def readSummary(filteredFiles, countDirectory, outputFile, log, printOnly=False, verbose=True, force=False):
# Print sort by ID
contentDict = {}
tsvFile = open(outputFile, "w")
print("# slamdunk summary v" + __version__, file=tsvFile)
if (countDirectory != None) :
f = tempfile.NamedTemporaryFile(delete=False)
for bam in filteredFiles:
slamseqInfo = SlamSeqInfo(bam)
sampleInfo = getSampleInfo(bam)
if (countDirectory != None) :
countedReads = 0
countFile = os.path.join(countDirectory, replaceExtension(os.path.basename(bam), ".tsv", "_tcount"))
if not os.path.exists(countFile):
print("TCount directory does not seem to contain tcount file for:\t" + countFile)
else :
print(sampleInfo.Name, countFile, sep='\t', file=f)
countedReads = sumCounts(countFile)
if(int(sampleInfo.ID) in contentDict):
ID = len(contentDict) + 1
else:
ID = sampleInfo.ID
contentDict[int(ID)] = "\t".join([bam, sampleInfo.Name, sampleInfo.Type, sampleInfo.Time, str(slamseqInfo.SequencedReads), str(slamseqInfo.MappedReads), str(slamseqInfo.DedupReads), str(slamseqInfo.MQFilteredReads), str(slamseqInfo.IdFilteredReads), str(slamseqInfo.NmFilteredReads), str(slamseqInfo.MultimapperReads), str(slamseqInfo.FilteredReads), str(countedReads), slamseqInfo.AnnotationName])
else :
if(int(sampleInfo.ID) in contentDict):
ID = len(contentDict) + 1
else:
ID = sampleInfo.ID
contentDict[int(ID)] = "\t".join([bam, sampleInfo.Name, sampleInfo.Type, sampleInfo.Time, str(slamseqInfo.SequencedReads), str(slamseqInfo.MappedReads), str(slamseqInfo.DedupReads), str(slamseqInfo.MQFilteredReads), str(slamseqInfo.IdFilteredReads), str(slamseqInfo.NmFilteredReads), str(slamseqInfo.MultimapperReads), str(slamseqInfo.FilteredReads), slamseqInfo.AnnotationName])
if (countDirectory != None) :
f.close()
callR(getPlotter("PCAPlotter") + " -f " + f.name + " -O " + replaceExtension(outputFile, ".pdf", "_PCA") + " -P " + replaceExtension(outputFile, ".txt", "_PCA"), log, dry=printOnly, verbose=verbose)
print("FileName", "SampleName", "SampleType", "SampleTime", "Sequenced", "Mapped", "Deduplicated", "MQ-Filtered", "Identity-Filtered", "NM-Filtered", "Multimap-Filtered", "Retained", "Counted", "Annotation", sep="\t", file=tsvFile)
else :
print("FileName", "SampleName", "SampleType", "SampleTime", "Sequenced", "Mapped", "Deduplicated", "MQ-Filtered", "Identity-Filtered", "NM-Filtered", "Multimap-Filtered", "Retained", "Annotation", sep="\t", file=tsvFile)
for key in sorted(contentDict):
print(contentDict[key], file=tsvFile)
tsvFile.close()
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 readSummary(filteredFiles,
countDirectory,
outputFile,
log,
printOnly=False,
verbose=True,
force=False):
# Print sort by ID
contentDict = {}
tsvFile = open(outputFile, "w")
print("# slamdunk summary v" + __version__, file=tsvFile)
if (countDirectory != None):
f = tempfile.NamedTemporaryFile(delete=False)
for bam in filteredFiles:
slamseqInfo = SlamSeqInfo(bam)
sampleInfo = getSampleInfo(bam)
if (countDirectory != None):
countedReads = 0
countFile = os.path.join(
countDirectory,
replaceExtension(os.path.basename(bam), ".tsv", "_tcount"))
if not os.path.exists(countFile):
print(
"TCount directory does not seem to contain tcount file for:\t"
+ countFile)
else:
print(sampleInfo.Name, countFile, sep='\t', file=f)
countedReads = sumCounts(countFile)
if (sampleInfo.ID in contentDict):
ID = len(contentDict) + 1
else:
ID = sampleInfo.ID
