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adapt_find.py
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adapt_find.py
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#!/usr/bin/env python
from __future__ import division
from __future__ import print_function
import sys
import os
import pandas as pd
import numpy as np
from scipy import stats
from scipy.stats import variation
from numpy import median
import operator
from collections import Counter
from itertools import groupby
import multiprocessing
import argparse
import itertools
from difflib import SequenceMatcher
from operator import itemgetter
import gzip
pd.options.mode.chained_assignment = None
#agrparse section
docstring= """
USAGE: python adapt_find.py <argument>
Example: python adapt_find.py ILLUMINA
Arguments:
Valid required arguments:
(1) ILLUMINA #(for Illumina sequencing)
(2) ION_TORRENT #(for Ion Torrent sequencing)
(3) 454 #(for 454 sequencing)
(4) SOLID #(for SOLID sequencing)
Use --help for more info
DESCRIPTION
Identifies adapter sequences from raw sequencing dataset, trims, maps to the genome, if an genome index is provided
"""
parser = argparse.ArgumentParser(usage = "\n" +"python %(prog)s sequencing_platform [--output_path path/to/folder] [--input_path path/to/folder] [--files list of files] \n" +"\n" + "Usage examples:\nFor Illumina as sequencing_platform and if the current directory has all FASTQ files\npython ADAPT_find.py ILLUMINA\nFor Illumina as sequencing_platform and to specify filenames explicitly\npython ADAPT_find.py ILLUMINA --files filename1.fastq filename2.fastq\nFor Illumina as sequencing_platform and to specify path to folder containing FASTQ files\npython ADAPT_find.py ILLUMINA --input_path path/to/folder\n" + "\n" + "Description:\nIdentifies adapter sequences from raw sequencing dataset, trims, maps to the genome, if an genome index is provided\n", add_help=False)
required = parser.add_argument_group('required arguments')
required.add_argument("sequencing_platform", help = """Valid arguments:
(1) ILLUMINA #(for Illumina sequencing)
(2) SOLID #(for SOLID sequencing)
(3) ION_TORRENT #(for Ion Torrent sequencing)
(4) 454 #(for 454 sequencing)""")
optional = parser.add_argument_group('optional arguments')
optional.add_argument('--min_len', default = 15, help= 'minimum length parameter for CUTADAPT')
optional.add_argument('--max_len', default = 50, help= 'maximum length parameter for CUTADAPT')
optional.add_argument('--index', help= 'paste path to bowtie index')
optional.add_argument('--input_path', help= 'paste path to FASTQ files')
optional.add_argument('--output_path', default = os.getcwd(), help= 'paste path to store output files')
optional.add_argument('--files', nargs='*', help= 'enter FASTQ files seperated by space')
optional.add_argument("-h", "--help", action='help', help='print help message')
args = parser.parse_args()
if (args.output_path!=None):
path = args.output_path
else:
path = os.getcwd()
# get the current working directory
if (args.input_path!=None) and (args.files!=None):
sys.exit('\nERROR: input path and files option cannnot be specified together. Only one of the two options can be specified. use ADAPT_find.py --help for more info\n%s'%(docstring))
elif (args.files!=None):
files= args.files
files = [f for f in files if f.split("/")[-1].endswith(".fastq") or f.split("/")[-1].endswith(".fastq.gz")]
else:
if (args.input_path==None):
cwd = os.getcwd()
else:
cwd= args.input_path
files = [cwd + "/" + f for f in os.listdir(cwd) if f.endswith(".fastq") or f.endswith(".fastq.gz")]
if len(files)==0:
sys.exit('\nERROR: Could not find any FASTQ files. Please check if the input path is specified correctly. use ADAPT_find.py --help for more info\n%s'%(docstring))
if (args.sequencing_platform!="ILLUMINA") and (args.sequencing_platform!="SOLID") and (args.sequencing_platform!="ION_TORRENT") and (args.sequencing_platform!="454"):
sys.exit('\nERROR: Invalid argument for sequencing_platform . use ADAPT_find.py --help for more info\n%s'%(docstring))
if not os.path.exists(path+ "/"+ "good-mapping"):
os.makedirs(path+ "/"+ "good-mapping")
if not os.path.exists(path+ "/"+ "bad-mapping"):
os.makedirs(path+ "/"+ "bad-mapping")
if not os.path.exists(path+ "/"+ "no_overepresented_sequences"):
os.makedirs(path+ "/"+ "no_overepresented_sequences")
if (args.index!=None):
index = args.index
#check if blast exists (version >= 2.7)
os.system("blastn -version > vers.txt")
infile= open("vers.txt", "r")
lines = infile.readlines()
if lines != []:
ver = float(lines[0].strip().split("blastn: ")[1].split("+")[0][2:])
else:
ver = "no"
if (ver == "no"):
sys.exit('\nERROR: blast not found. Please install blast 2.7 or any version released after 2.7\n')
elif (ver < 7.0):
sys.exit('\nERROR: adapt_find requires blast version 2.7 or higher\n')
