def prepareReport(ex, name, tot_counts, counts_primers, counts_primers_filtered,
tot_ambiguous=0, tot_discarded=0):
"""
Example::
Primer Total_number_of_reads nFiltered nValidReads
HoxD13 9406932 4296211 5110721
HoxD4 3835395 415503 3419892
HoxD9 6908054 594449 6313605
Discarded xxxx
Ambiguous xxx
Unclassified 1304048
Total 21454429
"""
tot_counts_primers = sum(counts_primers.values())
dataReport = unique_filename_in()
out = open(dataReport,'w')
out.write("Primer\tTotal_number_of_reads\tnFiltered\tnValidReads\n")
for k,v in counts_primers.iteritems():
if counts_primers_filtered[k]>0:
nFiltered=v-counts_primers_filtered[k]
else:
nFiltered=0
counts_primers_filtered[k]=v
out.write("\t".join([str(x) for x in [k,v,nFiltered,counts_primers_filtered[k]]])+"\n")
out.write("Discarded\t"+str(tot_discarded)+"\n")
out.write("Ambiguous\t"+str(tot_ambiguous)+"\n")
tot=tot_counts_primers+tot_discarded+tot_ambiguous
out.write("Unclassified\t"+str(tot_counts-tot)+"\n")
out.write("Total\t"+str(tot_counts)+"\n")
out.close()
return (tot_counts>tot_counts_primers,dataReport)
def filterSeq(ex, fastqFiles, seqToFilter, gid, grp_name, via='lsf'):
indexSeqToFilter = {}
indexFiles = {}
global bcDelimiter
for k, f in seqToFilter.iteritems():
if os.path.getsize(f) == 0: continue
ex.add(f,
description=set_file_descr(grp_name + "_" +
k.replace(bcDelimiter, "_") +
"_seqToFilter.fa",
groupId=gid,
step="filtering",
type="fa",
view="admin"))
if k in fastqFiles:
indexFiles[k] = bowtie_build.nonblocking(ex, f, via=via)
unalignedFiles = {}
futures = []
bwtarg = ["-a", "-q", "-n", "2", "-l", "20", "--un"]
for k, f in indexFiles.iteritems():
unalignedFiles[k] = unique_filename_in()
touch(ex, unalignedFiles[k])
futures.append(
bowtie.nonblocking(ex,
f.wait(),
fastqFiles[k],
bwtarg + [unalignedFiles[k]],
via='lsf'))
for f in futures:
f.wait()
return unalignedFiles
def fastqToFasta(fqFile, n=1, x=22, output=None):
if output is None: output = unique_filename_in()
return {
'arguments': [
"fastqToFasta.py", "-i", fqFile, "-o", output, "-n",
str(n), "-x",
str(x)
],
'return_value':
output
}
def getSeqToFilter(ex,primersFile):
filenames = {}
with open(primersFile,"r") as f:
for s in f:
if s[0] == '>': continue
s_split = s.split('|')
key = s_split[0].replace(">","")
filenames[key] = unique_filename_in()
with open(filenames[key],"w") as fout:
for i in range(4,len(s_split)):
if 'Exclude' not in s_split[i]:
fout.write(">seq"+str(i)+"\n"+s_split[i].replace(".","")+"\n")
return filenames
def getSeqToFilter(ex, primersFile):
filenames = {}
with open(primersFile, "r") as f:
for s in f:
if s[0] != '>': continue
s_split = s.split('|')
key = s_split[0].replace(">", "")
filenames[key] = unique_filename_in()
with open(filenames[key], "w") as fout:
for i in range(4, len(s_split)):
if 'Exclude' not in s_split[i]:
fout.write(">seq" + str(i) + "\n" +
s_split[i].replace(".", "") + "\n")
return filenames
def prepareReport(ex,
name,
tot_counts,
counts_primers,
counts_primers_filtered,
tot_ambiguous=0,
tot_discarded=0):
"""
Example::
Primer Total_number_of_reads nFiltered nValidReads
