text
term = Term(
reference=Reference(PREFIX, identifier, name),
definition=description,
)
for experiment in tree.findall('experiments'):
experiment_name = experiment.find('name').text
experiment_id = experiment.find('identifier').text
term.append_relationship(
has_part,
Reference(
'gwascentral.experiment',
identifier=experiment_id,
name=experiment_name,
))
return term
def _get_term_from_tree(tree: ElementTree.ElementTree) -> Term:
name = tree.find("name").text
description = tree.find("description").text
if description:
description = description.strip().replace("\n", " ")
identifier = tree.find("identifier").text
term = Term(
reference=Reference(PREFIX, identifier, name),
definition=description,
)
for experiment in tree.findall("experiments"):
experiment_name = experiment.find("name").text
experiment_id = experiment.find("identifier").text
term.append_relationship(
has_part,
Reference(
"gwascentral.experiment",
identifier=experiment_id,
name=experiment_name,
),
)
return term
def _parse_members(s) -> List[Tuple[Reference, str]]:
if pd.isna(s):
return []
rv = []
for member in s.split('|'):
entity_id, count = member.split('(')
count = count.rstrip(')')
if ':' in entity_id:
prefix, identifier = entity_id.split(':', 1)
else:
prefix, identifier = 'uniprot', entity_id
rv.append((Reference(prefix=prefix, identifier=identifier), count))
return rv
def _parse_members(s) -> List[Tuple[Reference, str]]:
if pd.isna(s):
return []
rv = []
for member in s.split("|"):
entity_id, count = member.split("(")
count = count.rstrip(")")
if ":" in entity_id:
prefix, identifier = entity_id.split(":", 1)
else:
prefix, identifier = "uniprot", entity_id
rv.append((Reference(prefix=prefix, identifier=identifier), count))
return rv
def get_terms(force: bool = False) -> Iterable[Term]:
"""Get CGNC terms."""
df = ensure_df(PREFIX,
url=URL,
name=f"{PREFIX}.tsv",
force=force,
header=0,
names=HEADER)
for i, (cgnc_id, entrez_id, ensembl_id, name, synonym_1, synoynm_2, _,
_) in enumerate(df.values):
if pd.isna(cgnc_id):
logger.warning(f"row {i} CGNC ID is none")
continue
try:
int(cgnc_id)
except ValueError:
logger.warning(f"row {i} CGNC ID is not int-like: {cgnc_id}")
continue
term = Term.from_triple(
prefix=PREFIX,
identifier=cgnc_id,
name=name,
)
term.set_species(identifier="9031", name="Gallus gallus")
if entrez_id and pd.notna(entrez_id):
term.append_xref(Reference(prefix="ncbigene",
identifier=entrez_id))
if pd.notna(ensembl_id):
term.append_xref(Reference(prefix="ensembl",
identifier=ensembl_id))
if synonym_1 and pd.notna(synonym_1):
term.append_synonym(synonym_1)
if synoynm_2 and pd.notna(synoynm_2):
term.append_synonym(synoynm_2)
yield term
def iter_terms() -> Iterable[Term]:
"""Get ITIS terms."""