else:
blastn = "blastn"
#check if bowtie version exists (version >= 1.1)
if (args.index!=None):
os.system("bowtie --version > vers.txt")
infile= open("vers.txt", "r")
lines = infile.readlines()
if lines != []:
ver1 = float(lines[0].strip().split("bowtie version")[1].split(" ")[1][:3])
else:
ver1 = "no"
if (ver1 == "no"):
sys.exit('\nERROR: bowtie not found. Please install bowtie version 1.1 or any version released after 1.1')
elif (ver1 < 1.1):
sys.exit('\nERROR: adapt_find requires bowtie 1.1 or higher\n')
else:
bowtie = "bowtie"
if os.popen("cutadapt --version").read().strip() == "":
os.system('\nERROR: Cutadapt not found. Please install cutadapt')
os.system("rm vers.txt")
def worker1(f):
filename = f.split("/")[-1].split(".")[0]
print("processing file " + filename + ".fastq")
command = "mkdir "+ "-p "+ path + "/aux_files/" + filename
os.system(command)
newpath = path + "/aux_files/" + filename + "/"
fastq_filename = filename + "_trimmed.fastq"
query_file = newpath + filename + "_query.fa"
subject_file = newpath + filename + "_subject.fa"
blast_file = newpath + filename + "_blast.csv"
blast2_file = newpath + filename + "_blast2.csv"
filename4 = newpath + filename + "_adapters.csv"
log = newpath + filename+ "_bowtie.txt"
log2 = newpath + filename+ "_cutadapt.txt"
if f.endswith(".fastq.gz"):
fastq_lst=gzip.open(f,'rb').readlines()[1::4]
fastq_lst = [line.strip().decode() for line in fastq_lst]
else:
infile= open(f)
fastq_lst = infile.readlines()[1::4]
fastq_lst = [line.strip() for line in fastq_lst]
abund = len(fastq_lst)
collapsed2 = Counter(fastq_lst)
collapsed = sorted(collapsed2.items(), key=operator.itemgetter(1), reverse=True)
ofile = open(query_file, "w")
q_fil = collapsed[0][1]
if ((abund > 5000000) and q_fil < 14000) or ((abund > 10000000) and len(collapsed[0][0]) >76) :
q_counter = 300
#elif abund > 9000000:
#q_counter = 50
else:
q_counter = 0
ind_counter = 0
for x,y in collapsed[q_counter:]:
if ind_counter < 51:
count_obj = Counter({'A':0,'T':0,'G':0,'C':0})
count_obj.update(x)
if ((min(count_obj.items(), key=itemgetter(1))[1])/len(x)) > 0.05:
ind_counter = ind_counter + 1
q_counter = q_counter + 1
ofile.write(">" + str(y) +"\n" + str(x) + "\n")
else:
continue
else:
break
ofile.close()
ofile = open(subject_file, "w")
if ((abund > 10000000) and len(collapsed[0][0]) >76) and q_counter < 400:
q_counter = 400
elif (abund > 10000000) and q_counter < 100:
q_counter = 101
else:
q_counter = q_counter
ind_counter = 0
for x,y in collapsed[q_counter:]:
if ind_counter < 201:
count_obj = Counter({'A':0,'T':0,'G':0,'C':0})
count_obj.update(x)
if ((min(count_obj.items(), key=itemgetter(1))[1])/len(x)) > 0.05:
ind_counter = ind_counter + 1
ofile.write(">" + str(y) +"\n" + str(x) + "\n")
else:
continue
else:
break
ofile.close()
new_df = pd.DataFrame(collapsed[:100], columns=['Sequence','Count'])
new_df2 = pd.DataFrame(collapsed[:40], columns=['Sequence','Count'])
if len(new_df) > 2:
new_df["Sequence_length"] = [len(word) for word in new_df['Sequence']]
new_df2["Sequence_length"] = [len(word) for word in new_df2['Sequence']]
seq_len = new_df2["Sequence_length"].tolist()
seq_median = new_df2["Sequence_length"].median()
if (len(seq_len)) > 1:
seq_var = variation(seq_len, axis=0)
else:
seq_var = 0
if ((len(seq_len)>1) and (seq_var > 0.17) and (max(seq_len) < 50)) or ((seq_median < 30) and (max(seq_len) < 50)):
print("There is no adapter sequence and the length distribution of top 100 collapsed reads is ..........")
print(list(set(new_df2["Sequence_length"].tolist())))
command = "cutadapt --trim-n -q 20 -m "+ str(args.min_len)+ " -M " + str(args.max_len) + " " + f + " 2> " + log2 + " | cutadapt -q 20 -m "+ str(args.min_len)+ " -M " + str(args.max_len) + " - > " + path + "/good-mapping/" + fastq_filename + " 2>> "+ log2
print(command)
os.system(command)
if (args.index!=None):
command = bowtie +" --best -v 1 -p 20 " + index + " -q " + path+ "/good-mapping/" + fastq_filename + " > check.sam 2> "+ log
os.system(command)
infile= open(log, "r")
lines = infile.readlines()
a = float(lines[1].strip().split("(")[1].split("%")[0])
no_reads = lines[0].strip().split("processed: ")[1]
if (a >=50):
element = [filename,"no adapter","na","na",abund,"na","na", no_reads,a,"good"]
return element
else:
command = "mv " + path + "/good-mapping/"+ fastq_filename + " " + path+ "/bad-mapping/" + fastq_filename
print(command)
os.system(command)
element = [filename,"no adapter","na","na",abund,"na","na", no_reads,a,"bad"]
return element
else:
element = [filename,"no adapter","na","na",abund,"na","na", "na","na","na"]
return element
sequences = new_df["Sequence"].tolist()
kmer = 25
while(kmer!=0):
kmers_collapsed = sorted(Counter([x[:kmer] for x in sequences]).items(), key=operator.itemgetter(1), reverse=True)
