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main.py
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main.py
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from posixpath import basename
from Bio import Entrez
from xml.dom.minidom import parse
from Bio.Align import MultipleSeqAlignment
from Bio.Entrez.Parser import ValidationError
from Bio.Entrez.Parser import CorruptedXMLError
import requests
import io
import pandas as pd
from pandas.errors import EmptyDataError
import re
import os
from copy import deepcopy
import subprocess
import sys
from urllib.error import HTTPError
from urllib.error import URLError
import time
import ftplib
from io import BytesIO
import ftplib
import gzip
import zlib
import json
import argparse
from numpy import nan
from Bio import SeqIO
import glob
import django
import tastypie
from tastypie import http
from urllib.request import urlopen
from socket import timeout
import http
from http import client
from http.client import IncompleteRead
import Bio
import matplotlib.pyplot as plt
import datetime
from subprocess import Popen
from subprocess import TimeoutExpired
import concurrent.futures
import requests
from requests.adapters import HTTPAdapter, Retry
import shutil
########### arguments ###########
parser = argparse.ArgumentParser(description="BGCSniffer")
parser.add_argument("-json", "--jsonfile", dest="jsonfile", help= "Please input the complete path of parameter file (JSON format)")
parser.add_argument("-workdir", "--workdir", dest="workdir", help= "Please input your working directory")
parser.add_argument("-database", "--database",dest="database",help="The directory of the genome database and GBF database")
args = parser.parse_args()
########### arguments ###########
########### functions ###########
def readPara(Parafile):
with open (Parafile) as Para:
parameters = json.load(Para)
return(parameters)
def extract_query_info(query_string):
id_list = []
if ('and' in query_string):
query_list = re.split(' and ', query_string)
for parenthesis_ele in query_list:
parenthesis_ele = parenthesis_ele.replace('(', '')
parenthesis_ele = parenthesis_ele.replace(')', '')
if 'AND' in parenthesis_ele:
for id in re.split(' AND ', parenthesis_ele):
id_list.append(id)
else:
for id in re.split(' OR ', parenthesis_ele):
id_list.append(id)
elif ('or' in query_string):
query_list = re.split(' or ', query_string)
for parenthesis_ele in query_list:
parenthesis_ele = parenthesis_ele.replace('(', '')
parenthesis_ele = parenthesis_ele.replace(')', '')
if 'AND' in parenthesis_ele:
for id in re.split(' AND ', parenthesis_ele):
id_list.append(id)
else:
for id in re.split(' OR ', parenthesis_ele):
id_list.append(id)
else:
query_list = re.split(' or ', query_string)
for parenthesis_ele in query_list:
parenthesis_ele = parenthesis_ele.replace('(', '')
parenthesis_ele = parenthesis_ele.replace(')', '')
if 'AND' in parenthesis_ele:
for id in re.split(' AND ', parenthesis_ele):
id_list.append(id)
else:
for id in re.split(' OR ', parenthesis_ele):
id_list.append(id)
return(id_list)
def get_next_link(headers, re_next_link):
if "Link" in headers:
match = re_next_link.match(headers["Link"])
if match:
return match.group(1)
def get_batch(batch_url, s, re_next_link):
while batch_url:
response = s.get(batch_url)
response.raise_for_status()
total = response.headers["x-total-results"]
yield response, total
batch_url = get_next_link(response.headers, re_next_link)
def searchUniprot(pf_id):
re_next_link = re.compile(r'<(.+)>; rel="next"')
retries = Retry(total=5, backoff_factor=0.25, status_forcelist=[500, 502, 503, 504])
session = requests.Session()
session.mount("https://", HTTPAdapter(max_retries=retries))
BASE = 'http://rest.uniprot.org'
KB_ENDPOINT = '/uniprotkb/'
QUERY_ID = pf_id
UNIPROT_URL = BASE + KB_ENDPOINT + 'search?query=%28' + QUERY_ID + '%20AND%20%28taxonomy_id%3A2%29%29&fields=accession,id,reviewed,protein_name,gene_names,organism_name,organism_id,length,sequence&format=tsv&size=500'
progress = 0
with open('query_res_tmp.tsv', 'w') as f:
for batch, total in get_batch(UNIPROT_URL, session, re_next_link):
