def statsComputeOverallRates(referenceFile, bam, minBaseQual, outputCSV, outputPDF, log, printOnly=False, verbose=True, force=False):
if(not checkStep([bam, referenceFile], [outputCSV], force)):
print("Skipped computing overall rates for file " + bam, file=log)
else:
# Init
totalRatesFwd = [0] * 25
totalRatesRev = [0] * 25
tcCount = [0] * 100
# Go through one chr after the other
testFile = SlamSeqBamFile(bam, referenceFile, None)
chromosomes = testFile.getChromosomes()
for chromosome in chromosomes:
readIterator = testFile.readsInChromosome(chromosome, minBaseQual)
for read in readIterator:
# Compute rates for current read
rates = read.conversionRates
# Get T -> C conversions for current read
tc = read.tcCount
tcCount[tc] += 1
# Add rates from read to total rates
if(read.direction == ReadDirection.Reverse):
totalRatesRev = sumLists(totalRatesRev, rates)
else:
totalRatesFwd = sumLists(totalRatesFwd, rates)
# Print rates in correct format for plotting
fo = open(outputCSV, "w")
print("# slamdunk rates v" + __version__, file=fo)
printRates(totalRatesFwd, totalRatesRev, fo)
fo.close()
if(not checkStep([bam, referenceFile], [outputPDF], force)):
print("Skipped computing overall rate pdfs for file " + bam, file=log)
else:
#f = tempfile.NamedTemporaryFile(delete=False)
#print(removeExtension(basename(bam)), outputCSV, sep='\t', file=f)
#f.close()
callR(getPlotter("compute_overall_rates") + " -f " + outputCSV + " -n " + removeExtension(os.path.basename(bam)) + " -O " + outputPDF, log, dry=printOnly, verbose=verbose)
def evaluateReads(bam, referenceFile, bed, outputFile, mainOutput):
print("Run " + bam)
# Go through one chr after the other
testFile = SlamSeqBamFile(bam, referenceFile, None)
chromosomes = testFile.getChromosomes()
bedTree = bedToIntervallTree(bed)
#evalHist = [0] *
outFile = open(outputFile, "w")
print("read.name", "read.chromosome", "read.startRefPos", "sim.utr", "read.utr", "sim.tcCount", "read.tcCount", sep = "\t", file=outFile)
total = 0
correct = 0
correcPosWrongTC = 0
wrongPos = 0
minBaseQual = 0
for chromosome in chromosomes:
readIterator = testFile.readsInChromosome(chromosome, minBaseQual)
for read in readIterator:
total += 1
simInfo = read.name.split("_")
utrSim = simInfo[0]
tcCountSim = int(simInfo[2])
utrFound = None
if read.chromosome in bedTree:
overlaps = list(bedTree[read.chromosome][read.startRefPos:read.endRefPos])
if len(overlaps) > 0:
utrFound = overlaps[0].data
if utrFound == utrSim:
if tcCountSim == read.tcCount:
correct += 1
else:
correcPosWrongTC += 1
else:
wrongPos += 1
print(read.name, read.chromosome, read.startRefPos, utrSim, utrFound, tcCountSim, read.tcCount, sep = "\t", file=outFile)
print(correct * 100.0 / total, correcPosWrongTC * 100.0 / total, wrongPos * 100.0 / total, total)
def dumpReadInfo(referenceFile, bam, minQual, outputCSV, snpsFile, log, printOnly=False, verbose=True, force=False):
if(not checkStep([bam, referenceFile], [outputCSV], force)):
print("Skipped computing T->C per reads position for file " + bam, file=log)
else:
snps = SNPtools.SNPDictionary(snpsFile)
snps.read()
outputFile = SlamSeqWriter(outputCSV)
#Go through one chr after the other
testFile = SlamSeqBamFile(bam, referenceFile, snps)
chromosomes = testFile.getChromosomes()
for chromosome in chromosomes:
readIterator = testFile.readsInChromosome(chromosome)
for read in readIterator:
outputFile.write(read)
outputFile.close()
def tcPerReadPos(referenceFile, bam, minQual, maxReadLength, outputCSV, outputPDF, snpsFile, log, printOnly=False, verbose=True, force=False):
if(not checkStep([bam, referenceFile], [outputCSV], force)):
print("Skipped computing T->C per reads position for file " + bam, file=log)
else:
totalReadCountFwd = [0] * maxReadLength
totalReadCountRev = [0] * maxReadLength
tcPerPosRev = [0] * maxReadLength
tcPerPosFwd = [0] * maxReadLength
allPerPosRev = [0] * maxReadLength
allPerPosFwd = [0] * maxReadLength
snps = SNPtools.SNPDictionary(snpsFile)
snps.read()
# Go through one chr after the other
testFile = SlamSeqBamFile(bam, referenceFile, snps)
chromosomes = testFile.getChromosomes()
for chromosome in chromosomes:
readIterator = testFile.readsInChromosome(chromosome, minQual)
for read in readIterator:
tcCounts = [0] * maxReadLength
mutCounts = [0] * maxReadLength
for mismatch in read.mismatches:
if(mismatch.isTCMismatch(read.direction == ReadDirection.Reverse)):
tcCounts[mismatch.readPosition] += 1
else :
mutCounts[mismatch.readPosition] += 1
query_length = len(read.sequence)
if(read.direction == ReadDirection.Reverse):
tcPerPosRev = sumLists(tcPerPosRev, tcCounts)
allPerPosRev = sumLists(allPerPosRev, mutCounts)
for i in range(0, query_length):
totalReadCountRev[i] += 1
else:
tcPerPosFwd = sumLists(tcPerPosFwd, tcCounts)
allPerPosFwd = sumLists(allPerPosFwd, mutCounts)
for i in range(0, query_length):
totalReadCountFwd[i] += 1
foTC = open(outputCSV, "w")
print("# slamdunk tcperreadpos v" + __version__, file=foTC)
for i in range(0, maxReadLength):
print(allPerPosFwd[i], allPerPosRev[i], tcPerPosFwd[i], tcPerPosRev[i], totalReadCountFwd[i], totalReadCountRev[i], sep='\t', file=foTC)
foTC.close()
if(not checkStep([outputCSV], [outputPDF], force)):
print("Skipped computing T->C per reads position plot for file " + bam, file=log)
else:
callR(getPlotter("conversion_per_read_position") + " -i " + outputCSV + " -o " + outputPDF, log, dry=printOnly, verbose=verbose)
def statsComputeTCContext(referenceFile, bam, minBaseQual, outputCSV, outputPDF, log, printOnly=False, verbose=True, force=False):
if(not checkStep([bam, referenceFile], [outputCSV], force)):
print("Skipped computing overall rates for file " + bam, file=log)
else:
# Init
# combinations = ["AT","CT","GT","TT","NT","AA","CA","GA","TA","NA"]
frontCombinations = ["AT", "CT", "GT", "TT", "NT"]
backCombinations = ["TA", "TC", "TG", "TT", "TN"]
counts = {}
counts['5prime'] = {}
counts['3prime'] = {}
counts['5prime']['fwd'] = {}
counts['5prime']['rev'] = {}
counts['3prime']['fwd'] = {}
counts['3prime']['rev'] = {}
for combination in frontCombinations :
counts['5prime']['fwd'][combination] = 0
counts['5prime']['rev'][combination] = 0
for combination in backCombinations:
counts['3prime']['fwd'][combination] = 0
counts['3prime']['rev'][combination] = 0
bamFile = pysam.AlignmentFile(bam, "rb")
# Go through one chr after the other
testFile = SlamSeqBamFile(bam, referenceFile, None)
chromosomes = testFile.getChromosomes()
for chromosome in chromosomes:
for read in bamFile.fetch(region=chromosome):
i = 0
while i < len(read.query_sequence):