if (float(kmers_collapsed[0][1])/float(len(new_df))) > 0.9:
break
else:
kmer = kmer - 1
forward_adapter = kmers_collapsed[0][0]
kmer = 25
while(kmer!=0):
kmers = Counter([x[-kmer:] for x in sequences])
kmers_collapsed = sorted(Counter([x[-kmer:] for x in sequences]).items(), key=operator.itemgetter(1), reverse=True)
if (float(kmers_collapsed[0][1])/float(len(new_df))) > 0.9:
break
else:
kmer = kmer - 1
reverse_adapter = kmers_collapsed[0][0]
if (len(forward_adapter) > 2) and (len(reverse_adapter) > 2):
command = "cutadapt -q 20 -m "+ str(args.min_len)+ " -M " + str(args.max_len) + " " + "-g ^" + forward_adapter + " " + f + " 2> " + log2 + " | cutadapt -q 20 -m "+ str(args.min_len)+ " -M " + str(args.max_len) + " -a " + reverse_adapter + " - > " + path + "/good-mapping/" + fastq_filename + " 2>> "+ log2
print(command)
os.system(command)
infile= open(log2, "r")
lines = infile.readlines()
cut_adapt = []
for line in lines:
if "Reads with adapters" in line:
cut_adapt.append(line.strip().replace(" ", "").split("(")[1].split(")")[0])
if (args.index!=None):
command = bowtie +" --best -v 1 -p 20 " + index + " -q " + path + "/good-mapping/" + fastq_filename + " > check.sam 2> "+ log
print(command)
os.system(command)
adapter = "5 prime = " +forward_adapter + " & 3 prime = " + reverse_adapter
infile= open(log, "r")
lines = infile.readlines()
a = float(lines[1].strip().split("(")[1].split("%")[0])
no_reads = lines[0].strip().split("processed: ")[1]
if (a >=50):
element = [filename,"5&3'-anchored",forward_adapter,reverse_adapter,abund,cut_adapt[0],cut_adapt[1],no_reads,a,"good"]
return element
else:
command = "mv " + path + "/good-mapping/"+ fastq_filename + " " + path + "/bad-mapping/" + fastq_filename
print(command)
os.system(command)
element = [filename,"5&3'-anchored",forward_adapter,reverse_adapter,abund,cut_adapt[0],cut_adapt[1],no_reads,a,"bad"]
return element
else:
element = [filename,"5&3'-anchored",forward_adapter,reverse_adapter,abund,cut_adapt[0],cut_adapt[1],"na","na","na"]
return element
#check now the median length
command = blastn + " -task blastn-short -ungapped -max_hsps 2 -query " + query_file + " -subject " + subject_file + " -outfmt '10 qseqid sseqid qseq length evalue qstart sstart' -out " + blast_file
os.system(command)
csv = pd.read_csv(blast_file)
csv.columns = ['query', 'subject', 'aligned_seq', 'aligned_length', 'evalue', 'qstart', 'sstart']
fo = open(blast_file,"w")
csv.to_csv(fo, sep = ",", index=False)
fo.close()
d = csv["sstart"].median()
e = csv["qstart"].median()
csv.insert(3, "adapter", "")
nom_length=csv["aligned_length"].median()
max_length=csv["aligned_length"].max()
print("Median length of aligned sequences for filename - " + str(filename) + " is " + str(nom_length))
if max_length > 100:
csv = csv.loc[~(csv["aligned_length"] <= 60)]
csv = csv.loc[~((csv["qstart"] <= 20) & (csv["sstart"] <= 20))]
csv = csv.loc[~((csv["qstart"] >= 100) & (csv["sstart"] >= 100))]
elif (nom_length >= 20) or (max_length > 50) or ((abund > 10000000) and len(collapsed[0][0]) >76):
if (seq_median-nom_length) <= 10:
csv = csv.loc[~((csv["qstart"] <= 5) & (csv["sstart"] <= 5))]
csv = csv.loc[~(csv["aligned_length"] < 10)]
elif (seq_median-nom_length) >= 40:
csv = csv.loc[~((csv["qstart"] <= 5) | (csv["sstart"] <= 5))]
csv = csv.loc[~((csv["qstart"] <= 15) & (csv["sstart"] <= 15))]
csv = csv.loc[~(csv["aligned_length"] < 8)]
else:
csv = csv.loc[~((csv["qstart"] <= 5) | (csv["sstart"] <= 5))]
csv = csv.loc[~((csv["qstart"] <= 15) & (csv["sstart"] <= 15))]
csv = csv.loc[~(csv["aligned_length"] < 15)]
if csv["sstart"].median() >= 15:
csv = csv.loc[~(csv["sstart"] < 15)]
if csv["qstart"].median() >= 15:
csv = csv.loc[~(csv["qstart"] < 15)]
csv.reset_index(drop=True,inplace=True)
elif (10 < nom_length < 20) :
csv = csv.loc[~((csv["qstart"] <= 5) | (csv["sstart"] <= 5))]
csv = csv.loc[~((csv["qstart"] <= 15) & (csv["sstart"] <= 15))]
csv = csv.loc[~(csv["aligned_length"] < 10)]
csv.reset_index(drop=True,inplace=True)
else:
csv = csv.loc[~((csv["qstart"] <= 15) | (csv["sstart"] <= 15))]
csv.reset_index(drop=True,inplace=True)
csv.sort_values('aligned_length', ascending=False, inplace=True)
csv.reset_index(drop=True,inplace=True)
if (nom_length <= 50) and (max_length >= 60):
csv = csv.loc[~(csv["aligned_length"] >= 60)]
csv.reset_index(drop=True,inplace=True)
if (len(csv)==0):
element = [filename,"no adapter","na","na",abund,"na","na","na","na","na"]
command = "cp "+ filename + ".fastq" + " no_overepresented_sequences/"
print(command)
os.system(command)
return element
nasa = pd.DataFrame([],columns=list(csv))
grouped = csv.groupby('query')
def trim(group):
group.reset_index(drop=True,inplace=True)
for i in range (0,len(group)):
b = group.at[i,'aligned_length']
if (b >= 21):