lines = batch.text.splitlines()
if not progress:
print(lines[0], file=f)
for line in lines[1:]:
print(line, file=f)
progress += len(lines[1:])
print(f'{progress} / {total}')
uniprot_df = pd.read_csv('query_res_tmp.tsv', sep="\t")
uniprot_df['ProteinFamily'] = pf_id
return(uniprot_df)
# uniprot_result = requests.get(UNIPROT_URL)
# if uniprot_result.ok:
# content = uniprot_result.text
# content = io.StringIO(content)
# uniprot_df = pd.read_csv(content, sep="\t", index_col=0)
# uniprot_df['ProteinFamily'] = pf_id
# return(uniprot_df)
# else:
# return('Something went wrong in Interpro searching ' + uniprot_result.status_code)
def findMutualProtein(queryString, combined_df):
if re.search('and', queryString):
queryString_list = re.split(' and ', queryString)
mutual_species_id = set(combined_df['Organism (ID)'].to_list())
for block in queryString_list:
block = block.replace('(', '')
block = block.replace(')', '')
tmp_block_list = []
for element in re.split(' OR ', block):
tmp_speciesID = combined_df[combined_df['ProteinFamily'] == element]['Organism (ID)'].to_list()
tmp_block_list.extend(tmp_speciesID)
tmp_block_list = list(set(tmp_block_list))
mutual_species_id = mutual_species_id.intersection(tmp_block_list)
filtered_df = combined_df[combined_df['Organism (ID)'].isin(list(mutual_species_id)) & ~combined_df['Organism'].isin(['uncultured bacterium','bioreactor metagenome'])]
else:
filtered_df = combined_df
return(filtered_df)
def downloadGenome(dataframe, work_dir,refdatabase):
species_id_list = []
organism_list = []
protein_family_info = {}
for row in range(0, dataframe.shape[0]):
species_name = dataframe['Organism'].iloc[row,]
try:
genus = re.split(' ', species_name)[0]
species = re.split(' ', species_name)[1]
#query_name = ' '.join([genus,species])
protein_ID = dataframe.iloc[row,].name
#protein_sequence = merged_df['Sequence'].iloc[row,]
organism = dataframe['Organism'].iloc[row,]
organism_id = dataframe['Organism (ID)'].iloc[row,]
## Get protein family information for each protein
proteinfamily_type = dataframe['ProteinFamily'].iloc[row,]
protein_family_info[protein_ID] = proteinfamily_type
species_id_list.append(str(organism_id))
organism_list.append(organism)
except IndexError:
next
# Retrieving genomes from the Entrez databases
if os.path.exists(work_dir +'/Candidate_genomes/'):
os.system("rm -rf " + work_dir + "/Candidate_genomes/*")
os.makedirs(work_dir + "/Candidate_genomes/", exist_ok = True)
else:
os.makedirs(work_dir + "/Candidate_genomes/", exist_ok = True)
content = '\n'.join(list(set(species_id_list)))
with open(os.path.join(work_dir ,'strains_ID.txt'), 'w+') as out:
out.write(content)
## 根据输入文件下载Refseq和genebank
# subprocess.run(['ncbi-genome-download', '--taxids' , os.path.join(work_dir,'strains_ID.txt'),
# 'bacteria' ,'--assembly-levels', 'complete,chromosome',
# '--flat-output','--parallel', '10','-r' , '10', '-o' ,
# work_dir + '/Candidate_genomes/', '-s', 'genbank', '-v',
# '-d'], stdout=subprocess.PIPE)
# subprocess.run(['ncbi-genome-download', '--taxids' , os.path.join(work_dir,'strains_ID.txt'),
# 'bacteria' ,'--assembly-levels', 'complete,chromosome',
# '-F' ,'fasta,genbank' ,'--flat-output','--parallel', '30', '-r' ,
# '50','-o' , work_dir + '/Candidate_genomes/', '-v', '-d'], stdout=subprocess.PIPE)
DatabaseFile = {}
with open(refdatabase,'r') as file:
for line in file:
(taxID,Genome,Gbff) = line.strip().split('\t')
if taxID in DatabaseFile.keys():
DatabaseFile[taxID].append(Genome)
DatabaseFile[taxID].append(Gbff)
else:
DatabaseFile[taxID] = [Genome,Gbff]
CandidateDir = os.path.join(work_dir,'Candidate_genomes/')
for target in list(set(species_id_list)):
if target in DatabaseFile.keys():
print('--' + str(target))
for gzfile in DatabaseFile[target]:
#shutil.copy(gzfile, CandidateDir)
if 'fna.gz' in gzfile:
ExtractPath = os.path.join(CandidateDir,os.path.basename(gzfile)[:-3])
with gzip.open(gzfile, 'rb') as gz_file, open(ExtractPath, 'wb') as output_file:
time.sleep(2)
shutil.copyfileobj(gz_file, output_file)
else:
time.sleep(2)
shutil.copy(gzfile, CandidateDir)
