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Read_processing_and_quality_QIAGEN.py
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# ImmuneReceptor_PROCESSING_PIPELINE developed by Rachael Bashford-Rogers (2020)
# at the University of Oxford and Univeristy of Cambridge
# E-mail: [email protected]
# If you find the methods in ImmuneReceptor_PROCESSING_PIPELINE, please cite the following reference:
# Bashford-Rogers, R. et al. Nature 2019 (https://www.nature.com/articles/s41586-019-1595-3.pdf)
# Copyright (C) 2020 Dr Rachael Bashford-Rogers
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
#!/usr/bin/python
import math
import sys
from collections import defaultdict
import os
import commands
import sys
from operator import itemgetter
import networkx as nx
import re
def fasta_iterator(fh):
while True:
line = fh.readline()
if line.startswith('>'): break
while True:
header = line[1:-1].rstrip()
sequence = fh.readline().rstrip()
while True:
line = fh.readline()
if not line: break
if line.startswith('>'): break
sequence += line.rstrip()
yield(header, sequence)
if not line: return
def fuzzy_substring(needle, haystack):
"""Calculates the fuzzy match of needle in haystack,
using a modified version of the Levenshtein distance
algorithm.
The function is modified from the levenshtein function
in the bktree module by Adam Hupp"""
m, n = len(needle), len(haystack)
# base cases
if m == 1:
return not needle in haystack
if not n:
return m
row1 = [0] * (n+1)
for i in range(0,m):
row2 = [i+1]
for j in range(0,n):
cost = ( needle[i] != haystack[j] )
row2.append( min(row1[j+1]+1, # deletion
row2[j]+1, #insertion
row1[j]+cost) #substitution
)
row1 = row2
return min(row1)
def Intialise_files(dir):
dirs_to_add=["FASTQ_FILES/", "ORIENTATED_SEQUENCES/", "ORIENTATED_SEQUENCES/TMP/", "ORIENTATED_SEQUENCES/NETWORKS/"]
for d in dirs_to_add:
c=commands.getoutput("ls "+dir+d)
if(c.count("No such file or directory")==1):commands.getoutput("mkdir "+dir+d)
return()
def QC_samples(dir,gene,id,source,length,species,barcode_group,quasr_location):
pre = source
reads1 = source
if(1==1):
print reads1
if(1==1):
threshold,length = "30","100"
command1 = "java -jar "+quasr_location+"qualityControl.jar -f "+reads1 +" -o "+dir+"FASTQ_FILES/Sequences_"+id+"_1 -m "+threshold+" -l "+length
command3 = "cat "+dir+"FASTQ_FILES/Sequences_"+id+"_1.qc.fq | perl -e '$i=0;while(<>){if(/^\@/&&$i==0){s/^\@/\>/;print;}elsif($i==1){s/\./N/g;print;$i=-3}$i++;}' > "+dir+"FASTQ_FILES/Sequences_"+id+"_1.fasta"
print command1
os.system(command1)
os.system(command3)
return()
def Get_paired_reads_overlapping(file1, file2, outfile,gene,paired,id,method):
(rc)= Init_rc()
(seqs1,seqs2)=Get_sequences(file1)
#(seqs2)=Get_sequences(file2)
print "Forward reads:", len(seqs1),"Reverse reads:", len(seqs2)
out,ind = '',0
fh = open (outfile, "w")
fh.close()
tot,f_joining,total,length,fail_no_pair=0,0,0,30,0
if(method in ["5RACE"]):length = 20
if(gene=="LAMBDA" or gene=="KAPPA" or gene =="IGK"):length=30
for id in seqs1:
if (id in seqs2):
total=total+1
(seq,failed)=Join_reads(seqs1[id], seqs2[id],rc,length)
if (failed==0 and len(seq)>180):
ind,tot = ind+1,tot+1
out = out+">"+id+"\n"+seq+"\n"
else:
seq2 = Reverse_comp(seqs2[id], rc)
(seq,failed)=Join_reads(seqs1[id], seq2,rc,length)
if (failed==0 and len(seq)>180):
ind,tot = ind+1,tot+1
out = out+">"+id+"\n"+seq+"\n"
else:
f_joining=f_joining+1
#print seqs1[id], seqs2[id]
if (ind >100):
Write_out(out, outfile)
out,ind = '',0
else:
fail_no_pair=fail_no_pair+1
Write_out(out, outfile)
del out,ind
print id+"\tTotal sequences:",total,"\tNumber of Failed sequences:",f_joining, "\tPercentage sequences failed:", f_joining*100.0/total,"%\tTotal remaining:",tot, "\tNo pairs:",fail_no_pair
return()
def Get_barcodes(barcode, barcode_file,rc):
barcode = barcode.split(",")
fh=open(barcode_file,"r")
barcodes,allbarcodes,barcode_stem,barcode_only,barcode_only_rc = [],[],[],[],[]
rc_barcodes = []
for l in fh:
if(l[0]!="#"):
l=l.strip().split()
allbarcodes.append(l[1])
if(l[0] in barcode):
barcodes.append(l[1])
rc_barcodes.append(Reverse_comp(l[1], rc))
barcode_only.append(l[2])
barcode_only.append(Reverse_comp(l[2], rc))
elif(l[0]=="STEM"):barcode_stem.append([l[1],l[1][0:len(l[1])/2], l[1][len(l[1])/2:len(l[1])]])
fh.close()
return(barcodes,allbarcodes,barcode_stem, rc_barcodes,barcode_only,barcode_only_rc)
def Init_rc():
b1 = ["A","T","G","C","N","."]
