#!/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 itertools
import collections
from typing import Dict, List
import xml.etree.cElementTree as ET
import pypath.share.curl as curl
import pypath.resources.urls as urls
import pypath.utils.mapping as mapping
import pypath.internals.intera as intera
[docs]
def spike_interactions(min_confidence: int = 2) -> List[tuple]:
"""
Args
min_confidence:
Confidence (integrity) levels in SPIKE span from 1 to 4, 1
being the highest confidence.
"""
url = urls.urls['spike']['url']
c = curl.Curl(
url,
silent = False,
large = True,
files_needed = ['LatestSpikeDB.xml'],
)
spikexml = c.result
xml = ET.parse(spikexml['LatestSpikeDB.xml'])
xmlroot = xml.getroot()
# iterating genes
bblock = xmlroot.find('BuildingBlock')
rblock = xmlroot.find('RegulationBlock')
iblock = xmlroot.find('InteractionBlock')
genes = {}
result = []
SpikeInteraction = collections.namedtuple(
'SpikeInteraction',
(
'entrez_a',
'genesymbol_a',
'entrez_b',
'genesymbol_b',
'directed',
'pmids',
'integrity',
'effect',
'assay',
'data_source',
'description',
'mechanism',
'regulation',
)
)
Gene = collections.namedtuple('Gene', ('entrez', 'genesymbol', 'type'))
for gene in bblock.findall('Gene'):
sy = '' if 'name' not in gene.attrib else gene.attrib['name']
genes[gene.attrib['id']] = [
Gene(
entrez = gene.find('XRef').attrib['id'],
genesymbol = sy,
type = 'gene',
)
]
for grp in bblock.findall('Group'):
if grp.attrib['type'] == 'Complex':
members = [m.attrib['ref'] for m in grp.findall('Member')]
if all(
m in genes and
genes[m][0].type != 'complex'
for m in members
):
uniprots = [
mapping.map_name(genes[m][0].entrez, 'entrez', 'uniprot')
for m in members
]
genes[grp.attrib['id']] = [
Gene(
entrez = cplex,
genesymbol = cplex,
type = 'complex',
)
for cplex in
(
intera.Complex(
name = grp.attrib['name'],
components = ups,
sources = 'SPIKE',
)
for ups in itertools.product(*uniprots)
)
]
for i in itertools.chain(
rblock.findall('Regulation'),
iblock.findall('Interaction'),
):
regulation = i.tag == 'Regulation'
src_tag = 'Source' if regulation else 'ProteinA'
tgt_tag = 'PhysicalTarget' if regulation else 'ProteinB'
ds = i.attrib['dataSource']
itg = i.attrib['integrity']
eff = i.attrib.get('effect', '')
mec = i.attrib.get('mechanism', '')
src = i.find(src_tag).attrib['ref']
tgt = i.find(tgt_tag).attrib['ref']
dcd = str(int(regulation))
dsc = (
''
if i.find('Description') is None else
i.find('Description').text.replace('\n', ' ')
)
asy = (
''
if 'biologicalAssay' not in i.attrib else
i.attrib['biologicalAssay']
)
refs = i.findall('Reference')
pmids = [r.attrib['pmid'] for r in refs]
if src in genes and tgt in genes:
if int(itg) <= min_confidence:
for _src, _tgt in itertools.product(genes[src], genes[tgt]):
result.append(
SpikeInteraction(
entrez_a = _src.entrez,
genesymbol_a = _src.genesymbol,
entrez_b = _tgt.entrez,
genesymbol_b = _tgt.genesymbol,
directed = dcd,
pmids = ';'.join(pmids),
integrity = itg,
effect = eff,
assay = asy,
data_source = ds,
description = dsc,
mechanism = mec,
regulation = regulation,
)
)
return result
[docs]
def spike_complexes(min_confidence: int = 2) -> Dict[str, intera.Complex]:
interactions = spike_interactions(min_confidence = min_confidence)
complexes = [
getattr(i, attr)
for i in interactions
for attr in ('entrez_a', 'entrez_b')
if isinstance(getattr(i, attr), intera.Complex)
]
return dict((cplx.__str__(), cplx) for cplx in complexes)