## 解压基因组fasta文件
# for gz_file in glob.glob(work_dir + '/Candidate_genomes/*genomic.fna.gz'):
# unzip_status = subprocess.run(['gunzip','-f', gz_file], stdout=subprocess.PIPE)
# if unzip_status.returncode:
# print(gz_file + ' unzip failed!!')
# subprocess.run(["rm", work_dir + "/Candidate_genomes/" + gz_file], shell=True)
# next
# else:
# pass
## 检查是否每一个解压后的基因组都有Refseq和对应genebank文件
# for refseq in glob.glob(work_dir + '/Candidate_genomes/*genomic.fna'):
# file_name = os.path.basename(refseq)
# Refseq_ID = re.split('_',file_name)[0:2]
# Refseq_ID = '_'.join(Refseq_ID)
# pattern = work_dir + '/Candidate_genomes/' + Refseq_ID + '*gz'
# file_list = glob.glob(pattern)
# if len(file_list) == 1 :
# pass
# else:
# subprocess.run(['rm', work_dir + '/Candidate_genomes/' + Refseq_ID + '*'])
return [species_id_list, organism_list, protein_family_info]
def downloadProtein(dataframe, work_dir):
dataframe = dataframe.drop_duplicates()
if os.path.exists(work_dir + '/All_proteins.fasta'):
os.system('rm '+ work_dir + '/All_proteins.fasta')
else:
pass
id_list = []
seq_list = []
content_list = []
for tmp_id in dataframe.index.values:
if tmp_id not in id_list:
id_list.append(tmp_id)
try:
tmp_seq = dataframe.loc[tmp_id,'Sequence'].to_list()[0]
seq_list.append(tmp_seq)
content ='>' + tmp_id + '\n' + tmp_seq
content_list.append(content)
except KeyError:
tmp_seq = dataframe.loc[tmp_id,'Sequence'].to_list()
seq_list.append(tmp_seq)
content ='>' + tmp_id + '\n' + tmp_seq
content_list.append(content)
except AttributeError:
tmp_seq = dataframe.loc[tmp_id,'Sequence']
seq_list.append(tmp_seq)
content ='>' + tmp_id + '\n' + tmp_seq
content_list.append(content)
else:
next
proteins = '\n'.join(content_list)
with open (work_dir + '/All_proteins.fasta', 'w+') as p_seq:
p_seq.write(proteins)
def sortDownloadedData(work_dir):
ID_dict = {}
datafolder = os.path.join(work_dir,'Candidate_genomes')
all_fna_files = glob.glob(os.path.join(datafolder, '*fna'))
ID_list = []
for fna_file in all_fna_files:
refseq_file_name = os.path.basename(fna_file)
tmp_folder = os.path.dirname(fna_file)
ID = re.split('_', refseq_file_name)[1]
ID = re.split('.fna', ID)[0]
gbff_file_pattern = os.path.join(tmp_folder, '*'+ ID + '*.gbff.gz')
## Check if relevant gbff file exists, jump to next id if not.
for gbff_file in glob.glob(gbff_file_pattern):
if os.path.isfile(gbff_file):
flag = 1
else:
flag = 0
if flag :
ID_list.append(ID)
else:
next
print(fna_file + ' 无对应gbff文件')
ID_dict['accessionIDnum'] = ID_list
all_strains_id = pd.DataFrame.from_dict(ID_dict)
all_strains_id.to_csv(work_dir + '/All_Strains_for_Antismash.xls', sep = '\t')
return(all_strains_id)
def buildHMMprofile(filtered_query, work_dir, query_string):
if 'and' in query_string or 'or' in query_string:
blocks = re.split(r"\s+(and|or)\s+", query_string)
blocks = [item for item in blocks if item not in ['and', 'or']]
else:
blocks = [query_string]
n=1
for block in blocks:
block = block.replace("(", "").replace(")", "")
pf_list = re.split(' OR ', block)
tmp_filtered_result = filtered_query[filtered_query['ProteinFamily'].isin(pf_list)]
tmp_block_fasta_file_name = work_dir + 'ProteinFamily' + str(n) + '.fasta'
with open(tmp_block_fasta_file_name, 'w') as fasta_file:
for index, row in tmp_filtered_result.iterrows():
entry = row['Entry']
sequence = row['Sequence']
fasta_file.write(f'>{entry}\n')
fasta_file.write(f'{sequence}\n')
n+=1
## 将所有蛋白整合到一起
# complete_fasta = work_dir + '/All_proteins.fasta'
#
# if ' and ' in query_string:
# blocks = re.split(' and ', query_string)
# n = 1
# for block in blocks:
# block = block.replace('(', '')
# block = block.replace(')', '')
# pf_list = re.split(' OR ', block)
# tmp_filtered_result = filtered_query[filtered_query['ProteinFamily'].isin(pf_list)]
# block_protein_list = tmp_filtered_result['Entry'].to_list()
#
# ## 从下载的蛋白序列中抓取block中包含的蛋白序列
#
# tmp_block_fasta_file_name = work_dir + 'ProteinFamily' + str(n) + '.fasta'
# n = n +1
#
# content_list = []
# for record in SeqIO.parse(complete_fasta, "fasta"):
# id = str(record.id)
# if id in block_protein_list:
# content = '>' + id + '\n' + str(record.seq)
# content_list.append(content)
# with open(tmp_block_fasta_file_name, 'w+') as out:
# out.write('\n'.join(content_list))
# out.close()
# else:
# next
#
#
# elif ' or ' in query_string:
# blocks = re.split(' or ', query_string)
# n = 1
# for block in blocks:
# block = block.replace('(', '')
# block = block.replace(')', '')
# pf_list = re.split(' OR ', block)
# tmp_filtered_result = filtered_query[filtered_query['ProteinFamily'].isin(pf_list)]
# block_protein_list = tmp_filtered_result['Entry'].to_list()
# print(pf_list)
# print(tmp_filtered_result)
# print(block_protein_list)
# ## 从下载的蛋白序列中抓取block中包含的蛋白序列
#
# tmp_block_fasta_file_name = work_dir + 'ProteinFamily' + str(n) + '.fasta'
# n = n +1
# content_list = []
# for record in SeqIO.parse(complete_fasta, "fasta"):
# id = str(record.id)
# if id in block_protein_list:
# content = '>' + id + '\n' + str(record.seq)
# content_list.append(content)
# with open(tmp_block_fasta_file_name, 'w+') as out:
# out.write('\n'.join(content_list))
# out.close()
# else:
# next
#
# else:
# n = 1
# query_string = query_string.replace('(', '')
# query_string = query_string.replace(')', '')
# pf_list = re.split(' OR ', query_string)
# tmp_filtered_result = filtered_query[filtered_query['ProteinFamily'].isin(pf_list)]
# block_protein_list = tmp_filtered_result['Entry'].to_list()
#
# ## 从下载的蛋白序列中抓取block中包含的蛋白序列
#
# tmp_block_fasta_file_name = work_dir + 'ProteinFamily' + str(n) + '.fasta'
#
# content_list = []
# for record in SeqIO.parse(complete_fasta, "fasta"):
# id = str(record.id)
# if id in block_protein_list:
# content = '>' + id + '\n' + str(record.seq)
# content_list.append(content)
# with open(tmp_block_fasta_file_name, 'w+') as out:
# out.write('\n'.join(content_list))
# out.close()
# else:
# next
## Reabundance
## Multiple Seq Alignment and Build HMM profile
for block_fasta in glob.glob(work_dir + 'ProteinFamily*.fasta'):
