def testHMMToBlast(self):
hmmFile = getTempFile()
fH = open(hmmFile, 'w')
##This is an HMM trained from some nanopore data.
fH.write("1 0.769849545837 0.192124461785 0.0373796764444 0.000454412327473 0.000191903606847 0.615582885081 0.384417114919 0.0 0.0 0.0 0.360492263331 0.0 0.639507736669 0.0 0.0 0.000837020116237 0.0 0.0 0.999162979884 0.0 0.00263503613846 0.0 0.0 0.0 0.997364963862 -83964693614.2\n")
fH.write("0.124467347093 0.0510185372341 0.055395149667 0.0165710761929 0.0424721479638 0.107314387884 0.0326918092026 0.0169710192012 0.0512318749092 0.0282356959149 0.112202089573 0.0215204542575 0.0384239342042 0.0479915657848 0.046228721193 0.207264189725 0.0660896084237 0.0593325150728 0.0603492612177 0.0582600197325 0.0584825157865 0.0522874453523 0.0584394811677 0.0575754515175 0.0527867639602 0.0495513728754 0.0503223877237 0.0558605997644 0.0825830058041 0.076482194432 0.0786344471539 0.0829629300156 0.061340281862 0.0603951822769 0.0697104777685 0.0624365718074 0.0515829309891 0.044694507015 0.0563446095066 0.0500495229974 0.0535285782675 0.0498949344494 0.0552268591741 0.0513874548672 0.0834191759666 0.0807522229048 0.088970651067 0.0802660390805 0.0585941736742 0.0666707645145 0.0671013181763 0.0540169346484 0.0546857600483 0.0614356162075 0.0637011684438 0.0493325269862 0.0497656880321 0.0565185406621 0.0574882564972 0.0453586109249 0.0756527827468 0.0866857198132 0.0833713253471 0.0696208132772 0.0489551609873 0.0485086714065 0.055299084522 0.0480626877811 0.0360480113306 0.0352929139202 0.0404027968092 0.0362560298668 0.0504819795621 0.0505241023095 0.0581098587574 0.0526378897109 0.107006368897 0.106127475919 0.11956635493 0.10672061329")
fH.close()
hmm = Hmm.loadHmm(hmmFile)
matchProbs, gapOpen, gapExtend = makeBlastScoringMatrix(hmm, ("TTTGG",))
writeLastzScoringMatrix(sys.stdout, matchProbs, gapOpen, gapExtend)
logger.info("Gap open: %s, Gap extend: %s, Match probs %s" % (gapOpen, gapExtend, " ".join(map(str, matchProbs))))
def testHMMToBlast(self):
hmmFile = getTempFile()
fH = open(hmmFile, 'w')
##This is an HMM trained from some nanopore data.
fH.write(
"1 0.769849545837 0.192124461785 0.0373796764444 0.000454412327473 0.000191903606847 0.615582885081 0.384417114919 0.0 0.0 0.0 0.360492263331 0.0 0.639507736669 0.0 0.0 0.000837020116237 0.0 0.0 0.999162979884 0.0 0.00263503613846 0.0 0.0 0.0 0.997364963862 -83964693614.2\n"
)
fH.write(
"0.124467347093 0.0510185372341 0.055395149667 0.0165710761929 0.0424721479638 0.107314387884 0.0326918092026 0.0169710192012 0.0512318749092 0.0282356959149 0.112202089573 0.0215204542575 0.0384239342042 0.0479915657848 0.046228721193 0.207264189725 0.0660896084237 0.0593325150728 0.0603492612177 0.0582600197325 0.0584825157865 0.0522874453523 0.0584394811677 0.0575754515175 0.0527867639602 0.0495513728754 0.0503223877237 0.0558605997644 0.0825830058041 0.076482194432 0.0786344471539 0.0829629300156 0.061340281862 0.0603951822769 0.0697104777685 0.0624365718074 0.0515829309891 0.044694507015 0.0563446095066 0.0500495229974 0.0535285782675 0.0498949344494 0.0552268591741 0.0513874548672 0.0834191759666 0.0807522229048 0.088970651067 0.0802660390805 0.0585941736742 0.0666707645145 0.0671013181763 0.0540169346484 0.0546857600483 0.0614356162075 0.0637011684438 0.0493325269862 0.0497656880321 0.0565185406621 0.0574882564972 0.0453586109249 0.0756527827468 0.0866857198132 0.0833713253471 0.0696208132772 0.0489551609873 0.0485086714065 0.055299084522 0.0480626877811 0.0360480113306 0.0352929139202 0.0404027968092 0.0362560298668 0.0504819795621 0.0505241023095 0.0581098587574 0.0526378897109 0.107006368897 0.106127475919 0.11956635493 0.10672061329"
)
fH.close()
hmm = Hmm.loadHmm(hmmFile)
matchProbs, gapOpen, gapExtend = makeBlastScoringMatrix(
hmm, ("TTTGG", ))
writeLastzScoringMatrix(sys.stdout, matchProbs, gapOpen, gapExtend)
logger.info("Gap open: %s, Gap extend: %s, Match probs %s" %
(gapOpen, gapExtend, " ".join(map(str, matchProbs))))
def testCPecanEmMultipleTrials(self):
"""Runs uns cPecanEm with multiple different trials.
