def loadHmmSubstitutionMatrix(hmmFile):
"""Load the substitution matrix from an HMM file
"""
hmm = Hmm.loadHmm(hmmFile)
m = hmm.emissions[:len(BASES)**2]
m = map(lambda i : m[i] / sum(m[4*(i/4):4*(1 + i/4)]), range(len(m))) #Normalise m
return dict(zip(product(BASES, BASES), m))
def loadHmmSubstitutionMatrix(hmmFile):
"""Load the substitution matrix from an HMM file
"""
hmm = Hmm.loadHmm(hmmFile)
m = hmm.emissions[:len(BASES)**2]
m = map(lambda i: m[i] / sum(m[4 * (i / 4):4 * (1 + i / 4)]),
range(len(m))) #Normalise m
return dict(zip(product(BASES, BASES), m))
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 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 testCPecanEm(self):
"""Runs cPecanEm.
"""
trial = 0
for modelType in ("fiveState", "fiveStateAsymmetric", "threeState", "threeStateAsymmetric"):
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")
#First run the script to generate a model and do one iteration of EM to
#get the likelihood to compare with the final likelihood
runCPecanEm(sequenceFiles=[ seqFile1, seqFile2 ],
alignmentsFile=alignmentsFile, outputModelFile=outputModelFile,
modelType=modelType,
jobTreeDir=jobTreeDir,
iterations=1, trials=1, randomStart=False, logLevel=getLogLevelString(),
setJukesCantorStartingEmissions=0.2,
#useDefaultModelAsStart=,
trainEmissions=True,
tieEmissions=True,
optionsToRealign="--diagonalExpansion=6 --splitMatrixBiggerThanThis=100")
hmm = Hmm.loadHmm(outputModelFile)
system("rm -rf %s" % jobTreeDir) #Cleanup the old jobTree
logger.info("For trial %s the likelihood after 1 iteration of EM is %s" % (trial, hmm.likelihood))
iterations = 5
runCPecanEm(sequenceFiles=[ seqFile1, seqFile2 ],
alignmentsFile=alignmentsFile, outputModelFile=outputModelFile, jobTreeDir=jobTreeDir,
optionsToRealign="--diagonalExpansion=6 --splitMatrixBiggerThanThis=100",
iterations=iterations, inputModelFile=outputModelFile, logLevel=getLogLevelString(),
maxAlignmentLengthPerJob=10000) #, updateTheBand=True)
hmm2 = Hmm.loadHmm(outputModelFile)
logger.info("For trial %s the likelihood after a further %s iterations of EM is %s" % (trial, iterations, hmm2.likelihood))
self.assertTrue(hmm.likelihood < hmm2.likelihood)
hmm2.normalise()
logger.info("Final transitions: %s" % " ".join(map(str, hmm2.transitions)))
logger.info("Final emissions: %s" % " ".join(map(str, hmm2.emissions)))
system("rm -rf %s" % tempDir)
trial += 1
def main():
#Parse the inputs args/options
parser = OptionParser(usage="usage: inputModel outputModel [options]",
version="%prog 0.1")
parser.add_option("--substitutionRate", dest="substitutionRate",
help="The probability per base of a difference between \
the sequenced reference and the reference the reads are aligned to. \
Value must be between 0 and 1.",
default=0.00, type=float)
parser.add_option("--gcContent", dest="gcContent",
help="The desired GC content of the model. \
By default no adjustment is made; value must be between 0 and 1.",
default=None, type=float)
parser.add_option("--setFlatIndelEmissions", dest="setFlatIndelEmissions",
help="Set all indel emissions probability to be equal regardless of base.",
default=False, action="store_true")
#Parse the options/arguments
options, args = parser.parse_args()
#Print help message if no input
if len(sys.argv) == 1:
parser.print_help()
sys.exit(0)
#Exit if the arguments are not what we expect
if len(args) != 2:
raise RuntimeError("Expected two arguments, got: %s" % " ".join(args))
#Load HMM
hmm = Hmm.loadHmm(sys.argv[1])
#Normalise background emission frequencies, if requested to GC% given
if options.gcContent != None:
if options.gcContent < 0 or options.gcContent > 1.0:
raise RuntimeError("Substitution rate is not a value between 0 and 1, got: %s" % options.gcContent)
normaliseHmmByReferenceGCContent(hmm, options.gcContent)
#Modify match emissions by proposed substitution rate
if options.substitutionRate < 0 or options.substitutionRate > 1.0:
raise RuntimeError("Substitution rate is not a value between 0 and 1, got: %s" % options.substitutionRate)
modifyHmmEmissionsByExpectedVariationRate(hmm, options.substitutionRate)
if options.setFlatIndelEmissions:
setHmmIndelEmissionsToBeFlat(hmm)
#Write out HMM
hmm.write(sys.argv[2])
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 learnModelFromSamFileTargetFn2(target, unnormalisedOutputModel, options):