basename = os.path.basename(block_fasta)
dirname = os.path.dirname(block_fasta)
basename = re.split('.fasta',basename)[0]
subprocess.run(['cd-hit','-i',block_fasta,'-o',block_fasta + '.reabun','-T','100','-M','20000','-n','3'])
subprocess.run(['FAMSA/famsa', block_fasta + '.reabun', os.path.join(work_dir, basename + '.st'),'-t','200'])
os.system('hmmbuild --amino ' + os.path.join(dirname, basename + '.hmm ') + ' ' + work_dir + basename + '.st')
def execute_command(command):
result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, shell=True)
return command, result.stdout, result.stderr
def run_commands(commands_list, max_workers):
results = []
with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
futures = {executor.submit(execute_command, command): command for command in commands_list}
for future in concurrent.futures.as_completed(futures):
command = futures[future]
try:
command, stdout, stderr = future.result()
results.append((command, stdout, stderr))
except Exception as e:
print(f"Error executing command: {command}, {e}")
return results
def runAntismash(query_string, work_dir, space_len,neighbour, n_threads):
## Prepare input files
block_hmm_list = []
hmm_file_name_list =[]
hmm_file_list = []
for block_hmm in glob.glob(os.path.join(work_dir, '*.hmm')):
## Copy hmm file to antismash folder
hmm_file_name = os.path.basename(block_hmm)
hmm_file_list.append(hmm_file_name)
hmm_file_name = re.split('.hmm',hmm_file_name)[0]
#os.system('cp ' + block_hmm + ' ' + hmm_folder + '/data/')
block_hmm_list.append(block_hmm)
hmm_file_name_list.append(hmm_file_name)
## Define rules in txt files
# RULE loose_test
# CATEGORY user_define
# COMMENT test for pilin and sortaseC
# CUTOFF 20
# NEIGHBOURHOOD 8
# CONDITIONS sortaseC and pilin
if 'and' in query_string:
# Loose rules file
content = "RULE" + " " + "loose_rules" + "\n\t"
content = content + "CATEGORY" + " " + "user_defined" + "\n\t"
content = content + "COMMENT" + " " + "user-defined BGC region" + "\n\t"
content = content + "CUTOFF" + " " + str(space_len) + "\n\t"
content = content + "NEIGHBOURHOOD" + " " + str(neighbour) + "\n\t"
content = content + "CONDITIONS" + " " + " and ".join(hmm_file_name_list)
with open (work_dir + '/loose.txt', 'w+') as out:
out.write(content)
# Relaxed rules file
content = "RULE" + " " + "relaxed_rules" + "\n\t"
content = content + "CATEGORY" + " " + "user_defined" + "\n\t"
content = content + "COMMENT" + " " + "user-defined BGC region" + "\n\t"
content = content + "CUTOFF" + " " + str(space_len) + "\n\t"
content = content + "NEIGHBOURHOOD" + " " + str(neighbour) + "\n\t"
content = content + "CONDITIONS" + " " + " and ".join(hmm_file_name_list)
with open (work_dir + '/relaxed.txt', 'w+') as out:
out.write(content)
# Stricted rules file
content = "RULE" + " " + "strict_rules" + "\n\t"
content = content + "CATEGORY" + " " + "user_defined" + "\n\t"
content = content + "COMMENT" + " " + "user-defined BGC region" + "\n\t"
content = content + "CUTOFF" + " " + str(space_len) + "\n\t"
content = content + "NEIGHBOURHOOD" + " " + str(neighbour) + "\n\t"
content = content + "CONDITIONS" + " " + " and ".join(hmm_file_name_list)
with open (work_dir + '/strict.txt', 'w+') as out:
out.write(content)
else:
# Loose rules file
content = "RULE" + " " + "loose_rules" + "\n\t"
content = content + "CATEGORY" + " " + "user_defined" + "\n\t"
content = content + "COMMENT" + " " + "user-defined BGC region" + "\n\t"
content = content + "CUTOFF" + " " + str(space_len) + "\n\t"
content = content + "NEIGHBOURHOOD" + " " + str(neighbour) + "\n\t"
content = content + "CONDITIONS" + " " + " or ".join(hmm_file_name_list)
with open (work_dir + '/loose.txt', 'w+') as out:
out.write(content)
# Relaxed rules file
content = "RULE" + " " + "relaxed_rules" + "\n\t"
content = content + "CATEGORY" + " " + "user_defined" + "\n\t"
content = content + "COMMENT" + " " + "user-defined BGC region" + "\n\t"
content = content + "CUTOFF" + " " + str(space_len) + "\n\t"
content = content + "NEIGHBOURHOOD" + " " + str(neighbour) + "\n\t"
content = content + "CONDITIONS" + " " + " or ".join(hmm_file_name_list)
with open (work_dir + '/relaxed.txt', 'w+') as out:
out.write(content)
# Stricted rules file
content = "RULE" + " " + "strict_rules" + "\n\t"
content = content + "CATEGORY" + " " + "user_defined" + "\n\t"
content = content + "COMMENT" + " " + "user-defined BGC region" + "\n\t"
content = content + "CUTOFF" + " " + str(space_len) + "\n\t"
content = content + "NEIGHBOURHOOD" + " " + str(neighbour) + "\n\t"
content = content + "CONDITIONS" + " " + " or ".join(hmm_file_name_list)
with open (work_dir + '/strict.txt', 'w+') as out:
out.write(content)
## Get path of antismash
antismash = subprocess.run(['which', 'antismash'], stdout=subprocess.PIPE)
if not antismash.returncode:
antismash= antismash.stdout.decode('utf-8')
antismash = antismash.replace('\n', '')
antismash = os.path.dirname(antismash)
path = subprocess.run(['find', '/opt/', '-name', 'hmm_detection'], stdout=subprocess.PIPE)
if not path.returncode:
path = path.stdout.decode('utf-8')
path = path.replace('\n', '')
else:
print("Error found when looking for hmm_detection folder in your docker image, please make sure it's installed correctedly!!")