b2 = ["T","A","C","G","N","."]
rc = {}
for i in range(0,6):
rc[b1[i]]=b2[i]
return(rc)
def Reverse_comp(seq, rc):
s = ''
l=len(seq)
for i in range(0,l):
j=l-i-1
if(seq[j] not in rc):
print seq
else:
s=s+rc[seq[j]]
return(s)
def Get_sequences(file):
command = "gunzip "+file
commands.getoutput(command)
fh = open (file, "r")
seqs1, seqs2 = {},{}
for header,sequence in fasta_iterator(fh):
header = header.split()
f = header[1][0]
header = header[0].replace(":","").split()[0].replace("-","")
header = header.split("#")[0].split("/")[0]
if(f=="1"):seqs1[header]=sequence
elif(f=="2"):seqs2[header]=sequence
fh.close()
return(seqs1, seqs2)
def Split_reads_and_join(file1, file2, outfile,gene,paired,id,method,primer_file,barcode_group,other,dir):
if(method=="Multiplex_FORWARD_BARCODE_GROUPED"):
Split_reads_and_join_pre(file1, file2, outfile,gene,paired,id,method,primer_file,barcode_group,other,dir)
Check_split(outfile, barcode_group,other)
elif(paired=="OVERLAPPING" and method in ["Multiplex"]):
Get_paired_reads_overlapping(Seq_file1, Seq_file2, Tmp_file,gene,paired,id,method)
return()
def Check_split(outfile, barcode_group,other):
rc = Init_rc()
barcode, barcode_file = other.split(",")[0], other.split(",")[1]
barcodes,allbarcodes,barcode_stem,rc_barcodes,barcode_only,barcode_only_rc = Get_barcodes(barcode, barcode_file,rc)
allbarcodes_rc = []
for b in allbarcodes:
allbarcodes_rc.append(Reverse_comp(b,rc))
out,ind = '',0
outfile1 = outfile.replace(".fasta","_CHECKED.fasta")
fh=open(outfile1,"w")
fh.close()
fh = open(outfile,"r")
seqs = {}
for header,sequence in fasta_iterator(fh):
seqs[header] = sequence
fh.close()
for header in seqs:
sequence = seqs[header]
score = []
for i in range(0,len(allbarcodes)):
p1 = len(Get_match(allbarcodes[i], sequence))
p2 = len(Get_match(allbarcodes_rc[i], sequence))
score.append(p1+p2)
if(sum(score)<=1):
out=out+">"+header+"\n"+sequence+"\n"
ind = ind+1
if(ind>500):
Write_out(out, outfile1)
out, ind = '',0
fh.close()
Write_out(out, outfile1)
return()
def Join_reads(s1, s2,rc,length):
seq=''
(s2)=Reverse_comp(s2,rc)
(l1,l2)=(len(s1),len(s2))
failed = 1
for i in range(0,100):
ind=(i*5)+5
if(ind<(l1-length)):
(s11,s22,p,overlap)=Trim(s1,s2,l1,l2,ind,length)
if(p==1):
seq = s1[0:s1.index(overlap)]+s2[(s2.index(overlap)):l2].lower()
if(len(seq)>120):
failed = 0
break
return(seq,failed)
def Trim(s1,s2,l1,l2,indent,length):
(i,p)=(indent,0)
sample1=s1[i:i+length]
index=s2.find(sample1)
s1a, s2a = s1, s2
if (index!=-1):
if(index>i):
s2a=s2a[index-i:l2]
else:
s1a=s1a[i-index:l1]
min_len=min([len(s1),len(s2)])
s1a=s1a[0:min_len]
s2a=s2a[0:min_len]
p=1
return (s1a, s2a, p,sample1)
def Get_match(primer, seq):
loc = []
if (seq.count(primer)!=0):
for m in re.finditer(primer, seq):
loc = loc+[m.start()]
return(loc)
def Split_reads_and_join_pre(file1, file2, outfile,gene,paired,id,method,primer_file,barcode_group,other,dir):
fh=open(outfile,"w")
fh.close()
rc = Init_rc()
file1 = dir+"FASTQ_FILES/Sequences_"+barcode_group+"_1.fasta"
file2 = dir+"FASTQ_FILES/Sequences_"+barcode_group+"_2.fasta"
print "\t",other
barcode, barcode_file = other.split(",")[0], other.split(",")[1]
barcodes,allbarcodes,barcode_stem,rc_barcodes,barcode_only,barcode_only_rc = Get_barcodes(barcode, barcode_file,rc)
print barcodes
(rc)= Init_rc()
(seqs1)=Get_sequences(file1)
(seqs2)=Get_sequences(file2)
print len(seqs1), len(seqs2)
out,ind = '',0
fh = open (outfile, "w")
fh.close()
tot,joining,total,length,primer_match=0,0,0,20,0
if(gene=="LAMBDA" or gene=="KAPPA" or gene =="IGK"):length=30
count_joined, count_barcode, count_barcode_saved = 0,0,0
for id in seqs1:
if (id in seqs2):
total=total+1
s1,s2 = seqs1[id], seqs2[id]
seq,failed =Join_reads(seqs1[id], seqs2[id],rc,length)
if(failed==0 and len(seq)>150):
seq,joining = seq.upper(),joining+1
count_joined = count_joined+1