seq = group.at[i,'aligned_seq'][:20]
group.at[i,'adapter']= seq
else:
seq = group.at[i,'aligned_seq']
group.at[i,'adapter']= seq
m=group["qstart"].tolist()
# group most_common output by frequency
freqs = groupby(Counter(m).most_common(), lambda x:x[1])
# pick off the first group (highest frequency)
q = [val for val,count in next(iter(freqs))[1]]
q.append(q[0]+1)
q.append(q[0]+2)
q.append(q[0]+3)
group = group.loc[group["qstart"].isin(q)]
group = group.loc[group["evalue"] < 0.5]
group.reset_index(drop=True,inplace=True)
return group
if (nom_length > 10):
for name,group in grouped:
result = trim(group)
if len(result) !=0:
nasa=nasa.append(result, ignore_index=True)
else:
nasa = csv.loc[(csv["aligned_length"] < 15)]
if (len(nasa)==0):
element = [filename,"no adpater","na","na", abund, "na","na","na", "na","na"]
command = "cp "+ filename + " no_overepresented_sequences/"
print(command)
os.system(command)
return element
print("Writing BLAST output")
nasa.insert(4, "adapter_length", "")
nasa['adapter_length'] = [len(word) for word in nasa['adapter']]
if (nom_length <= 10):
nasa = nasa.sort_values('aligned_seq', ascending=True )
nasa.reset_index(drop=True,inplace=True)
nasa['adapter'] = [word for word in nasa['aligned_seq']]
i=0
v= 0
master_df =[]
while(i<=(len(nasa)-2)):
seed = nasa.at[i,'aligned_seq']
match = nasa.at[i+1,'aligned_seq']
k = len(match)-len(seed)
if (seed == match[:-k]) or (seed == match):
j=i+1
while(j<=(len(nasa))-1):
seed = nasa.at[i,'aligned_seq']
match = nasa.at[j,'aligned_seq']
m = len(match)-len(seed)
if (seed == match) or (seed == match[:-m]):
j = j+1
else:
break
df = "nasa_" + str(v)
df_temp = nasa.iloc[i:j,0:8]
df_temp.reset_index(drop=True,inplace=True)
df =df_temp
master_df.append(df)
v=v+1
i = j
else:
i = i+1
if (len(master_df) >= 2):
master_df = sorted(master_df, key=len)
master_df = master_df[-2:]
adap_lst = []
for ad in master_df[::-1]:
adap_lst.append(ad.at[0,'aligned_seq'])
adapter = adap_lst[0]
for i in range(0,(len(adap_lst)-1)):
seed = adap_lst[i+1]
match = SequenceMatcher(None, adapter, seed).find_longest_match(0, len(adapter), 0, len(seed))
temp = (adapter[match.a: match.a + match.size])
if len(temp) >= 5:
adapter = temp
for elem in adap_lst:
if (adapter == elem[:len(adapter)]):
if len(elem) > len(adapter):
adapter = elem
fo = open(filename4,"w")
nasa.to_csv(fo, sep = ",", index=False)
fo.close()
k = len(adapter)-nom_length
if (k > 1):
adapter = adapter[:-int(k)]
elif (len(master_df) == 1):
max_df = max(master_df, key=len)
adapter = max_df.at[0,'aligned_seq']
print(nasa.head())
fo = open(filename4,"w")
nasa.to_csv(fo, sep = ",", index=False)
fo.close()
k = len(adapter)-nom_length
if (k > 1):
adapter = adapter[:-int(k)]
else:
nasa['adapter'] = [word for word in nasa['aligned_seq']]
fo = open(filename4,"w")
nasa.to_csv(fo, sep = ",", index=False)
fo.close()
print(nasa.head())
master=nasa["adapter"].tolist()
adapter = max(master, key=master.count)
k = len(adapter)-nom_length
if (k > 1):
adapter = adapter[:-int(k)]
nasa['adapter_length'] = [len(word) for word in nasa['adapter']]
print(nasa.head())
else:
m=nasa["adapter_length"].median()
#nasa = nasa.loc[nasa["adapter_length"] >= m]
nasa['adapter'] = [word[:int(m)] for word in nasa['adapter']]
fo = open(filename4,"w")
nasa.to_csv(fo, sep = ",", index=False)
fo.close()
print(nasa.head())
m=nasa["sstart"].tolist()
# group most_common output by frequency
freqs = groupby(Counter(m).most_common(), lambda x:x[1])
# pick off the first group (highest frequency)
q = [val for val,count in next(iter(freqs))[1]][0]
master=nasa["adapter"].tolist()
adap_elem = Counter(master)
collapsed = sorted(adap_elem.items(), key=operator.itemgetter(1), reverse=True)
lim = int(0.2 * len(nasa))
if (len(collapsed)>3) and (lim < collapsed[1][1]):
collapsed = [item for item in collapsed if item[1] >= lim]
elif (len(collapsed)>=3):
collapsed = collapsed[:3]
if (q_fil < 14500) or (q > 30) or (abund > 10000000):
fil = 0
for x,y in collapsed:
fil = fil + y
if ((len(collapsed) > 2) and ((float(collapsed[1][1]/fil)) >= 0.15)) or ((abund > 10000000) and len(collapsed[0][0]) >76):
lim = 3
else:
lim = 2
adap_lst = []
for x,y in collapsed[:lim]:
adap_lst.append(x)
if (len(adap_lst) > 1):
adapter = adap_lst[0]
for i in range(1,len(adap_lst)):
seed = adap_lst[i]
match = SequenceMatcher(None, adapter, seed).find_longest_match(0, len(adapter), 0, len(seed))
temp = (adapter[match.a: match.a + match.size])
if len(temp) > 10:
adapter = temp
for elem in adap_lst:
if (adapter == elem[:len(adapter)]):
if len(elem) > len(adapter):
adapter = elem
else:
adapter = collapsed[0][0]
else:
adapter = collapsed[0][0]
adap_lst = collapsed
print ("\n" + "Putative three prime end adapters for filename - " + filename + " is ")
print (adap_lst)
if len(forward_adapter) > 3:
print("Five prime end adapter sequence for filename - " + filename + " is " + str(forward_adapter))
print("Three prime end adapter sequence for filename - " + filename + " is " + str(adapter) + "\n" + "Trimming with CUTADAPT" + "\n")
if len(forward_adapter) > 3:
command = "cutadapt -g ^" + forward_adapter + " -m " + str(args.min_len) + " -M " + str(args.max_len) + " " + f + " 2> " + log2 + " | cutadapt -q 20 " + "-m " + str(args.min_len)+ " -M " + str(args.max_len) + " -a " + adapter + " - > " + path + "/good-mapping/" + fastq_filename + " 2>> "+ log2
print(command)
os.system(command)
infile= open(log2, "r")
lines = infile.readlines()
cut_adapt = []
for line in lines:
if "Reads with adapters" in line:
cut_adapt.append(line.strip().replace(" ", "").split("(")[1].split(")")[0])
if (args.index!=None):
command = bowtie +" --best -v 1 -p 20 " + index + " -q " + path + "/good-mapping/" + fastq_filename + " > check.sam 2> "+ log
print(command)
os.system(command)
infile= open(log, "r")
lines = infile.readlines()
a = float(lines[1].strip().split("(")[1].split("%")[0])
no_reads = lines[0].strip().split("processed: ")[1]
if (a >=50):
element = [filename,"5'-anchored&3'-normal", forward_adapter,adapter,abund, cut_adapt[0],cut_adapt[1],no_reads,a,"good"]
return element
else:
command = "mv " + path + "/good-mapping/"+ fastq_filename + " " + path + "/bad-mapping/" + fastq_filename
element = [filename,"5'-anchored&3'-normal", forward_adapter,adapter,abund, cut_adapt[0],cut_adapt[1],no_reads,a,"bad"]
return element
else:
element = [filename,"5'-anchored&3'-normal", forward_adapter,adapter,abund, cut_adapt[0],cut_adapt[1],"na","na","na"]
return element
else:
command = "cutadapt -q 20 -m "+ str(args.min_len)+ " -M " + str(args.max_len) + " -a " + adapter + " -o " + path + "/good-mapping/" + fastq_filename + " " + f + " > "+ log2
print(command)
os.system(command)
def cut(x):
infile= open(x, "r")
lines = infile.readlines()
for line in lines:
if "Reads with adapters" in line:
valu = float(line.strip().replace(" ", "").split("(")[1].split(")")[0].split("%")[0])
return valu
d = cut(log2)
if (d < 25):
new_lst = []
collapsed = sorted(collapsed2.items(), key=operator.itemgetter(1), reverse=True)
new_lst = []
for x,y in collapsed:
count_obj = Counter({'A':0,'T':0,'G':0,'C':0})
count_obj.update(x)
if (((min(count_obj.items(), key=itemgetter(1))[1])/len(x)) >= 0.10) and count_obj['N']==0:
new_lst.append((x,y))
print ("len of new_lst is " + str(filename) + " " + str(len(new_lst)))
filtered = [(x,y) for x,y in new_lst if y==1]
print ("len of filtered list is " + str(filename) + " " + str(len(filtered)))
ofile = open(query_file, "w")
for x,y in filtered[:1000]:
ofile.write(">" + str(y) +"\n" + str(x) + "\n")
ofile.close()
ofile = open(subject_file, "w")
for x,y in filtered[1000:5000]:
ofile.write(">" + str(y) +"\n" + str(x) + "\n")
ofile.close()
command = "blastn -task blastn-short -ungapped -max_hsps 1 -query " + query_file + " -subject " + subject_file + " -outfmt '10 qseqid sseqid qseq length evalue qstart sstart' -out " + blast2_file
os.system(command)
csv = pd.read_csv(blast2_file)
csv.columns = ['query', 'subject', 'aligned_seq', 'aligned_length', 'evalue', 'qstart', 'sstart']
csv.to_csv(blast2_file, sep = ",", index=False)
csv.insert(3,"adapter","")
csv['adapter'] = [word[:13] for word in csv['aligned_seq']]
#asan = csv.loc[csv["sstart"] >= 25]
#asan = asan.loc[asan["qstart"] >= 25]
asan = csv.loc[csv["aligned_length"] < 19]
asan = asan.loc[asan["aligned_length"] > 13]
asan = asan.loc[asan["evalue"] < 0.12]
asan = asan.loc[((asan["qstart"] >= 30) & (asan["sstart"] >= 30))]
fo = open(filename4,"w")
asan.to_csv(fo, sep = ",", index=False)
fo.close()
print (asan.head())
adap_lst = sorted(Counter(asan["adapter"].tolist()).items(), key=operator.itemgetter(1), reverse=True)
if len(adap_lst) > 10:
adap_lst = adap_lst[:10]
adap_lst2 = [ x for x,y in adap_lst]
if len(adap_lst2) > 1:
adap_lst = adap_lst2[:2]
print (adap_lst)
adapter = adap_lst2[0]
for i in range(1,len(adap_lst)):
seed = adap_lst[i]
match = SequenceMatcher(None, adapter, seed).find_longest_match(0, len(adapter), 0, len(seed))
temp = (adapter[match.a: match.a + match.size])
if len(temp) > 10:
adapter = temp
for elem in adap_lst2:
if (adapter == elem[:len(adapter)]):
if len(elem) > len(adapter):
adapter = elem
print ("adpater for filename " + str(filename) + " is " + str(adapter))
command = "cutadapt -q 20 -m "+ str(args.min_len)+ " -M " + str(args.max_len) + " -a " + adapter + " -o " + path + "/good-mapping/" + fastq_filename + " " + f + " > "+ log2
print (command)
os.system(command)
d = cut(log2)
d = str(d) + "%"
if (args.index!=None):
command = bowtie +" --best -v 1 -p 20 " + index + " -q " + path + "/good-mapping/" + fastq_filename + " > check.sam 2> "+ log
print(command)