zip_path = ensure_path(PREFIX, url=URL)
version = _get_version()
sqlite_dir = prefix_directory_join(PREFIX, version=version)
sqlite_path = prefix_directory_join(PREFIX,
name='ITIS.sqlite',
version=version)
if not os.path.exists(sqlite_path):
with zipfile.ZipFile(zip_path) as zip_file:
for x in zip_file.filelist:
if x.filename.endswith('.sqlite'):
zip_file.extract(x, sqlite_dir)
shutil.move(
os.path.join(sqlite_dir, f'itisSqlite{version}',
'ITIS.sqlite'), sqlite_path)
os.rmdir(os.path.join(sqlite_dir, f'itisSqlite{version}'))
if not os.path.exists(sqlite_path):
raise FileNotFoundError(f'file missing: {sqlite_path}')
conn = sqlite3.connect(sqlite_path)
with closing(conn.cursor()) as cursor:
cursor.execute(LONGNAMES_QUERY)
id_to_reference = {
str(identifier): Reference(prefix=PREFIX,
identifier=str(identifier),
name=name)
for identifier, name in cursor.fetchall()
}
with closing(conn.cursor()) as cursor:
cursor.execute(HIERARCHY_QUERY)
id_to_parents = multidict(
(str(child), str(parent)) for child, parent in cursor.fetchall())
for identifier, reference in id_to_reference.items():
parents = []
for parent_identifier in id_to_parents.get(identifier, []):
if parent_identifier == '0': # this means it's a plant
continue
parents.append(id_to_reference[parent_identifier])
term = Term(
reference=reference,
parents=parents,
)
yield term
def iter_terms(version: str) -> Iterable[Term]:
"""Iterate over ChEMBL compound's names."""
op = get_path(version=version)
logger.info('opening connection to %s', op)
with closing(sqlite3.connect(op)) as conn:
logger.info('using connection %s', conn)
with closing(conn.cursor()) as cursor:
logger.info('using cursor %s', cursor)
cursor.execute(QUERY)
for chembl_id, name in cursor.fetchall():
# TODO add xrefs to smiles, inchi, inchikey here
xrefs = []
yield Term(
reference=Reference(prefix=PREFIX,
identifier=chembl_id,
name=name),
xrefs=xrefs,
)
def iter_terms(version: str) -> Iterable[Term]:
"""Iterate over ChEMBL compounds."""
with chembl_downloader.connect(version=version) as conn:
logger.info("using connection %s", conn)
with closing(conn.cursor()) as cursor:
logger.info("using cursor %s", cursor)
cursor.execute(QUERY)
for chembl_id, name, smiles, inchi, inchi_key in cursor.fetchall():
# TODO add xrefs?
term = Term.from_triple(prefix=PREFIX,
identifier=chembl_id,
name=name)
if smiles:
term.append_property("smiles", smiles)
if inchi:
term.append_property("inchi", inchi)
if inchi_key:
term.append_xref(Reference("inchikey", inchi_key))
yield term
def iter_terms(version: str) -> Iterable[Term]:
"""Iterate over terms from GWAS Central Phenotype."""
for n in trange(1, 11000, desc=f'{PREFIX} download'):
try:
path = ensure_path(
PREFIX,
'phenotype',
version=version,
url=f'https://www.gwascentral.org/phenotype/HGVPM{n}?format=json',
name=f'HGVPM{n}.json',
)
except OSError as e:
tqdm.write(f'{n}: {e}')
continue
with open(path) as file:
j = json.load(file)
term = Term(
reference=Reference(PREFIX, j['identifier'], j['name']),
definition=j['description'].strip().replace('\n', ' '),
)
yield term
def iter_terms(version: str, force: bool = False) -> Iterable[Term]:
"""Get ITIS terms."""
zip_path = ensure_path(PREFIX, url=URL, force=force, version=version)
sqlite_dir = prefix_directory_join(PREFIX, version=version)
sqlite_path = prefix_directory_join(PREFIX, name="itis.sqlite", version=version)
if not os.path.exists(sqlite_path):
with zipfile.ZipFile(zip_path) as zip_file:
for file in zip_file.filelist:
if file.filename.endswith(".sqlite") and not file.is_dir():
zip_file.extract(file, sqlite_dir)
shutil.move(os.path.join(sqlite_dir, file.filename), sqlite_path)
os.rmdir(os.path.join(sqlite_dir, os.path.dirname(file.filename)))
if not os.path.exists(sqlite_path):
raise FileNotFoundError(f"file missing: {sqlite_path}")
conn = sqlite3.connect(sqlite_path.as_posix())
with closing(conn.cursor()) as cursor:
cursor.execute(LONGNAMES_QUERY)
id_to_reference = {
str(identifier): Reference(prefix=PREFIX, identifier=str(identifier), name=name)
for identifier, name in cursor.fetchall()
}
with closing(conn.cursor()) as cursor:
cursor.execute(HIERARCHY_QUERY)
id_to_parents = multidict((str(child), str(parent)) for child, parent in cursor.fetchall())
for identifier, reference in id_to_reference.items():
parents = []
for parent_identifier in id_to_parents.get(identifier, []):
if parent_identifier == "0": # this means it's a plant
continue
parents.append(id_to_reference[parent_identifier])
term = Term(
reference=reference,
parents=parents,
)
yield term
def iter_terms() -> Iterable[Term]:
"""Get ITIS terms."""
