def getCactusInputs_randomWithConstraints(regionNumber=0, tempDir=None):
sequenceDirs, newickTreeString = getCactusInputs_random(regionNumber=regionNumber, tempDir=tempDir)
constraints = getTempFile(rootDir=tempDir)
fileHandle = open(constraints, 'w')
for pairwiseAlignment in makeRandomConstraints(getFastasFromSequence(sequenceDirs)):
cigarWrite(fileHandle, pairwiseAlignment, withProbs=False)
fileHandle.close()
return sequenceDirs, newickTreeString, constraints
def getCactusInputs_randomWithConstraints(regionNumber=0, tempDir=None):
sequenceDirs, newickTreeString = getCactusInputs_random(regionNumber=regionNumber, tempDir=tempDir)
constraints = getTempFile(rootDir=tempDir)
fileHandle = open(constraints, 'w')
for pairwiseAlignment in makeRandomConstraints(getFastasFromSequence(sequenceDirs)):
cigarWrite(fileHandle, pairwiseAlignment, withProbs=False)
fileHandle.close()
return sequenceDirs, newickTreeString, constraints
def expectationMaximisation(target, sequences, alignments, outputModel, options):
#Iteratively run cPecanRealign to get expectations and load model.
if options.inputModel != None: #Read in the model
target.logToMaster("Loading the model from the input file %s" % options.inputModel)
hmm = Hmm.loadHmm(options.inputModel)
target.logToMaster("Loaded the model, has type %s" % hmm.modelType)
hmm.normalise()
else:
target.logToMaster("Making model of type %s" % options.modelType)
hmm = Hmm(options.modelType)
if options.randomStart: #Make random parameters
target.logToMaster("Using random starting parameters")
hmm.randomise()
else:
hmm.equalise()
if options.setJukesCantorStartingEmissions != None:
hmm.setEmissionsToJukesCantor(float(options.setJukesCantorStartingEmissions))
#Write out the first version of the output model
hmm.write(outputModel)
#Make a set of split alignment files
alignmentsLength = 0
splitAlignmentFiles = []
fH = None
for cigar in cigarRead(alignments):
if fH == None:
splitAlignmentFiles.append(os.path.join(target.getGlobalTempDir(), "alignments_%s.cigar" % len(splitAlignmentFiles)))
fH = open(splitAlignmentFiles[-1], 'w')
alignmentsLength += (abs(cigar.start1 - cigar.end1) + abs(cigar.start2 - cigar.end2))/2.0
cigarWrite(fH, cigar)
if alignmentsLength > options.maxAlignmentLengthPerJob:
fH.close()
fH = None
splitAlignmentFiles[-1] = (splitAlignmentFiles[-1], alignmentsLength)
alignmentsLength = 0
if fH != None:
fH.close()
splitAlignmentFiles[-1] = (splitAlignmentFiles[-1], alignmentsLength)
#Sample the alignment files so that we do EM on no more than options.maxAlignmentLengthToSample bases
random.shuffle(splitAlignmentFiles) #This ensures we don't just take the first N alignments in the provided alignments file
sampledSplitAlignmentFiles = []
totalSampledAlignmentLength = 0.0
for alignmentsFile, alignmentsLength in splitAlignmentFiles:
totalSampledAlignmentLength += alignmentsLength
sampledSplitAlignmentFiles.append(alignmentsFile)
if totalSampledAlignmentLength >= options.maxAlignmentLengthToSample:
break
target.logToMaster("We sampled: %s bases of alignment length and %s alignment files, of a possible %s base and %s files" % \
(totalSampledAlignmentLength, len(sampledSplitAlignmentFiles), sum(map(lambda x : x[1], splitAlignmentFiles)), len(splitAlignmentFiles)))
splitAlignmentFiles = sampledSplitAlignmentFiles
#Files to store expectations in
expectationsFiles = map(lambda i : os.path.join(target.getGlobalTempDir(), "expectation_%i.txt" % i), xrange(len(splitAlignmentFiles)))
assert len(splitAlignmentFiles) == len(expectationsFiles)
target.setFollowOnTargetFn(expectationMaximisation2, args=(sequences, splitAlignmentFiles, outputModel, expectationsFiles, 0, [], options))
def upconvertCoords(cigarPath, fastaPath, contigNum, outputFile):
"""Convert the coordinates of the given alignment, so that the
alignment refers to a set of trimmed sequences originating from a
contig rather than to the contig itself."""