contentDict[int(ID)] = "\t".join([
bam, sampleInfo.Name, sampleInfo.Type, sampleInfo.Time,
str(slamseqInfo.SequencedReads),
str(slamseqInfo.MappedReads),
str(slamseqInfo.DedupReads),
str(slamseqInfo.MQFilteredReads),
str(slamseqInfo.IdFilteredReads),
str(slamseqInfo.NmFilteredReads),
str(slamseqInfo.MultimapperReads),
str(slamseqInfo.FilteredReads),
str(countedReads), slamseqInfo.AnnotationName
])
else:
if (sampleInfo.ID in contentDict):
ID = len(contentDict) + 1
else:
ID = sampleInfo.ID
contentDict[int(ID)] = "\t".join([
bam, sampleInfo.Name, sampleInfo.Type, sampleInfo.Time,
str(slamseqInfo.SequencedReads),
str(slamseqInfo.MappedReads),
str(slamseqInfo.DedupReads),
str(slamseqInfo.MQFilteredReads),
str(slamseqInfo.IdFilteredReads),
str(slamseqInfo.NmFilteredReads),
str(slamseqInfo.MultimapperReads),
str(slamseqInfo.FilteredReads), slamseqInfo.AnnotationName
])
if (countDirectory != None):
f.close()
callR(getPlotter("PCAPlotter") + " -f " + f.name + " -O " +
replaceExtension(outputFile, ".pdf", "_PCA") + " -P " +
replaceExtension(outputFile, ".txt", "_PCA"),
log,
dry=printOnly,
verbose=verbose)
print("FileName",
"SampleName",
"SampleType",
"SampleTime",
"Sequenced",
"Mapped",
"Deduplicated",
"MQ-Filtered",
"Identity-Filtered",
"NM-Filtered",
"Multimap-Filtered",
"Retained",
"Counted",
"Annotation",
sep="\t",
file=tsvFile)
else:
print("FileName",
"SampleName",
"SampleType",
"SampleTime",
"Sequenced",
"Mapped",
"Deduplicated",
"MQ-Filtered",
"Identity-Filtered",
"NM-Filtered",
"Multimap-Filtered",
"Retained",
"Annotation",
sep="\t",
file=tsvFile)
for key in sorted(contentDict):
print(contentDict[key], file=tsvFile)
tsvFile.close()
def statsComputeOverallRatesPerUTR(referenceFile,
bam,
minBaseQual,
strictTCs,
outputCSV,
outputPDF,
utrBed,
maxReadLength,
log,
printOnly=False,
verbose=True,
force=False):
sampleInfo = getSampleInfo(bam)
slamseqInfo = SlamSeqInfo(bam)
if (not checkStep([bam, referenceFile], [outputCSV], force)):
print("Skipped computing overall rates for file " + bam, file=log)
else:
# Go through one chr after the other
testFile = SlamSeqBamFile(bam, referenceFile, None)
# UTR stats for MultiQC
utrStats = dict()
plotConversions = [
'A>T',
'A>G',
'A>C',
'C>A',
'C>G',
'C>T',
'G>A',
'G>C',
'G>T',
'T>A',
'T>G',
'T>C',
]
for conversion in plotConversions:
utrStats[conversion] = list()
f = tempfile.NamedTemporaryFile(delete=False)
for utr in BedIterator(utrBed):
readIterator = testFile.readInRegion(utr.chromosome, utr.start,
utr.stop, utr.strand,
maxReadLength, minBaseQual)
# Init
totalRates = [0] * 25
readCount = 0
for read in readIterator:
if (not read.isTcRead and strictTCs and read.tcCount > 0):
pass
else:
# Compute rates for current read
rates = read.conversionRates
# Add rates from read to total rates
totalRates = sumLists(totalRates, rates)
readCount += 1
print(utr.name,
utr.chromosome,
utr.start,
utr.stop,
utr.strand,
readCount,
"\t".join(str(x) for x in totalRates),
sep="\t",
file=f)
# Process rates for MultiQC
# Copied directly, too lazy to do it properly now
utrDict = {}
conversionSum = 0
A_A = totalRates[0]
conversionSum = +A_A
A_C = totalRates[1]
conversionSum = +A_C
A_G = totalRates[2]
conversionSum = +A_G
A_T = totalRates[3]
conversionSum = +A_T
C_A = totalRates[5]
conversionSum = +C_A
C_C = totalRates[6]
conversionSum = +C_C
C_G = totalRates[7]
conversionSum = +C_G
C_T = totalRates[8]
conversionSum = +C_T
G_A = totalRates[10]
conversionSum = +G_A