if(read.query_sequence[i] == "T" and not read.is_reverse) :
frontContext = None
backContext = None
if (i > 0) :
frontContext = read.query_sequence[i - 1]
if (i < (len(read.query_sequence) - 1)) :
backContext = read.query_sequence[i + 1]
if (frontContext != None) :
counts['5prime']['fwd'][frontContext + "T"] += 1
if (backContext != None) :
counts['3prime']['fwd']["T" + backContext] += 1
if(read.query_sequence[i] == "A" and read.is_reverse) :
frontContext = None
backContext = None
if (i > 0) :
backContext = read.query_sequence[i - 1]
if (i < (len(read.query_sequence) - 1)) :
frontContext = read.query_sequence[i + 1]
if (frontContext != None) :
counts['5prime']['rev'][complement(frontContext + "A")] += 1
if (backContext != None) :
counts['3prime']['rev'][complement("A" + backContext)] += 1
i += 1
# Print rates in correct format for plotting
fo = open(outputCSV, "w")
print("\t".join(frontCombinations), file=fo)
frontFwdLine = ""
frontRevLine = ""
backFwdLine = ""
backRevLine = ""
for combination in frontCombinations :
frontFwdLine += str(counts['5prime']['fwd'][combination]) + "\t"
frontRevLine += str(counts['5prime']['rev'][combination]) + "\t"
print(frontFwdLine.rstrip(), file=fo)
print(frontRevLine.rstrip(), file=fo)
print("\t".join(backCombinations), file=fo)
for combination in backCombinations :
backFwdLine += str(counts['3prime']['fwd'][combination]) + "\t"
backRevLine += str(counts['3prime']['rev'][combination]) + "\t"
print(backFwdLine.rstrip(), file=fo)
print(backRevLine.rstrip(), file=fo)
fo.close()
if(not checkStep([bam, referenceFile], [outputPDF], force)):
print("Skipped computing overall rate pdfs for file " + bam, file=log)
else:
f = tempfile.NamedTemporaryFile(delete=False)
print(removeExtension(os.path.basename(bam)), outputCSV, sep='\t', file=f)
f.close()
callR(getPlotter("compute_context_TC_rates") + " -f " + f.name + " -O " + outputPDF, log, dry=printOnly, verbose=verbose)
def genomewideConversionRates(referenceFile, snpsFile, bam, minBaseQual, outputBedGraphPrefix, conversionThreshold, coverageCutoff, log):
ref = pysam.FastaFile(referenceFile)
snps = SNPtools.SNPDictionary(snpsFile)
snps.read()
# Go through one chr after the other
testFile = SlamSeqBamFile(bam, referenceFile, snps)
chromosomes = testFile.getChromosomes()
bedGraphInfo = re.sub("_slamdunk_mapped.*","",basename(outputBedGraphPrefix))
print(bedGraphInfo)
fileBedGraphRatesPlus = open(outputBedGraphPrefix + "_TC_rates_genomewide.bedGraph", 'w')
fileBedGraphRatesMinus = open(outputBedGraphPrefix + "_AG_rates_genomewide.bedGraph", 'w')
fileBedGraphCoveragePlus = open(outputBedGraphPrefix + "_coverage_plus_genomewide.bedGraph", 'w')
fileBedGraphCoverageMinus = open(outputBedGraphPrefix + "_coverage_minus_genomewide.bedGraph", 'w')
fileBedGraphTCConversions = open(outputBedGraphPrefix + "_TC_conversions_genomewide.bedGraph", 'w')
fileBedGraphAGConversions = open(outputBedGraphPrefix + "_AG_conversions_genomewide.bedGraph", 'w')
fileBedGraphT = open(outputBedGraphPrefix + "_coverage_T_genomewide.bedGraph", 'w')
fileBedGraphA = open(outputBedGraphPrefix + "_coverage_A_genomewide.bedGraph", 'w')
print("track type=bedGraph name=\"" + bedGraphInfo + " tc-conversions\" description=\"# T->C conversions / # reads on T per position genome-wide\"", file=fileBedGraphRatesPlus)
