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#!/usr/bin/env python
from multiprocessing import Pool
from Bio import SeqIO
import multiprocessing
from subprocess import *
import os
import gzip
import quikr as q
import sys
import numpy as np
import argparse
import platform
import csv
# our defaults
kmer = 6
lamb = 10000
output_directory = ""
input_directory = ""
def main():
global kmer
global input_directory
global output_directory
global lamb
global trained_matrix
parser = argparse.ArgumentParser(description="MultifastaOTU")
parser.add_argument("-i", "--input-directory", help="directory containing fasta files", required=True)
parser.add_argument("-o", "--otu-table", help="otu_table", required=True)
parser.add_argument("-t", "--trained-matrix", help="your trained matrix ", required=True)
parser.add_argument("-f", "--trained-fasta", help="the fasta file used to train your matrix", required=True)
parser.add_argument("-d", "--output-directory", help="quikr output directory", required=True)
parser.add_argument("-l", "--lamb", type=int, help="the default lambda value is 10,000")
parser.add_argument("-k", "--kmer", type=int, help="specifies which kmer to use, default=6")
parser.add_argument("-j", "--jobs", type=int, help="specifies how many jobs to run at once, default=number of CPUs")
args = parser.parse_args()
# our defaults
jobs = multiprocessing.cpu_count()
trained_matrix = args.trained_matrix
input_directory = args.input_directory
output_directory = args.output_directory
# Make sure our input exist
if not os.path.isdir(args.input_directory):
parser.error("Input directory not found")
if not os.path.isdir(args.output_directory):
print "Output directory not found, creating directory"
os.mkdir(args.output_directory)
if not os.path.isfile(args.trained_matrix):
parser.error("Custom trained matrix not found")
if not os.path.isfile(args.trained_fasta):
parser.error("Fasta file of trained matrix not found")
# use alternative lambda
if args.lamb is not None:
lamb = args.lamb
if args.jobs is not None:
jobs = args.jobs
if args.kmer is not None:
kmer = args.kmer
# Load trained matrix
if q.is_compressed(args.trained_matrix):
trained_matrix_file = gzip.open(args.trained_matrix, "rb")
else:
trained_matrix_file = open(args.trained_matrix, "rb")
trained_matrix = np.load(trained_matrix_file)
# Return a list of the input directory
fasta_list = os.listdir(args.input_directory)
# Queue up and run our quikr functions.
pool = Pool(processes=jobs)
results = pool.map(quikr_call, fasta_list)
# Create an array of headers
headers = []
trained_matrix_headers = open(args.trained_fasta, "rU")
for header in SeqIO.parse(trained_matrix_headers, "fasta"):
headers.append(header.id)
trained_matrix_headers.close()
# create our number of reads matrix
number_of_reads = np.zeros((len(headers), len(fasta_list)))
# load the keys with values from each fasta result
for fasta, fasta_it in map(None, fasta_list, range(len(fasta_list))):
count_sequences = Popen(["grep", "-c" , "^>", args.input_directory + fasta], stdout=PIPE)
number_of_sequences = int(count_sequences.stdout.readline())
proportions = np.loadtxt(output_directory + fasta);
for proportion, proportion_it in map(None, proportions, range(len(proportions))):
number_of_reads[proportion_it, fasta_it] = round(proportion * number_of_sequences)
# remove empty rows from our matrix
final_headers = list()
final_data = list()
for row, header in map(None, number_of_reads, headers):
if row.sum() != 0:
final_headers.append(header)
final_data.append(row)
# convert from a list back into a numpy array
final_data = np.array(final_data, dtype=int)
# stack our final header and our number_of_reads matrix
number_of_reads = np.column_stack((final_headers, final_data))
# write our OTU table
output_file = open(args.otu_table, "wb")
writer = csv.writer(output_file, delimiter="\t")
#write out our fasta file row
writer.writerow(['# QIIME vGail OTU table'])
fasta_row = ['#OTU_ID']
fasta_row.append(' '.join(fasta_list))
fasta_row = [' '.join(fasta_row)]
writer.writerow(fasta_row)
# write out our results
for i in range(0, np.shape(number_of_reads)[0]):
writer.writerow(list(number_of_reads[i]))
output_file.close()
return 0
def quikr_call(fasta_file):
input_location = input_directory + fasta_file
output_location = output_directory + "/" + os.path.basename(fasta_file)
xstar = q.calculate_estimated_frequencies(input_location, trained_matrix, kmer, lamb)
np.savetxt(output_location, xstar, delimiter=",", fmt="%f")
return xstar
if __name__ == "__main__":
sys.exit(main())
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