for primer in barcodes:
p = Get_match(primer, seq)
if(len(p)==0):
seq = Reverse_comp(seq,rc)
p = Get_match(primer, seq)
break
else:break
if(len(p)!=0):
if(p[0]<len(seq)/2):
count_barcode = count_barcode+1
out=out+">"+id+"\n"+seq+"\n"
ind,primer_match = ind+1,primer_match+1
if(ind>200):
Write_out(out, outfile)
out,ind = '',0
else:
for prim in barcode_stem:
p = Get_match(prim[0], seq)
if(len(p)==0):
seq = Reverse_comp(seq,rc)
p = Get_match(prim[0], seq)
if(len(p)!=0):
start = p[0]+len(prim[0])-3
start = p[0]+len(prim[0])-6
for i in range(0,len(barcodes)):
bc_halves = [barcodes[i][2:len(barcodes[i])/2], barcodes[i][len(barcodes[i])/2:len(barcodes[i])-2]]
bc_shift = [2,len(barcodes[i])/2]
test = seq[start: start+len(barcodes[i])+10]
for j in range(0,len(bc_halves)):
if(test.count(bc_halves[j])!=0):
start = barcodes[i].index(barcode_only[i])
p=test.index(bc_halves[j])-bc_shift[j]
sub_test = test[p+start:p+len(barcode_only[i])+start]
a = fuzzy_substring(sub_test,barcode_only[i])
if(a<=1):
count_barcode, count_barcode_saved = count_barcode+1, count_barcode_saved+1
out=out+">"+id+"\n"+seq+"\n"
ind,primer_match = ind+1,primer_match+1
if(ind>200):
Write_out(out, outfile)
out,ind = '',0
Write_out(out, outfile)
del out,ind
print id+"\tTotal sequences:",total,"\tNumber of joined sequences:",joining, "Number of barcodes matched:",primer_match,"% retained:",primer_match*100.0/total, "IDs saved",count_barcode_saved
return()
def Single_J_barcoded_trimming_clustered(forward, reverse, barcoded_j, barcoded_v, rc,Tmp_file,Fail_file,Output_trim,primer_tag_file,tmp_file,gene,paired,species,primer_file,primer_tag_file_count,threshold_BARCODE,ref_const,inside,v_ref,cd_hit_directory):
Read_untrimmed_file_single(rc,Tmp_file,Fail_file,Output_trim, gene,paired,species,primer_file,primer_tag_file,tmp_file,primer_tag_file_count,forward, reverse,v_ref)
Check_barcodes_MALBAC(primer_tag_file_count,primer_tag_file,Fail_file,Output_trim,threshold_BARCODE,cd_hit_directory,tmp_file)
Separate_sequences(primer_tag_file_count,primer_tag_file, Output_trim,ref_const)
return()
def Assess_gene_score(consensus, word_dict,threshold):
scores,max_score = [],0
for hk in word_dict:
w = word_dict[hk]
score = [i for i in range(len(w)) if consensus.count(w[i][0])!=0]
if(len(score)>threshold/2):
if(len(score)>=max_score):
consensus1 = consensus
if(hk.count("HOUSEKEEPING")==0 and len(score)>threshold/2):
start = [consensus.index(w[score[i]][0])-w[score[i]][1] for i in range(len(score))]
start = max(set(start), key=start.count)
consensus1 = consensus[0:start]
if(max_score==len(score)):
scores.append([len(score),hk,consensus1])
else:
scores = []
scores.append([len(score),hk,consensus1])
max_score = len(score)
return(scores)
def Separate_sequences(primer_tag_file_count,primer_tag_file, Output_trim,ref_const):
fh=open(ref_const, "r")
word_length = 8
word_dict = {}
for header,sequence in fasta_iterator(fh):
header,sequence= header.replace("|",",").split("*")[0],sequence.upper()
words= []
for i in range(word_length,len(sequence)):
words.append([sequence[i-word_length:i],i-word_length])
word_dict[header] = words
fh.close()
region_primer_type = {}
fh=open(primer_tag_file_count,"r")
for l in fh:
if(l[0]!="#"):
l=l.strip().split()
const_reg,seq_type,rev_primer = l[len(l)-1],l[4],l[6]
region_primer_type[l[0].split(":")[0]] = [const_reg,seq_type,rev_primer]
fh.close()
fh=open(primer_tag_file,"r")
hk_score,non_hkscore = [],[]
seqs,classifications = Tree(),{}
for l in fh:
if(l[0]!="#"):
l=l.strip().split()
if(l[6]=="YES"):
consensus = l[7]
id = l[0].split("__")[0]
seqs[consensus][id].value = 1
fh.close()
print len(seqs)
out,ind = '',0
seq_info, seq_uniq = {},Tree()
classes = {}
threshold = 5
for s in seqs:
for id in seqs[s]:
break
scores= Assess_gene_score(s,word_dict,threshold)
if(len(scores)>0):