HoxD13 9406932 4296211 5110721
HoxD4 3835395 415503 3419892
HoxD9 6908054 594449 6313605
Discarded xxxx
Ambiguous xxx
Unclassified 1304048
Total 21454429
"""
tot_counts_primers = sum(counts_primers.values())
dataReport = unique_filename_in()
out = open(dataReport, 'w')
out.write("Primer\tTotal_number_of_reads\tnFiltered\tnValidReads\n")
for k, v in counts_primers.iteritems():
if counts_primers_filtered[k] > 0:
nFiltered = v - counts_primers_filtered[k]
else:
nFiltered = 0
counts_primers_filtered[k] = v
out.write("\t".join(
[str(x)
for x in [k, v, nFiltered, counts_primers_filtered[k]]]) + "\n")
out.write("Discarded\t" + str(tot_discarded) + "\n")
out.write("Ambiguous\t" + str(tot_ambiguous) + "\n")
tot = tot_counts_primers + tot_discarded + tot_ambiguous
out.write("Unclassified\t" + str(tot_counts - tot) + "\n")
out.write("Total\t" + str(tot_counts) + "\n")
out.close()
return (tot_counts > tot_counts_primers, dataReport)
def filterSeq(ex,fastqFiles,seqToFilter,gid,grp_name,via='lsf'):
#seqToFilter=`awk -v primer=${curPrimer} '{if($0 ~ /^>/){n=split($0,a,"|");curPrimer=a[1];gsub(">","",curPrimer); if(curPrimer == primer){seqToFilter="";for(i=5;i<n;i++){if(a[i] !~ /Exclude=/){seqToFilter=seqToFilter""a[i]","}} if(a[n] !~ /Exclude=/){seqToFilter=seqToFilter""a[n]}else{gsub(/,$/,"",seqToFilter)};print seqToFilter}}}' ${primersFile}`
indexSeqToFilter = {}
indexFiles = {}
for k,f in seqToFilter.iteritems():
if os.path.getsize(f) == 0: continue
ex.add(f,description=set_file_descr(grp_name+"_"+k+"_seqToFilter.fa",
groupId=gid,step="filtering",
type="fa",view="admin"))
if k in fastqFiles:
indexFiles[k] = bowtie_build.nonblocking(ex,f,via=via)
unalignedFiles = {}
futures = []
bwtarg = ["-a","-q","-n","2","-l","20","--un"]
for k,f in indexFiles.iteritems():
unalignedFiles[k] = unique_filename_in()
touch(ex,unalignedFiles[k])
futures.append(bowtie.nonblocking( ex, f.wait(), fastqFiles[k],
bwtarg+[unalignedFiles[k]], via='lsf'))
for f in futures: f.wait()
return unalignedFiles
def filterSeq(ex,fastqFiles,seqToFilter,gid,grp_name,via='lsf'):
indexSeqToFilter = {}
indexFiles = {}
global bcDelimiter
for k,f in seqToFilter.iteritems():
if os.path.getsize(f) == 0: continue
ex.add(f,description=set_file_descr(grp_name+"_"+k.replace(bcDelimiter,"_")+"_seqToFilter.fa",
groupId=gid,step="filtering",
type="fa",view="admin"))
if k in fastqFiles:
indexFiles[k] = bowtie_build.nonblocking(ex,f,via=via)
unalignedFiles = {}
futures = []
bwtarg = ["-a","-q","-n","2","-l","20","--un"]
for k,f in indexFiles.iteritems():
unalignedFiles[k] = unique_filename_in()
touch(ex,unalignedFiles[k])
futures.append(bowtie.nonblocking( ex, f.wait(), fastqFiles[k],
bwtarg+[unalignedFiles[k]], via='lsf'))
for f in futures: f.wait()
return unalignedFiles
def demultiplex_workflow(ex, job, gl, file_path="../", via='lsf',
logfile=sys.stdout, debugfile=sys.stderr):
script_path=gl['script_path']
file_names = {}
job_groups=job.groups
resFiles={}
for gid, group in job_groups.iteritems():
file_names[gid] = {}
primersFilename = 'group_' + group['name'] + "_barcode_file.fa"