with open(fastaPath) as f:
seqRanges = getSequenceRanges(f)
validateRanges(seqRanges)
sortedCigarPath = sortCigarByContigAndPos(cigarPath, contigNum)
sortedCigarFile = open(sortedCigarPath)
currentContig = None
currentRangeIdx = None
currentRange = None
for alignment in cigarRead(sortedCigarFile):
# contig1 and contig2 are reversed in python api!!
contig = alignment.contig2 if contigNum == 1 else alignment.contig1
minPos = min(alignment.start2,
alignment.end2) if contigNum == 1 else min(
alignment.start1, alignment.end1)
maxPos = max(alignment.start2,
alignment.end2) if contigNum == 1 else max(
alignment.start1, alignment.end1)
if contig in seqRanges:
if contig != currentContig:
currentContig = contig
currentRangeIdx = 0
currentRange = seqRanges[contig][0]
while (minPos >= currentRange[1] or minPos < currentRange[0]
) and currentRangeIdx < len(seqRanges[contig]) - 1:
currentRangeIdx += 1
currentRange = seqRanges[contig][currentRangeIdx]
if currentRange[0] <= minPos < currentRange[1]:
if maxPos - 1 > currentRange[1]:
raise RuntimeError("alignment on %s:%d-%d crosses "
"trimmed sequence boundary" %\
(contig,
minPos,
maxPos))
if contigNum == 1:
alignment.start2 -= currentRange[0]
alignment.end2 -= currentRange[0]
alignment.contig2 = contig + ("|%d" % currentRange[0])
else:
alignment.start1 -= currentRange[0]
alignment.end1 -= currentRange[0]
alignment.contig1 = contig + ("|%d" % currentRange[0])
else:
raise RuntimeError("No trimmed sequence containing alignment "
"on %s:%d-%d" % (contig, minPos, maxPos))
cigarWrite(outputFile, alignment, False)
os.remove(sortedCigarPath)
def upconvertCoords(cigarPath, fastaPath, contigNum, outputFile):
"""Convert the coordinates of the given alignment, so that the
alignment refers to a set of trimmed sequences originating from a
contig rather than to the contig itself."""
with open(fastaPath) as f:
seqRanges = getSequenceRanges(f)
validateRanges(seqRanges)
sortedCigarPath = sortCigarByContigAndPos(cigarPath, contigNum)
sortedCigarFile = open(sortedCigarPath)
currentContig = None
currentRangeIdx = None
currentRange = None
for alignment in cigarRead(sortedCigarFile):
# contig1 and contig2 are reversed in python api!!
contig = alignment.contig2 if contigNum == 1 else alignment.contig1
minPos = min(alignment.start2, alignment.end2) if contigNum == 1 else min(alignment.start1, alignment.end1)
maxPos = max(alignment.start2, alignment.end2) if contigNum == 1 else max(alignment.start1, alignment.end1)
if contig in seqRanges:
if contig != currentContig:
currentContig = contig
currentRangeIdx = 0
currentRange = seqRanges[contig][0]
while (minPos >= currentRange[1] or minPos < currentRange[0]) and currentRangeIdx < len(seqRanges[contig]) - 1:
currentRangeIdx += 1
currentRange = seqRanges[contig][currentRangeIdx]
if currentRange[0] <= minPos < currentRange[1]:
if maxPos - 1 > currentRange[1]:
raise RuntimeError("alignment on %s:%d-%d crosses "
"trimmed sequence boundary" %\
(contig,
minPos,
maxPos))
if contigNum == 1:
alignment.start2 -= currentRange[0]
alignment.end2 -= currentRange[0]
alignment.contig2 = contig + ("|%d" % currentRange[0])
else:
alignment.start1 -= currentRange[0]
alignment.end1 -= currentRange[0]
alignment.contig1 = contig + ("|%d" % currentRange[0])
else:
raise RuntimeError("No trimmed sequence containing alignment "
"on %s:%d-%d" % (contig,
minPos,
maxPos))
cigarWrite(outputFile, alignment, False)
os.remove(sortedCigarPath)
def testCigarReadWrite(self):
tempFile = getTempFile()
self.tempFiles.append(tempFile)
for test in range(0, self.testNo):
cigarNumber = random.choice(range(10))
l = [getRandomPairwiseAlignment() for i in range(cigarNumber)]
fileHandle = open(tempFile, 'w')
for cigar in l:
cigarWrite(fileHandle, cigar)
fileHandle.close()
fileHandle = open(tempFile, 'r')
l.reverse()
for cigar in cigarRead(fileHandle):
cigarWrite(sys.stdout, l[-1])
cigarWrite(sys.stdout, cigar)
assert cigar == l.pop()
assert len(l) == 0
fileHandle.close()
def testCigarReadWrite(self):
"""Tests the C code for reading and writing cigars against the python parser for cigars.