"""
for seqFile1, seqFile2 in seqFilePairGenerator():
tempDir = getTempDirectory(rootDir=os.getcwd())
jobTreeDir = os.path.join(tempDir, "jobTree")
alignmentsFile = os.path.join(tempDir, "alignments.cigars")
computeAlignments(seqFile1, seqFile2, alignmentsFile)
logger.info("Computed alignments for seqs %s and %s" % (seqFile1, seqFile2))
outputModelFile = os.path.join(tempDir, "outputModel.txt")
outputModelXMLFile = os.path.join(tempDir, "outputModel.xml")
outputBlastFile = os.path.join(tempDir, "outputBlast.txt")
#First run the script to generate a model and do one iteration of EM to
#get the likelihood to compare with the final likelihood
trials=3
runCPecanEm(sequenceFiles=[ seqFile1, seqFile2 ],
alignmentsFile=alignmentsFile, outputModelFile=outputModelFile,
jobTreeDir=jobTreeDir,
trials=trials,
outputTrialHmms=True,
iterations=5, randomStart=True, logLevel=getLogLevelString(),
optionsToRealign="--diagonalExpansion=6 --splitMatrixBiggerThanThis=100",
outputXMLModelFile=outputModelXMLFile,
blastScoringMatrixFile=outputBlastFile)
trialHmms = [ Hmm.loadHmm(outputModelFile + ("_%i" % i)) for i in xrange(trials) ]
hmm = Hmm.loadHmm(outputModelFile)
node = ET.parse(outputModelXMLFile).getroot()
logger.info("After multiple trials and iterations of EM the best likelihood found was %s, the likelihoods of the variants were: %s" %
(hmm.likelihood, " ".join(map(lambda x : str(x.likelihood), trialHmms))))
matchProbs, gapOpen, gapExtend = makeBlastScoringMatrix(hmm, ("ACTG",))
logger.info("Gap open: %s, Gap extend: %s, Match probs %s" % (gapOpen, gapExtend, " ".join(map(str, matchProbs))))
self.assertTrue(float(node.attrib["maxLikelihood"]) == hmm.likelihood)
#Now use the blast file to compute a new matrix
computeAlignments(seqFile1, seqFile2, alignmentsFile, lastzArguments=("--ambiguous=iupac --scores=%s" % outputBlastFile))
#Run modifyHmm to check it works
system("cPecanModifyHmm %s %s --gcContent=0.5 --substitutionRate=0.05 --setFlatIndelEmissions" % (outputModelFile, outputModelFile))
hmm = Hmm.loadHmm(outputModelFile)
node = ET.parse(outputModelXMLFile).getroot()
system("rm -rf %s" % tempDir)
def testCPecanEmMultipleTrials(self):
"""Runs uns cPecanEm with multiple different trials.
"""
for seqFile1, seqFile2 in seqFilePairGenerator():
tempDir = getTempDirectory(rootDir=os.getcwd())
jobTreeDir = os.path.join(tempDir, "jobTree")
alignmentsFile = os.path.join(tempDir, "alignments.cigars")
computeAlignments(seqFile1, seqFile2, alignmentsFile)
logger.info("Computed alignments for seqs %s and %s" %
(seqFile1, seqFile2))
outputModelFile = os.path.join(tempDir, "outputModel.txt")
outputModelXMLFile = os.path.join(tempDir, "outputModel.xml")
outputBlastFile = os.path.join(tempDir, "outputBlast.txt")
#First run the script to generate a model and do one iteration of EM to
#get the likelihood to compare with the final likelihood
trials = 3
runCPecanEm(
sequenceFiles=[seqFile1, seqFile2],
alignmentsFile=alignmentsFile,
outputModelFile=outputModelFile,
jobTreeDir=jobTreeDir,
trials=trials,
outputTrialHmms=True,
iterations=5,
randomStart=True,
logLevel=getLogLevelString(),
optionsToRealign=
"--diagonalExpansion=6 --splitMatrixBiggerThanThis=100",
outputXMLModelFile=outputModelXMLFile,
blastScoringMatrixFile=outputBlastFile)
trialHmms = [
Hmm.loadHmm(outputModelFile + ("_%i" % i))
for i in xrange(trials)
]
hmm = Hmm.loadHmm(outputModelFile)
node = ET.parse(outputModelXMLFile).getroot()
logger.info(
"After multiple trials and iterations of EM the best likelihood found was %s, the likelihoods of the variants were: %s"
% (hmm.likelihood, " ".join(
map(lambda x: str(x.likelihood), trialHmms))))
matchProbs, gapOpen, gapExtend = makeBlastScoringMatrix(
hmm, ("ACTG", ))
logger.info("Gap open: %s, Gap extend: %s, Match probs %s" %
(gapOpen, gapExtend, " ".join(map(str, matchProbs))))
self.assertTrue(
float(node.attrib["maxLikelihood"]) == hmm.likelihood)
#Now use the blast file to compute a new matrix
computeAlignments(seqFile1,
seqFile2,
alignmentsFile,
lastzArguments=("--ambiguous=iupac --scores=%s" %
outputBlastFile))
#Run modifyHmm to check it works
system(
"cPecanModifyHmm %s %s --gcContent=0.5 --substitutionRate=0.05 --setFlatIndelEmissions"
% (outputModelFile, outputModelFile))
hmm = Hmm.loadHmm(outputModelFile)
node = ET.parse(outputModelXMLFile).getroot()
system("rm -rf %s" % tempDir)
