def computeTconversions(ref,
bed,
snpsFile,
bam,
maxReadLength,
minQual,
outputCSV,
outputBedgraphPlus,
outputBedgraphMinus,
conversionThreshold,
log,
mle=False):
referenceFile = pysam.FastaFile(ref)
sampleInfo = getSampleInfo(bam)
slamseqInfo = SlamSeqInfo(bam)
#readNumber = slamseqInfo.MappedReads
readNumber = slamseqInfo.FilteredReads
bedMD5 = md5(bed)
if (mle):
fileNameTest = replaceExtension(outputCSV, ".tsv", "_perread")
fileTest = open(fileNameTest, 'w')
print("#slamdunk v" + __version__,
__count_version__,
"sample info:",
sampleInfo.Name,
sampleInfo.ID,
sampleInfo.Type,
sampleInfo.Time,
sep="\t",
file=fileTest)
print("#annotation:",
os.path.basename(bed),
bedMD5,
sep="\t",
file=fileTest)
#print("utr", "n", "k", file=fileTest)
print(SlamSeqInterval.Header, file=fileTest)
fileCSV = open(outputCSV, 'w')
print("#slamdunk v" + __version__,
__count_version__,
"sample info:",
sampleInfo.Name,
sampleInfo.ID,
sampleInfo.Type,
sampleInfo.Time,
sep="\t",
file=fileCSV)
print("#annotation:",
os.path.basename(bed),
bedMD5,
sep="\t",
file=fileCSV)
print(SlamSeqInterval.Header, file=fileCSV)
snps = SNPtools.SNPDictionary(snpsFile)
snps.read()
#Go through one chr after the other
testFile = SlamSeqBamFile(bam, ref, snps)
if not testFile.bamVersion == __bam_version__:
raise RuntimeError("Wrong filtered BAM file version detected (" +
testFile.bamVersion + "). Expected version " +
__bam_version__ + ". Please rerun slamdunk filter.")
bedMD5 = md5(bed)
if slamseqInfo.AnnotationMD5 != bedMD5:
print(
"Warning: MD5 checksum of annotation (" + bedMD5 +
") does not matched MD5 in filtered BAM files (" +
slamseqInfo.AnnotationMD5 +
"). Most probably the annotation filed changed after the filtered BAM files were created.",
file=log)
conversionBedGraph = {}
for utr in BedIterator(bed):
Tcontent = 0
slamSeqUtr = SlamSeqInterval(utr.chromosome, utr.start, utr.stop,
utr.strand, utr.name, Tcontent, 0, 0, 0,
0, 0, 0, 0)
slamSeqUtrMLE = SlamSeqInterval(utr.chromosome, utr.start, utr.stop,
utr.strand, utr.name, Tcontent, 0, 0,
0, 0, 0, 0, 0)
if (not utr.hasStrand()):
raise RuntimeError(
"Input BED file does not contain stranded intervals.")
if utr.start < 0:
raise RuntimeError(
"Negativ start coordinate found. Please check the following entry in your BED file: "
+ utr)
# Retreive reference sequence
region = utr.chromosome + ":" + str(utr.start + 1) + "-" + str(
utr.stop)
if (utr.chromosome in list(referenceFile.references)):
#print(refRegion,file=sys.stderr)
# pysam-0.15.0.1
#refSeq = referenceFile.fetch(region=region).upper()
refSeq = referenceFile.fetch(reference=utr.chromosome,
start=utr.start,
end=utr.stop).upper()
if (utr.strand == "-"):
#refSeq = complement(refSeq[::-1])
Tcontent = refSeq.count("A")
else:
Tcontent = refSeq.count("T")
slamSeqUtr._Tcontent = Tcontent
readIterator = testFile.readInRegion(utr.chromosome, utr.start,
utr.stop, utr.strand,
maxReadLength, minQual,
conversionThreshold)
tcCountUtr = [0] * utr.getLength()
coverageUtr = [0] * utr.getLength()
tInReads = []
tcInRead = []
countFwd = 0
tcCountFwd = 0
countRev = 0
tCountRev = 0
multiMapFwd = 0
multiMapRev = 0
for read in readIterator:
# Overwrite any conversions for non-TC reads (reads with < 2 TC conversions)
if (not read.isTcRead):
read.tcCount = 0
read.mismatches = []
read.conversionRates = 0.0