primersFile = os.path.join(file_path,primersFilename)
ex.add(primersFile,description=set_file_descr(primersFilename,groupId=gid,step="init",type="fa"))
paramsFilename = 'group_' + group['name'] + "_param_file.txt"
paramsFile = os.path.join(file_path,paramsFilename)
ex.add(paramsFile,description=set_file_descr(paramsFilename,groupId=gid,step="init",type="txt"))
params = load_paramsFile(paramsFile)
infiles = []
tot_counts = 0
allSubFiles = []
for rid,run in group['runs'].iteritems():
infiles.append(run)
n=count_lines(ex,run)
tot_counts += n/4
if n>10000000:
allSubFiles.extend(split_file(ex,run,n_lines=8000000))
else:
allSubFiles.append(run)
(resExonerate, tot_ambiguous, tot_discarded) = parallel_exonerate( ex, allSubFiles, primersFile,
(gid, group['name']), via=via, **params )
gzipfile(ex,run)
ex.add(run+".gz",description=set_file_descr(group['name']+"_full_fastq.gz",
groupId=gid,step='exonerate',view='debug',type="fastq"))
logfile.write("Will get sequences to filter\n");logfile.flush()
seqToFilter = getSeqToFilter(ex,primersFile)
logfile.write("Will filter the sequences\n")
filteredFastq = filterSeq(ex,resExonerate,seqToFilter,gid,group['name'],via=via)
logfile.write("After filterSeq, filteredFastq=%s\n" %filteredFastq);logfile.flush()
counts_primers = {}
counts_primers_filtered = {}
if len(filteredFastq):
archive = unique_filename_in()
tgz = tarfile.open(archive, "w:gz")
for k,f in resExonerate.iteritems():
counts_primers[k] = count_lines(ex,f)/4
if k in filteredFastq:
file_names[gid][k] = group['name']+"_"+k+"_filtered"
ex.add(filteredFastq[k],description=set_file_descr(file_names[gid][k]+".fastq",
groupId=gid,step="final",
type="fastq"))
counts_primers_filtered[k] = count_lines(ex,filteredFastq[k])/4
tgz.add( f, arcname=group['name']+"_"+k+".fastq" )
else:
file_names[gid][k] = group['name']+"_"+k
ex.add(f,description=set_file_descr(file_names[gid][k]+".fastq",
groupId=gid,step="final",
type="fastq"))
counts_primers_filtered[k] = 0
if len(filteredFastq):
tgz.close()
ex.add(archive,description=set_file_descr(group['name']+"_unfiltered_fastq.tgz",
groupId=gid,step="exonerate",
type="tar"))
# Prepare report per group of runs
report_ok,reportFile = prepareReport(ex,group['name'],
tot_counts, counts_primers,counts_primers_filtered,
tot_ambiguous, tot_discarded)
ex.add(reportFile,description = set_file_descr(
group['name']+"_report_demultiplexing.txt",
groupId=gid,step="final",type="txt",view="admin"))
if report_ok:
reportFile_pdf = unique_filename_in()
createReport(ex,reportFile,reportFile_pdf,script_path)
ex.add(reportFile_pdf,description=set_file_descr(
group['name']+"_report_demultiplexing.pdf",
groupId=gid,step="final",type="pdf"))
else:
logfile.write("*** Probable ambiguous classification: total_reads < sum(reads_by_primers) ***\n");logfile.flush()
add_pickle( ex, file_names,
set_file_descr('file_names',step="final",type='py',view='admin') )
return resFiles
def split_exonerate(filename,minScore,l=30,n=1):
correction = {}
files = {}
filenames = {}
alignments = {}
prev_idLine = ''