G_C = totalRates[11]
conversionSum = +G_C
G_G = totalRates[12]
conversionSum = +G_G
G_T = totalRates[13]
conversionSum = +G_T
T_A = totalRates[15]
conversionSum = +T_A
T_C = totalRates[16]
conversionSum = +T_C
T_G = totalRates[17]
conversionSum = +T_G
T_T = totalRates[18]
conversionSum = +T_T
if utr.strand == "-":
A_A, T_T = T_T, A_A
G_G, C_C = C_C, G_G
A_C, T_G = T_G, A_C
A_G, T_C = T_C, A_G
A_T, T_A = T_A, A_T
C_A, G_T = G_T, C_A
C_G, G_C = G_C, C_G
C_T, G_A = G_A, C_T
if conversionSum > 0:
Asum = A_A + A_C + A_G + A_T
Csum = C_A + C_C + C_G + C_T
Gsum = G_A + G_C + G_G + G_T
Tsum = T_A + T_C + T_G + T_T
if Asum > 0:
A_T = A_T / float(Asum) * 100
A_G = A_G / float(Asum) * 100
A_C = A_C / float(Asum) * 100
else:
A_T = 0
A_G = 0
A_C = 0
if Csum > 0:
C_A = C_A / float(Csum) * 100
C_G = C_G / float(Csum) * 100
C_T = C_T / float(Csum) * 100
else:
C_A = 0
C_G = 0
C_T = 0
if Gsum > 0:
G_A = G_A / float(Gsum) * 100
G_C = G_C / float(Gsum) * 100
G_T = G_T / float(Gsum) * 100
else:
G_A = 0
G_C = 0
G_T = 0
if Tsum > 0:
T_A = T_A / float(Tsum) * 100
T_G = T_G / float(Tsum) * 100
T_C = T_C / float(Tsum) * 100
else:
T_A = 0
T_G = 0
T_C = 0
utrStats['A>T'].append(A_T)
utrStats['A>G'].append(A_G)
utrStats['A>C'].append(A_C)
utrStats['C>A'].append(C_A)
utrStats['C>G'].append(C_G)
utrStats['C>T'].append(C_T)
utrStats['G>A'].append(G_A)
utrStats['G>T'].append(G_T)
utrStats['G>C'].append(G_C)
utrStats['T>A'].append(T_A)
utrStats['T>G'].append(T_G)
utrStats['T>C'].append(T_C)
f.close()
fo = open(outputCSV, "w")
print("# slamdunk utrrates v" + __version__, file=fo)
print("# Median-Conversions=", end="", file=fo)
first = True
for conversion in plotConversions:
if (not first):
print(',', file=fo, end="")
else:
first = False
print(conversion + ":" + str(np.median(utrStats[conversion])),
file=fo,
end="")
print(file=fo)
print("Name",
"Chr",
"Start",
"End",
"Strand",
"ReadCount",
sep="\t",
end="\t",
file=fo)
for i in range(0, 5):
for j in range(0, 5):
print(toBase[i].upper() + "_" + toBase[j].upper(),
end="",
file=fo)
if (i != 4 or j != 4):
print("\t", end="", file=fo)
print(file=fo)
with open(f.name, "rb") as valueFile:
fo.write(valueFile.read())
fo.close()
if (not checkStep([bam, referenceFile], [outputPDF], force)):
print("Skipped computing global rate pdfs for file " + bam, file=log)
else:
f = tempfile.NamedTemporaryFile(delete=False)
print(sampleInfo.Name, outputCSV, sep='\t', file=f)
f.close()
callR(getPlotter("globalRatePlotter") + " -f " + f.name + " -O " +
outputPDF,
log,
dry=printOnly,
verbose=verbose)
def statsComputeOverallRatesPerUTR(referenceFile, bam, minBaseQual, strictTCs, outputCSV, outputPDF, utrBed, maxReadLength, log, printOnly=False, verbose=True, force=False):
sampleInfo = getSampleInfo(bam)
slamseqInfo = SlamSeqInfo(bam)
if(not checkStep([bam, referenceFile], [outputCSV], force)):
print("Skipped computing overall rates for file " + bam, file=log)
else:
# Go through one chr after the other
testFile = SlamSeqBamFile(bam, referenceFile, None)
# UTR stats for MultiQC
utrStats = dict()
plotConversions = ['A>T', 'A>G', 'A>C',
'C>A', 'C>G', 'C>T',
'G>A', 'G>C', 'G>T',
'T>A', 'T>G', 'T>C',
]
for conversion in plotConversions:
utrStats[conversion] = list()
f = tempfile.NamedTemporaryFile(delete=False)
for utr in BedIterator(utrBed):
readIterator = testFile.readInRegion(utr.chromosome, utr.start, utr.stop, utr.strand, maxReadLength, minBaseQual)
# Init
totalRates = [0] * 25