os.system(command)
infile= open(log, "r")
lines = infile.readlines()
a = float(lines[1].strip().split("(")[1].split("%")[0])
no_reads = lines[0].strip().split("processed: ")[1]
if (a >=50):
element = [filename,"3'-normal","na",adapter,abund,"na",str(d),no_reads,a,"good"]
return element
else:
command = "mv " + path + "/good-mapping/"+ fastq_filename + " " + path + "/bad-mapping/" + fastq_filename
print(command)
os.system(command)
element = [filename,"3'-normal","na",adapter,abund,"na",str(d),no_reads,a,"bad"]
return element
else:
element = [filename,"3'-normal","na",adapter,abund,"na",str(d),"na","na","na"]
return element
def worker2(f):
filename = f.split(".")[0]
command = "mkdir "+ "-p "+ path + "aux_files/" + filename
os.system(command)
newpath = path + "/aux_files/" + filename + "/"
fastq_filename = filename + "_trimmed.fastq"
query_file = newpath + filename + "_query.fa"
subject_file = newpath + filename + "_subject.fa"
over_rep = newpath + filename + "_overrep.csv"
over_rep2 = newpath + filename + "_overrep2.csv"
blast_file = newpath + filename + "_blast.csv"
filename4 = newpath + filename + "_adapters.csv"
log = newpath + filename+ "_bowtie.txt"
log2 = newpath + filename+ "_cutadapt.txt"
if f.endswith(".fastq.gz"):
fastq_lst=gzip.open(f,'rb').readlines()[1::4]
fastq_lst = [line.strip().decode() for line in fastq_lst]
else:
infile= open(f)
fastq_lst = infile.readlines()[1::4]
fastq_lst = [line.strip() for line in fastq_lst]
abund = len(fastq_lst)
collapsed = Counter(fastq_lst)
collapsed = sorted(collapsed.items(), key=operator.itemgetter(1), reverse=True)
ofile = open(query_file, "w")
if len(collapsed) > 365:
q_counter = 200
else:
q_counter = 0
ind_counter = 0
print("Total length of unique sequences is " + str(len(collapsed)))
for x,y in collapsed[q_counter:]:
q_counter = q_counter + 1
if ind_counter < 15:
count_obj = Counter({'A':0,'T':0,'G':0,'C':0})
count_obj.update(x)
if ((count_obj[min(count_obj)])/len(x) > 0.01) or (len(collapsed)<365):
ind_counter = ind_counter + 1
ofile.write(">" + str(y) +"\n" + str(x) + "\n")
else:
continue
else:
break
ofile.close()
ofile = open(subject_file, "w")
if (len(collapsed)> 500) and (q_counter <= 300):
q_counter = 300
ind_counter = 0
for x,y in collapsed[q_counter:]:
if ind_counter < 100:
count_obj = Counter({'A':0,'T':0,'G':0,'C':0})
count_obj.update(x)
if ((count_obj[min(count_obj)])/len(x) > 0.01) or (len(collapsed)<365):
ind_counter = ind_counter + 1
ofile.write(">" + str(y) +"\n" + str(x) + "\n")
else:
continue
else:
break
ofile.close()
new_df2 = pd.DataFrame(collapsed[:15], columns=['Sequence','Count'])
if len(new_df2) > 2:
new_df2["Sequence_length"] = [len(word) for word in new_df2['Sequence']]
seq_len = new_df2["Sequence_length"].tolist()
seq_median = new_df2["Sequence_length"].median()
if (len(seq_len)) > 1:
seq_var = variation(seq_len, axis=0)
else:
seq_var = 0
if ((len(seq_len)>1) and (seq_var > 0.17) and (max(seq_len) < 50)) or ((seq_median < 30) and (max(seq_len) < 50)):
print("There is no adapter sequences and the length distribution of sequences is ..........")
print(seq_len)
command = "cutadapt --trim-n" + " -m "+ str(args.min_len)+ " -M " + str(args.max_len) + " " + f + " 2> " + log2 + " | cutadapt -q 20 -m "+ str(args.min_len)+ " -M " + str(args.max_len) + " " + " - > " + path + "/good-mapping/" + fastq_filename + " 2>> "+ log2
print(command)
os.system(command)
if (args.index!=None):
command = bowtie + " --best -v 1 -p 20 " + index + " -q " + path + "/good-mapping/" + fastq_filename + " > check.sam 2> "+ log
os.system(command)
infile= open(log, "r")
lines = infile.readlines()
a = float(lines[1].strip().split("(")[1].split("%")[0])
no_reads = lines[0].strip().split("processed: ")[1]
if (a >=50):
element = [filename,"na","na","na",abund,"na","na", no_reads,a,"good"]
return element
else:
command = "mv " + path + "/good-mapping/"+ fastq_filename + " " + path + "/bad-mapping/" + fastq_filename
print(command)
os.system(command)
element = [filename,"na","na","na",abund,"na","na", no_reads,a,"bad"]
return element
else:
element = [filename,"na","na","na",abund,"na","na","na","na","na"]
return element
#check now the median length
command = blastn + " -task blastn-short -ungapped -max_hsps 2 -query " + query_file + " -subject " + subject_file + " -outfmt '10 qseqid sseqid qseq length evalue qstart sstart' -out " + blast_file
print (command)
os.system(command)
csv = pd.read_csv(blast_file)
csv.columns = ['query', 'subject', 'aligned_seq', 'aligned_length', 'evalue', 'qstart', 'sstart']
fo = open(blast_file,"w")
csv.to_csv(fo, sep = ",", index=False)
fo.close()
df1 = csv.loc[csv["qstart"] < 15]
df2 = csv.loc[csv["qstart"] >= 15]
master=df1["aligned_seq"].tolist()
adap_elem = Counter(master)
collapsed = sorted(adap_elem.items(), key=operator.itemgetter(1), reverse=True)