# Is ambiguous a read with multiple equally good exonerate alignments (i.e. with a same score >= minScore)
# Will often be empty as it contains only good alignments (>minScore).
# Other ambiguous but "bad" alignments will be in "unaligned"
alignments["ambiguous"] = unique_filename_in()
files["ambiguous"] = open(alignments["ambiguous"],"w")
alignments["ambiguous_fastq"] = unique_filename_in()
files["ambiguous_fastq"] = open(alignments["ambiguous_fastq"],"w")
# Is unaligned a read with score < minScore (that is score between my_minScore and minScore)
# my_minScore is the score used for exonerate and is ~ equal to half the length of the primer sequence
alignments["unaligned"] = unique_filename_in()
files["unaligned"] = open(alignments["unaligned"],"w")
# discarded: if score ok but len(seq) < l (that is the remaining sequence is too short)
alignments["discarded"] = unique_filename_in()
files["discarded"] = open(alignments["discarded"],"w")
line_buffer = []
def _process_line(line_buffer):
if len(line_buffer) == 1:
files[line_buffer[0][3]].write(line_buffer[0][1])
elif len(line_buffer)>1:
for buf in sorted(line_buffer):
files["ambiguous"].write(" ".join(buf[2])+"\n")
# add the corresponding ambiguous read to the ambiguous_fastq file (only once)
files["ambiguous_fastq"].write(" ".buf[1]+"\n")
with open(filename,"r") as f:
for s in f:
if not s[:7] == 'vulgar:': continue
s = s.strip().split(' ')
s_split = s[5].split('|')
key = s_split[0]
info = s[1].split('_')
idLine = info[0]
if idLine != prev_idLine:
_process_line(line_buffer)
line_buffer = []
prev_idLine = idLine
if key not in correction:
if len(s_split) > 3:
correction[key] = len(s_split[1])-len(s_split[3])+n-1
else:
correction[key] = len(s_split[1])+n-1
filenames[key] = unique_filename_in()
files[key] = open(filenames[key],"w")
k = int(s[3])-int(s[7])+correction[key]
l_linename = len(info[0])
l_seq = len(info[1])
full_qual = s[1][int(l_linename)+int(l_seq)+2:int(l_linename)+2*int(l_seq)+2]
seq = info[1][k:l+k]
qual = full_qual[k:l+k]
if int(s[9]) < minScore:
files["unaligned"].write(" ".join(s)+"\n")
elif len(seq) >= l:
line_buffer.append((s[9],"@"+info[0]+"\n"+seq+"\n+\n"+qual+"\n",s,key))
else:
files["discarded"].write("@"+info[0]+"\n"+seq+"\n+\n"+qual+"\n")
_process_line(line_buffer)
for f in files.itervalues():
f.close()
return (filenames,alignments)
def fastqToFasta(fqFile,n=1,x=22,output=None):
if output is None: output = unique_filename_in()
return {'arguments': ["fastqToFasta.py","-i",fqFile,"-o",output,"-n",str(n),"-x",str(x)],
'return_value': output}
def parallel_exonerate(ex, subfiles, dbFile, grp_descr,
minScore=77, n=1, x=22, l=30, q=True, via="local"):
futures = [fastqToFasta.nonblocking(ex,sf,n=n,x=x,via=via) for sf in subfiles]
futures2 = []
res = []
resExonerate = []
faSubFiles = []
all_ambiguous = []
all_ambiguous_fastq = []
all_unaligned = []
all_discarded = []
gid, grp_name = grp_descr
my_minscore = _get_minscore(dbFile)
for sf in futures:
subResFile = unique_filename_in()
faSubFiles.append(sf.wait())
futures2.append(exonerate.nonblocking(ex, faSubFiles[-1], dbFile, minScore=my_minscore,
via=via, stdout=subResFile, memory=6))
resExonerate.append(subResFile)
for nf,f in enumerate(resExonerate):
futures2[nf].wait()
(resSplitExonerate,alignments) = split_exonerate(f,minScore,l=l,n=n)
all_unaligned.append(alignments["unaligned"])
all_ambiguous.append(alignments["ambiguous"])
all_ambiguous_fastq.append(alignments["ambiguous_fastq"])
all_discarded.append(alignments["discarded"])
res.append(resSplitExonerate)
gzipfile(ex,cat(all_unaligned[1:],out=all_unaligned[0]))
ex.add(all_unaligned[0]+".gz",
description=set_file_descr(grp_name+"_unaligned.txt.gz",
groupId=gid,step="exonerate",type="txt",
view="admin", comment="scores between %i and %i"%(my_minscore,minScore)) )
# add ambiguous file only if it is not empty
n = count_lines(ex,all_ambiguous[0])
if n > 1:
gzipfile(ex,cat(all_ambiguous[1:],out=all_ambiguous[0]))
ex.add(all_ambiguous[0]+".gz",
description=set_file_descr(grp_name+"_ambiguous.txt.gz",
groupId=gid,step="exonerate",type="txt",
view="admin", comment="multiple equally good classifications") )
# add ambiguous fastq file only if it is not empty
tot_ambiguous = count_lines(ex,all_ambiguous_fastq[0])/4
if n > 1:
gzipfile(ex,cat(all_ambiguous_fastq[1:],out=all_ambiguous_fastq[0]))
ex.add(all_ambiguous_fastq[0]+".gz",
description=set_file_descr(grp_name+"_ambiguous.fastq.gz",
groupId=gid,step="exonerate",type="fastq", comment="multiple equally good classifications") )