zip_path = ensure_path(PREFIX, URL)
sqlite_path = prefix_directory_join(PREFIX, 'itisSqlite043020',
'ITIS.sqlite')
if not os.path.exists(sqlite_path):
with zipfile.ZipFile(zip_path, 'r') as zip_ref:
zip_ref.extractall(get_prefix_directory(PREFIX))
if not os.path.exists(sqlite_path):
raise FileNotFoundError(f'file missing: {sqlite_path}')
conn = sqlite3.connect(sqlite_path)
with closing(conn.cursor()) as cursor:
cursor.execute(LONGNAMES_QUERY)
id_to_reference = {
str(identifier): Reference(prefix=PREFIX,
identifier=str(identifier),
name=name)
for identifier, name in cursor.fetchall()
}
with closing(conn.cursor()) as cursor:
cursor.execute(HIERARCHY_QUERY)
id_to_parents = multidict(
(str(child), str(parent)) for child, parent in cursor.fetchall())
for identifier, reference in id_to_reference.items():
parents = []
for parent_identifier in id_to_parents.get(identifier, []):
if parent_identifier == '0': # this means its a plant
continue
parents.append(id_to_reference[parent_identifier])
term = Term(
reference=reference,
parents=parents,
)
yield term
def iter_terms(version: str) -> Iterable[Term]:
"""Iterate over terms in Rhea."""
terms = {}
directions = ensure_df(
PREFIX,
url='ftp://ftp.expasy.org/databases/rhea/tsv/rhea-directions.tsv',
version=version)
for master, lr, rl, bi in directions.values:
terms[master] = Term(reference=Reference(PREFIX, master))
terms[lr] = Term(reference=Reference(PREFIX, lr))
terms[rl] = Term(reference=Reference(PREFIX, rl))
terms[bi] = Term(reference=Reference(PREFIX, bi))
terms[master].append_relationship(has_lr, terms[lr])
terms[master].append_relationship(has_rl, terms[rl])
terms[master].append_relationship(has_bi, terms[bi])
terms[lr].append_parent(terms[master])
terms[rl].append_parent(terms[master])
terms[bi].append_parent(terms[master])
hierarchy = ensure_df(
PREFIX,
url='ftp://ftp.expasy.org/databases/rhea/tsv/rhea-relationships.tsv',
version=version)
for source, relation, target in hierarchy.values:
if relation != 'is_a':
raise ValueError(f'RHEA unrecognized relation: {relation}')
terms[source].append_parent(terms[target])
for xref_prefix, url in [
('ecocyc', 'rhea2ecocyc'),
('kegg.reaction', 'rhea2kegg_reaction'),
('reactome', 'rhea2reactome'),
('macie', 'rhea2macie'),
('metacyc', 'rhea2metacyc'),
]:
xref_df = ensure_df(
PREFIX,
url=f'ftp://ftp.expasy.org/databases/rhea/tsv/{url}.tsv',
version=version)
for rhea_id, _, _, xref_id in xref_df.values:
if rhea_id not in terms:
logger.warning('[%s] could not find %s:%s for xref %s:%s',
PREFIX, PREFIX, rhea_id, xref_prefix, xref_id)
continue
terms[rhea_id].append_xref(Reference(xref_prefix, xref_id))
# TODO are EC codes equivalent?
# TODO uniprot enabled by (RO:0002333)
# TODO names?
url = 'ftp://ftp.expasy.org/databases/rhea/rdf/rhea.rdf.gz'
graph = pystow.ensure_rdf('pyobo', 'raw', PREFIX, version, url=url)
result = graph.query('''
PREFIX rh:<http://rdf.rhea-db.org/>
SELECT ?reaction ?reactionId ?reactionLabel WHERE {
?reaction rdfs:subClassOf rh:Reaction .