if(scores[0][0]>threshold): ## for non-housekeeping genes, trimmed sequences to remove constant region
if(len(scores)==1):
if(len(scores[0][2])>50):seq = scores[0][2]
else:seq = s
seq_uniq[s][id].value = 1
seq_info[id] = [len(seqs[s]), seq,scores[0][1]]
classes[scores[0][1]] = 1
del seqs
print len(classes)
classes_all = []
for c in classes:
classes_all.append(c)
classes_all.sort()
classes_order ={}
for i in range(0,len(classes_all)):
classes_order[classes_all[i]] = i
header = "_".join(classes_all)
for s in seq_uniq:
if(len(seq_uniq[s])>0):
f=[0]*len(classes_all)
for id in seq_uniq[s]:
c = seq_info[id][2]
f[classes_order[c]] = f[classes_order[c]]+seq_info[id][0]
out=out+">"+id.split("__")[0]+"__"+"_".join(map(str, f))+"|"+header+"\n"+s+"\n"
ind = ind+1
if(ind>100):
Write_out(out, Output_trim)
out, ind = '',0
Write_out(out, Output_trim)
return()
def Check_barcodes_MALBAC(primer_tag_file_count,primer_tag_file,Fail_file,Output_trim,threshold,cd_hit_directory,tmp_file):
outs = ['','#ID\tnumber_of_reads\ttotal_reads_with_BC\tJ_barcode\tV_barcode\tbp_mismatches_to_consensus\tBC_accepted\tconsensus\tsequence\n']
files = [Output_trim,primer_tag_file]
for i in range(0,len(files)):
fh=open(files[i],"w")
fh.write(outs[i])
fh.close()
fh=open(primer_tag_file_count,"r")
seqs = Tree()
for l in fh:
if (l[0]!="#"):
l=l.strip().split()
v_tag, j_tag, sequence, header = l[2],l[1],l[3],l[0]
seqs[j_tag+"\t"+v_tag][sequence][header].value = 1
fh.close()
print len(seqs), "Unique tags"
min_depth = (1.0/(1-threshold))
total_tags,ind,passed_seqs_total = 0,0,0
fail_less_than_threshold = 0
outp=''
for t1 in seqs:
t,u_seq, u_freq,u_header = 0,[],[],[]
for s in seqs[t1]:
f = 0
for h in seqs[t1][s]:
f = f+int(h.split(":")[1])
total_tags,u_seq, u_freq, u_header, ind =total_tags+f, u_seq+[s], u_freq+[f], u_header+[h], ind+1
f = sum(u_freq)
h = h.split(":")[0].split("__")[0]+"__"+str(sum(u_freq))
if(sum(u_freq)>20):print "BC group size:\t",len(u_freq),t1.split()
if(len(u_freq)==1):
passed_seqs_total = passed_seqs_total+f
outp=outp+h+"\t"+str(f)+"\t"+str(f)+"\t"+t1+"\t0\tYES\t"+s+"\t"+s+"\n"
elif(len(u_freq)<500):
#print "BC group size:\t",len(u_freq),t1.split(), u_freq#, u_seq
if(max(u_freq)>sum(u_freq)*threshold):
nz = [i for i in range(len(u_freq)) if u_freq[i]!=max(u_freq)]
passed_seqs_total = passed_seqs_total+f
consensus = u_seq[nz[0]]
outp = outp+"\t".join(map(str, [h, len(u_freq), sum(u_freq),t1,0,"YES",consensus, consensus ]))+"\n"
elif(len(u_freq)>15): ## clustering first then alignment
out_cluster,ids = '',{}
for i in range(0,len(u_seq)):
out_cluster = out_cluster+">"+u_header[i]+"\n"+u_seq[i]+"\n"
ids[u_header[i]] = [u_seq[i], u_freq[i]]
consensus,pass_consensus = Get_consensus_sequence_large(out_cluster, Fail_file,len(u_seq[i]), ids,sum(u_freq),threshold,tmp_file,cd_hit_directory)
if(consensus!='' and pass_consensus==1):
outp = outp+"\t".join(map(str, [h, len(u_freq), sum(u_freq),t1,0,"YES",consensus, consensus ]))+"\n"
passed_seqs_total = passed_seqs_total+f
else:fail_less_than_threshold = fail_less_than_threshold+1
else:
consensus, pass_consensus=Get_consensus_sequence_cluster(u_seq, u_freq,tmp_file,threshold)
if(consensus.count("_")==0 and pass_consensus==1):
outp = outp+"\t".join(map(str, [h, len(u_freq), sum(u_freq),t1,0,"YES",consensus, consensus ]))+"\n"
passed_seqs_total = passed_seqs_total+f
else:
fail_less_than_threshold = fail_less_than_threshold+1
if(ind>200):
Write_out(outp, primer_tag_file)
outp, ind = '',0
Write_out(outp, primer_tag_file)
outp, ind = '',0
print total_tags, passed_seqs_total, fail_less_than_threshold
return()
def Get_consensus_sequence_cluster(u_seq, u_freq,tmp_file,threshold):
out=''
for i in range(0,len(u_seq)):
out=out+">"+str(i)+"\n"+u_seq[i]+"\n"
fh=open(tmp_file+"txt", "w")
fh.write(out)
fh.close()
insert = ''
if( len(u_seq) > 2000):insert = '--parttree'