# add discarded file only if it is not empty
tot_discarded = count_lines(ex,all_discarded[0])/4
if n > 1:
gzipfile(ex,cat(all_discarded[1:],out=all_discarded[0]))
ex.add(all_discarded[0]+".gz",
description=set_file_descr(grp_name+"_discarded.fastq.gz",
groupId=gid, step="exonerate", type="fastq",
view="admin", comment="< %i bps" %l ) )
gzipfile(ex,faSubFiles[0])
ex.add(faSubFiles[0]+".gz",
description=set_file_descr(grp_name+"_input_part.fa.gz",
groupId=gid, step="init", type="fa",
view="admin", comment="part") )
gzipfile(ex,resExonerate[0])
ex.add(resExonerate[0]+".gz",
description=set_file_descr(grp_name+"_exonerate_part.txt.gz",
groupId=gid, step="exonerate", type="txt",
view="admin", comment="part") )
resFiles = dict((k,'') for d in res for k in d.keys())
for k in resFiles.keys():
v = [d[k] for d in res if k in d]
resFiles[k] = cat(v[1:],out=v[0])
return (resFiles, tot_ambiguous, tot_discarded)
def demultiplex_workflow(ex,
job,
gl,
file_path="../",
via='lsf',
logfile=sys.stdout,
debugfile=sys.stderr):
script_path = gl['script_path']
file_names = {}
job_groups = job.groups
resFiles = {}
for gid, group in job_groups.iteritems():
file_names[gid] = {}
primersFilename = 'group_' + group['name'] + "_barcode_file.fa"
primersFile = group.get("primersfile",
os.path.join(file_path, primersFilename))
ex.add(primersFile,
description=set_file_descr(primersFilename,
groupId=gid,
step="init",
type="fa"))
paramsFilename = 'group_' + group['name'] + "_param_file.txt"
paramsFile = group.get("paramsfile",
os.path.join(file_path, paramsFilename))
ex.add(paramsFile,
description=set_file_descr(paramsFilename,
groupId=gid,
step="init",
type="txt"))
params = load_paramsFile(paramsFile)
infiles = []
tot_counts = 0
allSubFiles = []
for rid, run in group['runs'].iteritems():
infiles.append(run)
n = count_lines(ex, run)
tot_counts += n / 4
if n > 10000000:
allSubFiles.extend(split_file(ex, run, n_lines=8000000))
else:
allSubFiles.append(run)
(resExonerate, tot_ambiguous,
tot_discarded) = parallel_exonerate(ex,
allSubFiles,
primersFile, (gid, group['name']),
via=via,
**params)
gzipfile(ex, cat(infiles))
ex.add(run + ".gz",
description=set_file_descr(group['name'] + "_full_fastq.gz",
groupId=gid,
step='exonerate',
view='debug',
type="fastq"))
logfile.write("Will get sequences to filter\n")
logfile.flush()
seqToFilter = getSeqToFilter(ex, primersFile)
logfile.write("Will filter the sequences\n")
filteredFastq = filterSeq(ex,
resExonerate,
seqToFilter,
gid,
group['name'],
via=via)
logfile.write("After filterSeq, filteredFastq=%s\n" % filteredFastq)
logfile.flush()
counts_primers = {}
counts_primers_filtered = {}
global bcDelimiter
if len(filteredFastq):
archive = unique_filename_in()
tgz = tarfile.open(archive, "w:gz")
for k, f in resExonerate.iteritems():
counts_primers[k] = count_lines(ex, f) / 4
if k in filteredFastq:
k2 = k.replace(bcDelimiter, "_")
file_names[gid][k2] = group['name'] + "_" + k2 + "_filtered"
ex.add(filteredFastq[k],
description=set_file_descr(file_names[gid][k2] +
".fastq",
groupId=gid,
step="final",
type="fastq"))
counts_primers_filtered[k] = count_lines(ex,
filteredFastq[k]) / 4
tgz.add(f,
arcname=group['name'] + "_" +
k.replace(bcDelimiter, "_") + ".fastq")
else:
k2 = k.replace(bcDelimiter, "_")
file_names[gid][k2] = group['name'] + "_" + k2
ex.add(f,
description=set_file_descr(file_names[gid][k2] +
".fastq",
groupId=gid,
step="final",
type="fastq"))
counts_primers_filtered[k] = 0
if len(filteredFastq):
tgz.close()
ex.add(archive,
description=set_file_descr(group['name'] +
"_unfiltered_fastq.tgz",
groupId=gid,
step="exonerate",
type="tar"))
# Prepare report per group of runs
report_ok, reportFile = prepareReport(ex, group['name'], tot_counts,
counts_primers,
counts_primers_filtered,
tot_ambiguous, tot_discarded)
ex.add(reportFile,
description=set_file_descr(group['name'] +
"_report_demultiplexing.txt",
groupId=gid,
step="final",
type="txt",
view="admin"))
if report_ok:
reportFile_pdf = unique_filename_in()
createReport(ex, reportFile, reportFile_pdf, script_path)
ex.add(reportFile_pdf,
description=set_file_descr(group['name'] +
"_report_demultiplexing.pdf",
groupId=gid,
step="final",
type="pdf"))
else:
logfile.write(
"*** Probable ambiguous classification: total_reads < sum(reads_by_primers) ***\n"
)
logfile.flush()
add_pickle(
ex, file_names,
set_file_descr('file_names', step="final", type='py', view='admin'))
return resFiles
def split_exonerate(filename, primersDict, minScore, l=30, n=1, trim=True):
correction = {}
files = {}
filenames = {}
alignments = {}
prev_idLine = ''