?reaction rh:id ?reactionId .
?reaction rdfs:label ?reactionLabel .
}
''')
for _, identifier, name in result:
identifier = str(identifier)
if identifier not in terms:
logger.warning('isolated element in rdf: rhea:%s ! %s', identifier,
name)
continue
terms[identifier].reference.name = name
# TODO participants?
yield from terms.values()
# -*- coding: utf-8 -*-
"""Converter for Rhea."""
import logging
from typing import Iterable
import bioversions
import pystow
from pyobo.struct import Obo, Reference, Term, TypeDef
from pyobo.utils.path import ensure_df
logger = logging.getLogger(__name__)
PREFIX = 'rhea'
has_lr = TypeDef(Reference(PREFIX, 'has_lr_reaction'))
has_rl = TypeDef(Reference(PREFIX, 'has_rl_reaction'))
has_bi = TypeDef(Reference(PREFIX, 'has_bi_reaction'))
def get_obo() -> Obo:
"""Get Rhea as OBO."""
version = bioversions.get_version(PREFIX)
return Obo(
ontology=PREFIX,
name='Rhea',
iter_terms=iter_terms,
iter_terms_kwargs=dict(version=version),
data_version=version,
auto_generated_by=f'bio2obo:{PREFIX}',
typedefs=[has_lr, has_bi, has_rl],
def get_terms(version: str) -> Iterable[Term]:
"""Get ComplexPortal terms."""
df = get_df(version=version)
df.rename(
inplace=True,
columns={
"Aliases for complex": "aliases",
"Identifiers (and stoichiometry) of molecules in complex": "members",
"Taxonomy identifier": "taxonomy_id",
"Cross references": "xrefs",
"Description": "definition",
"Recommended name": "name",
"#Complex ac": "complexportal_id",
},
)
df["aliases"] = df["aliases"].map(lambda s: s.split("|") if pd.notna(s) else [])
df["members"] = df["members"].map(_parse_members)
df["xrefs"] = df["xrefs"].map(_parse_xrefs)
taxnomy_id_to_name = get_id_name_mapping("ncbitaxon")
df["taxonomy_name"] = df["taxonomy_id"].map(taxnomy_id_to_name.get)
slim_df = df[
[
"complexportal_id",
"name",
"definition",
"aliases",
"xrefs",
"taxonomy_id",
"taxonomy_name",
"members",
]
]
it = tqdm(slim_df.values, total=len(slim_df.index), desc=f"mapping {PREFIX}")
unhandled_xref_type = set()
for (
complexportal_id,
name,
definition,
aliases,
xrefs,
taxonomy_id,
taxonomy_name,
members,
) in it:
synonyms = [Synonym(name=alias) for alias in aliases]
_xrefs = []
provenance = []
for reference, note in xrefs:
if note == "identity":
_xrefs.append(reference)
elif note == "see-also" and reference.prefix == "pubmed":
provenance.append(reference)
elif (note, reference.prefix) not in unhandled_xref_type:
logger.debug(f"unhandled xref type: {note} / {reference.prefix}")
unhandled_xref_type.add((note, reference.prefix))
term = Term(
reference=Reference(prefix=PREFIX, identifier=complexportal_id, name=name),
definition=definition.strip() if pd.notna(definition) else None,
synonyms=synonyms,
xrefs=_xrefs,
provenance=provenance,
)
term.set_species(identifier=taxonomy_id, name=taxonomy_name)
for reference, _count in members:
term.append_relationship(has_part, reference)
yield term