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ENSP_to_uniprotID.py
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ENSP_to_uniprotID.py
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import glob
import math
import os
import re
import time
import json
import zlib
from xml.etree import ElementTree
from urllib.parse import urlparse, parse_qs, urlencode
import requests
from requests.adapters import HTTPAdapter, Retry
import pandas as pd
import xlrd
# following two lines needed to prevent error when importing xlsx using pd (at home)
# xlrd.xlsx.ensure_elementtree_imported(False, None)
# xlrd.xlsx.Element_has_iter = True
# this is adapted from https://www.uniprot.org/help/id_mapping example
POLLING_INTERVAL = 3
API_URL = "https://rest.uniprot.org"
retries = Retry(total=5, backoff_factor=0.25, status_forcelist=[500, 502, 503, 504])
session = requests.Session()
session.mount("https://", HTTPAdapter(max_retries=retries))
def check_response(response):
try:
response.raise_for_status()
except requests.HTTPError:
print(response.json())
raise
def submit_id_mapping(from_db, to_db, ids):
request = requests.post(
f"{API_URL}/idmapping/run",
data={"from": from_db, "to": to_db, "ids": ",".join(ids)},
)
check_response(request)
return request.json()["jobId"]
def get_next_link(headers):
re_next_link = re.compile(r'<(.+)>; rel="next"')
if "Link" in headers:
match = re_next_link.match(headers["Link"])
if match:
return match.group(1)
def check_id_mapping_results_ready(job_id):
while True:
request = session.get(f"{API_URL}/idmapping/status/{job_id}")
check_response(request)
j = request.json()
if "jobStatus" in j:
if j["jobStatus"] == "RUNNING":
print(f"Retrying in {POLLING_INTERVAL}s")
time.sleep(POLLING_INTERVAL)
else:
raise Exception(j["jobStatus"])
else:
return bool(j["results"] or j["failedIds"])
def get_batch(batch_response, file_format, compressed):
batch_url = get_next_link(batch_response.headers)
while batch_url:
batch_response = session.get(batch_url)
batch_response.raise_for_status()
yield decode_results(batch_response, file_format, compressed)
batch_url = get_next_link(batch_response.headers)
def combine_batches(all_results, batch_results, file_format):
if file_format == "json":
for key in ("results", "failedIds"):
if key in batch_results and batch_results[key]:
all_results[key] += batch_results[key]
elif file_format == "tsv":
return all_results + batch_results[1:]
else:
return all_results + batch_results
return all_results
def get_id_mapping_results_link(job_id):
url = f"{API_URL}/idmapping/details/{job_id}"
request = session.get(url)
check_response(request)
return request.json()["redirectURL"]
def decode_results(response, file_format, compressed):
if compressed:
decompressed = zlib.decompress(response.content, 16 + zlib.MAX_WBITS)
if file_format == "json":
j = json.loads(decompressed.decode("utf-8"))
return j
elif file_format == "tsv":
return [line for line in decompressed.decode("utf-8").split("\n") if line]
elif file_format == "xlsx":
return [decompressed]
elif file_format == "xml":
return [decompressed.decode("utf-8")]
else:
return decompressed.decode("utf-8")
elif file_format == "json":
return response.json()
elif file_format == "tsv":
return [line for line in response.text.split("\n") if line]
elif file_format == "xlsx":
return [response.content]
elif file_format == "xml":
return [response.text]
return response.text
def get_xml_namespace(element):
m = re.match(r"\{(.*)\}", element.tag)
return m.groups()[0] if m else ""
def merge_xml_results(xml_results):
merged_root = ElementTree.fromstring(xml_results[0])
for result in xml_results[1:]:
root = ElementTree.fromstring(result)
for child in root.findall("{http://uniprot.org/uniprot}entry"):
merged_root.insert(-1, child)
ElementTree.register_namespace("", get_xml_namespace(merged_root[0]))
return ElementTree.tostring(merged_root, encoding="utf-8", xml_declaration=True)
def print_progress_batches(batch_index, size, total):
n_fetched = min((batch_index + 1) * size, total)
print(f"Fetched: {n_fetched} / {total}")
def get_id_mapping_results_search(url):
parsed = urlparse(url)
query = parse_qs(parsed.query)
file_format = query["format"][0] if "format" in query else "json"
if "size" in query:
size = int(query["size"][0])
else:
size = 500
query["size"] = size
compressed = (
query["compressed"][0].lower() == "true" if "compressed" in query else False
)
parsed = parsed._replace(query=urlencode(query, doseq=True))
url = parsed.geturl()
request = session.get(url)
check_response(request)
results = decode_results(request, file_format, compressed)
total = int(request.headers["x-total-results"])
print_progress_batches(0, size, total)
# for i in results['results']:
# print(i['from'])
# print(i['to']['primaryAccession'])
for i, batch in enumerate(get_batch(request, file_format, compressed), 1):
results = combine_batches(results, batch, file_format)
print_progress_batches(i, size, total)
if file_format == "xml":
return merge_xml_results(results)
return results
def get_id_mapping_results_stream(url):
if "/stream/" not in url:
url = url.replace("/results/", "/results/stream/")
request = session.get(url)
check_response(request)
parsed = urlparse(url)
query = parse_qs(parsed.query)
file_format = query["format"][0] if "format" in query else "json"
compressed = (
query["compressed"][0].lower() == "true" if "compressed" in query else False
)
return decode_results(request, file_format, compressed)
def get_ENSP_IDs(path_to_xlsx, xlsx):
df = pd.read_excel(path_to_xlsx + xlsx)
ENSP_IDs = df['stringId_B']
return (ENSP_IDs.tolist())
def get_ID_from_mapping_API(id_list):
ids_left = id_list
n_ids_left = len(id_list)
input_ENSP = []
retrieved_IDs = []
print("total ids to aquire " + str(n_ids_left))
run = 0
while n_ids_left > 0:
if run == 0:
print("starting first query ...")