# Is ambiguous a read with multiple equally good exonerate alignments (i.e. with a same score >= minScore)
# Will often be empty as it contains only good alignments (>minScore).
# Other ambiguous but "bad" alignments will be in "unaligned"
alignments["ambiguous"] = unique_filename_in()
files["ambiguous"] = open(alignments["ambiguous"], "w")
alignments["ambiguous_fastq"] = unique_filename_in()
files["ambiguous_fastq"] = open(alignments["ambiguous_fastq"], "w")
# Is unaligned a read with score < minScore (that is score between my_minScore and minScore)
# my_minScore is the score used for exonerate and is ~ equal to half the length of the primer sequence
alignments["unaligned"] = unique_filename_in()
files["unaligned"] = open(alignments["unaligned"], "w")
# discarded: if score ok but len(seq) < l (that is the remaining sequence is too short)
alignments["discarded"] = unique_filename_in()
files["discarded"] = open(alignments["discarded"], "w")
line_buffer = []
def _process_line(line_buffer):
if len(line_buffer) == 1:
files[line_buffer[0][3]].write(line_buffer[0][1])
elif len(line_buffer) > 1:
for buf in sorted(line_buffer):
files["ambiguous"].write(" ".join(buf[2]) + "\n")
# add the corresponding ambiguous read to the ambiguous_fastq file (only once)
#files["ambiguous_fastq"].write(" ".join(buf[1])+"\n")
files["ambiguous_fastq"].write(buf[1])
with open(filename, "r") as f:
for s in f:
if not s[:7] == 'vulgar:': continue
s = s.strip().split(' ')
s_split = s[5].split('|')
key = s_split[0]
info = s[1].split('_')
idLine = info[0]
if idLine != prev_idLine:
_process_line(line_buffer)
line_buffer = []
prev_idLine = idLine
closestBarcode = key
global bcDelimiter
if key.split(bcDelimiter)[0] in primersDict: ## has barcode
pr = key.split(bcDelimiter)[0]
closestBarcode = getClosestBarcode(
info[1],
primersDict[pr]) ## replace key with the closest barcode
if key not in correction:
if len(s_split) > 3:
correction[key] = len(s_split[1]) - len(s_split[3]) + n - 1
else:
correction[key] = len(s_split[1]) + n - 1
if len(key.split(bcDelimiter)) > 1:
correction[key] = correction[
key] - 1 ## remove the bcDelimiter from the correction
filenames[key] = unique_filename_in()
files[key] = open(filenames[key], "w")
k = int(s[3]) - int(s[7]) + correction[key]
if len(key.split(bcDelimiter)) > 1:
k = k - len(primersDict[key.split(bcDelimiter)[0]][key])
l_linename = len(info[0])
l_seq = len(info[1])
full_qual = s[1][int(l_linename) + int(l_seq) + 2:int(l_linename) +
2 * int(l_seq) + 2]
if trim:
seq = info[1][k:l + k] if l + k < l_seq else info[1][k:]
## if not take the remaining length
qual = full_qual[k:l + k] if l + k < l_seq else full_qual[k:]
else:
seq = info[1]
qual = full_qual
if int(s[9]) < minScore:
files["unaligned"].write(" ".join(s) + "\n")
elif len(seq) >= l / 2:
if key == closestBarcode or closestBarcode == "amb": # if not => not the primer_barcode to which the read comes from + add if ambiguous barcode
#line_buffer.append((s[9],"@"+info[0]+"\n"+seq+"***"+info[1]+"\n+\n"+qual+"\n",s,key))
line_buffer.append((s[9], "@" + info[0] + "\n" + seq +
"\n+\n" + qual + "\n", s, key))
else: ## will be discarded if remaining read length is too short. Arbitrarly set to < l/2. It used to be < l but will not happen as we now accept l close to the read length.
