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Download_GenomeJP.py
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Download_GenomeJP.py
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#!/usr/bin/python
# Written in Python 3.8 in 2023 by A.L.O. Gaenssle
# MODULE: DOWNLOAD GENE/PROTEIN DATA from Genome.jp
# -> downloads all IDs associated with the input (domain) id
# -> exports the otained data into a table
# -> download protein data from UniProt (e.g. organims, domains and sequence)
import pandas as pd
import re
import math
import urllib.request
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
##-------------------------------------------------------------------------------------------------
## DOWNLOAD FUNCTIONS -----------------------------------------------------------------------------
##-------------------------------------------------------------------------------------------------
## ================================================================================================
## Download gene list from Genome.jp
def DownloadGeneList(url, getAmount=False):
Amount = 0
with urllib.request.urlopen(url) as File:
Add = False
List = []
for Line in File:
Line = Line.decode("utf-8").strip()
Line = re.sub('<[^>]*>', '', Line)
if Line:
if Add == False:
if Line.startswith("Hits:"):
Amount = Line.split(" ",2)[1]
Amount = math.ceil(int(Amount)/1000)
# Start of list of entries
elif Line.startswith("----------"):
Add = True
else:
# End of list of entries
if Line.startswith("DBGET integrated"):
break
else:
List.append(Line.strip())
print(url, "downloaded")
print(len(List), "gene IDs added")
if getAmount:
return(List, Amount) # Required for the first call to get the number of pages
else:
return(List) # For all subsequent pages
## ================================================================================================
## Download UniProt protein entry
def DownloadEntryUniProt(ID):
url = "https://www.genome.jp/entry/up:" + str(ID)
with urllib.request.urlopen(url) as Input:
Dict = {"ID": ID, "Sequence": ""}
TaxString = ""
InSequence = False
InDomain = False
DomainID = 0
for Line in Input:
Line = Line.decode("utf-8").strip()
Line = re.sub('<[^>]*>', '', Line)
if Line:
try:
if Line.startswith("ID"):
Dict["Length"] = Line.rsplit(";",1)[1].rsplit(" ",1)[0].strip()
elif Line.startswith("OS"):
Dict["Organism"] = Line.split(" ",1)[1].replace(".", "").strip()
elif Line.startswith("OC"):
TaxString += Line.split(" ",1)[1].replace(".", "").strip()
elif Line.startswith("DR"):
if "KEGG" in Line:
Dict["KEGG"] = Line.split(";")[1].strip()
# Each domain is stored in 3 columns: Name-D[n], Start-D[n], End-D[n]
elif Line.startswith("FT"):
if "DOMAIN" in Line:
InDomain = True
DomainID += 1
DID = "-" + "D" + str(DomainID)
String = Line.rsplit(" ",1)[1]
Dict["Start" + DID], Dict["End" + DID] = String.split("..",1)
elif InDomain and "/note=" in Line:
Dict["Name" + DID] = Line.split("\"")[1]
InDomain = False
# The amino acid sequence is the last information on the page
elif InSequence:
# Separator between data sets of different proteins
if Line.startswith("//"):
break
else:
Dict["Sequence"] += Line.replace(" ", "")
elif Line.startswith("SQ"):
InSequence = True
except IndexError:
pass
# Extract and store the taxonomic classification and Kingdom-Phylum
try:
Kingdom, Phylum = TaxString.split("; ",3)[:2]
Dict["Taxonomy"] = Kingdom + "-" + Phylum
except ValueError:
Dict["Taxonomy"] = "-".join(TaxString.split("; ",))
print("ID", ID, "downloaded")
return(Dict)
##-------------------------------------------------------------------------------------------------
## CLEANUP DATA FUNCTIONS -------------------------------------------------------------------------
##-------------------------------------------------------------------------------------------------
## ================================================================================================
## Convert downloaded KEGG gene text to table
def CleanKEGG(Data):
ListOfDicts = []
for Line in Data:
Dict = {}
try:
Dict["ID"], String = Line.strip().split(" ",1)
if "no KO assigned" in String:
Dict["Description"] = String.split("|")[1].split(") ")[1]
else:
if "|" in String:
String = String.split("|")[0]
if "[EC" in String:
String, Dict["#EC"] = String.rsplit(" [EC:")
Dict["#EC"] = Dict["#EC"].replace("]", "").strip()
Dict["KO ID"], Dict["Description"] = String.strip().split(" ", 1)
except ValueError:
pass
ListOfDicts.append(Dict)
DataFrame = pd.DataFrame(ListOfDicts)
return(DataFrame)
## ================================================================================================
## Convert downloaded UniProt/SWISS-Prot gene text to table
def CleanUniProt(Data):
ListOfDicts = []
IDList = []
for Line in Data:
Dict = {}
try:
Dict["ID"], String = Line.split(" ",1)
String = String.strip().split("Full=",1)[1]
try:
Dict["Description"], IDString = String.split(" {",1)
IDList = IDString.split(",",1)[0].split("}",1)[0].split("|")
for ID in IDList:
if len(ID) > 10:
Type, ID = ID.strip().split(":",1)
Dict[Type] = ID
except ValueError:
Dict["Description"] = String.split("; ",1)[0]
ListOfDicts.append(Dict)
except ValueError:
pass
DataFrame = pd.DataFrame(ListOfDicts)
return(DataFrame)
## ================================================================================================
## Convert downloaded linked gene text to table (e.g. PDB)
def CleanPDB(Data):
ListOfDicts = []
for Line in Data:
Dict = {}
try:
Dict["ID"], Dict["Description"] = Line.strip().split(" ",1)
except ValueError:
pass
ListOfDicts.append(Dict)
DataFrame = pd.DataFrame(ListOfDicts)
return(DataFrame)