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Copy pathXspecT_mini_alt.py
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XspecT_mini_alt.py
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import search_filter
import os
from Bio import SeqIO, SeqRecord, Seq
import Classifier
from OXA_Table import OXATable
import warnings
from copy import deepcopy
import time
import csv
import pickle
import matplotlib.pyplot as plt
warnings.filterwarnings("ignore")
def xspecT_mini(file_path, XspecT, ClAssT, oxa, file_format, read_amount, csv_table, metagenome):
"""performs a BF-lookup for a set of genomes for testing purpose"""
itemlist = ["albensis", "apis", "baretiae", "baumannii", "baylyi", "beijerinckii", "bereziniae",
"bohemicus", "boissieri", "bouvetii", "brisouii", "calcoaceticus",
"celticus", "chengduensis", "chinensis", "colistiniresistens", "courvalinii", "cumulans",
"defluvii", "dispersus", "equi", "gandensis", "gerneri", "gs06", "gs16", "guerrae",
"guillouiae", "gyllenbergii", "haemolyticus", "halotolerans", "harbinensis", "idrijaensis", "indicus",
"johnsonii", "junii", "kanungonis", "kookii", "kyonggiensis", "lactucae", "lanii", "larvae",
"lwoffii", "marinus", "modestus", "nectaris", "nosocomialis", "oleivorans", "parvus",
"piscicola", "pittii", "pollinis", "populi", "portensis", "pseudolwoffii", "pullicarnis",
"pragensis", "proteolyticus", "puyangensis",
"qingfengensis", "radioresistens", "rathckeae", "rongchengensis", "rudis", "schindleri", "seifertii",
"seohaensis", "shaoyimingii", "sichuanensis", "soli", "stercoris", "tandoii", "terrae",
"terrestris", "tianfuensis", "tjernbergiae", "towneri", "ursingii", "variabilis", "venetianus",
"vivianii", "wanghuae", "wuhouensis", "sp."]
print("Preparing Bloomfilter...")
start = time.time()
if XspecT:
BF = search_filter.pre_processing()
# BF_1 = search_filter.pre_processing_prefilter()
BF_1_1 = search_filter.pre_processing_prefilter2()
if ClAssT:
BF_2 = search_filter.pre_processing_ClAssT()
if oxa:
BF_3 = search_filter.pre_processing_oxa()
#if BioMonitoring:
#BF = search_filter.pre_processing_Culicidae_species()
#BF_1_1 = search_filter.pre_processing_prefilter_Culicidae()
end = time.time()
needed = round(end - start, 2)
print("Time needed for preprocessing: ", needed)
try:
files = sorted(os.listdir(file_path))
except FileNotFoundError:
print("Error: Invalid filepath!")
quit()
if file_format == "fna" or file_format == "fasta" or file_format == "fa":
for i in range(len(files) - 1, -1, -1):
if 'fna' in files[i] or 'fasta' in files[i]:
continue
else:
del files[i]
elif file_format == "fastq" or file_format == "fq":
for i in range(len(files) - 1, -1, -1):
if 'fastq' in files[i] or 'fq' in files[i]:
continue
else:
del files[i]
if len(files) == 0:
print("Error: No " + str(file_format) + " files in directory!")
quit()
paths = files[:]
file_path2 = file_path[:]
for i in range(len(file_path2)):
if file_path2[i] == "\\":
list_temp = list(file_path2)
list_temp[i] = '/'
file_path2 = ''.join(list_temp)
start = time.time()
for i in range(len(files)):
paths[i] = file_path2 + "/" + paths[i]
# Testing purpose delete later
# extracts the GCF-Number
# files_split = []
# for i in range(len(files)):
# files_split.append(files[i].split("_"))
# try:
# files_split[i] = files_split[i][0] + "_" + files_split[i][1]
# except:
# files_split[i] = files[i].split('.')[-2]
# GCF_taxon = []
# for i in range(len(files)):
# with open(paths[i]) as file:
# head = file.readline()
# head = head.split()
# if head[2] == "sp.":
# GCF_taxon.append("none")
# else:
# GCF_taxon.append(head[2])
# if head[2] != "baumannii":
# del files[i]
# del paths[i]
# excelv3 = []
# for i in range(len(files)):
# excelv3.append(files_split[i] + "," + GCF_taxon[i])
# for i in range(0, len(excelv3)):
# excelv3[i] = [excelv3[i]]
# with open(r'Results/XspecT_mini_csv/Test.csv', 'w', newline='') as file:
# writer = csv.writer(file)
# writer.writerows(excelv3)
if XspecT:
predictions, scores = xspecT(
BF, BF_1_1, files, paths, file_format, read_amount, metagenome)
if ClAssT:
predictions_ClAssT, scores_ClAssT = clAssT(
BF_2, files, paths, file_format, read_amount)
if oxa:
scores_oxa, scores_oxa_ind = blaOXA(BF_3, files, paths, file_format, read_amount)
#if BioMonitoring:
#predictions, scores = xspecT(
#BF, BF_1_1, files, paths, file_format, read_amount, metagenome, BioMonitoring)
print("Preparing results...")