files["discarded"].write("@" + info[0] + "\n" + seq + "\n+\n" +
qual + "\n")
_process_line(line_buffer)
for f in files.itervalues():
f.close()
return (filenames, alignments)
def parallel_exonerate(ex,
subfiles,
dbFile,
grp_descr,
minScore=77,
n=1,
x=22,
l=30,
trim=True,
via="local"):
futures = [
fastqToFasta.nonblocking(ex, sf, n=n, x=x, via=via) for sf in subfiles
]
futures2 = []
res = []
resExonerate = []
faSubFiles = []
all_ambiguous = []
all_ambiguous_fastq = []
all_unaligned = []
all_discarded = []
gid, grp_name = grp_descr
my_minscore = _get_minscore(dbFile)
primersDict = get_primersList(dbFile)
for sf in futures:
subResFile = unique_filename_in()
faSubFiles.append(sf.wait())
futures2.append(
exonerate.nonblocking(ex,
faSubFiles[-1],
dbFile,
minScore=my_minscore,
via=via,
stdout=subResFile,
memory=6))
resExonerate.append(subResFile)
for nf, f in enumerate(resExonerate):
futures2[nf].wait()
(resSplitExonerate, alignments) = split_exonerate(f,
primersDict,
minScore,
l=l,
n=n,
trim=trim)
all_unaligned.append(alignments["unaligned"])
all_ambiguous.append(alignments["ambiguous"])
all_ambiguous_fastq.append(alignments["ambiguous_fastq"])
all_discarded.append(alignments["discarded"])
res.append(resSplitExonerate)
# add unaligned file only if it is not empty
n = count_lines(ex, all_unaligned[0])
if n > 1:
catfile = cat(all_unaligned)
gzipfile(ex, catfile)
ex.add(catfile + ".gz",
description=set_file_descr(grp_name + "_unaligned.txt.gz",
groupId=gid,
step="exonerate",
type="txt",
view="admin",
comment="scores between %i and %i" %
(my_minscore, minScore)))
# add ambiguous file only if it is not empty
n = count_lines(ex, all_ambiguous[0])
if n > 1:
catfile = cat(all_ambiguous)
gzipfile(ex, catfile)
ex.add(catfile + ".gz",
description=set_file_descr(
grp_name + "_ambiguous.txt.gz",
groupId=gid,
step="exonerate",
type="txt",
view="admin",
comment="multiple equally good classifications"))
# add ambiguous fastq file only if it is not empty
tot_ambiguous = count_lines(ex, all_ambiguous_fastq[0]) / 4
if tot_ambiguous > 1:
catfile = cat(all_ambiguous_fastq)
gzipfile(ex, catfile)
ex.add(catfile + ".gz",
description=set_file_descr(
grp_name + "_ambiguous.fastq.gz",
groupId=gid,
step="exonerate",
type="fastq",
comment="multiple equally good classifications"))
# add discarded file only if it is not empty
tot_discarded = count_lines(ex, all_discarded[0]) / 4
if tot_discarded > 1:
catfile = cat(all_discarded)
gzipfile(ex, catfile)
ex.add(catfile + ".gz",
description=set_file_descr(grp_name + "_discarded.fastq.gz",
groupId=gid,
step="exonerate",
type="fastq",
view="admin",
comment="remaining seq too short"))
## add part input fasta file only if it is not empty
n = count_lines(ex, faSubFiles[0])
if n > 1:
gzipfile(ex, faSubFiles[0])
ex.add(faSubFiles[0] + ".gz",
description=set_file_descr(grp_name + "_input_part.fa.gz",
groupId=gid,
step="init",
type="fa",
view="admin",
comment="part"))
## add part res exonerate only if it is not empty
n = count_lines(ex, resExonerate[0])
if n > 1:
gzipfile(ex, resExonerate[0])
ex.add(resExonerate[0] + ".gz",
description=set_file_descr(grp_name + "_exonerate_part.txt.gz",
groupId=gid,
step="exonerate",
type="txt",
view="admin",
comment="part"))
resFiles = dict((k, '') for d in res for k in d.keys())
for k in resFiles.keys():
v = [d[k] for d in res if k in d]
resFiles[k] = cat(v[1:], out=v[0])
return (resFiles, tot_ambiguous, tot_discarded)
def split_exonerate(filename,primersDict,minScore,l=30,n=1,trim=True):
correction = {}
files = {}
filenames = {}
alignments = {}
prev_idLine = ''