mafft = "/apps/well/mafft/7.149/bin/mafft "
command1 = mafft+" --retree 2 "+insert+" "+tmp_file+"txt > "+tmp_file+"aligned"
commands.getoutput(command1)
fh=open(tmp_file+"aligned","r")
max_seqs={}
for header,sequence in fasta_iterator(fh):
max_seqs[sequence.upper()]=int(header)
fh.close()
bases=["A","T","G","C","-"]
base_dict = {}
for b in range(0,len(bases)):
base_dict[bases[b]]=b
consensus = ''
start,pass_consensus = 0,1
for i in range(0,len(sequence)):
f = [0]*len(bases)
for s in max_seqs:
if(s[i] not in base_dict):
print i, s[i], max_seqs
f[base_dict[s[i]]]=f[base_dict[s[i]]]+u_freq[max_seqs[s]]
if(f[4]==0):start==1
if(max(f)*1.0/sum(f) >= threshold):
if(bases[f.index(max(f))]!="-"):
consensus=consensus+bases[f.index(max(f))]
else:
pass_consensus = 0
if(start==1):
for j in range(0,5):
if(f[j]!=0):
consensus=consensus+"|"+bases[j]+":"+str('%s' % float('%.3g' % (f[j]*1.0/sum(f))))
consensus=consensus+"_"
else:
f = f[0:4]
if(bases[f.index(max(f))]!="-"):
consensus=consensus+bases[f.index(max(f))]
else:
for j in range(0,4):
if(f[j]!=0):
consensus=consensus+"|"+bases[j]+":"+str('%s' % float('%.3g' % (f[j]*1.0/sum(f))))
consensus=consensus+"_"
return(consensus,pass_consensus)
def Get_consensus_sequence_large(out_cluster, Fail_file,l1,ids,sum_u_freq,threshold,tmp_file,cd_hit_directory):
fh=open(Fail_file,"w")
fh.write(out_cluster)
fh.close()
Cluster_i(Fail_file,Fail_file+"cls",(l1-3.0)/l1,cd_hit_directory)
cluster = Tree()
fh=open(Fail_file+"cls.bak.clstr","r")
for l in fh:
l=l.strip().split()
clust, id = l[0],l[2].replace("...","").replace(">","")
cluster[clust][id].value = 1
fh.close()
max_s, max_clust = 0,''
for c in cluster:
f = 0
for id in cluster[c]:
f = f+ids[id][1]
if(max_s<f):max_clust,max_s = c,f
consensus,pass_consensus = '',0
if(sum_u_freq*threshold<max_s):
seq_align,s_freq = [],[]
for id in cluster[max_clust]:
seq_align.append(ids[id][0])
s_freq.append(ids[id][1])
consensus = Get_consensus_sequence(seq_align,s_freq,tmp_file,threshold)
if(consensus.count("_")==0 and len(consensus)>3):pass_consensus=1
else:pass_consensus = 0
return(consensus, pass_consensus)
def Read_untrimmed_file_single(rc,Tmp_file,Fail_file,Output_trim, gene,paired,species,primer_file,primer_tag_file,tmp_file,primer_tag_file_count,forward, reverse,v_ref):
for f in [primer_tag_file_count]:
fh=open(f, "w")
fh.close()
fh = open (Tmp_file, "r")
seqs = Tree()
minl,maxl=110,1000000
if(gene=="HEAVY" or gene == "IGH"):minl= 120
if(gene=="KAPPA" or gene=="IGK"):minl= 110
if(gene=="LAMBDA" or gene == "IGL"):(minl, maxl)= (90, 150)
J_found,v_found,tot,indexing=0,0,0,0
seqs1,t=Tree(),0
for header,seq in fasta_iterator(fh):
seq=seq.upper()
seqs1[seq][header].value=1
t=t+1
out,ind = "#ID\tJ_tag\tV_tag\tSequence\ttype_rev\ttype_for\tprimer_rev\tprimer_for\n", 0
total,pass_r, pass_f, pass_all = 0,0,0,0
print "start"
for seq in seqs1:
for header in seqs1[seq]:
break
header = header+":"+str(len(seqs1[seq]))
number = len(seqs1[seq])
passes,j_tag,v_tag=0,'',''
type_rev, type_for = '',''
primer_rev,primer_for = '',''
total = total+1
#if(seq.count("CAGCACGCTTCAGGCT")!=0): print header
for i in range(0,len(reverse)):
pj=Get_match(reverse[i][2], seq)
if(max(pj+[-1])==-1 or len(pj)==0):
seq = Reverse_comp(seq, rc)
pj=Get_match(reverse[i][2], seq)
if(max(pj+[-1])!=-1):
bc_len = len(reverse[i][3])#len(reverse[i][2])/5
if(pj[0]>bc_len-3):
#j_tag = seq[pj[0]-bc_len:pj[0]]
j_tag = seq[pj[0]+len(reverse[i][2]): pj[0]+len(reverse[i][2])+bc_len]
if(len(j_tag)>bc_len/2):
seq = seq[pj[0]+bc_len:len(seq)]
seq = Reverse_comp(seq, rc)
type_rev,primer_rev = reverse[i][1], reverse[i][5]
passes = 1
break
if(passes!=1):
for i in range(0,len(reverse)):
words =reverse[i][6]
p=[]
for w in words:
pj=Get_match(w[0], seq)
if(max(pj+[-1])!=-1 and len(pj)>0):
if(pj[0]<len(seq)/2):p=p+[max([0,pj[0]-w[1]])]