print("")
end = time.time()
needed = round(end - start, 2)
print("Time needed: ", needed)
print("")
header_filename = "Filename"
spaces = []
space = " "
underscore = "________"
name_max = len(max(itemlist, key=len))
if XspecT:
for i in range(len(predictions)):
while len(predictions[i]) < name_max:
predictions[i] += " "
file_max = len(max(files, key=len))
while len(header_filename) < file_max:
header_filename += " "
underscore += "_"
for j in range(len(files)):
for i in range(len(header_filename)-len(files[j])):
space += " "
spaces.append(space)
space = " "
excel = []
# formatting
if ClAssT:
for i in range(len(predictions_ClAssT)):
if predictions_ClAssT[i] != "none" and predictions_ClAssT[i] != "None":
predictions_ClAssT[i] += " "
if XspecT and ClAssT:
for i in range(len(scores_ClAssT)):
if scores[i] == "1.0":
scores[i] += " "
if XspecT and ClAssT and oxa:
excelv2 = []
for i in range(len(files)):
excel.append(files[i] + spaces[i] + predictions[i] + " " + scores[i] + " " +
predictions_ClAssT[i] + " " + scores_ClAssT[i] + " " + str(scores_oxa[i]) + " " + str(scores_oxa_ind[i][0]) + " " + str(scores_oxa_ind[i][1]))
excelv2.append(files[i] + "," + predictions[i] + "," + scores[i] +
predictions_ClAssT[i] + "," + scores_ClAssT[i] + "," + str(scores_oxa[i]))
print(header_filename + " Species Score Sub-Type Score blaOXA-Family blaOXA-Gene Score")
print(underscore + "___________________________________________________________________________________________________________________________________________")
for i in excel:
print(i)
for i in range(0, len(excelv2)):
excelv2[i] = [excelv2[i]]
if csv_table:
with open(r'Results/XspecT_mini_csv/Results.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerows(excelv2)
print("")
print("")
elif XspecT and not ClAssT and not oxa:
excelv2 = []
for i in range(len(files)):
excel.append(files[i] + spaces[i] +
predictions[i] + " " + scores[i])
excelv2.append(files[i] + "," + predictions[i] + "," + scores[i])
print(header_filename + " Species Score")
print(underscore + "_________________________________________")
for i in excel:
print(i)
for i in range(0, len(excelv2)):
excelv2[i] = [excelv2[i]]
if csv_table:
with open(r'Results/XspecT_mini_csv/Results_XspecT.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerows(excelv2)
print("")
print("")
elif ClAssT and not XspecT and not oxa:
excelv2 = []
for i in range(len(files)):
excel.append(
files[i] + spaces[i] + predictions_ClAssT[i] + " " + scores_ClAssT[i])
excelv2.append(
files[i] + "," + predictions_ClAssT[i] + "," + scores_ClAssT[i])
print(header_filename + " Sub-Type Score")
print(underscore + "________________________________")
for i in excel:
print(i)
print("")
print("")
for i in range(0, len(excelv2)):
excelv2[i] = [excelv2[i]]
if csv_table:
with open(r'Results/XspecT_mini_csv/Results_ClAssT.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerows(excelv2)
elif oxa and not ClAssT and not XspecT:
excelv2 = []
for i in range(len(files)):
excel.append(files[i] + spaces[i] + str(scores_oxa[i]) + " " + str(scores_oxa_ind[i][0]) + " " + str(scores_oxa_ind[i][1]))
excelv2.append(files[i] + "," + str(scores_oxa[i]))
print(header_filename + " blaOXA-Family blaOXA-Gene Score")
print(underscore + "_______________________________________________________________________")
for i in excel:
print(i)
print("")
print("")
for i in range(0, len(excelv2)):
excelv2[i] = [excelv2[i]]
if csv_table:
with open(r'Results/XspecT_mini_csv/Results_Oxa.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerows(excelv2)
elif XspecT and ClAssT and not oxa:
excelv2 = []
for i in range(len(files)):
excel.append(files[i] + spaces[i] + predictions[i] + " " + scores[i] +
" " + predictions_ClAssT[i] + " " + scores_ClAssT[i])
excelv2.append(files[i] + "," + predictions[i] + "," + scores[i] +
"," + predictions_ClAssT[i] + "," + scores_ClAssT[i])
print(header_filename +
" Species Score Sub-Type Score")
print(underscore + "________________________________________________________________________")
for i in excel:
print(i)
print("")
print("")
for i in range(0, len(excelv2)):
excelv2[i] = [excelv2[i]]
if csv_table:
with open(r'Results/XspecT_mini_csv/Results.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerows(excelv2)
elif XspecT and oxa and not ClAssT:
excelv2 = []
for i in range(len(files)):
excel.append(files[i] + spaces[i] + predictions[i] +
" " + scores[i] + " " + str(scores_oxa[i]) + " " + str(scores_oxa_ind[i][0]) + " " + str(scores_oxa_ind[i][1]))
excelv2.append(files[i] + "," + predictions[i] +
"," + scores[i] + str(scores_oxa[i]))
print(header_filename +
" Species Score blaOXA-Family blaOXA-Gene Score")
print(underscore + "_______________________________________________________________________________________________________________")
for i in excel:
print(i)
for i in range(0, len(excelv2)):
excelv2[i] = [excelv2[i]]
if csv_table:
with open(r'Results/XspecT_mini_csv/Results.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerows(excelv2)
print("")
print("")
elif ClAssT and oxa and not XspecT:
excelv2 = []
for i in range(len(files)):
excel.append(files[i] + spaces[i] + predictions_ClAssT[i] +
" " + scores_ClAssT[i] + " " + str(scores_oxa[i]) + " " + str(scores_oxa_ind[i][0]) + " " + str(scores_oxa_ind[i][1]))
excelv2.append(files[i] + "," + predictions_ClAssT[i] +
"," + scores_ClAssT[i] + "," + str(scores_oxa[i]))
print(header_filename +
" Sub-Type Score blaOXA-Family blaOXA-Gene Score")
print(underscore + "______________________________________________________________________________________________________")
for i in excel:
print(i)
for i in range(0, len(excelv2)):
excelv2[i] = [excelv2[i]]
if csv_table:
with open(r'Results/XspecT_mini_csv/Results.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerows(excelv2)
print("")
print("")
def xspecT(BF, BF_1_1, files, paths, file_format, read_amount, metagenome):
"""performs a BF-lookup for a set of genomes for testing purpose"""
print("Starting taxonomic assignment on species-level...")
predictions = []
scores = []
counterx = 0
for i in range(len(files)):
if i == int(len(files)/6) or i == int(len(files)/3) or i == int(len(files)/2) or i == int(len(files)/1.5) or i == int(len(files)/1.2):
print("...")
BF.number_of_kmeres = 0
BF.hits_per_filter = [0] * BF.clonetypes
BF_1_1.number_of_kmeres = 0
BF_1_1.hits_per_filter = [0]
if file_format == "fasta" or file_format == "fna" or file_format =="fa":
if metagenome:
reads = []
for sequence in SeqIO.parse(paths[i], "fasta"):
# reverse_sequence = sequence.seq.reverse_complement()
read_kmers = []
# if counter < read_amount:
# counter += 1
# else:
# break
#k1 = str(sequence.seq[0:BF_1_1.k]) # first k-mer
#k2 = str(sequence.seq[len(str(sequence.seq)) - BF_1_1.k:]) # last k-mer
#mid = len(str(sequence.seq)) // 2
#k3 = str(sequence.seq[mid:mid + BF_1_1.k]) # k-mer in middle
#k4 = str(sequence.seq[BF_1_1.k:BF_1_1.k * 2])
#k5 = str(sequence.seq[mid + BF_1_1.k:mid + BF_1_1.k * 2])
# Taking sum of list as reference, if sum has not increased after testing those 3 kmeres,
# then the read won't be tested further
hit_sum = sum(BF_1_1.hits_per_filter)