# Is ambiguous a read with multiple equally good exonerate alignments (i.e. with a same score >= minScore)
# Will often be empty as it contains only good alignments (>minScore).
# Other ambiguous but "bad" alignments will be in "unaligned"
alignments["ambiguous"] = unique_filename_in()
files["ambiguous"] = open(alignments["ambiguous"],"w")
alignments["ambiguous_fastq"] = unique_filename_in()
files["ambiguous_fastq"] = open(alignments["ambiguous_fastq"],"w")
# Is unaligned a read with score < minScore (that is score between my_minScore and minScore)
# my_minScore is the score used for exonerate and is ~ equal to half the length of the primer sequence
alignments["unaligned"] = unique_filename_in()
files["unaligned"] = open(alignments["unaligned"],"w")
# discarded: if score ok but len(seq) < l (that is the remaining sequence is too short)
alignments["discarded"] = unique_filename_in()
files["discarded"] = open(alignments["discarded"],"w")
line_buffer = []
def _process_line(line_buffer):
if len(line_buffer) == 1:
files[line_buffer[0][3]].write(line_buffer[0][1])
elif len(line_buffer)>1:
for buf in sorted(line_buffer):
files["ambiguous"].write(" ".join(buf[2])+"\n")
# add the corresponding ambiguous read to the ambiguous_fastq file (only once)
#files["ambiguous_fastq"].write(" ".join(buf[1])+"\n")
files["ambiguous_fastq"].write(buf[1])
with open(filename,"r") as f:
for s in f:
if not s[:7] == 'vulgar:': continue
s = s.strip().split(' ')
s_split = s[5].split('|')
key = s_split[0]
info = s[1].split('_')
idLine = info[0]
if idLine != prev_idLine:
_process_line(line_buffer)
line_buffer = []
prev_idLine = idLine
closestBarcode = key
global bcDelimiter
if key.split(bcDelimiter)[0] in primersDict: ## has barcode
pr = key.split(bcDelimiter)[0]
closestBarcode = getClosestBarcode(info[1],primersDict[pr]) ## replace key with the closest barcode
if key not in correction:
if len(s_split) > 3:
correction[key] = len(s_split[1])-len(s_split[3])+n-1
else:
correction[key] = len(s_split[1])+n-1
if len(key.split(bcDelimiter)) > 1:
correction[key] = correction[key] - 1 ## remove the bcDelimiter from the correction
filenames[key] = unique_filename_in()
files[key] = open(filenames[key],"w")
k = int(s[3])-int(s[7])+correction[key]
if len(key.split(bcDelimiter)) > 1:
k = k - len(primersDict[key.split(bcDelimiter)[0]][key])
l_linename = len(info[0])
l_seq = len(info[1])
full_qual = s[1][int(l_linename)+int(l_seq)+2:int(l_linename)+2*int(l_seq)+2]
if trim:
seq = info[1][k:l+k] if l+k < l_seq else info[1][k:]; ## if not take the remaining length
qual = full_qual[k:l+k] if l+k < l_seq else full_qual[k:]
else:
seq = info[1]
qual = full_qual
if int(s[9]) < minScore:
files["unaligned"].write(" ".join(s)+"\n")
elif len(seq) >= l/2:
if key == closestBarcode or closestBarcode == "amb": # if not => not the primer_barcode to which the read comes from + add if ambiguous barcode
#line_buffer.append((s[9],"@"+info[0]+"\n"+seq+"***"+info[1]+"\n+\n"+qual+"\n",s,key))
line_buffer.append((s[9],"@"+info[0]+"\n"+seq+"\n+\n"+qual+"\n",s,key))
else: ## will be discarded if remaining read length is too short. Arbitrarly set to < l/2. It used to be < l but will not happen as we now accept l close to the read length.
files["discarded"].write("@"+info[0]+"\n"+seq+"\n+\n"+qual+"\n")
_process_line(line_buffer)
for f in files.itervalues():
f.close()
return (filenames,alignments)
