#!/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 typing import Union
import re
import collections
import pypath.utils.mapping as mapping
import pypath.share.curl as curl
import pypath.resources.urls as urls
import pypath.formats.obo as obo
[docs]
def hpo_annotations() -> dict[str, set[tuple]]:
"""
Human Phenotype Ontology annotations.
Returns
Dict of proteins as keys and sets of HPO annotations as values.
"""
url = urls.urls['hpo']['gene']
c = curl.Curl(url, large = True, silent = False)
_ = next(c.result)
fields = ('entrez_gene_id','entrez_gene_symbol','hpo_id')
HpoAnnotation = collections.namedtuple(
'HpoAnnotation',
fields,defaults = ("",) * len(fields)
)
result = collections.defaultdict(set)
for r in c.result:
r = r.strip().split('\t')
uniprots = mapping.map_name(r[0], 'entrez', 'uniprot')
for uniprot in uniprots:
result[uniprot].add(
HpoAnnotation(
entrez_gene_id = r[0],
entrez_gene_symbol = r[1],
hpo_id = r[2],
)
)
return dict(result)
[docs]
def hpo_terms() -> dict[str, str]:
"""
Human Phenotype Ontology accession to term mapping.
"""
return hpo_ontology()['terms']
[docs]
def hpo_diseases() -> dict[str, set[tuple]]:
"""
HPO term-disease relationships from Human Phenotype Ontology.
Returns
A set of disease records for each HPO term.
"""
url = urls.urls['hpo']['disease']
c = curl.Curl(url, large = True, silent = False)
HpoDisease = collections.namedtuple(
'HpoDisease',
(
'omim',
'name',
'pmid',
'qualifier',
'evidence',
'onset',
'frequency',
'sex',
'modifier',
'aspect',
),
)
result = collections.defaultdict(set)
for r in c.result:
if r[0] == '#': continue
r = r.split('\t')
pmid = re.sub('^PMID:', '', r[4]) if r[4][:4] == 'PMID' else None
result[r[3]].add(
HpoDisease(
omim = r[0],
name = r[1],
pmid = pmid,
qualifier = r[2] or None,
evidence = r[5] or None,
onset = r[6] or None,
frequency = r[7] or None,
sex = r[8] or None,
modifier = r[9] or None,
aspect = r[10],
)
)
return dict(result)
[docs]
def hpo_ontology() -> dict[str, dict[str, Union[str, set[str]]]]:
"""
Ontology data from HPO.
Returns
Five dictionaries with term names, term definitions, parents in the
ontology tree, term synonyms and cross references to other databases.
The dicts "terms" and "defs" are one-to-one, while "parents",
"synonyms" and "xrefs" are one-to-many mappings, the keys are always
HPO terms.
"""
url = urls.urls['hpo']['ontology']
reader = obo.Obo(url)
result = {
'terms': {},
'defs': {},
'parents': collections.defaultdict(set),
'synonyms': collections.defaultdict(set),
'xrefs': collections.defaultdict(set),
}
for r in reader:
if r.stanza != 'Term': continue
if (
r.name is None or
r.name.value == 'obsolete' or
r.attrs.get('is_obsolete')
):
continue
term = r.id.value
name = (
(r.name.value, r.name.modifiers)
if r.name.modifiers else
r.name.value
)
if isinstance(name, tuple): name = ' '.join(n for n in name if n)
result['terms'][term] = name
result['defs'][term] = r.definition.value if r.definition else None
for key, obokey in (
('parents', 'is_a'),
('synonyms', 'synonym'),
('xrefs', 'xref'),
):
proc = (
lambda x: tuple(x.split(':'))
if key == 'xrefs' else
lambda x: x
)
for x in r.attrs.get(obokey, ()):
y = proc(x.value)
result[key][term].update(
{
y(x.value)
if type(y) != tuple else
y
}
)
return {k: dict(v) for k, v in result.items()}