hits_per_filter_copy = BF_1_1.hits_per_filter[:]
sample_size = int(len(str(sequence.seq)) ** 0.5)
threshold_contig = sample_size * 0.7
for i in range(0, len(str(sequence.seq)) - BF_1_1.k, sample_size):
if "N" not in str(sequence.seq[i: i + BF_1_1.k]):
BF_1_1.lookup(str(sequence.seq[i: i + BF_1_1.k]).upper())
#BF_1_1.lookup(k1)
#BF_1_1.lookup(k2)
#BF_1_1.lookup(k3)
#BF_1_1.lookup(k4)
#BF_1_1.lookup(k5)
# needs at least 70% hits to continue with the contig
counter = 0
#print((sum(BF_1_1.hits_per_filter) - hit_sum), threshold_contig)
if (sum(BF_1_1.hits_per_filter) - hit_sum) > threshold_contig:
#print("added")
for j in range(len(str(sequence.seq)) - BF_1_1.k):
if "N" not in str(sequence.seq[j: j + BF_1_1.k]):
read_kmers.append(str(sequence.seq[j: j + BF_1_1.k]).upper())
counter += 1
# how many kmers? to use
if counter >= 50000:
break
# read_kmers.append(str(reverse_sequence[j: j + BF_1_1.k]))
reads.append(read_kmers)
BF_1_1.hits_per_filter = hits_per_filter_copy
else:
# resetting hit counter
BF_1_1.hits_per_filter = hits_per_filter_copy
reads_filtered = set()
threshold_dic = {}
counter = 0
#print(reads)
#print("Anzahl contigs: ", len(reads))
for i in range(len(reads)):
#print("Read-Länge: ", len(reads[i]))
read_kmers_filtered = []
threshold = 0
temp = []
for j in range(len(reads[i])):
BF_1_1.number_of_kmeres += 1
hits_per_filter_copy = BF_1_1.hits_per_filter[:]
BF_1_1.lookup(reads[i][j])
if hits_per_filter_copy != BF_1_1.hits_per_filter:
threshold += 1
temp.append(reads[i][j])
count = threshold_dic.get(threshold, 0)
threshold_dic[threshold] = count + 1
# parameter value needs to be determined
#print("Länge temp: ", len(temp))
#print("Threshold: ", 0.7*len(reads[i]))
if threshold >= (0.7*len(reads[i])):
#print("added")
reads_filtered.update(temp)
counter += len(temp)
#print(counter)
if counter >= 50000:
break
#reads_for_blast = set()
#for sequence in range(len(reads)):
#reads[sequence] = "".join(reads[sequence])
x, y = [], []
threshold_dic = dict(sorted(threshold_dic.items()))
for i in reads_filtered:
hits_per_filter_copy = BF.hits_per_filter[:]
BF.lookup(i)
if ((sum(BF.hits_per_filter) - sum(hits_per_filter_copy)) <= 5 and ((sum(BF.hits_per_filter) - sum(hits_per_filter_copy)) != 0)):
BF.number_of_kmeres += 1
#for sequence in reads:
#if i in sequence:
#reads_for_blast.update(sequence)
else:
BF.hits_per_filter = hits_per_filter_copy[:]
#print("Sequences: ", reads_for_blast)
print("Kmers searched in Acinetobacter-Filter: ", BF_1_1.number_of_kmeres)
print("Kmers found in Acinetobacter-Filter: ", BF_1_1.hits_per_filter[0])
print("Kmers discarded: ", (BF_1_1.number_of_kmeres - BF_1_1.hits_per_filter[0]))
print("Kmers used for species assignment: ", BF.number_of_kmeres)
else:
for sequence in SeqIO.parse(paths[i], "fasta"):
for j in range(0, len(sequence.seq) - BF.k, 500):
#hits_per_filter_copy = BF_1_1.hits_per_filter[:]
#BF_1_1.lookup(str(sequence.seq[j: j + BF.k]))
#if hits_per_filter_copy != BF_1_1.hits_per_filter:
BF.number_of_kmeres += 1
BF.lookup(str(sequence.seq[j: j + BF.k]))
score = BF.get_score()
#print("Scores: ", score)
#print("Hits: ", BF.hits_per_filter)
names = []
BioMonitoring = False
if BioMonitoring:
with open(r'filter/FilterCulicidaeSpecies.txt', 'rb') as fp:
names = pickle.load(fp)
else:
with open(r'filter/FilterSpecies.txt', 'rb') as fp:
names = pickle.load(fp)
score_edit = [str(x) for x in score]
score_edit = ",".join(score_edit)
elif file_format == "fastq" or file_format == "fq":
if metagenome:
counter = 0
reads = []
for sequence in SeqIO.parse(paths[i], "fastq"):
# reverse_sequence = sequence.seq.reverse_complement()
read_kmers = []