read.tcRate = 0.0
if (read.direction == ReadDirection.Reverse):
countRev += 1
if read.tcCount > 0:
tCountRev += 1
if read.isMultimapper:
multiMapRev += 1
else:
countFwd += 1
if read.tcCount > 0:
tcCountFwd += 1
if read.isMultimapper:
multiMapFwd += 1
for mismatch in read.mismatches:
if (mismatch.isTCMismatch(
read.direction == ReadDirection.Reverse)
and mismatch.referencePosition >= 0
and mismatch.referencePosition < utr.getLength()):
tcCountUtr[mismatch.referencePosition] += 1
testN = read.getTcount()
testk = 0
for mismatch in read.mismatches:
if (mismatch.referencePosition >= 0
and mismatch.referencePosition < utr.getLength()):
if (mismatch.isT(read.direction == ReadDirection.Reverse)):
testN += 1
if (mismatch.isTCMismatch(
read.direction == ReadDirection.Reverse)):
testk += 1
#print(utr.name, read.name, read.direction, testN, testk, read.sequence, sep="\t")
tInReads.append(testN)
tcInRead.append(testk)
#print(utr.name, testN, testk, sep="\t", file=fileTest)
for i in xrange(read.startRefPos, read.endRefPos):
if (i >= 0 and i < utr.getLength()):
coverageUtr[i] += 1
if ((utr.strand == "+" and countFwd > 0)
or (utr.strand == "-" and countRev > 0)):
tcRateUtr = [
x * 100.0 / y if y > 0 else 0
for x, y in zip(tcCountUtr, coverageUtr)
]
readCount = countFwd
tcReadCount = tcCountFwd
multiMapCount = multiMapFwd
if (utr.strand == "-"):
readCount = countRev
tcReadCount = tCountRev
multiMapCount = multiMapRev
if ((utr.strand == "-" and countFwd > countRev)
or (utr.strand == "+" and countRev > countFwd)):
print(
"Warning: " + utr.name + " is located on the " +
utr.strand +
" strand but read counts are higher for the opposite strand (fwd: "
+ countFwd + ", rev: " + countRev + ")",
file=sys.stderr)
refSeq = readIterator.getRefSeq()
# Get number of covered Ts/As in the UTR and compute average conversion rate for all covered Ts/As
coveredTcount = 0
avgConversationRate = 0
coveredPositions = 0
# Get number of reads on T positions and number of reads with T->C conversions on T positions
coverageOnTs = 0
conversionsOnTs = 0
for position in xrange(0, len(coverageUtr)):
if (coverageUtr[position] > 0
and ((utr.strand == "+" and refSeq[position] == "T") or
(utr.strand == "-" and refSeq[position] == "A"))):
coveredTcount += 1
avgConversationRate += tcRateUtr[position]
coverageOnTs += coverageUtr[position]
conversionsOnTs += tcCountUtr[position]
conversionBedGraph[utr.chromosome + ":" +
str(utr.start + position) + ":" +
str(utr.strand)] = tcRateUtr[position]
if (coverageUtr[position] > 0):
coveredPositions += 1
if (coveredTcount > 0):
avgConversationRate = avgConversationRate / coveredTcount
else:
avgConversationRate = 0
# reads per million mapped to the UTR
readsCPM = 0
if (readNumber > 0):
readsCPM = readCount * 1000000.0 / readNumber
# Convert to SlamSeqInterval and print
conversionRate = 0
if (coverageOnTs > 0):
conversionRate = float(conversionsOnTs) / float(coverageOnTs)
slamSeqUtr = SlamSeqInterval(utr.chromosome, utr.start, utr.stop,
utr.strand, utr.name, Tcontent,
readsCPM, coverageOnTs,
conversionsOnTs, conversionRate,
readCount, tcReadCount, multiMapCount)
slamSeqUtrMLE = SlamSeqInterval(
utr.chromosome, utr.start, utr.stop, utr.strand, utr.name,
Tcontent, readsCPM, coverageOnTs, conversionsOnTs,
conversionRate, ",".join(str(x) for x in tInReads),