else:pj[0]=-1
if(len(p)>1):
pj = max(set(p), key=p.count)
bc_len = len(reverse[i][3])
if(pj>min([bc_len-3])):
j_tag = seq[pj-bc_len:pj+1]
if(len(j_tag)>bc_len/2):
seq = seq[pj+bc_len:len(seq)]
seq = Reverse_comp(seq, rc)
type_rev,primer_rev = reverse[i][1], reverse[i][5]
passes = 1
break
#if(passes!=1):
# seq = Reverse_comp(seq, rc)
# for i in range(0,len(reverse)):
# words =reverse[i][6]
# p=[]
# for w in words:
# pj=Get_match(w[0], seq)
# if(max(pj+[-1])!=-1 and len(pj)>0):
# if(pj[0]<len(seq)/2):p=p+[max([0,pj[0]-w[1]])]
# else:pj[0]=-1
# if(len(p)>1):
# bc_len = len(reverse[i][3])
# pj = max(set(p), key=p.count)
# if(pj>min([bc_len-3])):
# j_tag = seq[pj-bc_len:pj+1]
# print header, j_tag, seq[pj-bc_len-10: pj+len(reverse[i][2])+bc_len]
# if(len(j_tag)>bc_len/2):
# seq = seq[pj+bc_len:len(seq)]
# seq = Reverse_comp(seq, rc)
# type_rev,primer_rev = reverse[i][1], reverse[i][5]
# passes = 1
# break
if(passes==1):
pass_r = pass_r+1
for i in range(0,len(forward)):
pv=Get_match(forward[i][0], seq)
if(max(pv+[-1])!=-1):
v_tag='-'
seq = seq[pv[0]+len(forward[i][0])-1 :len(seq)]
type_for, primer_for = forward[i][1],forward[i][2]
passes = 2
break
if(passes!=2):
for i in range(0,len(forward)):
words =forward[i][3]
p=0
for w in words:
pv=Get_match(w[0], seq)
if(max(pv+[-1])!=-1 and len(pv)>0):
if(pv[0]<len(seq)/2):p=p+1
else:pv[0]=-1
if(max(pv+[-1])!=-1 and len(pv)>0 and p>1):
v_tag='-'
seq = seq[pv[0]+len(forward[i][2])-1 :len(seq)]
type_for, primer_for = forward[i][1],forward[i][2]
passes = 2
break
if (passes!=2):
p=[]
for i in range(0,len(v_ref)):
words= v_ref[i][3]
p1 = []
for w in words:
pv=Get_match(w[0], seq)
if(max(pv+[-1])!=-1 and len(pv)>0):
if(pv[0]<len(seq)/2):p=p+[pv[0]]
if(len(p1)>0):p1 = p1+[min(p1)]
if(len(p)>=2):
pv = min(p)
v_tag='-'
seq = seq[pv:len(seq)]
type_for, primer_for = v_ref[i][1],v_ref[i][2]
passes = 2
if (passes ==2):
pass_f = pass_f+1
if(len(seq)>minl and len(seq)<maxl):
if(seq.count("N")==0):
#if(type_rev==type_for):
out=out+"\t".join(map(str,[header, j_tag, v_tag, seq, type_rev,type_for,primer_rev,primer_for]))+"\n"
pass_all = pass_all+1
ind = ind+1
if(ind>200):
Write_out(out, primer_tag_file_count)
out, ind = '',0
fh.close()
Write_out(out, primer_tag_file_count)
out, ind = '',0
print "\tTotal sequences:\t"+str(total)+"\tNumber of REV primers found:\t"+str(pass_r)+"\tNumber of FOR primers found:\t"+str(pass_f)+"\tPassed sequences:\t"+str(pass_all)
return()
def Get_consensus_sequence(u_seq, u_freq,tmp_file,threshold):
out=''
for i in range(0,len(u_seq)):
out=out+">"+str(i)+"\n"+u_seq[i]+"\n"
fh=open(tmp_file+"txt", "w")
fh.write(out)
fh.close()
insert = ''
if( len(u_seq) > 2000):insert = '--parttree'
command1 = "/software/pubseq/bin/mafft-6.857/bin/mafft --retree 2 "+insert+" "+tmp_file+"txt > "+tmp_file+"aligned"
commands.getoutput(command1)
fh=open(tmp_file+"aligned","r")
max_seqs={}
pfh= Check_fasta_not_empty(fh)
if(pfh==1):
for header,sequence in fasta_iterator(fh):
max_seqs[sequence.upper()]=int(header)
fh.close()
bases=["A","T","G","C","-"]
base_dict = {}
for b in range(0,len(bases)):
base_dict[bases[b]]=b
consensus = ''
start = 0
for i in range(0,len(sequence)):
f = [0]*len(bases)
for s in max_seqs:
f[base_dict[s[i]]]=f[base_dict[s[i]]]+u_freq[max_seqs[s]]
if(f[4]==0):start==1
if(max(f)*1.0/sum(f) >= threshold):
if(bases[f.index(max(f))]!="-"):
consensus=consensus+bases[f.index(max(f))]
else:
if(start==1):
for j in range(0,5):
if(f[j]!=0):
consensus=consensus+"|"+bases[j]+":"+str('%s' % float('%.3g' % (f[j]*1.0/sum(f))))
consensus=consensus+"_"
else:
f = f[0:4]
if(bases[f.index(max(f))]!="-"):
consensus=consensus+bases[f.index(max(f))]
else:
for j in range(0,4):
if(f[j]!=0):
consensus=consensus+"|"+bases[j]+":"+str('%s' % float('%.3g' % (f[j]*1.0/sum(f))))
consensus=consensus+"_"
else:
print "ERROR", u_freq, u_seq
