def build_gene(self):
self.cpdb_gene = set()
for entity in self._entities:
# we add the components of the complexes to the protein data
# frame; I don't know if it's necessary but does not harm I guess
if hasattr(entity, 'components'):
components = entity.components
else:
components = (entity, )
for comp in components:
name = mapping.map_name0(comp, 'uniprot', 'genesymbol')
ensembl_genes = mapping.map_name(comp, 'uniprot', 'ensembl')
for ensembl in ensembl_genes:
self.cpdb_gene.add(
CellPhoneDBGene(
gene_name=name,
uniprot=comp,
hgnc_symbol=name,
ensembl=ensembl,
))
def _icellnet_get_components(line, idx):
return [
uniprot
for uniprot in (mapping.map_name0(line[i], 'genesymbol', 'uniprot')
for i in idx) if uniprot
]
def get_stoichiometry(rec):
if not rec['stoichiometry']:
return get_uniprots(rec)
return tuple(
mapping.map_name0(genesymbol, 'genesymbol', 'uniprot')
for genesymbol in rec['stoichiometry'].split(';'))
def _cellphonedb_get_entity(name, complexes):
if name in complexes:
return (complexes[name], )
if ':' in name:
name = name.split(':')[1]
if '_' in name:
name = mapping.map_name0(name, 'name-entry', 'name')
if not uniprot_input.is_uniprot(name):
uniprot = mapping.map_name0(name, 'genesymbol', 'uniprot')
name = uniprot or name
name = _cellphonedb_hla(name)
return (name, ) if isinstance(name, common.basestring) else name
def genesymbols(self):
return sorted(
(
mapping.map_name0(uniprot, 'uniprot', 'genesymbol') or
uniprot
)
for uniprot in self.components.keys()
)
def get_id_name(entity):
id_ = entity.__str__()
name = (id_ if 'COMPLEX' in id_ else mapping.map_name0(
id_,
'uniprot',
'uniprot-entry',
))
return id_, name
def stoichiometry_str_genesymbols(self):
return ';'.join(
itertools.chain(*(((mapping.map_name0(
uniprot,
'uniprot',
'genesymbol',
) or uniprot), ) * cnt for uniprot, cnt in sorted(
iteritems(self.components),
key=lambda comp_cnt: comp_cnt[0],
))))
def build_protein(self):
integrins = annot.db.annots['Integrins']
self.cpdb_protein = set()
for entity in self._entities:
# we add the components of the complexes to the protein data
# frame; I don't know if it's necessary but does not harm I guess
if hasattr(entity, 'components'):
components = entity.components
else:
components = (entity, )
for comp in components:
classes = self.intercell.classes_by_entity(comp)
self.cpdb_protein.add(
CellPhoneDBProtein(
uniprot=comp.__str__(),
protein_name=mapping.map_name0(
comp,
'uniprot',
'uniprot-entry',
),
transmembrane='transmembrane' in classes,
peripheral='cell_surface' in classes,
secreted='secreted' in classes,
secreted_desc='',
secreted_highlight='',
receptor='receptor' in classes,
receptor_desc='',
integrin=comp in integrins,
other='',
other_desc='',
tags='',
tags_reason='',
tags_description='',
))
def zhong2015_annotations():
"""
From 10.1111/nyas.12776 (PMID 25988664).
"""
types = {
'i': 'iCAM',
'm': 'matrix adhesion',
'ag': 'axonal guidance',
'aj': 'adherens junction',
'c': 'cell-cell adhesion',
'fa': 'focal adhesion',
'tj': 'tight junction',
'my': 'myelin interactions',
}
Zhong2015Annotation = collections.namedtuple(
'Zhong2015Annotation',
['type'],
)
result = collections.defaultdict(set)
url = urls.urls['zhong2015']['url']
c = curl.Curl(url, silent=False, large=True)
_ = next(c.result)
for rec in c.result:
rec = rec.strip().split('\t')
uniprot = mapping.map_name0(rec[0], 'genesymbol', 'uniprot')
if uniprot:
result[uniprot].add(Zhong2015Annotation(type=types[rec[2]]))
return result
def _bootstrap(self, identifier, id_type, entity_type, taxon):
if self._is_complex(identifier):
entity_type = 'complex'
id_type = 'complex'
taxon = (
identifier.ncbi_tax_id
if hasattr(identifier, 'ncbi_tax_id') else
taxon
)
taxon = taxon or settings.get('default_organism')
if not entity_type:
if id_type and id_type in self._id_type_to_entity_type:
entity_type = self._id_type_to_entity_type[id_type]
if not id_type:
id_type, entity_type = mapping.guess_type(
identifier,
entity_type = entity_type,
)
if not id_type and (not entity_type or entity_type == 'protein'):