if counter < read_amount:
counter += 1
else:
break
k1 = str(sequence.seq[0:BF_1_1.k]) # first k-mer
k2 = str(sequence.seq[len(str(sequence.seq)) - BF_1_1.k:]) # last k-mer
mid = len(str(sequence.seq)) // 2
k3 = str(sequence.seq[mid:mid + BF_1_1.k]) # k-mer in middle
k4 = str(sequence.seq[BF_1_1.k:BF_1_1.k * 2])
k5 = str(sequence.seq[mid + BF_1_1.k:mid + BF_1_1.k * 2])
# Taking sum of list as reference, if sum has not increased after testing those 3 kmeres,
# then the read won't be tested further
hit_sum = sum(BF_1_1.hits_per_filter)
hits_per_filter_copy = BF_1_1.hits_per_filter[:]
#sample_size = int(len(str(sequence.seq)) ** 0.5)
#threshold_read = sample_size * 0.7
#for i in range(0, len(str(sequence.seq)) - BF_1_1.k, sample_size):
# if "N" not in str(sequence.seq[i: i + BF_1_1.k]):
# BF_1_1.lookup(str(sequence.seq[i: i + BF_1_1.k]))
if "N" not in str(sequence.seq):
BF_1_1.lookup(k1)
BF_1_1.lookup(k2)
BF_1_1.lookup(k3)
BF_1_1.lookup(k4)
BF_1_1.lookup(k5)
# needs at least 2 of 3 hits to continue with read
if (sum(BF_1_1.hits_per_filter) - hit_sum) > 3:
for j in range(len(str(sequence.seq)) - BF_1_1.k):
if "N" not in str(sequence.seq[j: j + BF_1_1.k]):
read_kmers.append(str(sequence.seq[j: j + BF_1_1.k]))
# read_kmers.append(str(reverse_sequence[j: j + BF_1_1.k]))
reads.append(read_kmers)
BF_1_1.hits_per_filter = hits_per_filter_copy
else:
# resetting hit counter
BF_1_1.hits_per_filter = hits_per_filter_copy
amplicon = True
if not amplicon:
reads_filtered = set()
# reads_filtered = []
threshold_dic = {}
#print("Anzahl reads: ", len(reads))
for i in range(len(reads)):
read_kmers_filtered = []
threshold = 0
# hits_per_filter_copy = BF_1.hits_per_filter[:]
# read_len = len(reads[i])
# BF_1.lookup(reads[i][0])
# BF_1.lookup(reads[i][int(read_len/2)])
# BF_1.lookup(reads[i][-1])
# if (BF_1.hits_per_filter[0] - hits_per_filter_copy[0]) < 2:
# continue
temp = []
for j in range(len(reads[i])):
BF_1_1.number_of_kmeres += 1
hits_per_filter_copy = BF_1_1.hits_per_filter[:]
BF_1_1.lookup(reads[i][j])
if hits_per_filter_copy != BF_1_1.hits_per_filter:
threshold += 1
temp.append(reads[i][j])
count = threshold_dic.get(threshold, 0)
threshold_dic[threshold] = count + 1
#print("Threshold: ", 0.8 * len(reads[i]), " Value: ", threshold)
if threshold >= 0.8 * len(reads[i]):
reads_filtered.update(temp)
# reads_filtered.add(reads[i][j])
# reads_filtered.append(reads[i][j])
# reads_filtered.append(list(read_kmers_filtered_unique))
#print("Kmer Anzahl: ", len(reads_filtered))
x, y = [], []
threshold_dic = dict(sorted(threshold_dic.items()))
else:
#reads_filtered = set()
reads_filtered = []
threshold_dic = {}
#print("Anzahl reads: ", len(reads))
for i in range(len(reads)):
read_kmers_filtered = []
threshold = 0
temp = []
for j in range(len(reads[i])):
BF_1_1.number_of_kmeres += 1
hits_per_filter_copy = BF_1_1.hits_per_filter[:]
BF_1_1.lookup(reads[i][j])
if hits_per_filter_copy != BF_1_1.hits_per_filter:
threshold += 1
temp.append(reads[i][j])
#count = threshold_dic.get(threshold, 0)
#threshold_dic[threshold] = count + 1
#print("Threshold: ", 0.8 * len(reads[i]), " Value: ", threshold)
if threshold >= 0.8 * len(reads[i]):
reads_filtered += temp
#print("Kmer Anzahl: ", len(reads_filtered))
#x, y = [], []
#threshold_dic = dict(sorted(threshold_dic.items()))
# for key, value in threshold_dic.items():
# x.append(key)
# y.append(value)
# plt.plot(x, y)
# plt.ylabel('frequency')
# plt.xlabel('kmer-hits')
# plt.title('Read-Hit Coverage')
# plt.show()
# plt.clf()
# print(dict(sorted(threshold_dic.items())))
#print("Kmer Anzahl: ", len(reads_filtered))