",".join(str(x) for x in tcInRead), multiMapCount)
print(slamSeqUtr, file=fileCSV)
if (mle):
print(slamSeqUtrMLE, file=fileTest)
fileCSV.close()
if (mle):
fileTest.close()
fileBedgraphPlus = open(outputBedgraphPlus, 'w')
fileBedgraphMinus = open(outputBedgraphMinus, 'w')
for position in conversionBedGraph:
positionData = position.split(":")
if (positionData[2] == "+"):
print(positionData[0],
positionData[1],
int(positionData[1]) + 1,
conversionBedGraph[position],
file=fileBedgraphPlus)
else:
print(positionData[0],
positionData[1],
int(positionData[1]) + 1,
conversionBedGraph[position],
file=fileBedgraphMinus)
fileBedgraphPlus.close()
fileBedgraphMinus.close()
if (mle):
fileNameMLE = replaceExtension(outputCSV, ".tsv", "_mle")
callR(
getPlotter("compute_conversion_rate_mle") + " -f " + fileNameTest +
" -r " + "0.024" + " -o " + fileNameMLE + " &> /dev/null")
def addTcConversions(bed,
readInFile,
readOutFile,
pulseTimePoint,
chaseTimePoint,
utrSummaryFile,
conversionRate,
librarySize,
sampleInfo,
labeledTranscripts=-1.0):
# Read utrs from BED file
utrs = parseUtrBedFile(bed)
readOutTemp = readOutFile + "_tmp.sam"
#bamheader = { 'HD': {'VN': '1.0'} }
#readOutBAM = pysam.AlignmentFile(readOutTemp, "wb", header=bamheader, add_sq_text=False)
readOutSAM = open(readOutTemp, "w")
print("@HD\tVN:1.0\tSO:unsorted", file=readOutSAM)
utrSummary = open(utrSummaryFile, "w")
bedMD5 = md5(bed)
print("#slamdunk v" + __version__,
__count_version__,
"sample info:",
sampleInfo.Name,
sampleInfo.ID,
sampleInfo.Type,
sampleInfo.Time,
sep="\t",
file=utrSummary)
print("#annotation:",
os.path.basename(bed),
bedMD5,
sep="\t",
file=utrSummary)
print(SlamSeqInterval.Header, file=utrSummary)
reads = []
lastUtrName = None
utrName = None
fasta_sequences = SeqIO.parse(open(readInFile), 'fasta')
for entry in fasta_sequences:
# TODO: Uncomment to go back to pysam
#with pysam.FastxFile(readInFile) as fh:
#for entry in fh:
#utrName = getUtrName(entry.name)
utrName = getUtrName(entry.id)
if (utrName == lastUtrName):
reads.append(entry)
elif (lastUtrName == None):
reads.append(entry)
else:
readsCPM = len(reads) * 1000000.0 / librarySize
readToConvertPercent = computeConversionRate(
utrs[lastUtrName].score, pulseTimePoint, chaseTimePoint,
labeledTranscripts)
readsWithTC, totalTCount, totalTcCount = addTcConversionsToReads(
utrs[lastUtrName], reads, readToConvertPercent, conversionRate,
readOutSAM)
printUtrSummary(utrs[lastUtrName], len(reads), readsWithTC,
totalTCount, totalTcCount, utrSummary, readsCPM,
readToConvertPercent)
reads = []
lastUtrName = utrName
# Last UTR
readsCPM = len(reads) * 1000000.0 / librarySize
readToConvertPercent = computeConversionRate(utrs[lastUtrName].score,
pulseTimePoint,
chaseTimePoint,
labeledTranscripts)
readsWithTC, totalTCount, totalTcCount = addTcConversionsToReads(
utrs[lastUtrName], reads, readToConvertPercent, conversionRate,
readOutSAM)
printUtrSummary(utrs[lastUtrName], len(reads), readsWithTC, totalTCount,
totalTcCount, utrSummary, readsCPM, readToConvertPercent)
readOutSAM.close()
utrSummary.close()
readOutTempBAM = readOutFile + "_tmp.bam"
# Convert to BAM
run("samtools view -Sb " + readOutTemp + " > " + readOutTempBAM)
#samFile = pysam.AlignmentFile(readOutTemp, "r", check_header = False, check_sq = False)