print("track type=bedGraph name=\"" + bedGraphInfo + " ag-conversions\" description=\"# A->G conversions / # reads on A per position genome-wide\"", file=fileBedGraphRatesMinus)
print("track type=bedGraph name=\"" + bedGraphInfo + " plus-strand coverage\" description=\"# Reads on plus strand genome-wide\"", file=fileBedGraphCoveragePlus)
print("track type=bedGraph name=\"" + bedGraphInfo + " minus-strand coverage\" description=\"# Reads on minus strand genome-wide\"", file=fileBedGraphCoverageMinus)
print("track type=bedGraph name=\"" + bedGraphInfo + " T->C conversions\" description=\"# T->C conversions on plus strand genome-wide\"", file=fileBedGraphTCConversions)
print("track type=bedGraph name=\"" + bedGraphInfo + " A->G conversions\" description=\"# A->G conversions on minus strand genome-wide\"", file=fileBedGraphAGConversions)
print("track type=bedGraph name=\"" + bedGraphInfo + " T-coverage\" description=\"# Plus-strand reads on Ts genome-wide\"", file=fileBedGraphT)
print("track type=bedGraph name=\"" + bedGraphInfo + " A-coverage\" description=\"# Minus-strand reads on As genome-wide\"", file=fileBedGraphA)
for chromosome in chromosomes:
chrLength = testFile.getChromosomeLength(chromosome)
tcCount = [0] * chrLength
agCount = [0] * chrLength
coveragePlus = [0] * chrLength
coverageMinus = [0] * chrLength
tCoverage = [0] * chrLength
aCoverage = [0] * chrLength
readIterator = testFile.readsInChromosome(chromosome, minBaseQual, conversionThreshold)
for read in readIterator:
if (not read.isTcRead) :
read.tcCount = 0
read.mismatches = []
read.conversionRates = 0.0
read.tcRate = 0.0
for mismatch in read.mismatches:
if(mismatch.isTCMismatch(read.direction == ReadDirection.Reverse) and mismatch.referencePosition >= 0 and mismatch.referencePosition < chrLength):
if read.direction == ReadDirection.Reverse:
agCount[mismatch.referencePosition] += 1
else :
tcCount[mismatch.referencePosition] += 1
for i in range(read.startRefPos, read.endRefPos):
if(i >= 0 and i < chrLength):
if read.direction == ReadDirection.Reverse:
coverageMinus[i] += 1
else :
coveragePlus[i] += 1
prevCoveragePlus = 0
prevCoveragePlusPos = 0
prevCoverageMinus = 0
prevCoverageMinusPos = 0
prevTCConversionRate = 0
prevTCConversionRatePos = 0
prevAGConversionRate = 0
prevAGConversionRatePos = 0
prevTCConversions = 0
prevTCConversionPos = 0
prevAGConversions = 0
prevAGConversionPos = 0
prevTCoverage = 0
prevTCoveragePos = 0
prevACoverage = 0
prevACoveragePos = 0
for pos in range(0, chrLength):
if prevCoveragePlus != coveragePlus[pos]:
print(chromosome + "\t" + str(prevCoveragePlusPos + 1) + "\t" + str(pos + 1) + "\t" + str(prevCoveragePlus), file = fileBedGraphCoveragePlus)
prevCoveragePlus = coveragePlus[pos]
prevCoveragePlusPos = pos
if prevCoverageMinus != coverageMinus[pos]:
print(chromosome + "\t" + str(prevCoverageMinusPos + 1) + "\t" + str(pos + 1) + "\t" + str(prevCoverageMinus), file = fileBedGraphCoverageMinus)
prevCoverageMinus = coverageMinus[pos]
prevCoverageMinusPos = pos
tCoverage = 0
if coveragePlus[pos] > 0:
base = ref.fetch(reference=chromosome, start = pos + 1, end = pos + 2)
if base.upper() == "T":
tCoverage = coveragePlus[pos]
aCoverage = 0
if coverageMinus[pos] > 0:
base = ref.fetch(reference=chromosome, start = pos + 1, end = pos + 2)