else:consensus =""
return(consensus)
def Trim_sequences_BCR_TCR(Tmp_file,Fail_file,Output_trim, gene,paired,species,primer_file,primer_tag_file,tmp_file,primer_tag_file_count,sample,ref_const,reverse_primer_group,cd_hit_directory):
(rc)= Init_rc()
forward, reverse, barcoded_j, barcoded_v,v_ref = Get_primers_split(rc, primer_file)
fh_out = open(Output_trim,"w")
fh_out.close()
fh_out = open(Fail_file,"w")
fh_out.close()
threshold_BARCODE = 0.80 ### Certainty for accepting a barcode
print barcoded_j, barcoded_v
if(barcoded_j==1 and barcoded_v==0):
inside = 1
print "J barcoded"
Single_J_barcoded_trimming_clustered(forward, reverse, barcoded_j, barcoded_v, rc,Tmp_file,Fail_file,Output_trim,primer_tag_file,tmp_file,gene,paired,species,primer_file,primer_tag_file_count,threshold_BARCODE,ref_const,inside,v_ref,cd_hit_directory)
if(barcoded_j==0 and barcoded_v==0):
print "Unbarcoded"
inside = 0
Non_barcoded_trimming(forward, reverse, barcoded_j, barcoded_v, rc,Tmp_file,Fail_file,Output_trim,primer_tag_file,tmp_file,gene,paired,species,primer_file,primer_tag_file_count,threshold_BARCODE,ref_const,inside,v_ref)
if(barcoded_j==1 and barcoded_v==1):
print "V-J barcoded"
Double_barcoded_trimming(forward, reverse, barcoded_j, barcoded_v, rc,Tmp_file,Fail_file,Output_trim,primer_tag_file,tmp_file,gene,paired,species,primer_file,primer_tag_file_count,threshold_BARCODE,ref_const)
return()
def Double_barcoded_trimming(forward, reverse, barcoded_j, barcoded_v, rc,Tmp_file,Fail_file,Output_trim,primer_tag_file,tmp_file,gene,paired,species,primer_file,primer_tag_file_count,threshold_BARCODE,ref_const):
Read_untrimmed_file_double(rc,Tmp_file,Fail_file,Output_trim, gene,paired,species,primer_file,primer_tag_file,tmp_file,primer_tag_file_count,forward, reverse)
Check_barcodes_MALBAC(primer_tag_file_count,primer_tag_file,Fail_file,Output_trim,threshold_BARCODE,cd_hit_directory,Tmp_file)
inside = 0
Print_trimmed_sequences(Output_trim, primer_tag_file, primer_tag_file_count,inside,ref_const)
return()
def Print_trimmed_sequences(Output_trim, primer_tag_file, primer_tag_file_count,inside,ref_const):
constant_region,regions,uniq_bcs = {},{},{}
print "inside",inside
if(inside in [0,1]):
fh=open(primer_tag_file_count,"r")
for l in fh:
if(l[0]!="#"):
l=l.strip().split()
const_reg = l[len(l)-2].replace("REVERSE","REV")
if(const_reg.count("VH")==0):
bc = l[1]+":"+l[2]
constant_region[l[0].split(":")[0]] = const_reg
regions[const_reg]=1
uniq_bcs[bc] = 1
fh.close()
print "Number of unique BCs:",len(uniq_bcs)
del uniq_bcs
fh=open(primer_tag_file,"r")
unique_seqs,seqs = Tree(),{}
passed,failed,uniq_failed = 0,0,{}
if(inside in [0,1]):
for l in fh:
if(l[0]!="#"):
l=l.strip().split()
if(l[6]=="YES"):
passed = passed+1
consensus = l[7]
if(consensus.count("human_BC_REV_CONST")!=0):
print l
seqs[l[3]+":"+l[4]] = [l[0],consensus]
else:
failed = failed+1
if(l[3] in uniq_failed):uniq_failed[l[3]]= uniq_failed[l[3]]+1
else:uniq_failed[l[3]] = 1
print "Passed:", passed, "Failed:",failed,"(",failed*100/(failed+passed),"%)"
#n_seqs_associated_with_failed_BCs = []
#for n in uniq_failed:
# n_seqs_associated_with_failed_BCs.append(uniq_failed[n])
#n_seqs_associated_with_failed_BCs.sort()
#print "\t\tMean failed BC multiplicity:",mean(n_seqs_associated_with_failed_BCs),"Max failed BC multiplicity:", max(n_seqs_associated_with_failed_BCs), "Sum:",sum(n_seqs_associated_with_failed_BCs)
fh.close()
if(inside==0):
for bc in seqs:
id = seqs[bc][0].split(":")[0].split("__")[0]
unique_seqs[seqs[bc][1]][id].value = 1
del seqs
out,ind = '',0
for seq in unique_seqs:
f = len(unique_seqs[seq])
for id in unique_seqs[seq]:
break
out=out+">"+id.split("__")[0]+"__"+str(f)+"\n"+seq+"\n"
ind = ind+1
if(ind>100):
Write_out(out, Output_trim)
out, ind = '',0
Write_out(out, Output_trim)
elif(inside==1):
print "NOT YET DEVELOPED!"