collapsed = [x for x in collapsed if len(x[0])>17]
collapsed = [x for x in collapsed if len(x[0])<20]
adap_lst = []
for x,y in collapsed[:3]:
adap_lst.append(x)
adapter =adap_lst[0]
for elem in adap_lst[1:]:
seed = elem
match = SequenceMatcher(None, adapter, seed).find_longest_match(0, len(adapter), 0, len(seed))
temp = (adapter[match.a: match.a + match.size])
if len(temp) > 12:
adapter = temp
fiveprime_adapter = adapter
master=df2["aligned_seq"].tolist()
adap_elem = Counter(master)
collapsed = sorted(adap_elem.items(), key=operator.itemgetter(1), reverse=True)
collapsed = [x for x in collapsed if len(x[0]) > 17]
adap_lst = []
for x,y in collapsed[:3]:
adap_lst.append(x)
adapter =adap_lst[0]
for elem in adap_lst[1:]:
seed = elem
match = SequenceMatcher(None, adapter, seed).find_longest_match(0, len(adapter), 0, len(seed))
temp = (adapter[match.a: match.a + match.size])
if len(temp) > 12:
adapter = temp
for elem in adap_lst:
if (adapter == elem[:len(adapter)]):
if len(elem) > len(adapter):
adapter = elem
threeprime_adapter = adapter
d = df1["qstart"].mode()[0]
e = df2["qstart"].mode()[0]
if d in range(0,6):
print("5 prime adapter OK")
else:
print("WARNING: Unusual query start position for fiveprime_adapter")
if e in range(0,6):
print("WARNING: Unusual query start position for threeprime_adapter")
else:
print("3 prime adapter OK")
print("5prime_adapter is " + fiveprime_adapter)
print("3prime_adapter is " + threeprime_adapter)
adapter = "5 prime = " +fiveprime_adapter + " & 3 prime = " + threeprime_adapter
command = "cutadapt -q 20 -e 0.25 -m "+ str(args.min_len)+ " -M " + str(args.max_len) + " -g " + fiveprime_adapter + " " + f + " 2> " + log2 + " | cutadapt -q 20 -m "+ str(args.min_len)+ " -M " + str(args.max_len) + " -a " + threeprime_adapter + " - > " + path + "/good-mapping/" + fastq_filename + " 2>> "+ log2
print(command)
os.system(command)
infile= open(log2, "r")
lines = infile.readlines()
cut_adapt = []
for line in lines:
if "Reads with adapters" in line:
cut_adapt.append(line.strip().replace(" ", "").split("(")[1].split(")")[0])
if (args.index!=None):
command = bowtie +" --best -v 1 -p 20 " + index + " -q " + path + "/good-mapping/" + fastq_filename + " > check.sam 2> "+ log
print(command)
os.system(command)
infile= open(log, "r")
lines = infile.readlines()
a = float(lines[1].strip().split("(")[1].split("%")[0])
no_reads = lines[0].strip().split("processed: ")[1]
if (a >=50):
element = [filename,"both",fiveprime_adapter,threeprime_adapter,abund,cut_adapt[0],cut_adapt[1],no_reads,a,"good"]
return element
else:
command = "mv " + path + "/good-mapping/"+ fastq_filename + " " + path + "/bad-mapping/" + fastq_filename
print(command)
os.system(command)
element = [filename,"both",fiveprime_adapter,threeprime_adapter,abund,cut_adapt[0],cut_adapt[1], no_reads,a,"bad"]
return element
else:
element = [filename,"both",fiveprime_adapter,threeprime_adapter,abund,cut_adapt[0],cut_adapt[1],"na","na","na"]
return element
def worker3(f):
print("processing file " + f)
if f.endswith(".fastq.gz"):
os.system("gunzip " + f)
f = f.split(".fastq.gz")[0] + ".fastq"
os.system("perl -X csfq2fq.pl " + f + " > q_fastq/" + f.split(".fastq")[0] + "_q.fastq")
if f.endswith(".fastq.gz"):
os.system("gzip " + f)
filename = f.split(".")[0]
command = "mkdir "+ "-p "+ "/aux_files/" + filename
os.system(command)
newpath = path + "aux_files/" + filename + "/"
query_file = newpath + filename + "_query.fa"
subject_file = newpath + filename + "_subject.fa"
blast_file = newpath + filename + "_blast.csv"
infile= open("q_fastq/" + f.split(".fastq")[0] + "_q.fastq")
fastq_lst = infile.readlines()[1::4]
fastq_lst = [line.strip() for line in fastq_lst]
abund = len(fastq_lst)
collapsed = Counter(fastq_lst)
collapsed = sorted(collapsed.items(), key=operator.itemgetter(1), reverse=True)
ofile = open(query_file, "w")
q_counter = 0
ind_counter = 0
for x,y in collapsed[q_counter:]:
if ind_counter < 51:
count_obj = Counter({'A':0,'T':0,'G':0,'C':0})
count_obj.update(x)
if ((min(count_obj.items(), key=itemgetter(1))[1])/len(x)) > 0.05:
ind_counter = ind_counter + 1
q_counter = q_counter + 1
ofile.write(">" + str(y) +"\n" + str(x) + "\n")
else:
continue
ofile.close()
ind_counter = 0
ofile = open(subject_file, "w")
for x,y in collapsed[q_counter:]:
if ind_counter < 201:
count_obj = Counter({'A':0,'T':0,'G':0,'C':0})
count_obj.update(x)
if ((min(count_obj.items(), key=itemgetter(1))[1])/len(x)) > 0.05:
ind_counter = ind_counter + 1
q_counter = q_counter + 1
ofile.write(">" + str(y) +"\n" + str(x) + "\n")
else:
continue
ofile.close()
command = blastn + " -task blastn-short -ungapped -max_hsps 2 -query " + query_file + " -subject " + subject_file + " -outfmt '10 qseqid sseqid qseq length evalue qstart sstart' -out " + blast_file