#bamFile = pysam.AlignmentFile(readOutTempBAM, "wb", template=samFile)
#for read in samFile:
# bamFile.write(read)
#bamFile.close()
#samFile.close()
# Sort reads by query name (doesn't matter for mapping, but makes evaluation easier
#pysam.sort("-o", readOutFile, readOutTempBAM) # @UndefinedVariable
run("samtools sort -o " + readOutFile + " " + readOutTempBAM)
os.unlink(readOutTemp)
os.unlink(readOutTempBAM)
def Filter(inputBAM, outputBAM, log, bed, MQ=2, minIdentity=0.8, NM=-1, printOnly=False, verbose=True, force=False):
if(printOnly or checkStep([inputBAM], [outputBAM], force)):
mappedReads = 0
unmappedReads = 0
filteredReads = 0
mqFiltered = 0
idFiltered = 0
nmFiltered = 0
multimapper = 0
infile = pysam.AlignmentFile(inputBAM, "rb")
outfile = pysam.AlignmentFile(outputBAM, "wb", template=infile)
# Default filtering without bed
if (bed == None) :
print("#No bed-file supplied. Running default filtering on " + inputBAM + ".",file=log)
for read in infile:
if(not read.is_secondary and not read.is_supplementary):
if(read.is_unmapped):
unmappedReads += 1
else:
mappedReads += 1
if(read.is_unmapped):
continue
if(read.mapping_quality < MQ):
mqFiltered += 1
continue
if(float(read.get_tag("XI")) < minIdentity):
idFiltered += 1
continue
if(NM > -1 and int(read.get_tag("NM")) > NM):
nmFiltered += 1
continue
if(not read.is_secondary and not read.is_supplementary):
filteredReads += 1
outfile.write(read)
print("Criterion\tFiltered reads",file=log)
print("MQ < " + str(MQ) + "\t" + str(mqFiltered),file=log)
print("ID < " + str(minIdentity) + "\t" + str(idFiltered),file=log)
print("NM > " + str(NM) + "\t" + str(nmFiltered),file=log)
print("MM\t0",file=log)
else :
# Multimap retention strategy filtering when bed is supplied
random.seed(1)
print("#Bed-file supplied. Running multimap retention filtering strategy on " + inputBAM + ".",file=log)
mappedReads, unmappedReads, filteredReads, mqFiltered, idFiltered, nmFiltered, multimapper = multimapUTRRetainment (infile, outfile, bed, minIdentity, NM, log)
#mappedReads, unmappedReads, filteredReads = multimapUTRRetainment (infile, outfile, bed, minIdentity, NM, log)
# Add number of sequenced and number of mapped reads to the read group description
# Used for creating summary file
inFileBamHeader = outfile.header
if('RG' in inFileBamHeader and len(inFileBamHeader['RG']) > 0):
slamseqInfo = SlamSeqInfo()
slamseqInfo.SequencedReads = mappedReads + unmappedReads
slamseqInfo.MappedReads = mappedReads
slamseqInfo.FilteredReads = filteredReads
slamseqInfo.MQFilteredReads = mqFiltered
slamseqInfo.IdFilteredReads = idFiltered
slamseqInfo.NmFilteredReads = nmFiltered
slamseqInfo.MultimapperReads = multimapper
if (bed != None) :
slamseqInfo.AnnotationName = os.path.basename(bed)
slamseqInfo.AnnotationMD5 = md5(bed)
else :
slamseqInfo.AnnotationName = ""
slamseqInfo.AnnotationMD5 = ""
if not isinstance(inFileBamHeader, dict):
inFileBamHeader = inFileBamHeader.to_dict()
inFileBamHeader['RG'][0]['DS'] = str(slamseqInfo)
#inFileBamHeader['RG'][0]['DS'] = "{'sequenced':" + str(mappedReads + unmappedReads) + "," + "'mapped':" + str(mappedReads) + "," + "'filtered':" + str(filteredReads) + "}"
slamDunkPG = { 'ID': 'slamdunk', 'PN': 'slamdunk filter v' + __version__, 'VN': __bam_version__ }
if('PG' in inFileBamHeader):
inFileBamHeader['PG'].append(slamDunkPG)
