#!/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/
#
import collections
import itertools
import pypath.utils.mapping as mapping
import pypath.resources.urls as urls
import pypath.share.curl as curl
import pypath.inputs.uniprot_db as uniprot_db
import pypath.share.session as session
_logger = session.Logger(name = 'homologene_input')
_log = _logger._log
[docs]
def get_homologene():
"""
Downloads the latest release of the NCBI HomoloGene database.
Returns file pointer.
"""
url = urls.urls['homologene']['url_rescued']
c = curl.Curl(
url = url,
silent = False,
large = True,
timeout = 1800,
ignore_content_length = True,
)
return c.result
[docs]
def homologene_dict(source, target, id_type):
"""
Returns orthology translation table as dict, obtained
from NCBI HomoloGene data.
Args
source (int): NCBI Taxonomy ID of the source species (keys).
target (int): NCBI Taxonomy ID of the target species (values).
id_type (str): ID type to be used in the dict. Possible values:
'RefSeq', 'Entrez', 'GI', 'GeneSymbol'.
Returns
Dict of sets: keys are IDs of the source organism, values are sets
of IDs of the target organism.
"""
ids = {
'refseq': 5,
'refseqp': 5,
'genesymbol': 3,
'gi': 4,
'entrez': 2
}
try:
id_col = ids[id_type.lower()]
except KeyError:
_log(
'Unknown ID type: `%s`. Please use RefSeq, '
'Entrez, GI or GeneSymbol.' % id_type
)
raise
hg = get_homologene()
hgroup = None
result = collections.defaultdict(set)
for l in hg:
l = l.strip().split('\t')
this_hgroup = l[0].strip()
if this_hgroup != hgroup:
this_source = None
this_target = None
hgroup = this_hgroup
this_taxon = int(l[1].strip())
if this_taxon == source:
this_source = l[id_col]
elif this_taxon == target:
this_target = l[id_col]
if (
this_source and
this_target
):
result[this_source].add(this_target)
return dict(result)
[docs]
def homologene_uniprot_dict(source, target, only_swissprot = True):
"""
Returns orthology translation table as dict from UniProt to Uniprot,
obtained from NCBI HomoloGene data. Uses RefSeq and Entrez IDs for
translation.
Args
source (int): NCBI Taxonomy ID of the source species (keys).
target(int): NCBI Taxonomy ID of the target species (values).
only_swissprot (bool): Use only SwissProt IDs.
Returns
Dict of sets: keys are UniProt IDs of the source organism, values
are sets of UniProt IDs of the target organism.
"""
result = {}
hge = homologene_dict(source, target, 'entrez')
hgr = homologene_dict(source, target, 'refseq')
all_source = set(uniprot_db.all_uniprots(
organism = source,
swissprot = 'YES',
))
if not only_swissprot:
all_source_trembl = uniprot_db.all_uniprots(
organism = source,
swissprot = 'NO',
)
all_source.update(set(all_source_trembl))
for u in all_source:
source_e = mapping.map_name(u, 'uniprot', 'entrez', source)
source_r = mapping.map_name(u, 'uniprot', 'refseqp', source)
target_u = set()
target_r = set()
target_e = set()
for e in source_e:
if e in hge:
target_e.update(hge[e])
for r in source_r:
if r in hgr:
target_r.update(hgr[r])
for e in target_e:
target_u.update(
mapping.map_name(e, 'entrez', 'uniprot', target)
)
for r in target_r:
target_u.update(
mapping.map_name(e, 'refseqp', 'uniprot', target)
)
target_u = (
itertools.chain(
*map(
lambda tu:
mapping.map_name(tu, 'uniprot', 'uniprot', target),
target_u
)
)
)
result[u] = sorted(list(target_u))
return result