for i in reads_filtered:
hits_per_filter_copy = BF.hits_per_filter[:]
BF.lookup(i)
if ((sum(BF.hits_per_filter) - sum(hits_per_filter_copy)) <= 5) and ((sum(BF.hits_per_filter) - sum(hits_per_filter_copy)) != 0):
BF.number_of_kmeres += 1
elif (sum(BF.hits_per_filter) - sum(hits_per_filter_copy)) > 5:
BF.hits_per_filter = hits_per_filter_copy[:]
# for i in range(len(reads_filtered)):
# hits_per_filter_copy = BF.hits_per_filter[:]
# read_len = len(reads_filtered[i])
# BF.lookup(reads[i][0])
# BF.lookup(reads[i][int(read_len/2)])
# BF.lookup(reads[i][-1])
# for j in range(len(reads_filtered[i])):
# hits_per_filter_copy = BF.hits_per_filter[:]
# BF.lookup(reads_filtered[i][j])
# if (sum(BF.hits_per_filter) - sum(hits_per_filter_copy)) == 0:
# continue
# if ((sum(BF.hits_per_filter) - sum(hits_per_filter_copy)) <= 5) and ((sum(BF.hits_per_filter) - sum(hits_per_filter_copy)) != 0):
# BF.number_of_kmeres += 1
# if BF.hits_per_filter[50] != hits_per_filter_copy[50]:
# print(reads_filtered[i][j])
# elif (sum(BF.hits_per_filter) - sum(hits_per_filter_copy)) > 5:
# BF.hits_per_filter = hits_per_filter_copy[:]
print("Kmers searched in Acinetobacter-Filter: ", BF_1_1.number_of_kmeres)
print("Kmers found in Acinetobacter-Filter: ", BF_1_1.hits_per_filter[0])
print("Kmers discarded: ", (BF_1_1.number_of_kmeres - BF_1_1.hits_per_filter[0]))
print("Kmers used for species assignment: ", BF.number_of_kmeres)
# for sequence in SeqIO.parse(paths[i], "fastq"):
# if counter < read_amount:
# counter += 1
# for j in range(0, len(sequence.seq) - BF.k+1, 10):
# BF.number_of_kmeres += 1
# BF.lookup(str(sequence.seq[j: j + BF.k]))
# else:
# break
# counter = 0
# for sequence in SeqIO.parse(paths[i], "fastq"):
# reverse_sequence = sequence.seq.reverse_complement()
# if counter < read_amount:
# counter += 1
# for j in range(0, len(reverse_sequence) - BF.k+1, 10):
# BF.number_of_kmeres += 1
# BF.lookup(str(reverse_sequence[j: j + BF.k]))
# else:
# break
else:
counter = 0
for sequence in SeqIO.parse(paths[i], "fastq"):
if counter < read_amount:
counter += 1
for j in range(0, len(sequence.seq) - BF.k+1, 10):
BF.number_of_kmeres += 1
BF.lookup(str(sequence.seq[j: j + BF.k]))
else:
break
counter = 0
for sequence in SeqIO.parse(paths[i], "fastq"):
reverse_sequence = sequence.seq.reverse_complement()
if counter < read_amount:
counter += 1
for j in range(0, len(reverse_sequence) - BF.k+1, 10):
BF.number_of_kmeres += 1
BF.lookup(str(reverse_sequence[j: j + BF.k]))
else:
break
score = BF.get_score()
print("Scores: ", score)
print("Hits: ", BF.hits_per_filter)
names = []
BioMonitoring = False
if BioMonitoring:
with open(r'filter/FilterCulicidaeSpecies.txt', 'rb') as fp:
names = pickle.load(fp)
else:
with open(r'filter/FilterSpecies.txt', 'rb') as fp:
names = pickle.load(fp)
score_edit = [str(x) for x in score]
score_edit = ",".join(score_edit)
# making prediction
if not metagenome and not BioMonitoring:
prediction = Classifier.classify(r'Training_data/Training_data_spec.csv', score, True)
else:
index_result = max(range(len(score)), key=score.__getitem__)
prediction = names[index_result]
if max(score) < 0.3:
predictions.append("unknown")
scores.append(str(max(score)))
else:
if BioMonitoring:
predictions.append(prediction)
scores.append(str(max(score)))
else:
predictions.append("A. " + prediction)
scores.append(str(max(score)))
if BioMonitoring:
for i in range(len(predictions)):
print(files[i] + ": " + "Predicted species: ", predictions[i], " Score: ", scores[i])
print("Taxonomic assignment done...")