else:
inFileBamHeader['PG'] = [ slamDunkPG ]
infile.close()
outfile.close()
# Sort afterwards
bamSort(outputBAM, log, inFileBamHeader, verbose)
pysamIndex(outputBAM)
#pysamFlagstat(outputBAM)
#runFlagstat(outputBAM, log, verbose=verbose, dry=printOnly)
else:
print("Skipped filtering for " + inputBAM, file=log)
def Filter(inputBAM,
outputBAM,
log,
bed,
MQ=2,
minIdentity=0.8,
NM=-1,
printOnly=False,
verbose=True,
force=False):
if (printOnly or checkStep([inputBAM], [outputBAM], force)):
mappedReads = 0
unmappedReads = 0
filteredReads = 0
mqFiltered = 0
idFiltered = 0
nmFiltered = 0
multimapper = 0
infile = pysam.AlignmentFile(inputBAM, "rb")
outfile = pysam.AlignmentFile(outputBAM, "wb", template=infile)
# Default filtering without bed
if (bed == None):
print("#No bed-file supplied. Running default filtering on " +
inputBAM + ".",
file=log)
for read in infile:
if (not read.is_secondary and not read.is_supplementary):
if (read.is_unmapped):
unmappedReads += 1
else:
mappedReads += 1
if (read.is_unmapped):
continue
if (read.mapping_quality < MQ):
mqFiltered += 1
continue
if (float(read.get_tag("XI")) < minIdentity):
idFiltered += 1
continue
if (NM > -1 and int(read.get_tag("NM")) > NM):
nmFiltered += 1
continue
if (not read.is_secondary and not read.is_supplementary):
filteredReads += 1
outfile.write(read)
print("Criterion\tFiltered reads", file=log)
print("MQ < " + str(MQ) + "\t" + str(mqFiltered), file=log)
print("ID < " + str(minIdentity) + "\t" + str(idFiltered),
file=log)
print("NM > " + str(NM) + "\t" + str(nmFiltered), file=log)
print("MM\t0", file=log)
else:
# Multimap retention strategy filtering when bed is supplied
random.seed(1)
print(
"#Bed-file supplied. Running multimap retention filtering strategy on "
+ inputBAM + ".",
file=log)
mappedReads, unmappedReads, filteredReads, mqFiltered, idFiltered, nmFiltered, multimapper = multimapUTRRetainment(
infile, outfile, bed, minIdentity, NM, log)
#mappedReads, unmappedReads, filteredReads = multimapUTRRetainment (infile, outfile, bed, minIdentity, NM, log)
# Add number of sequenced and number of mapped reads to the read group description
# Used for creating summary file
inFileBamHeader = outfile.header
if ('RG' in inFileBamHeader and len(inFileBamHeader['RG']) > 0):
slamseqInfo = SlamSeqInfo()
slamseqInfo.SequencedReads = mappedReads + unmappedReads
slamseqInfo.MappedReads = mappedReads
slamseqInfo.FilteredReads = filteredReads
slamseqInfo.MQFilteredReads = mqFiltered
slamseqInfo.IdFilteredReads = idFiltered
slamseqInfo.NmFilteredReads = nmFiltered
slamseqInfo.MultimapperReads = multimapper
if (bed != None):
slamseqInfo.AnnotationName = os.path.basename(bed)
slamseqInfo.AnnotationMD5 = md5(bed)
else:
slamseqInfo.AnnotationName = ""
slamseqInfo.AnnotationMD5 = ""
if not isinstance(inFileBamHeader, dict):
inFileBamHeader = inFileBamHeader.to_dict()
inFileBamHeader['RG'][0]['DS'] = str(slamseqInfo)
#inFileBamHeader['RG'][0]['DS'] = "{'sequenced':" + str(mappedReads + unmappedReads) + "," + "'mapped':" + str(mappedReads) + "," + "'filtered':" + str(filteredReads) + "}"
slamDunkPG = {
'ID': 'slamdunk',
'PN': 'slamdunk filter v' + __version__,
'VN': __bam_version__
}
if ('PG' in inFileBamHeader):
inFileBamHeader['PG'].append(slamDunkPG)
else:
inFileBamHeader['PG'] = [slamDunkPG]