hmm = Hmm.loadHmm(unnormalisedOutputModel)
setHmmIndelEmissionsToBeFlat(hmm)
#Normalise background emission frequencies, if requested to GC% given
normaliseHmmByReferenceGCContent(hmm, 0.5)
hmm.write(options.outputModel)
def main():
#Parse the inputs args/options
parser = OptionParser(usage="usage: inputModel outputModel [options]",
version="%prog 0.1")
parser.add_option("--substitutionRate",
dest="substitutionRate",
help="The probability per base of a difference between \
the sequenced reference and the reference the reads are aligned to. \
Value must be between 0 and 1.",
default=0.00,
type=float)
parser.add_option("--gcContent",
dest="gcContent",
help="The desired GC content of the model. \
By default no adjustment is made; value must be between 0 and 1.",
default=None,
type=float)
parser.add_option(
"--setFlatIndelEmissions",
dest="setFlatIndelEmissions",
help=
"Set all indel emissions probability to be equal regardless of base.",
default=False,
action="store_true")
#Parse the options/arguments
options, args = parser.parse_args()
#Print help message if no input
if len(sys.argv) == 1:
parser.print_help()
sys.exit(0)
#Exit if the arguments are not what we expect
if len(args) != 2:
raise RuntimeError("Expected two arguments, got: %s" % " ".join(args))
#Load HMM
hmm = Hmm.loadHmm(sys.argv[1])
#Normalise background emission frequencies, if requested to GC% given
if options.gcContent != None:
if options.gcContent < 0 or options.gcContent > 1.0:
raise RuntimeError(
"Substitution rate is not a value between 0 and 1, got: %s" %
options.gcContent)
normaliseHmmByReferenceGCContent(hmm, options.gcContent)
#Modify match emissions by proposed substitution rate
if options.substitutionRate < 0 or options.substitutionRate > 1.0:
raise RuntimeError(
"Substitution rate is not a value between 0 and 1, got: %s" %
options.substitutionRate)
modifyHmmEmissionsByExpectedVariationRate(hmm, options.substitutionRate)
if options.setFlatIndelEmissions:
setHmmIndelEmissionsToBeFlat(hmm)
#Write out HMM
hmm.write(sys.argv[2])
def checkHmm(self, hmmFile):
Hmm.loadHmm(
hmmFile) #This performs a bunch of internal consistency checks
def checkHmm(self, hmmFile):
Hmm.loadHmm(hmmFile) #This performs a bunch of internal consistency checks
def learnModelFromSamFileTargetFn2(target, unnormalisedOutputModel, options):
hmm = Hmm.loadHmm(unnormalisedOutputModel)
setHmmIndelEmissionsToBeFlat(hmm)
#Normalise background emission frequencies, if requested to GC% given
normaliseHmmByReferenceGCContent(hmm, 0.5)
hmm.write(options.outputModel)
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 testCPecanEm(self):
"""Runs cPecanEm.
"""
trial = 0
for modelType in ("fiveState", "fiveStateAsymmetric", "threeState",
"threeStateAsymmetric"):
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")
#First run the script to generate a model and do one iteration of EM to
#get the likelihood to compare with the final likelihood
runCPecanEm(
sequenceFiles=[seqFile1, seqFile2],
alignmentsFile=alignmentsFile,
outputModelFile=outputModelFile,
modelType=modelType,
jobTreeDir=jobTreeDir,
iterations=1,
trials=1,
randomStart=False,
logLevel=getLogLevelString(),
setJukesCantorStartingEmissions=0.2,
#useDefaultModelAsStart=,
trainEmissions=True,
tieEmissions=True,
optionsToRealign=
"--diagonalExpansion=6 --splitMatrixBiggerThanThis=100")
hmm = Hmm.loadHmm(outputModelFile)
system("rm -rf %s" % jobTreeDir) #Cleanup the old jobTree
logger.info(
"For trial %s the likelihood after 1 iteration of EM is %s"
% (trial, hmm.likelihood))
iterations = 5
runCPecanEm(
sequenceFiles=[seqFile1, seqFile2],
alignmentsFile=alignmentsFile,
outputModelFile=outputModelFile,
jobTreeDir=jobTreeDir,
optionsToRealign=
"--diagonalExpansion=6 --splitMatrixBiggerThanThis=100",
iterations=iterations,
inputModelFile=outputModelFile,
logLevel=getLogLevelString(),
maxAlignmentLengthPerJob=10000) #, updateTheBand=True)
hmm2 = Hmm.loadHmm(outputModelFile)
logger.info(
"For trial %s the likelihood after a further %s iterations of EM is %s"
% (trial, iterations, hmm2.likelihood))
self.assertTrue(hmm.likelihood < hmm2.likelihood)
hmm2.normalise()
logger.info("Final transitions: %s" %
" ".join(map(str, hmm2.transitions)))
logger.info("Final emissions: %s" %
" ".join(map(str, hmm2.emissions)))
system("rm -rf %s" % tempDir)
trial += 1