id_type, entity_type = 'genesymbol', 'protein'
if id_type in self._label_types:
_identifier = mapping.id_from_label0(
label = identifier,
label_id_type = id_type,
ncbi_tax_id = taxon,
)
if _identifier and _identifier != identifier:
id_type = mapping.mapper.label_type_to_id_type[id_type]
identifier = _identifier
if id_type == 'mir-pre':
_identifier = mapping.map_name0(
identifier,
id_type,
'mirbase',
ncbi_tax_id = taxon,
)
if _identifier and _identifier != identifier:
identifier = _identifier
id_type = 'mirbase'
entity_type = entity_type or self._get_entity_type(identifier)
self.identifier = identifier
self.id_type = id_type
self.entity_type = entity_type
self.taxon = taxon
def topdb_annotations(ncbi_tax_id = 9606):
TopdbAnnotation = collections.namedtuple(
'TopdbAnnotation',
['membrane', 'topology', 'score', 'tmregions'],
)
result = collections.defaultdict(set)
url = urls.urls['topdb']['url']
c = curl.Curl(
url,
large = True,
default_mode = 'rb',
silent = False,
)
parser = etree.iterparse(c.fileobj, events = ('start', 'end'))
result = collections.defaultdict(set)
root = next(parser)
used_elements = []
for ev, elem in parser:
if ev == 'end' and elem.tag == 'TOPDB':
used_elements.append(elem)
organism = elem.find('Organism').text
if (
organism not in taxonomy.latin_name_to_ncbi_tax_id or
taxonomy.latin_name_to_ncbi_tax_id[organism] != ncbi_tax_id
):
continue
tag_uniprots = elem.find('./CrossRef/UniProt')
if tag_uniprots is None:
continue
uniprots = [u.text for u in tag_uniprots.findall('AC')]
uniprots = set(
mapping.map_name0(
u,
'uniprot',
'uniprot',
ncbi_tax_id = ncbi_tax_id,
)
for u in uniprots
)
if not uniprots:
continue
membranes = set(
mem
for tag_mem in elem.findall('Membrane')
for mem in tag_mem.text.split(';')
)
ntm = 0
score = 0
topologies = ()
tag_topo = elem.find('Topology')
if tag_topo is not None:
ntm = int(tag_topo.find('Numtm').attrib['Count'])
score = int(tag_topo.find('Reliability').text)
topologies = set(
tag_reg.attrib['Loc']
for tag_reg in tag_topo.findall('./Regions/Region')
)
if not membranes:
membranes = (None,)
if not topologies:
topologies = (None,)
for topology, membrane, uniprot in itertools.product(
topologies,
membranes,
uniprots,
):
result[uniprot].add(
TopdbAnnotation(
membrane = membrane,
topology = topology,
tmregions = ntm,
score = score,
)
)
# removing used elements to keep memory low
if len(used_elements) > 2000:
for _ in xrange(1000):
e = used_elements.pop(0)
e.clear()
# closing the XML
c.fileobj.close()
del c
return result
def opm_annotations(organism = 9606):
reparentheses = re.compile(r'\((.*)\)')
regenesymbol = re.compile(r' ([A-Z0-9]{3,}) ')
def get_dict(name):
result = {}
url = urls.urls['opm'][name]
c = curl.Curl(url, large = True, silent = False)
data = csv.DictReader(c.result, delimiter = ',')
for rec in data:
result[rec['id']] = rec['name']
return result
OpmAnnotation = collections.namedtuple(
'OpmAnnotation',
['membrane', 'family', 'transmembrane'],
)
result = collections.defaultdict(set)
organism_name = (
taxonomy.phosphoelm_taxids[organism]
if organism in taxonomy.phosphoelm_taxids else
None
)
types = get_dict('types')
families = get_dict('families')
url = urls.urls['opm']['proteins']
c = curl.Curl(url, silent = False, large = True)
data = csv.DictReader(c.result, delimiter = ',')
for rec in data:
if organism_name and rec['species_name_cache'] != organism_name:
continue
name = rec['name']
names = [
name,
name.split('(')[0],
name.split(',')[0],
]
m = reparentheses.search(name)
if m:
names.append(m.groups()[0])
genesymbols = regenesymbol.findall(name)
for this_name in names:
uniprot = mapping.map_name0(this_name, 'protein-name', 'uniprot')
if uniprot:
break
if not uniprot:
for gs in genesymbols:
uniprot = (
mapping.map_name0(this_name, 'genesymbol', 'uniprot')
)
if uniprot:
break
if not uniprot:
continue
result[uniprot].add(
OpmAnnotation(
membrane = rec['membrane_name_cache'],
family = rec['family_name_cache'],
transmembrane = types[rec['type_id']] == 'Transmembrane',
)
)
return result