os.system(command)
csv = pd.read_csv(blast_file)
csv.columns = ['query', 'subject', 'aligned_seq', 'aligned_length', 'evalue', 'qstart', 'sstart']
#csv =csv.loc[csv["aligned_length"] >=15]
adapter = sorted(Counter(csv["aligned_seq"].tolist()).items(), key=operator.itemgetter(1), reverse=True)[0][0][:21]
print("Predicted adapter sequence for filename " + filename + " is " + adapter)
print("Trimming ~" + filename)
newpath = "aux_files/" + filename + "/"
log = newpath + filename + "_bowtie.txt"
log2 = newpath + filename + "_cutadapt.txt"
tsv_file = newpath + filename+ "_sam.tsv"
fastq_filename = "solid-adapter-trimmed/"+ filename + "_trimmed.fastq"
sam = newpath + filename + "_mapped.sam"
if float(os.popen("cutadapt --version").read().strip()) > 1.18:
sys.exit('\nERROR: Cutadapt version should be less than or equal to 1.18. Colorspace reads are supported only in cutadapt version 1.18 or earlier \n')
command = "cutadapt -c --format=sra-fastq -a " + adapter + " -q 20 -m 15 -M50 " + filename + ".fastq 2> " + log2 + " | cutadapt -c -q 20 -m 15 -M50 - > " + fastq_filename + " 2>> "+ log2
print(command)
os.system(command)
infile= open(log2, "r")
lines = infile.readlines()
cut_adapt = []
for line in lines:
if "Reads with adapters" in line:
cut_adapt.append(line.strip().replace(" ", "").split("(")[1].split(")")[0])
if (args.index!=None):
index = args.index
command = bowtie +" --best -C -v 3 -p 20 " + index + " -q " + fastq_filename + " -S 2> " + log + "| samtools view -Sh -F 4 - > " + sam
print(command)
os.system(command)
os.system("rm " + fastq_filename)
os.system("egrep -v '@HD|@SQ|@PG' " + sam + " > " + tsv_file)
tsv = pd.read_csv(tsv_file,sep='\t', header = None, quoting=3)
if len(list(tsv)) == 16:
tsv.columns = ['header', 'flag', 'reference', 'sequence_start', 'MAPQ','CIGAR', 'REf-MATE', 'REF-MATE-POS', 'INSERT-SIZE', 'READ', 'ASCII', 'OPT-1','OPT-2','OPT-3','OPT-4','OPT-5']
else:
tsv.columns = ['header', 'flag', 'reference', 'sequence_start', 'MAPQ','CIGAR', 'REf-MATE', 'REF-MATE-POS', 'INSERT-SIZE', 'READ', 'ASCII', 'OPT-1','OPT-2','OPT-3','OPT-4']
seq = []
ofile = open(fastq_filename, "w")
seq = tsv[['READ', 'ASCII']].apply(tuple, axis=1).tolist()
i = 0
for x,y in seq:
ofile.write(">" + filename + "-" + str(i) +"\n" + str(x) + "\n"+ "+" +"\n"+ str(y)+"\n")
i = i + 1
ofile.close()
infile= open(log, "r")
lines = infile.readlines()
a = float(lines[1].strip().split("(")[1].split("%")[0])
no_reads = lines[0].strip().split("processed: ")[1]
os.system(command)
if (a >=50):
return [filename,"3prime","na",adapter,abund,"na",cut_adapt[0],no_reads,a,"good"]
else:
command = "mv " + path + "/good-mapping/"+ fastq_filename + " " + path + "/bad-mapping/" + fastq_filename
print(command)
os.system(command)
return [filename,"3prime","na",adapter,abund,"na",cut_adapt[0],no_reads,a,"bad"]
else:
return [filename,"3prime","na",adapter,abund,"na",cut_adapt[0],"na","na","na"]
if __name__ == "__main__":
if (args.sequencing_platform == "ILLUMINA"):
worker = worker1
elif (args.sequencing_platform == "454") or (args.sequencing_platform == "ION_TORRENT"):
worker = worker2
elif (args.sequencing_platform == "SOLID"):
if not os.path.exists("q_fastq"):
os.makedirs("q_fastq")
if not os.path.exists("solid-adapter-trimmed"):
os.makedirs("solid-adapter-trimmed")
if (args.index!=None):
os.system("samtools --version > vers.txt")
infile= open("vers.txt", "r")
lines = infile.readlines()
os.system("rm vers.txt")
if lines != []:
ver = lines[0].strip().split("samtools ")[1]
else:
ver = "no"
if (ver=="no"):
sys.exit('\nERROR: SAMtools not found. Please install samtools \n')
os.system("wget https://gist.github.com/pcantalupo/9c30709fe802c96ea2b3/archive/b5a290a3993a4845d3766a018837557bd0f0047b.zip")
os.system("unzip -j b5a290a3993a4845d3766a018837557bd0f0047b.zip 9c30709fe802c96ea2b3-b5a290a3993a4845d3766a018837557bd0f0047b/csfq2fq.pl")
os.system("rm -r b5a290a3993a4845d3766a018837557bd0f0047b.zip")
worker = worker3
else:
sys.exit('\nERROR: Invalid argument. \n%s'%(docstring))
size = {}
for f in files:
size[f] = os.path.getsize(f)
size = sorted(size.items(), key=operator.itemgetter(1))
asan = [k for k, v in size if v >= 10000000000]
nasa = [k for k, v in size if v < 10000000000]
print("Total number of input files = " + str(len(files)) + "\n" + " Number of bigger files (>=10GB) = " + str(len(asan)) + "\n" + " Number of smaller files (<10 GB) = " + str(len(nasa)))
result_list =[]
if (len(nasa) != 0):
cp_count = multiprocessing.cpu_count()
print("number of CPU is " + str(cp_count))
if ((len(nasa)/cp_count) < 2):
pool = multiprocessing.Pool(processes = int(cp_count/2))
print("Processing " + str(len(nasa)) + " files")
result_list.append(pool.map(worker, [f for f in nasa ]))
pool.close()
pool.join()
else:
denom = int(len(nasa)/(cp_count/2))