#!/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 __future__ import annotations
from typing import Iterable, Literal
import collections
import pypath.resources.urls as urls
import pypath.share.curl as curl
import pypath.share.common as common
CONFIDENCE_THRESHOLDS = {
'highest_confidence': 900,
'high_confidence': 700,
'medium_confidence': 400,
'low_confidence': .150,
}
[docs]
def string_effects(
ncbi_tax_id: int = 9606,
stimulation: str | Iterable[str] = 'activation',
inhibition: str | Iterable[str] = 'inhibition',
exclude: str | Iterable[str] = 'expression',
score_threshold: int = 0,
) -> list[tuple]:
StringEffectsInteraction = collections.namedtuple(
'StringEffectsInteraction',
(
'source',
'target',
'effect',
),
)
effects = []
stimulation = common.to_set(stimulation)
inhibition = common.to_set(inhibition)
exclude = common.to_set(exclude)
url = urls.urls['string']['actions'] % ncbi_tax_id
c = curl.Curl(url, silent = False, large = True)
_ = next(c.result)
for l in c.result:
if hasattr(l, 'decode'):
l = l.decode('ascii')
l = l.strip().split('\t')
if l and l[4] == 't' and int(l[6]) >= score_threshold:
effect = (
'+'
if l[2] in stimulation else
'-'
if l[2] in inhibition else
'*'
if l[2] not in exclude else
None
)
source = l[0].split('.')[1] if l[5] == 't' else l[1].split('.')[1]
target = l[1].split('.')[1] if l[5] == 't' else l[0].split('.')[1]
if effect is not None:
effects.append(
StringEffectsInteraction(
source = source,
target = target,
effect = effect,
)
)
return effects
[docs]
def string_links_interactions(
ncbi_tax_id: int = 9606,
score_threshold: int | Literal[
'highest_confidence',
'high_confidence',
'medium_confidence',
'low_confidence',
] = 'highest_confidence',
physical_interaction_score: bool = True,
) -> list[tuple]:
"""
Downloads protein network data, including subscores per channel.
The output contains both functional and physical protein associations.
The combined physical interaction score is defined between the proteins
for which we have evidence of their binding or forming a physical complex.
Args
score_threshold: Minimum required interaction score. user can use
pre-defined confidence limits or can define a custom value.
"""
StringLinksInteraction = collections.namedtuple(
'StringLinksInteraction',
(
'protein_a',
'protein_b',
'neighborhood_score',
'fusion',
'cooccurence',
'coexpression',
'experimental',
'database',
'textmining',
'combined_score',
'physical_combined_score',
),
)
if physical_interaction_score:
phy_links = dict(
(
(i.protein_a,i.protein_b),
i.combined_score
)
for i in
string_physical_interactions(
ncbi_tax_id = ncbi_tax_id,
score_threshold = 0,
)
)
url = urls.urls['string']['links'] % ncbi_tax_id
c = curl.Curl(url, silent = False, large = True)
_ = next(c.result)
min_score = CONFIDENCE_THRESHOLDS.get(score_threshold, score_threshold)
for l in c.result:
l = l.strip().split(' ')
prot_a_id = l[0].split('.')[1]
prot_b_id = l[1].split('.')[1]
if int(l[9]) < min_score:
continue
phy_score = (
phy_links.get((prot_a_id, prot_b_id), None)
if physical_interaction_score else
None
)
yield StringLinksInteraction(
protein_a = prot_a_id,
protein_b = prot_b_id,
neighborhood_score = int(l[2]),
fusion = int(l[3]),
cooccurence = int(l[4]),
coexpression = int(l[5]),
experimental = int(l[6]),
database = int(l[7]),
textmining = int(l[8]),
combined_score = int(l[9]),
physical_combined_score = phy_score,
)
[docs]
def string_physical_interactions(
ncbi_tax_id: int = 9606,
score_threshold: int | Literal[
'highest_confidence',
'high_confidence',
'medium_confidence',
'low_confidence',
] = 'highest_confidence',
) -> list[tuple]:
"""
Downloads protein physical subnetwork data, including subscores per
channel. The interactions indicate that the proteins are part of a
physical complex.
Args
score_threshold: Minimum required interaction score. user can use
pre-defined confidence limits or can define a custom value.
"""
StringPhysicalInteraction = collections.namedtuple(
'StringPhysicalInteraction',
(
'protein_a',
'protein_b',
'experimental',
'database',
'textmining',
'combined_score',
),
)
links = []
url = urls.urls['string']['physical_links'] % ncbi_tax_id
c = curl.Curl(url, silent = False, large = True)
_ = next(c.result)
min_score = CONFIDENCE_THRESHOLDS.get(score_threshold, score_threshold)
for l in c.result:
l = l.strip().split(' ')
if int(l[5]) >= min_score:
links.append(
StringPhysicalInteraction(
protein_a= l[0].split('.')[1],
protein_b= l[1].split('.')[1],
experimental= int(l[2]),
database= int(l[3]),
textmining= int(l[4]),
combined_score= int(l[5]),
)
)
return links
[docs]
def string_species() -> dict[int, str]:
"""
Downloads list of organisms in STRING.
Returns
Dict of tax ids as keys and scientific names of organisms as values.
"""
species = {}
url = urls.urls['string']['species']
c = curl.Curl(url, silent = False, large = True)
_ = next(c.result)
for l in c.result:
l = l.strip().split('\t')
tax_id = l[0]
official_name = l[3]
species[tax_id] = official_name
return species
