def get_specific_chebi_id(chebi_ids, name):
# NOTE: this function is mainly factored out to be able to use cacheing, it
# requires a frozenset as input to work.
# First, if we have a manual override, we just do that
manual_id = manual_chebi_map.get(name)
if manual_id:
return manual_id
# The first thing we do is eliminate the secondary IDs by mapping them to
# primaries
primary_ids = {chebi_client.get_primary_id(cid) for cid in chebi_ids}
# Occasinally, invalid ChEBI IDs are given that don't have corresponding
# primary IDs, which we can filter out
primary_ids = {pi for pi in primary_ids if pi is not None}
# We then get rid of generic IDs which are never useful for grounding
non_generic_ids = primary_ids - generic_chebi_ids
# We then try name-based grounding to see if any of the names in the list
# match the name of the entity well enough
grounding_names = [
chebi_client.get_chebi_name_from_id(p) for p in non_generic_ids
]
for grounding_name, grounding_id in zip(grounding_names, non_generic_ids):
if grounding_name and (name.lower() == grounding_name.lower()):
return grounding_id
# If we still have no best grounding, we try to distill the IDs down to
# the most specific one based on the hierarchy
specific_chebi_id = chebi_client.get_specific_id(non_generic_ids)
return specific_chebi_id
def fix_id_standards(db_ns, db_id):
if db_ns == 'CHEBI':
if not db_id.startswith('CHEBI:'):
db_id = f'CHEBI:{db_id}'
db_id = chebi_client.get_primary_id(db_id)
elif db_ns == 'HGNC' and db_id.startswith('HGNC:'):
db_id = db_id[5:]
return db_ns, db_id
def generate_chebi_terms():
fname = os.path.join(indra_resources, 'chebi_entries.tsv')
logger.info('Loading %s' % fname)
terms = []
for row in read_csv(fname, header=True, delimiter='\t'):
db = 'CHEBI'
id = 'CHEBI:' + row['CHEBI_ID']
name = row['NAME']
term = Term(normalize(name), name, db, id, name, 'name', 'chebi')
terms.append(term)
logger.info('Loaded %d terms' % len(terms))
# Now we add synonyms
# NOTE: this file is not in version control. The file is available
# at ftp://ftp.ebi.ac.uk/pub/databases/chebi/Flat_file_
# tab_delimited/names_3star.tsv.gz, it needs to be decompressed
# into the INDRA resources folder.
fname = os.path.join(indra_resources, 'names_3star.tsv')
if not os.path.exists(fname):
import pandas as pd
chebi_url = 'ftp://ftp.ebi.ac.uk/pub/databases/chebi/' \
'Flat_file_tab_delimited/names_3star.tsv.gz'
logger.info('Loading %s into memory. You can download and decompress'
' it in the indra/resources folder for faster access.'
% chebi_url)
df = pd.read_csv(chebi_url, sep='\t')
rows = (row for _, row in df.iterrows())
else:
rows = read_csv(fname, header=True, delimiter='\t')
added = set()
for row in rows:
chebi_id = chebi_client.get_primary_id(str(row['COMPOUND_ID']))
if not chebi_id:
logger.info('Could not get valid CHEBI ID for %s' %
row['COMPOUND_ID'])
continue
db = 'CHEBI'
id = 'CHEBI:%s' % chebi_id
name = str(row['NAME'])
chebi_name = \
chebi_client.get_chebi_name_from_id(chebi_id, offline=True)
if chebi_name is None:
logger.info('Could not get valid name for %s' % chebi_id)
continue
term_args = (normalize(name), name, db, id, chebi_name, 'synonym',
'chebi')
if term_args in added:
continue
else:
term = Term(*term_args)
terms.append(term)
added.add(term_args)
logger.info('Loaded %d terms' % len(terms))
return terms
def generate_chebi_terms():
# We can get standard names directly from the OBO
terms = _generate_obo_terms('chebi', ignore_mappings=True, map_to_ns={})
# Now we add synonyms
# NOTE: this file is not in version control. The file is available
# at ftp://ftp.ebi.ac.uk/pub/databases/chebi/Flat_file_
# tab_delimited/names_3star.tsv.gz, it needs to be decompressed
# into the INDRA resources folder.
fname = os.path.join(indra_resources, 'names_3star.tsv')
if not os.path.exists(fname):
import pandas as pd
chebi_url = 'ftp://ftp.ebi.ac.uk/pub/databases/chebi/' \
'Flat_file_tab_delimited/names_3star.tsv.gz'
logger.info('Loading %s into memory. You can download and decompress'
' it in the indra/resources folder for faster access.' %
chebi_url)
df = pd.read_csv(chebi_url, sep='\t')
rows = (row for _, row in df.iterrows())
else:
rows = read_csv(fname, header=True, delimiter='\t')
added = set()
for row in rows:
chebi_id = chebi_client.get_primary_id(str(row['COMPOUND_ID']))
if not chebi_id:
logger.info('Could not get valid CHEBI ID for %s' %
row['COMPOUND_ID'])
continue
db = 'CHEBI'
name = str(row['NAME'])
chebi_name = \
chebi_client.get_chebi_name_from_id(chebi_id, offline=True)
if chebi_name is None:
logger.info('Could not get valid name for %s' % chebi_id)
continue
# We skip entries of the form Glu-Lys with synonyms like EK since
# there are highly ambiguous with other acronyms, and are unlikely
# to be used in practice.
if is_aa_sequence(chebi_name) and re.match(r'(^[A-Z-]+$)', name):
continue
term_args = (normalize(name), name, db, chebi_id, chebi_name,
'synonym', 'chebi')
if term_args in added:
continue
else:
term = Term(*term_args)
terms.append(term)
added.add(term_args)
logger.info('Loaded %d terms' % len(terms))
return terms
def sanitize_chebi_ids(chebi_ids, name):
chebi_ids = {chebi_id if chebi_id.startswith('CHEBI:')
else 'CHEBI:%s' % chebi_id for chebi_id in chebi_ids}
chebi_ids = {chebi_client.get_primary_id(chebi_id)
for chebi_id in chebi_ids}
# Make sure we eliminate Nones here which can appear as a result
# of failed primary ID lookups
chebi_ids = {ci for ci in chebi_ids if ci is not None}
if not chebi_ids:
return []
elif len(chebi_ids) == 1:
return list(chebi_ids)
specific_chebi_id = get_specific_chebi_id(frozenset(chebi_ids), name)
return specific_chebi_id
def _get_primary_id_wrapper(chebi_id):
return get_primary_id(chebi_id)[6:]
def test_chebi_to_primary():
assert chebi_client.get_primary_id('CHEBI:6281') == 'CHEBI:17490'
assert chebi_client.get_primary_id('CHEBI:161680') == 'CHEBI:161680'