else:
print("Error found when looking for antismash in your docker image, please make sure it's installed correctedly!!")
if os.path.isdir(os.path.join(os.path.dirname(path), 'hmm_detection_backup/')):
pass
else:
os.system("cp -r {0} {1}".format(path, os.path.join(os.path.dirname(path), 'hmm_detection_backup/')))
## Write hmmdetails file
content_list = []
for proteinfamily in hmm_file_name_list:
content = proteinfamily + "\t" + proteinfamily + "\t" + str(space_len) + "\t" + proteinfamily + '.hmm'
content_list.append(content)
all_content = '\n'.join(content_list)
with open(os.path.join(path, 'data','hmmdetails.txt'), 'a+') as HMMDETAIL:
HMMDETAIL.write(all_content)
os.system('cp ' + os.path.join(path, 'data','hmmdetails.txt') + " " + work_dir)
## Write category json file
category_dict = {
"user_defined": {
"description": "user-defined BGC region",
"version": 1}
}
with open(work_dir + "/categories.json","w+") as f:
json.dump(category_dict,f,indent = 4)
## Copy files
os.system('cp ' + work_dir + "/categories.json " + os.path.join(path, 'data'))
os.system('cp ' + work_dir + "strict.txt " + os.path.join(path, 'cluster_rules'))
os.system('cp ' + work_dir + "relaxed.txt " + os.path.join(path, 'cluster_rules'))
os.system('cp ' + work_dir + "loose.txt " + os.path.join(path, 'cluster_rules'))
os.system('cp ' + work_dir + "/*hmm " + os.path.join(path, 'data'))
print('Check param files in antismash')
os.system('ls ' + os.path.join(path, 'data','*hmm'))
os.system('ls ' + os.path.join(path, 'cluster_rules','*'))
print("yes")
## run antismash
ID_list = []
candidate_gbk_file_list = glob.glob(os.path.join(work_dir,'Candidate_genomes','*.gbff.gz'))
for candidate_gbk_file in candidate_gbk_file_list:
file_name = os.path.basename(candidate_gbk_file)
ID = re.split('_', file_name)[0:2]
ID = '_'.join(ID)
refseq_accession = 'GCF_'+str(ID)
os.makedirs(os.path.join(work_dir, 'Anitismash_Result', refseq_accession), exist_ok = True)
ID_list.append(ID)
input = zip(ID_list, candidate_gbk_file_list)
command_list = []
for ID, input_file in input:
refseq_accession = 'GCF_'+str(ID)
command = ' '.join(['antismash', '--hmmdetection-strictness','strict' , '--genefinding-tool prodigal', '-c', '10', '--taxon', 'bacteria', input_file , '--output-dir', os.path.join(work_dir, 'Anitismash_Result', refseq_accession)])
with open (os.path.join(work_dir, 'antismash.sh' ), 'a') as file:
file.write(command + '\n')
command_list.append(command)
max_parallel_tasks = 40 # 设置最大并行任务数
results = run_commands(command_list, max_parallel_tasks)
with open(os.path.join(work_dir,'Antismash.log'),'a') as file:
for command, stdout, stderr in results:
file.write(f"Command: {command}\n")
file.write(f"Standard Output:\n{stdout}\n")
file.write(f"Standard Error:\n{stderr}\n")
file.write("=" * 30)
for i in range(0, len(command_list), n_threads):
tmp_command_list = command_list[i:i+n_threads]
procs = [ Popen(i, shell=True, stderr=subprocess.PIPE, stdout=subprocess.PIPE) for i in tmp_command_list]
for p in procs:
try:
outs, errs = p.communicate(timeout = 400)
if errs:
print(errs.decode('ascii'))
except TimeoutExpired:
p.kill()
outs, errs = p.communicate()
print(errs.decode('ascii'))
def summaryAntismashResult(Result_folder, work_dir):
all_strain_dict = {}
strainID2ncID = {}
assemblyID2ncID = {}
assemblyIDnumer = {}
n=0
for root, subdir, files in os.walk(Result_folder):
n+=1
print(str(n) + '\r', end='', flush=True)
for file in files:
if re.search('region', file):
if file.endswith('gbk'):
region_id = os.path.basename(file)
strain_id = os.path.basename(root)
region_id = re.split('.gbk',region_id )[0]
region_dict = {}
file_path = os.path.join(root,file)
for seq_record in SeqIO.parse(file_path, "genbank"):
organism = seq_record.description
organism = re.split(',', organism)[0]
organism = organism.replace(' plasmid', '')
organism = organism.replace(' chromosome', '')
## 获得taxonomy信息
try:
if len(seq_record.annotations['taxonomy']) < 6:
genus = seq_record.annotations['taxonomy'][4] ## Genus level，某些物种缺失某些层级信息
else:
genus = seq_record.annotations['taxonomy'][5] ## Genus level
except IndexError:
genus = seq_record.annotations['taxonomy'][-1]
print(file_path)
print('文件物种层级存在一些问题，请检查')
print(seq_record.annotations['taxonomy'])
species = ' '.join(re.split(' ', organism)[0:2])
ID = seq_record.id
for block_hmm in glob.glob(os.path.join(work_dir, '*.hmm')):
block_name_num = 0
block_name = os.path.basename(block_hmm)
block_name = re.split('.hmm', block_name)[0]
for feature in seq_record.features:
if feature.type == "CDS":
try:
for function in feature.qualifiers['gene_functions']:
if re.search(block_name, function):
block_name_num = block_name_num + 1
else:
next
except KeyError:
next
else:
next
region_dict[block_name] = block_name_num
region_dict['organism'] = organism
region_dict['genus'] = genus
region_dict['ID'] = ID
region_dict['species'] = species
#region_dict['StrainID']= strain_id
region_dict['regionID'] = region_id
all_strain_dict[region_id] = region_dict
else:
next
else:
if file.endswith('gbk'):
AssemblyID_backup = re.split('_',file)
AssemblyID_number = AssemblyID_backup[1]
AssemblyID_backup = AssemblyID_backup[0:2]
AssemblyID_backup = '_'.join(AssemblyID_backup)
#AssemblyID_backup = AssemblyID_backup.replace('.gbk', '')
file_path = os.path.join(root,file)
for seq_record in SeqIO.parse(file_path, "genbank"):
ID = seq_record.id
for feature in seq_record.features:
try:
content = ''.join(feature.qualifiers.get("db_xref"))
if re.search('taxon', content):
taxon = content
taxonID = re.split(':',taxon )[-1]
else:
next
except TypeError:
next
strainID2ncID[ID] = taxonID
Assembly_ID = AssemblyID_backup
assemblyID2ncID[ID] = Assembly_ID
assemblyIDnumer[ID] = AssemblyID_number
else:
next
## 获得物种基因组序列ID对应的taxaID
Stat_df = pd.DataFrame(all_strain_dict).T
Stat_df['TaxonID'] = nan
Stat_df['AssemblyID'] = nan
Stat_df['AssemblyIDnum'] = nan
Stat_df['Genus_branches'] = nan
## 增加taxonID和AssemblyID信息
Assembly_id_list = []
for key in strainID2ncID.keys():
Stat_df[Stat_df['ID'] == key] = Stat_df[Stat_df['ID'] == key].assign(TaxonID = strainID2ncID[key])
Stat_df[Stat_df['ID'] == key] = Stat_df[Stat_df['ID'] == key].assign(AssemblyID = assemblyID2ncID[key])
Stat_df[Stat_df['ID'] == key] = Stat_df[Stat_df['ID'] == key].assign(AssemblyIDnum = assemblyIDnumer[key])
Assembly_id_list.append(assemblyID2ncID[key])
Assembly_id_list = list(set(Assembly_id_list))
# with open ('strain_list.txt', 'w') as strain:
# content = '\n'.join(Assembly_id_list)
# strain.write(content)
# strain.close
## 获取构建进化树的AssemblyID
Tree_genome_list = []
Tree_genome_dict = {}
tree_content = []
for Genus in list(set(Stat_df['genus'].to_list())):
tmp_df = Stat_df[Stat_df['genus'] == Genus]
species_num = len(set(tmp_df['species'].to_list()))
species_num = int(species_num)
Stat_df[Stat_df['genus'] == Genus] = Stat_df[Stat_df['genus'] == Genus].assign(**{"Genus_branches" : species_num})
ref_assembly_id = tmp_df['AssemblyID'].to_list()[0]
Tree_genome_list.append(ref_assembly_id)
Tree_genome_dict[ref_assembly_id] = Genus
Stat_df.to_csv(os.path.join(work_dir,'BGC_stat.xls'), sep = '\t')
for key in Tree_genome_dict.keys():
line = key + '\t' + Tree_genome_dict[key]
tree_content.append(line)
with open (os.path.join(work_dir, 'tree_genomeIDs.txt'), 'w') as genomes:
content = '\n'.join(tree_content)
genomes.write(content)
genomes.close
def StrainClassification(BGC_stat, db_file, work_dir):
df = BGC_stat
db = pd.read_csv(db_file,sep='\t',encoding = "ISO-8859-1",dtype=str)
df = df.drop_duplicates()
df = df.reset_index()
df['Type'] = nan
for assemblyid in list(set(df['AssemblyID'].to_list())):
assemblyid_trans = assemblyid.replace('GCF_', '')
strain_name = df[df['AssemblyID'] == assemblyid]['organism'].to_list()[0]
strain_name = re.sub('genome*assembly','',strain_name)
strain_name = re.sub('complete*genome','',strain_name)
strain_name = re.sub('DNA','',strain_name)
strain_name = re.sub('draft*genome','',strain_name)