fh=open(ref_const,"r")
word_size,ref_words = 10,{}
for header,seq in fasta_iterator(fh):
words,seq = [],seq.upper()
for i in range(word_size,len(seq)):
words.append(seq[i-word_size:i])
ref_words[header.split("|")[0]]=words
for bc in seqs:
seq,id = seqs[bc][1], seqs[bc][0]
score = []
#print bc, seqs[bc]
##id = seqs[bc][0].split(":")[0].split("__")[0]
#if(id in constant_region):
# unique_seqs[seqs[bc][1]][constant_region[id]][id].value = 1
#print seqs[bc][1], constant_region[id], id
del seqs
def Read_untrimmed_file_double(rc,Tmp_file,Fail_file,Output_trim, gene,paired,species,primer_file,primer_tag_file,tmp_file,primer_tag_file_count,forward, reverse):
for f in [primer_tag_file_count]:
fh=open(f, "w")
fh.close()
fh = open (Tmp_file, "r")
seqs = Tree()
maxl=1000000
if(gene=="HEAVY" or gene == "IGH"):minl= 120
if(gene=="KAPPA" or gene=="IGK"):minl= 110
if(gene=="LAMBDA" or gene == "IGL"):(minl, maxl)= (90, 150)
J_found,v_found,tot,indexing=0,0,0,0
seqs1,t=Tree(),0
for header,seq in fasta_iterator(fh):
seq=seq.upper()
seqs1[seq][header].value=1
t=t+1
out,ind = "#ID\tJ_tag\tV_tag\tSequence\ttype_rev\ttype_for\tprimer_rev\tprimer_for\n", 0
total,pass_r, pass_f, pass_all = 0,0,0,0
for seq in seqs1:
for header in seqs1[seq]:
break
header = header+":"+str(len(seqs1[seq]))
number = len(seqs1[seq])
passes,j_tag,v_tag=0,'',''
type_rev, type_for = '',''
primer_rev,primer_for = '',''
total = total+1
for i in range(0,len(reverse)):
pj=Get_match(reverse[i][2], seq)
if(max(pj+[-1])==-1 or len(pj)==0):
seq = Reverse_comp(seq, rc)
pj=Get_match(reverse[i][2], seq)
if(max(pj+[-1])!=-1):
bc_len = len(reverse[i][3])+3#len(reverse[i][2])/5
if(pj[0]>bc_len):
#start = pj[0]
#primer_match_seq = seq[start:start+len(reverse[i][2])]
j_tag = seq[pj[0]+len(reverse[i][2]):pj[0]+len(reverse[i][2])+bc_len]
#print primer_match_seq, reverse[i][2], seq[start:start+len(reverse[i][2])+30],j_tag
seq = seq[0:pj[0]+bc_len]
type_rev,primer_rev = reverse[i][1], reverse[i][5]
passes = 1
break
#if(passes!=1):
if(passes ==1000):
for i in range(0,len(reverse)):
words =reverse[i][7]
p=[]
for w in words:
pj=Get_match(w[0], seq)
if(max(pj+[-1])!=-1 and len(pj)>0):
if(pj[0]<len(seq)/2):p=p+[max([0,pj[0]-w[1]])]
else:pj[0]=-1
if(len(p)>1):
bc_len = len(reverse[i][3])+3
pj = max(set(p), key=p.count)
start = pj
j_tag = seq[pj-bc_len:pj+1]
#start = pj-bc_len
primer_match_seq = seq[start:start+len(reverse[i][6])]
print primer_match_seq, reverse[i][2], seq[start:start+len(reverse[i][6])+30]
seq = seq[pj+bc_len:len(seq)]
seq = Reverse_comp(seq, rc)
type_rev,primer_rev = reverse[i][1], reverse[i][5]
passes = 1
break
if(passes!=1):
seq = Reverse_comp(seq, rc)
for i in range(0,len(reverse)):
words =reverse[i][6]
p=[]
for w in words:
pj=Get_match(w[0], seq)
if(max(pj+[-1])!=-1 and len(pj)>0):
if(pj[0]<len(seq)/2):p=p+[max([0,pj[0]-w[1]])]
else:pj[0]=-1
if(len(p)>1):
bc_len = len(reverse[i][3])+3
pj = max(set(p), key=p.count)
j_tag = seq[pj-bc_len:pj+1]
start = pj-bc_len
primer_match_seq = seq[start:start+len(reverse[i][6])]
print primer_match_seq, reverse[i][2], seq[start:start+len(reverse[i][6])+30]
seq = seq[pj+bc_len:len(seq)]
seq = Reverse_comp(seq, rc)
type_rev,primer_rev = reverse[i][1], reverse[i][5]
passes = 1
break
if(passes==1):
pass_r = pass_r+1
for i in range(0,len(forward)):
pv=Get_match(forward[i][0], seq)
if(max(pv+[-1])!=-1):
v_tag='-'
seq = seq[pv[0]+len(forward[i][0])-1 :len(seq)]
type_for, primer_for = forward[i][1],forward[i][2]
passes = 2
break
if(passes!=2):
for i in range(0,len(forward)):
words =forward[i][6]
p=[]
for w in words:
pv=Get_match(w[0], seq)
if(max(pv+[-1])!=-1 and len(pv)>0):
if(pv[0]<len(seq)/2):p=p+[max([0,pv[0]-w[1]])]
else:pv[0]=-1
if(len(p)>3):
pv = max(set(p), key=p.count)
bc_len = len(forward[i][3])+1
v_tag = seq[pv-bc_len:pv]
type_for, primer_for = forward[i][1],forward[i][2]
#print type_for, primer_for,v_tag,seq[pv-bc_len-6:pv+4], p
seq = seq[pv+len(forward[i][2])-1 :len(seq)]
type_for, primer_for = forward[i][1],forward[i][2]
passes = 2
break