if base.upper() == "A":
aCoverage = coverageMinus[pos]
if prevTCoverage != tCoverage:
print(chromosome + "\t" + str(prevTCoveragePos + 1) + "\t" + str(pos + 1) + "\t" + str(prevTCoverage), file = fileBedGraphT)
prevTCoverage = tCoverage
prevTCoveragePos = pos
if prevACoverage != aCoverage:
print(chromosome + "\t" + str(prevACoveragePos + 1) + "\t" + str(pos + 1) + "\t" + str(prevACoverage), file = fileBedGraphA)
prevACoverage = aCoverage
prevACoveragePos = pos
if prevTCConversions != tcCount[pos]:
print(chromosome + "\t" + str(prevTCConversionPos + 1) + "\t" + str(pos + 1) + "\t" + str(prevTCConversions), file = fileBedGraphTCConversions)
prevTCConversions = tcCount[pos]
prevTCConversionPos = pos
if prevAGConversions != agCount[pos]:
print(chromosome + "\t" + str(prevAGConversionPos + 1) + "\t" + str(pos + 1) + "\t" + str(prevAGConversions), file = fileBedGraphAGConversions)
prevAGConversions = agCount[pos]
prevAGConversionPos = pos
TCconversionRate = 0
if coveragePlus[pos] > 0 and coveragePlus[pos] >= coverageCutoff:
TCconversionRate = float(tcCount[pos]) / float(coveragePlus[pos])
AGconversionRate = 0
if coverageMinus[pos] > 0 and coverageMinus[pos] >= coverageCutoff:
AGconversionRate = float(agCount[pos]) / float(coverageMinus[pos])
if prevTCConversionRate != TCconversionRate:
print(chromosome + "\t" + str(prevTCConversionRatePos + 1) + "\t" + str(pos + 1) + "\t" + str(prevTCConversionRate), file = fileBedGraphRatesPlus)
prevTCConversionRate = TCconversionRate
prevTCConversionRatePos = pos
if prevAGConversionRate != AGconversionRate:
print(chromosome + "\t" + str(prevAGConversionRatePos + 1) + "\t" + str(pos + 1) + "\t" + str(prevAGConversionRate), file = fileBedGraphRatesMinus)
prevAGConversionRate = AGconversionRate
prevAGConversionRatePos = pos
fileBedGraphRatesPlus.close()
fileBedGraphRatesMinus.close()
fileBedGraphCoveragePlus.close()
fileBedGraphCoverageMinus.close()
fileBedGraphTCConversions.close()
fileBedGraphAGConversions.close()
fileBedGraphT.close()
fileBedGraphA.close()
def evaluateReads(bam, referenceFile, bed, outputFile, mainOutput):
print("Run " + bam)
# Go through one chr after the other
testFile = SlamSeqBamFile(bam, referenceFile, None)
chromosomes = testFile.getChromosomes()
bedTree = bedToIntervallTree(bed)
#evalHist = [0] *
outFile = open(outputFile, "w")
print("read.name",
"read.chromosome",
"read.startRefPos",
"sim.utr",
"read.utr",
"sim.tcCount",
"read.tcCount",
sep="\t",
file=outFile)
total = 0
correct = 0
correcPosWrongTC = 0
wrongPos = 0
minBaseQual = 0
for chromosome in chromosomes:
readIterator = testFile.readsInChromosome(chromosome, minBaseQual)
for read in readIterator:
total += 1
simInfo = read.name.split("_")
utrSim = simInfo[0]
tcCountSim = int(simInfo[2])
utrFound = None
if read.chromosome in bedTree:
overlaps = list(
bedTree[read.chromosome][read.startRefPos:read.endRefPos])
if len(overlaps) > 0:
utrFound = overlaps[0].data
if utrFound == utrSim:
if tcCountSim == read.tcCount:
correct += 1
else:
correcPosWrongTC += 1
else:
wrongPos += 1
print(read.name,
read.chromosome,
read.startRefPos,
utrSim,
utrFound,
tcCountSim,
read.tcCount,
sep="\t",
file=outFile)
print(correct * 100.0 / total, correcPosWrongTC * 100.0 / total,
wrongPos * 100.0 / total, total)