strain_name = re.sub('genome','',strain_name)
strain_name = re.sub('assembly','',strain_name)
strain_name = strain_name.rstrip()
strain_name = re.sub(' +', ' ', strain_name)
strain_name = re.sub('\(','.',strain_name)
strain_name = re.sub('\)','.',strain_name)
strain_name = re.sub('[-_]','.',strain_name)
strain_name = re.sub(r'\s+','.',strain_name)
taxid = df[df['AssemblyID'] == assemblyid]['TaxonID'].to_list()[0]
## 根据assemblyID注释
if db['assembly_accession'].str.contains(assemblyid_trans, na=False).values.any():
df[df['AssemblyID'] == assemblyid] = df[df['AssemblyID'] == assemblyid].assign(**{'Type':db[db['assembly_accession'].str.contains(assemblyid_trans, na=False)]['Type'].to_list()[0]})
elif db[db['genome_name'].str.contains(strain_name, regex=True,case=False) == True].shape[0] > 0:
## 根据菌株名字注释
type = db[db['genome_name'].str.contains(strain_name, regex=True,case=False) == True]['Type'].to_list()[0]
df[df['AssemblyID'] == assemblyid] = df[df['AssemblyID'] == assemblyid].assign(**{'Type':type})
else:
df[df['AssemblyID'] == assemblyid] = df[df['AssemblyID'] == assemblyid].assign(**{'Type':'Others'})
df['Pathogen'] = ''
df['Industrial workhorse'] = ''
df['others'] = ''
for row in range(0,df.shape[0]):
if df.loc[row,'Type'] == 'Pathogen':
df.loc[row,'Pathogen'] = 'yes'
df.loc[row,'Industrial workhorse'] = 'no'
df.loc[row,'others'] = 'no'
elif df.loc[row,'Type'] == 'Industrial workhorse':
df.loc[row,'Pathogen'] = 'no'
df.loc[row,'Industrial workhorse'] = 'yes'
df.loc[row,'others'] = 'no'
else:
df.loc[row,'others'] = 'yes'
df.loc[row,'Pathogen'] = 'no'
df.loc[row,'Industrial workhorse'] = 'no'
df.to_csv(os.path.join(work_dir, 'Strain_classification.xls'), sep = '\t')
Block_names = []
for col in list(df.columns):
if 'ProteinFamily' in col:
Block_names.append(col)
else:
next
for row in range(0,df.shape[0]):
Patter_num_list = []
for tmp_name in Block_names:
tmp_num = df.loc[row, tmp_name]
Patter_num_list.append(str(tmp_num))
pattern = ','.join(Patter_num_list)
df.loc[row,'BGCPattern'] = pattern
df.to_csv(os.path.join(work_dir, 'Strain_classification.xls'), sep = '\t')
d = {}
count = 1
d['BGCPattern_Name'] = []
d['Pattern'] = []
d['BGCPattern number'] = []
for temp_str in list(set(df['BGCPattern'])):
d['BGCPattern_Name'].append('BGCPattern'+str(count))
d['Pattern'].append(temp_str)
d['BGCPattern number'].append(df[df['BGCPattern'] == temp_str].shape[0])
count = count + 1
pattern_df = pd.DataFrame(d)
pattern_df.to_csv(work_dir + 'Pattern_stat.xls', sep= '\t')
## 绘制pielplot
labels = list(set(df['Type'].to_list()))
sizes = [(len(df[df['Type'] == x]['AssemblyID'].drop_duplicates().to_list())/(len(df['AssemblyID'].drop_duplicates().to_list()))) * 100 for x in labels]
#fig, ax = plt.subplots()
#ax.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90)
#patches, texts = plt.pie(sizes, startangle=90)
colours = {
'Pathogen':'C0',
'Industrial workhorse':'C1',
'Others':'C2'
}
patches, texts, _ = plt.pie(sizes, autopct='%1.1f%%', colors=[colours[key] for key in labels], startangle=90)
texts[0].set_fontsize(4)
plt.legend(patches, labels, loc="best")
plt.axis('equal')
plt.savefig(work_dir + 'classification.png', format = 'png', dpi = 250)
# Construct tree metadata
for row in range(0,df.shape[0]):
pattern = df.loc[row,'BGCPattern']
for p in pattern_df['BGCPattern_Name']:
tmp_pattern = pattern_df[pattern_df['BGCPattern_Name'] == p]['Pattern'].to_list()[0]
if pattern == tmp_pattern:
df.loc[row,'BGCPatternID'] = p
else:
next
for i in pattern_df['BGCPattern_Name']:
df[i] = nan
for assemblyid in list(set(df['AssemblyID'].to_list())):
tmp_df = df[df['AssemblyID'] == assemblyid]
tmp_pattern_lsit = tmp_df['BGCPatternID'].to_list()
for i in pattern_df['BGCPattern_Name']:
count = tmp_pattern_lsit.count(i)
df[df['AssemblyID'] == assemblyid] = df[df['AssemblyID'] == assemblyid].assign(**{i:count})
df.to_csv(os.path.join(work_dir, 'Tree_metadata.xls'), sep = '\t')
def buildPhylogenetic_tree(work_dir, input_genomes_folder_name):