infile.close()
outfile.close()
# Sort afterwards
bamSort(outputBAM, log, inFileBamHeader, verbose)
pysamIndex(outputBAM)
#pysamFlagstat(outputBAM)
#runFlagstat(outputBAM, log, verbose=verbose, dry=printOnly)
else:
print("Skipped filtering for " + inputBAM, file=log)
def addTcConversions(bed, readInFile, readOutFile, pulseTimePoint, chaseTimePoint, utrSummaryFile, conversionRate, librarySize, sampleInfo, labeledTranscripts = -1.0):
# Read utrs from BED file
utrs = parseUtrBedFile(bed)
readOutTemp = readOutFile + "_tmp.sam"
#bamheader = { 'HD': {'VN': '1.0'} }
#readOutBAM = pysam.AlignmentFile(readOutTemp, "wb", header=bamheader, add_sq_text=False)
readOutSAM = open(readOutTemp, "w")
print("@HD\tVN:1.0\tSO:unsorted", file=readOutSAM)
utrSummary = open(utrSummaryFile, "w")
bedMD5 = md5(bed)
print("#slamdunk v" + __version__, __count_version__, "sample info:", sampleInfo.Name, sampleInfo.ID, sampleInfo.Type, sampleInfo.Time, sep="\t", file=utrSummary)
print("#annotation:", os.path.basename(bed), bedMD5, sep="\t", file=utrSummary)
print(SlamSeqInterval.Header, file=utrSummary)
reads = []
lastUtrName = None
utrName = None
fasta_sequences = SeqIO.parse(open(readInFile),'fasta')
for entry in fasta_sequences:
# TODO: Uncomment to go back to pysam
#with pysam.FastxFile(readInFile) as fh:
#for entry in fh:
#utrName = getUtrName(entry.name)
utrName = getUtrName(entry.id)
if(utrName == lastUtrName):
reads.append(entry)
elif(lastUtrName == None):
reads.append(entry)
else:
readsCPM = len(reads) * 1000000.0 / librarySize;
readToConvertPercent = computeConversionRate(utrs[lastUtrName].score, pulseTimePoint, chaseTimePoint, labeledTranscripts)
readsWithTC, totalTCount, totalTcCount = addTcConversionsToReads(utrs[lastUtrName], reads, readToConvertPercent, conversionRate, readOutSAM)
printUtrSummary(utrs[lastUtrName], len(reads), readsWithTC, totalTCount, totalTcCount, utrSummary, readsCPM, readToConvertPercent)
reads = []
lastUtrName = utrName
# Last UTR
readsCPM = len(reads) * 1000000.0 / librarySize;
readToConvertPercent = computeConversionRate(utrs[lastUtrName].score, pulseTimePoint, chaseTimePoint, labeledTranscripts)
readsWithTC, totalTCount, totalTcCount = addTcConversionsToReads(utrs[lastUtrName], reads, readToConvertPercent, conversionRate, readOutSAM)
printUtrSummary(utrs[lastUtrName], len(reads), readsWithTC, totalTCount, totalTcCount, utrSummary, readsCPM, readToConvertPercent)
readOutSAM.close()
utrSummary.close()
readOutTempBAM = readOutFile + "_tmp.bam"
# Convert to BAM
run("samtools view -Sb " + readOutTemp + " > " + readOutTempBAM)
#samFile = pysam.AlignmentFile(readOutTemp, "r", check_header = False, check_sq = False)
#bamFile = pysam.AlignmentFile(readOutTempBAM, "wb", template=samFile)
#for read in samFile:
# bamFile.write(read)
#bamFile.close()
#samFile.close()
# Sort reads by query name (doesn't matter for mapping, but makes evaluation easier
#pysam.sort("-o", readOutFile, readOutTempBAM) # @UndefinedVariable
run("samtools sort -o " + readOutFile + " " + readOutTempBAM)
os.unlink(readOutTemp)
os.unlink(readOutTempBAM)
def Filter(inputBAM,
outputBAM,
log,
bed,
MQ=2,
minIdentity=0.8,
NM=-1,
printOnly=False,
verbose=True,
force=False,
paired=False):
inputBAM = os.path.expanduser(inputBAM)
outputBAM = os.path.expanduser(outputBAM)
