#!/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.utils import iteritems
import os
import collections
import bs4
import pandas as pd
import pypath.resources.urls as urls
import pypath.share.curl as curl
import pypath.share.progress as progress
import pypath.share.cache as cache_mod
import pypath.share.session as session
_logger = session.Logger(name = 'pepcyber_input')
_log = _logger._log
[docs]
def pepcyber_interactions(dataframe = False, cache = True):
"""
Downloads phosphoprotein binding protein interactions
from the PepCyber database (http://www.pepcyber.org/).
Args
dataframe (bool): Return a pandas data frame instead of list of
tuples.
cache (bool): Read the data from an intermediate cache file,
if available.
"""
PepcyberInteraction = collections.namedtuple(
'PepcyberInteraction',
(
'ppdb_class',
'ppdb_genesymbol',
'substrate_genesymbol',
'binding_seq',
'binding_pos',
'all_evidences',
'n_records',
'category',
'substrate_residue',
'ppdb_uniprot',
'ppdb_refseq',
'substrate_uniprot',
'substrate_refseq',
'evidence',
'pmid',
),
)
def get_cells(row):
cells = row.find_all('td')
if len(cells) == 10:
sp = cells[4].find('span')
if (
sp is not None and
'class' in sp.attrs and
'sequence' in sp.attrs['class']
):
return cells
cachefile = os.path.join(
cache_mod.get_cachedir(),
'pepcyber_details.tsv',
)
if cache and os.path.exists(cachefile):
_log('Reading data from cache file `%s`.' % cachefile)
tbl = pd.read_csv(cachefile, sep = '\t', dtype = {'pmid': 'string'})
result = [
PepcyberInteraction(
*(f if pd.notna(f) else None for f in row)
)
for row in tbl.itertuples(index = False)
]
else:
_log('Downloading PepCyber data.')
url = urls.urls['pepcyber']['rescued']
# this is huge, takes a few minutes!
c = curl.Curl(
url,
silent = False,
timeout = 600,
encoding = 'iso-8859-1',
)
data = c.result
soup = bs4.BeautifulSoup(data, 'html.parser')
rows = soup.find_all('tr')
result = []
prg = progress.Progress(
len(rows),
'Retrieving and processing PepCyber data',
7,
)
for row in rows:
prg.step()
cells = get_cells(row)
if cells is None:
continue
row_txt = [c.text.strip() for c in cells]
if len(row_txt) > 9 and row_txt[5].isdigit():
inum = int(row.find('a')['name'])
row_txt[9] = (
None
if 'p' not in row_txt[4] else
row_txt[4][row_txt[4].index('p') + 1]
)
details = pepcyber_details(inum)
row_txt.extend(
details[row_txt[2]]
if row_txt[2] in details else
[None, None]
)
row_txt.extend(
details[row_txt[3]]
if row_txt[3] in details else
[None, None]
)
refs = details['_refs'] or [(None,) * 3]
for ref in refs:
this_record = row_txt[1:] + list(ref[1:])
this_record[4] = int(this_record[4])
this_record[6] = int(this_record[6])
result.append(
PepcyberInteraction(*this_record)
)
tbl = pd.DataFrame.from_records(
result,
columns = PepcyberInteraction._fields,
)
_log('Saving data to `%s`.' % cachefile)
tbl.to_csv(cachefile, sep = '\t', index = False)
return tbl if dataframe else result
[docs]
def pepcyber_details(num):
"""
Retrieves detailed information about an interaction from the PepCyber
database.
Returns
Dict with gene symbols as keys and lists of length 2 as values,
with UniProt ID and RefSeq protein ID. A special key `_refs`
holds a list of dictionaries, each with category, evidence type and
PubMed reference information.
"""
PepcyberReference = collections.namedtuple(
'PepcyberReference',
('category', 'evidence', 'reference')
)
result = {'_refs': []}
url = urls.urls['pepcyber']['details_rescued'] % num
c = curl.Curl(url, encoding = 'iso-8859-1')
data = c.result
if data:
soup = bs4.BeautifulSoup(data, 'html.parser')
gname = None
prev = ''
for td in soup.find_all('td'):
if prev.startswith('Gene name'):
gname = td.text.strip().split('(')[0]
if prev.startswith('RefSeq'):
refseq = td.text.strip()
if prev.startswith('SwissProt') and gname is not None:
swprot = td.text.strip()
if gname and gname[0] != u'\xce':
result[gname] = [swprot, refseq]
gname = None
prev = td.text.strip()
if soup.find(text = 'Records:'):
refs = (
soup.find(text = 'Records:').
parent.
parent.
parent.
next_sibling.
find('table').
find_all('tr')
)[1:]
result['_refs'] = [
PepcyberReference(
*(
td.a.a.text if td.find('a') else td.text
for td in tr.find_all('td')
)
)
for tr in refs
]
return result