readCount = 0
for read in readIterator:
if (not read.isTcRead and strictTCs and read.tcCount > 0) :
pass
else :
# Compute rates for current read
rates = read.conversionRates
# Add rates from read to total rates
totalRates = sumLists(totalRates, rates)
readCount += 1
print(utr.name, utr.chromosome, utr.start, utr.stop, utr.strand, readCount, "\t".join(str(x) for x in totalRates), sep="\t", file=f)
# Process rates for MultiQC
# Copied directly, too lazy to do it properly now
utrDict = {}
conversionSum = 0
A_A = totalRates[0]
conversionSum =+ A_A
A_C = totalRates[1]
conversionSum =+ A_C
A_G = totalRates[2]
conversionSum =+ A_G
A_T = totalRates[3]
conversionSum =+ A_T
C_A = totalRates[5]
conversionSum =+ C_A
C_C = totalRates[6]
conversionSum =+ C_C
C_G = totalRates[7]
conversionSum =+ C_G
C_T = totalRates[8]
conversionSum =+ C_T
G_A = totalRates[10]
conversionSum =+ G_A
G_C = totalRates[11]
conversionSum =+ G_C
G_G = totalRates[12]
conversionSum =+ G_G
G_T = totalRates[13]
conversionSum =+ G_T
T_A = totalRates[15]
conversionSum =+ T_A
T_C = totalRates[16]
conversionSum =+ T_C
T_G = totalRates[17]
conversionSum =+ T_G
T_T = totalRates[18]
conversionSum =+ T_T
if utr.strand == "-":
A_A, T_T = T_T,A_A
G_G, C_C = C_C,G_G
A_C, T_G = T_G, A_C
A_G, T_C = T_C, A_G
A_T, T_A = T_A, A_T
C_A, G_T = G_T, C_A
C_G, G_C = G_C, C_G
C_T, G_A = G_A, C_T
if conversionSum > 0:
Asum = A_A + A_C + A_G + A_T
Csum = C_A + C_C + C_G + C_T
Gsum = G_A + G_C + G_G + G_T
Tsum = T_A + T_C + T_G + T_T
if Asum > 0 :
A_T = A_T / float(Asum) * 100
A_G = A_G / float(Asum) * 100
A_C = A_C / float(Asum) * 100
else :
A_T = 0
A_G = 0
A_C = 0
if Csum > 0:
C_A = C_A / float(Csum) * 100
C_G = C_G / float(Csum) * 100
C_T = C_T / float(Csum) * 100
else :
C_A = 0
C_G = 0
C_T = 0
if Gsum > 0:
G_A = G_A / float(Gsum) * 100
G_C = G_C / float(Gsum) * 100
G_T = G_T / float(Gsum) * 100
else :
G_A = 0
G_C = 0
G_T = 0
if Tsum > 0:
T_A = T_A / float(Tsum) * 100
T_G = T_G / float(Tsum) * 100
T_C = T_C / float(Tsum) * 100
else :
T_A = 0
T_G = 0
T_C = 0
utrStats['A>T'].append(A_T)
utrStats['A>G'].append(A_G)
utrStats['A>C'].append(A_C)
utrStats['C>A'].append(C_A)
utrStats['C>G'].append(C_G)
utrStats['C>T'].append(C_T)
utrStats['G>A'].append(G_A)
utrStats['G>T'].append(G_T)
utrStats['G>C'].append(G_C)
utrStats['T>A'].append(T_A)
utrStats['T>G'].append(T_G)
utrStats['T>C'].append(T_C)
f.close()
fo = open(outputCSV, "w")
print("# slamdunk utrrates v" + __version__, file=fo)
print("# Median-Conversions=",end="",file=fo)
first = True
for conversion in plotConversions:
if (not first) :
print(',',file=fo, end="")
else :
first = False
print(conversion + ":" + str(np.median(utrStats[conversion])),file=fo, end="")
print(file=fo)
print("Name", "Chr", "Start", "End", "Strand", "ReadCount", sep="\t", end="\t", file=fo)
for i in range(0, 5):
for j in range(0, 5):
print(toBase[i].upper() + "_" + toBase[j].upper(), end="", file=fo)
if(i != 4 or j != 4):
print("\t", end="", file=fo)
print(file=fo)
with open(f.name, "rb") as valueFile:
fo.write(valueFile.read())
fo.close()
if(not checkStep([bam, referenceFile], [outputPDF], force)):
print("Skipped computing global rate pdfs for file " + bam, file=log)
else:
f = tempfile.NamedTemporaryFile(delete=False)
print(sampleInfo.Name, outputCSV, sep='\t', file=f)
f.close()
callR(getPlotter("globalRatePlotter") + " -f " + f.name + " -O " + outputPDF, log, dry=printOnly, verbose=verbose)