"""
tempFile = getTempFile()
self.tempFiles.append(tempFile)
for test in range(0, self.testNo):
pairwiseAlignmentNumber = random.choice(range(10))
l = [
getRandomPairwiseAlignment()
for i in range(pairwiseAlignmentNumber)
]
fileHandle = open(tempFile, 'w')
keepProbs = random.random() > 0.5
if keepProbs == False:
for pA in l:
for op in pA.operationList:
op.score = 0.0
for pairwiseAlignment in l:
cigarWrite(fileHandle, pairwiseAlignment, keepProbs)
fileHandle.close()
#Now call sonLib_cigarsTest and read and write chains
command = "sonLib_cigarTest %s %s" % (tempFile, keepProbs)
#return
system(command)
#Now check the chain is okay
fileHandle = open(tempFile, 'r')
l.reverse()
for pairwiseAlignment in cigarRead(fileHandle):
pairwiseAlignment2 = l.pop()
cigarWrite(sys.stdout, pairwiseAlignment, keepProbs)
cigarWrite(sys.stdout, pairwiseAlignment2, keepProbs)
assert pairwiseAlignment == pairwiseAlignment2
assert len(l) == 0
fileHandle.close()
def expectationMaximisation(target, sequences, alignments, outputModel,
options):
#Iteratively run cPecanRealign to get expectations and load model.
if options.inputModel != None: #Read in the model
target.logToMaster("Loading the model from the input file %s" %
options.inputModel)
hmm = Hmm.loadHmm(options.inputModel)
target.logToMaster("Loaded the model, has type %s" % hmm.modelType)
hmm.normalise()
else:
target.logToMaster("Making model of type %s" % options.modelType)
hmm = Hmm(options.modelType)
if options.randomStart: #Make random parameters
target.logToMaster("Using random starting parameters")
hmm.randomise()
else:
hmm.equalise()
if options.setJukesCantorStartingEmissions != None:
hmm.setEmissionsToJukesCantor(
float(options.setJukesCantorStartingEmissions))
#Write out the first version of the output model
hmm.write(outputModel)
#Make a set of split alignment files
alignmentsLength = 0
splitAlignmentFiles = []
fH = None
for cigar in cigarRead(alignments):
if fH == None:
splitAlignmentFiles.append(
os.path.join(target.getGlobalTempDir(),
"alignments_%s.cigar" % len(splitAlignmentFiles)))
fH = open(splitAlignmentFiles[-1], 'w')
alignmentsLength += (abs(cigar.start1 - cigar.end1) +
abs(cigar.start2 - cigar.end2)) / 2.0
cigarWrite(fH, cigar)
if alignmentsLength > options.maxAlignmentLengthPerJob:
fH.close()
fH = None
splitAlignmentFiles[-1] = (splitAlignmentFiles[-1],
alignmentsLength)
alignmentsLength = 0
if fH != None:
fH.close()
splitAlignmentFiles[-1] = (splitAlignmentFiles[-1], alignmentsLength)
#Sample the alignment files so that we do EM on no more than options.maxAlignmentLengthToSample bases
random.shuffle(
splitAlignmentFiles
) #This ensures we don't just take the first N alignments in the provided alignments file
sampledSplitAlignmentFiles = []
totalSampledAlignmentLength = 0.0
for alignmentsFile, alignmentsLength in splitAlignmentFiles:
totalSampledAlignmentLength += alignmentsLength
sampledSplitAlignmentFiles.append(alignmentsFile)
if totalSampledAlignmentLength >= options.maxAlignmentLengthToSample:
break
target.logToMaster("We sampled: %s bases of alignment length and %s alignment files, of a possible %s base and %s files" % \
(totalSampledAlignmentLength, len(sampledSplitAlignmentFiles), sum(map(lambda x : x[1], splitAlignmentFiles)), len(splitAlignmentFiles)))
splitAlignmentFiles = sampledSplitAlignmentFiles
#Files to store expectations in
expectationsFiles = map(
lambda i: os.path.join(target.getGlobalTempDir(), "expectation_%i.txt"
% i), xrange(len(splitAlignmentFiles)))
assert len(splitAlignmentFiles) == len(expectationsFiles)
target.setFollowOnTargetFn(expectationMaximisation2,
args=(sequences, splitAlignmentFiles,
outputModel, expectationsFiles, 0, [],
options))