return predictions, scores
def clAssT(BF_2, files, paths, file_format, read_amount):
print("Starting strain-typing on sub-type-level...")
predictions_ClAssT = []
scores_ClAssT = []
for i in range(len(files)):
if i == int(len(files)/6) or i == int(len(files)/3) or i == int(len(files)/2) or i == int(len(files)/1.5) or i == int(len(files)/1.2):
print("...")
BF_2.number_of_kmeres = 0
BF_2.hits_per_filter = [0] * BF_2.clonetypes
if file_format == "fasta" or file_format == "fna":
for sequence in SeqIO.parse(paths[i], "fasta"):
# Originally 10
for j in range(0, len(sequence.seq) - BF_2.k, 500):
BF_2.number_of_kmeres += 1
BF_2.lookup(str(sequence.seq[j: j + BF_2.k]))
elif file_format == "fastq" or file_format == "fq":
counter = 0
for sequence in SeqIO.parse(paths[i], "fastq"):
if counter < read_amount:
counter += 1
for j in range(0, len(sequence.seq) - BF_2.k+1, 10):
BF_2.number_of_kmeres += 1
BF_2.lookup(str(sequence.seq[j: j + BF_2.k]))
else:
break
score_ClAssT = BF_2.get_score()
score_edit_ClAssT = [str(x) for x in score_ClAssT]
score_edit_ClAssT = ",".join(score_edit_ClAssT)
prediction_ClAssT = Classifier.classify(r'Training_data/Training_data_IC.csv', score_ClAssT, [True,True,True,True,True,True,True,True,False])
predictions_ClAssT.append(prediction_ClAssT)
scores_ClAssT.append(str(max(score_ClAssT)))
print("Strain-typing on sub-type-level done...")
return predictions_ClAssT, scores_ClAssT
def blaOXA(BF_3, files, paths, file_format, read_amount):
start = time.time()
print("Start screening for blaOXA-genes...")
paths_oxa = sorted(os.listdir(r"filter/OXAs/families"))
BF_families = BF_3["OXA-families"]
oxas = []
scores_oxa = []
scores_oxa_ind = []
for i in paths_oxa:
oxas.append(i[:-4])
for i in range(len(files)):
oxa_dic = {}
if i == int(len(files)/6) or i == int(len(files)/3) or i == int(len(files)/2) or i == int(len(files)/1.5) or i == int(len(files)/1.2):
print("...")
