#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# This file is part of the `pypath` python module
#
# Copyright 2014-2023
# EMBL, EMBL-EBI, Uniklinik RWTH Aachen, Heidelberg University
#
# Authors: see the file `README.rst`
# Contact: Dénes Türei (turei.denes@gmail.com)
#
# Distributed under the GPLv3 License.
# See accompanying file LICENSE.txt or copy at
# https://www.gnu.org/licenses/gpl-3.0.html
#
# Website: https://pypath.omnipathdb.org/
#
from past.builtins import xrange, range
import collections
from lxml import etree
import pypath.share.curl as curl
import pypath.resources.urls as urls
import pypath.inputs.uniprot_db as uniprot_db
import pypath.utils.taxonomy as taxonomy
import pypath.utils.mapping as mapping
[docs]
def locate_localizations(
organism = 9606,
literature = True,
external = True,
predictions = False,
):
record = collections.namedtuple(
'LocateAnnotation',
('source', 'location', 'cls', 'pmid', 'score'),
)
record.__new__.__defaults__ = (None, None, None)
organism_uniprots = set(
uniprot_db.all_uniprots(organism = organism, swissprot = True)
)
organism_str = taxonomy.taxids[organism]
url = urls.urls['locate']['url_rescued'] % organism_str
fname = url.split('/')[-1][:-4]
c = curl.Curl(
url,
large = True,
default_mode = 'rb',
silent = False,
files_needed = [fname],
)
c.result[fname]
parser = etree.iterparse(c.result[fname], events = ('start', 'end'))
result = collections.defaultdict(set)
root = next(parser)
used_elements = []
for ev, elem in parser:
if ev == 'end' and elem.tag == 'LOCATE_protein':
tag_protein = elem.find('protein')
this_uniprot = None
this_uniprots = None
this_entrez = None
this_organism = (
tag_protein.find('organism').text
if tag_protein is not None else
None
)
this_class = (
tag_protein.find('class').text
if tag_protein is not None else
None
)
xrefs = elem.find('xrefs')
if xrefs is None:
continue
for xref in xrefs.findall('xref'):
src = xref.find('source')
src_name = src.find('source_name').text
if src_name == 'UniProtKB-SwissProt':
this_uniprot = src.find('accn').text
if src_name == 'Entrez Gene':
this_entrez = src.find('accn').text
if src_name == 'UniProt/SPTrEMBL' and this_uniprot is None:
this_uniprot = src.find('accn').text
# if we don't know what it is, does not make sense to proceed
if this_uniprot is None and this_entrez is None:
continue
if this_uniprot:
this_uniprots = mapping.map_name(
this_uniprot,
'uniprot',
'uniprot',
ncbi_tax_id = organism,
)
if not this_uniprots and this_entrez:
this_uniprots = mapping.map_name(
this_entrez,
'entrez',
'uniprot',
ncbi_tax_id = organism,
)
this_uniprots = set(this_uniprots) & organism_uniprots
# if we don't know what it is, does not make sense to proceed
if not this_uniprots:
continue
if external:
# External database annotations
extannot = elem.find('externalannot')
if extannot is not None:
for extannotref in extannot.findall('reference'):
sources = []
for src in extannotref.findall('source'):
src_name = src.find('source_name')
if src_name is not None:
sources.append(src_name.text)
sources = ';'.join(sources) if sources else None
locations = extannotref.find('locations')
if locations is not None:
for location in locations.findall('location'):
for loc in location.iterchildren():
if loc.tag[:4] == 'tier':
this_loc = loc.text.lower().split(',')
for uniprot in this_uniprots:
for _loc in this_loc:
result[uniprot].add(record(
source = sources,
location = _loc.strip(),
cls = this_class,
score = None,
))
if predictions:
# Predictions
sclpred = elem.find('scl_prediction')
if sclpred is not None:
for sclpred_src in sclpred.findall('source'):
score = float(sclpred_src.find('evaluation').text)
if score == 0.0:
continue
this_src = sclpred_src.find('method').text
this_loc = sclpred_src.find('location').text.lower()
if this_loc == 'no prediction':
continue
for uniprot in this_uniprots:
result[uniprot].add(record(
source = this_src,
location = this_loc,
cls = this_class,
score = score,
))
if literature:
# Literature curation
lit = elem.find('literature')
if lit is not None:
for litref in lit.findall('reference'):
locs = set()
for lloc in (
litref.find('locations').findall('location')
):
for loc in lloc.iterchildren():
if loc.tag[:4] == 'tier':
locs.add(loc.text.lower())
pmid = litref.find('source')
pmid = (
None if pmid is None else pmid.find('accn').text
)
for loc in locs:
for uniprot in this_uniprots:
result[uniprot].add(
record(
source = 'literature',
location = loc,
pmid = pmid,
cls = this_class,
score = None,
)
)
used_elements.append(elem)
# removing used elements to keep memory low
if len(used_elements) > 1000:
for _ in xrange(500):
e = used_elements.pop(0)
e.clear()
# closing the XML
c.fileobj.close()
del c
return dict(result)