job_id = submit_id_mapping(
from_db="STRING", to_db="UniProtKB", ids=ids_left
)
if check_id_mapping_results_ready(job_id):
link = get_id_mapping_results_link(job_id)
uni_entries = get_id_mapping_results_search(link)
# save results
ids_fetched = []
for i in range(len(uni_entries['results'])):
input_ENSP += [uni_entries['results'][i]['from']]
retrieved_IDs += [uni_entries['results'][i]['to']['primaryAccession']]
ids_fetched += [uni_entries['results'][i]['from']]
n_fetched = len(uni_entries['results'])
print("total ids fetched " + str(n_fetched))
ids_left = list(set(ids_left) - set(ids_fetched))
n_ids_left = n_ids_left - n_fetched
print("ids left after this run " + str(n_ids_left))
run += 1
else:
print("-----------------")
print("starting next query ...")
job_id = submit_id_mapping(
from_db="STRING", to_db="UniProtKB", ids=ids_left
)
if check_id_mapping_results_ready(job_id):
link = get_id_mapping_results_link(job_id)
uni_entries = get_id_mapping_results_search(link)
if len(uni_entries['results']) == 0:
print("Fetching was stopped - could not retrieve IDs for " + str(n_ids_left) + "x ENSPs:")
print(ids_left)
break
# save results
ids_fetched = []
for i in range(len(uni_entries['results'])):
input_ENSP += [uni_entries['results'][i]['from']]
retrieved_IDs += [uni_entries['results'][i]['to']['primaryAccession']]
n_fetched = len(uni_entries['results'])
print("total ids fetched " + str(n_fetched))
ids_left = list(set(ids_left) - set(ids_fetched))
n_ids_left = n_ids_left - n_fetched
print("ids left after this run " + str(n_ids_left))
print("control" + str(len(ids_left)))
print("-----------------")
run += 1
translation_df = pd.concat([pd.Series(input_ENSP), pd.Series(retrieved_IDs)], axis=1)
translation_df.rename(columns={0: "ENSP", 1: "ID"}, inplace=True)
return (translation_df)
### import interactors retrieved from stringDB via get_stringDB.py
path = 'path/to/folder'
file = "interactors_stringDB.xlsx"
# import stringDB df
df = pd.read_excel(path + file)
ENSP_IDs = df['stringId_B']
# somehow~ IDs are fetched in a way that their order does no match entirely the order of submitted ENSP IDs
# thus, translation df is created and used as dictionary
ENSP_input = ENSP_IDs.drop_duplicates()
# create translation df with ENSPs and fetched IDs
translation_df = get_ID_from_mapping_API(ENSP_input)
# use translation_df as dictionary to create new series to append to results xlsx
all_uni_ID = pd.Series(dtype='float64')
for i, ENSP in enumerate(ENSP_IDs):
temp_id = translation_df[translation_df["ENSP"] == ENSP]["ID"]
if len(temp_id) == 0:
temp_id = pd.Series(float("NaN"))
all_uni_ID = pd.concat([all_uni_ID, temp_id], ignore_index=True)
# save results in a new column of df
df['uniprot_ID_proteinB'] = all_uni_ID
df.rename(columns={'Unnamed: 0': 'string_index'}, inplace=True)
df.drop('index', axis = 'columns', inplace=True)
# export df
export_path = path.replace("/OG_stringDB_data/", "/uniprot_ID/")
try:
os.mkdir(export_path)
except FileExistsError:
pass
translation_df.to_excel(export_path + file[0:len(file) - 5] + "_translation.xlsx")
df.to_excel(export_path + file[0:len(file) - 5] + "_ID.xlsx")