# Checking file type
# if the file is fasta -> concat lines
reads = []
BF_families.number_of_kmeres = 0
BF_families.hits_per_filter = [0] * BF_families.clonetypes
BF_families.table = OXATable()
BF_families.table.read_dic(r'filter/OXAs_dict/oxa_dict.txt')
if file_format == "fasta" or file_format == "fna":
for sequence in SeqIO.parse(paths[i], "fasta"):
reads.append(str(sequence.seq))
BF_families.lookup_oxa(reads, ".fna")
elif file_format == "fastq" or file_format == "fq":
counter = 0
for sequence in SeqIO.parse(paths[i], "fastq"):
if counter < read_amount:
counter += 1
reads.append(str(sequence.seq))
else:
break
BF_families.lookup_oxa(reads, ".fq")
# print("Reads used: ", counter)
score_oxa = BF_families.get_oxa_score()
for i in range(len(oxas)):
oxa_dic[oxas[i]] = score_oxa[i]
for i in range(len(oxa_dic)):
if oxa_dic[oxas[i]] < 0.3:
del oxa_dic[oxas[i]]
if len(oxa_dic) == 0:
oxa_dic = "None"
if oxa_dic != "None":
oxa_dic = dict(sorted(oxa_dic.items(), key=lambda item: item[1]))
scores_oxa.append(oxa_dic)
# prepare data for next taxonomic level
oxa_names = []
for oxa_family in oxa_dic:
oxa_names.append(oxa_family[:-7])
for oxa_family in oxa_names:
oxa_dic_ind = {}
## TODO:
BF_ind = BF_3[oxa_family]
BF_ind.number_of_kmeres = 0
BF_ind.hits_per_filter = [0] * BF_ind.clonetypes
BF_ind.table = OXATable()
BF_ind.table.read_dic(r'filter/OXAs_dict/oxa_dict.txt')
paths_oxa = sorted(os.listdir(r"filter/OXAs/individual/" + oxa_family))
oxas_ind = []
for i in paths_oxa:
oxas_ind.append(i[:-4])
if file_format == "fasta" or file_format == "fna":
BF_ind.lookup_oxa(reads, ".fna")
elif file_format == "fastq" or file_format == "fq":
BF_ind.lookup_oxa(reads, ".fq")
score_oxa = BF_ind.get_oxa_score()
# build dict with oxa-gen and its score
for i in range(len(oxas_ind)):
oxa_dic_ind[oxas_ind[i]] = score_oxa[i]
# filter dict by score
if len(oxa_dic_ind) == 0:
scores_oxa_ind.append("None")
else:
scores_oxa_ind.append([max(oxa_dic_ind, key=oxa_dic_ind.get), oxa_dic_ind[max(oxa_dic_ind, key=oxa_dic_ind.get)]])
end = time.time()
needed = round(end - start, 2)
print("Time needed: ", needed)
print("Screening for blaOXA-genes done...")
return scores_oxa, scores_oxa_ind
def main():
print("")
print("XspecT performs a taxonomic assignment on the species-level for bacteria of the genus Acinetobacter.")
print("ClAssT performs a strain-typing one sub-type-level for A. baumannii.")
print("You can also screen your file for blaOXA-genes.")
print("XspecT/ClAssT needs a file path to all files where an assignment shall be performed.")
print("")
print("Run XspecT: (y/n)?")
XspecT = input()
if XspecT == "y":
XspecT = True
elif XspecT == "n":
XspecT = False
else:
print("Error: Wrong Input, use y/n (y=yes, n=no)!")
quit()
print("Run ClAssT: (y/n)?")
ClAssT = input()
if ClAssT == "y":
ClAssT = True
elif ClAssT == "n":
ClAssT = False
else:
print("Error: Wrong Input, use y/n (y=yes, n=no)!")
quit()
print("Screen for blaOXA-Genes: (y/n)?")
oxa = input()
if oxa == "y":
oxa = True
elif oxa == "n":
oxa = False
else:
print("Error: Wrong Input, use y/n (y=yes, n=no)!")
quit()
#print("Run BioMonitoring for mosquitos: (y/n)?")
#BioMonitoring = input()
#if BioMonitoring == "y":
#BioMonitoring = True
#elif BioMonitoring == "n":
#BioMonitoring = False
#else:
#print("Error: Wrong Input, use y/n (y=yes, n=no)!")
#quit()
print("Metagenome-Mode?: (y/n)?")
metagenome = input()
if metagenome == "y":
metagenome = True
elif metagenome == "n":
metagenome = False
else:
print("Error: Wrong Input, use y/n (y=yes, n=no)!")
quit()
print("Enter file-format: (fasta/fastq)")
file_format = input()
if file_format != "fasta" and file_format != "fastq":
print("Error: Invalid file-format, use fasta/fastq!")
quit()
if file_format == "fastq":
print("How many reads should be used: ")
read_amount = int(input())
else:
read_amount = 543789
if read_amount < 0:
print("Error: Invalid read amount")
print("Enter file-path:")
file_path = input()
print("Save results as CSV-table: (y/n)?")
csv_table = input()
print("")
if csv_table == "y":
csv_table = True
elif csv_table == "n":
csv_table = False
else:
print("Error: Wrong Input, use y/n (y=yes, n=no)!")
quit()
if XspecT == False and ClAssT == False and oxa == False:
print("No tool selected, closing application...")
quit()
xspecT_mini(file_path, XspecT, ClAssT, oxa, file_format, read_amount, csv_table, metagenome)
if __name__ == '__main__':
main()