classification_file = os.path.join(work_dir, 'Strain_classification.xls')
classification_df = pd.read_csv(classification_file, index_col=0, sep = '\t', dtype=str)
#annotated_df = classification_df
annotated_df = classification_df[classification_df['others'] != 'yes']
tree_strain_list = list(set(annotated_df['AssemblyIDnum'].to_list()))
if os.path.isdir(os.path.join(work_dir,'StrainPhylogeneticTree/Input')):
pass
else:
subprocess.run(['mkdir','-p',os.path.join(work_dir,'StrainPhylogeneticTree/Input/')])
## Look for input genomes and copy them into folder
input_genomes_folder = subprocess.run(['find', work_dir, '-name', input_genomes_folder_name], stdout=subprocess.PIPE)
if not input_genomes_folder.returncode:
input_genomes_folder = input_genomes_folder.stdout.decode('utf-8')
input_genomes_folder = input_genomes_folder.replace('\n', '')
else:
print('Can\'t find input genomes folder in' + input_genomes_folder_name)
input_tree_list_fna = glob.glob(os.path.join(input_genomes_folder, '*fna'))
input_tree_list_fasta = glob.glob(os.path.join(input_genomes_folder, '*fasta'))
input_tree_list = input_tree_list_fna + input_tree_list_fasta
for input_tree_file in input_tree_list:
print(input_tree_file)
# 获取Input Genomes的NC ID
with open(input_tree_file) as handle:
nc_id_list = []
for record in SeqIO.parse(handle, "fasta"):
if record.id in annotated_df['ID'].to_list():
next
else:
subprocess.run(['cp', input_tree_file, os.path.join(work_dir,'StrainPhylogeneticTree/Input/')])
for strain_id in tree_strain_list:
for fasta_file in glob.glob(os.path.join(work_dir, 'Candidate_genomes') + '/*' + str(strain_id) + '*'):
if fasta_file.endswith('fasta') or fasta_file.endswith('fna'):
file_size = os.path.getsize(fasta_file)
if file_size == 0:
next
else:
subprocess.run(['cp', fasta_file, os.path.join(work_dir,'StrainPhylogeneticTree/Input/')])
else:
next
## Create strain tree file
subprocess.run(['JolyTree.sh', '-i', os.path.join(work_dir,'StrainPhylogeneticTree/Input/'),'-b', os.path.join(work_dir,'StrainPhylogeneticTree/','Strain_tree'), '-t', '200'])
def Infer_candidate_strains(work_dir):
annotated_df = pd.read_table(os.path.join(work_dir,'Strain_classification.xls'), sep = '\t',dtype=str)
# 去掉ID栏多余的前缀
newID_list = []
for id in annotated_df['ID'].to_list():
new_id = re.sub(r'.+_', '',id)
newID_list.append(new_id)
annotated_df['ID'] = newID_list
distance_file = glob.glob(work_dir + '/StrainPhylogeneticTree/' + 'Strain_tree' + '.d')
distance_file = distance_file[0]
distance_matrtix = pd.read_csv(distance_file, skiprows = 1, index_col = 0,sep ='\s+',header=None)
newname = []
for i in distance_matrtix.index.values:
newname.append(re.split('_', i)[1])
name = pd.Series(newname)
distance_matrtix = distance_matrtix.set_index(name)
distance_matrtix.columns = newname
for input_tree_file in glob.glob(os.path.join(work_dir,'Input_genomes/*')):
if input_tree_file.endswith('fasta') or input_tree_file.endswith('fna'):
# 获取Input Genomes的NC ID
with open(input_tree_file) as handle:
Assemlbly_ID_list = []
for record in SeqIO.parse(handle, "fasta"):
tmp_id = re.split('_',record.id)[1]
if tmp_id in annotated_df['ID'].to_list():
Assemlbly_ID_num = annotated_df[annotated_df['ID'] == tmp_id]['AssemblyID'].to_list()[0]
Assemlbly_ID_num = re.split('_',Assemlbly_ID_num)[1]
Assemlbly_ID_list.append(Assemlbly_ID_num)
else:
next
else:
next
content = '候选菌株信息:\n'
if len(Assemlbly_ID_list) == 0:
content = '未能找到候选工程菌'
else:
pass
candidate_dict = {}
for pathogen in set(Assemlbly_ID_list):
tmp_pathogen = annotated_df[annotated_df['AssemblyIDnum'] == pathogen]['organism'].to_list()[0]
#tmp_df = annotated_df
tmp_df = annotated_df[annotated_df['Type'] == 'Industrial workhorse']
content = content + tmp_pathogen + '的候选工程菌为:\n'
tmp_distance_series = distance_matrtix.loc[pathogen,]
tmp_distance_series = tmp_distance_series.sort_values(ascending = True)
tmp_candidate = tmp_distance_series.rename('ID')
## 只挑选前10的物种输出到候选菌株信息.txt中