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XspecT_mini_alt2.py
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XspecT_mini_alt2.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
import random
from numpy import array
from numpy import sum
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:
contigs = []
contigs_classified = {}
for sequence in SeqIO.parse(paths[i], "fasta"):
contigs = []
# reverse_sequence = sequence.seq.reverse_complement()
contigs_kmers = []
# Taking sum of list as reference, if sum has not increased after testing those 3 kmeres,
# then the contigs 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())
# 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]):
contigs_kmers.append(str(sequence.seq[j: j + BF_1_1.k]).upper())
counter += 1
# how many kmers? to use
if counter >= 50000:
break
# contigs_kmers.append(str(reverse_sequence[j: j + BF_1_1.k]))
contigs.append(contigs_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
continue
contigs_filtered = set()
threshold_dic = {}
counter = 0
#print(contigs)
#print("Anzahl contigs: ", len(contigs))
# Since we classify individual contigs now, the var contigs only contains one item which makes those loops unneccesary
for i in range(len(contigs)):
#print("contigs-Länge: ", len(contigs[i]))
contigs_kmers_filtered = []
threshold = 0
temp = []
for j in range(len(contigs[i])):
BF_1_1.number_of_kmeres += 1
hits_per_filter_copy = BF_1_1.hits_per_filter[:]
BF_1_1.lookup(contigs[i][j])
if hits_per_filter_copy != BF_1_1.hits_per_filter:
threshold += 1
temp.append(contigs[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(contigs[i]))
if threshold >= (0.7*len(contigs[i])):
#print("added")
contigs_filtered.update(temp)
counter += len(temp)
#print(counter)
if counter >= 50000:
break
x, y = [], []
threshold_dic = dict(sorted(threshold_dic.items()))
# since we do ind contig classifications we need to reset the BF vars
BF.number_of_kmeres = 0
BF.hits_per_filter = [0] * BF.clonetypes
for i in contigs_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
else:
BF.hits_per_filter = hits_per_filter_copy[:]
score = BF.get_score()
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
index_result = max(range(len(score)), key=score.__getitem__)
prediction = names[index_result]
if max(score) < 0.3:
if "unkown" not in contigs_classified:
contigs_classified["unknown"] = [1]
else:
contigs_classified["unknown"][0] += 1
#scores.append(str(max(score)))
else:
if BioMonitoring:
predictions.append(prediction)
scores.append(str(max(score)))
else:
if ("A. " + prediction) not in contigs_classified:
contigs_classified["A. " + prediction] = [max(score), 1]
else:
contigs_classified["A. " + prediction][1] += 1
contigs_classified["A. " + prediction][0] += max(score)
#scores.append(str(max(score)))
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)
for key, value in contigs_classified.items():
if key == "unknown":
continue
value.append(value[0]/value[1])
value.pop(0)
value[1] = "Score: ~" + str(value[1])
value[0] = "Contigs: " + str(value[0])
contigs_classified[key] = value
print("Species: ", key)
print(value[0])
print(value[1])
print()
elif file_format == "fastq" or file_format == "fq":
if metagenome:
counter = 0
reads = []
reads_classified = {}
for sequence in SeqIO.parse(paths[i], "fastq"):
# reverse_sequence = sequence.seq.reverse_complement()
read_kmers = []
reads = []
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
continue
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)
else:
continue
# 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))
#print("Kmer Anzahl: ", len(reads_filtered))
if len(reads_filtered) == 0:
continue
BF.number_of_kmeres = 0
BF.hits_per_filter = [0] * BF.clonetypes
BF.kmer_hits_single = []
for i in reads_filtered:
hits_per_filter_copy = BF.hits_per_filter[:]
BF.lookup(i)
BF.number_of_kmeres += 1
#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[:]
#print(len(BF.kmer_hits_single))
score = BF.get_score()
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
index_result = max(range(len(score)), key=score.__getitem__)
prediction = names[index_result]
# bootstrapping
bootstrap_n = 100
samples = bootstrap(BF.kmer_hits_single, BF.number_of_kmeres, bootstrap_n)
sample_scores = bootstrap_scores(samples, BF.number_of_kmeres, BF.clonetypes)
bootstrap_score = 0
for i in range(len(sample_scores)):
if max(range(len(sample_scores[i])), key=sample_scores[i].__getitem__) == index_result:
bootstrap_score += 1
bootstrap_score = bootstrap_score/bootstrap_n
print(bootstrap_score)
if max(score) < 0.3:
if "unkown" not in reads_classified:
reads_classified["unknown"] = [1]
else:
reads_classified["unknown"][0] += 1
#scores.append(str(max(score)))
else:
if BioMonitoring:
predictions.append(prediction)
scores.append(str(max(score)))
else:
if ("A. " + prediction) not in reads_classified:
reads_classified["A. " + prediction] = [max(score), 1]
else:
reads_classified["A. " + prediction][1] += 1
reads_classified["A. " + prediction][0] += max(score)
# 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()
for key, value in reads_classified.items():
if key == "unknown":
continue
value.append(value[0]/value[1])
value.pop(0)
value[1] = "Score: ~" + str(value[1])
value[0] = "Reads: " + str(value[0])
reads_classified[key] = value
print("Species: ", key)
print(value[0])
print(value[1])
print()
# 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 bootstrap(data, sample_amount, size):
samples = []
for iteration in range(size):
sample = []
for i in range(sample_amount):
sample.append(random.choice(data))
sample = array(sample)
temp = sum(sample, 0)
samples.append(list(temp))
return samples
def bootstrap_scores(samples, number_of_kmeres, number_of_filters):
scores = []
# calculates float for each value in [hits per filter]
for i in range(len(samples)):
score = []
for j in range(number_of_filters):
if samples[i][j] == 0:
score.append(0.0)
else:
score.append(round(float(samples[i][j]) / float(number_of_kmeres), 2))
scores.append(score)
return scores
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()