if printOnly or checkStep([inputBAM], [outputBAM], force):
(mappedReads, unmappedReads, filteredReads, mqFiltered, idFiltered,
nmFiltered, multimapper) = 0, 0, 0, 0, 0, 0, 0
infile = pysam.AlignmentFile(inputBAM, "rb")
outfile = pysam.AlignmentFile(outputBAM, "wb", template=infile)
# Default filtering without bed
if bed is None:
print("#No bed-file supplied. Running default filtering on " +
inputBAM + ".",
file=log)
if paired:
read1 = None
read2 = None
for read in infile:
if paired:
if not read.is_paired or read.mate_is_unmapped or read.is_duplicate:
unmappedReads += 1
continue
if read.is_read2:
read2 = read
else:
read1 = read
read2 = None
continue
if not read.is_secondary and not read.is_supplementary:
if read.is_unmapped:
unmappedReads += 1
continue
else:
mappedReads += 1
if not paired:
if read.mapping_quality < MQ:
mqFiltered += 1
continue
if float(read.get_tag("XI")) < minIdentity:
idFiltered += 1
continue
if -1 < NM < int(read.get_tag("NM")):
nmFiltered += 1
continue
filteredReads += 1
outfile.write(read)
else:
if read1 is None or read2 is None:
continue
if read1.query_name != read2.query_name:
continue
if read1.mapping_quality < MQ and read2.mapping_quality < MQ:
mqFiltered += 1
continue
if float(read1.get_tag("XI")) < minIdentity and float(
read2.get_tag("XI")) < minIdentity:
idFiltered += 1
continue
if -1 < NM < int(read1.get_tag("NM")) and -1 < NM < int(
read2.get_tag("NM")):
nmFiltered += 1
continue
filteredReads += 1
outfile.write(read1)
outfile.write(read2)
print("Criterion\tFiltered reads", file=log)
print("MQ < 0\t0", file=log)
print("ID < %s\t%s" % (minIdentity, idFiltered), file=log)
print("NM > %s\t%s" % (NM, nmFiltered), file=log)
print("MM\t0", file=log)
else:
# Multimap retention strategy filtering when bed is supplied
print(
"#Bed-file supplied. Running multimap retention filtering strategy on "
+ inputBAM + ".",
file=log)
(mappedReads, unmappedReads, filteredReads, mqFiltered, idFiltered,
nmFiltered,
multimapper) = multimapUTRRetainment(infile, outfile, bed,
minIdentity, NM, MQ, log)
# Add number of sequenced and number of mapped reads to the read group description
# Used for creating summary file
inFileBamHeader = outfile.header
if "RG" in inFileBamHeader and len(inFileBamHeader["RG"]) > 0:
slamseqInfo = SlamSeqInfo()
slamseqInfo.SequencedReads = mappedReads + unmappedReads
slamseqInfo.MappedReads = mappedReads
slamseqInfo.FilteredReads = filteredReads
slamseqInfo.MQFilteredReads = mqFiltered
slamseqInfo.IdFilteredReads = idFiltered
slamseqInfo.NmFilteredReads = nmFiltered
slamseqInfo.MultimapperReads = multimapper
if bed:
slamseqInfo.AnnotationName = os.path.basename(bed)
slamseqInfo.AnnotationMD5 = md5(bed)
else:
slamseqInfo.AnnotationName = ""
slamseqInfo.AnnotationMD5 = ""
if not isinstance(inFileBamHeader, dict):
inFileBamHeader = inFileBamHeader.to_dict()
inFileBamHeader["RG"][0]["DS"] = str(slamseqInfo)
slamDunkPG = {
"ID": "slamdunk",
"PN": "slamdunk filter v" + __version__,
"VN": __bam_version__
}
if "PG" in inFileBamHeader:
inFileBamHeader["PG"].append(slamDunkPG)
else:
inFileBamHeader["PG"] = [slamDunkPG]
infile.close()
outfile.close()
# Sort afterwards
bamSort(outputBAM, log, inFileBamHeader, paired=False, verbose=verbose)
if not paired:
pysamIndex(outputBAM)
else:
print("Skipped filtering for " + inputBAM, file=log)