def mainCmpH5(options):
alnReader = AlignmentSet(options.inputCmpH5,
referenceFastaFname=options.referenceFilename)
if options.fofn is not None:
alnReader.attach(options.fofn)
if options.referenceFilename:
referenceTable = loadReferences(options.referenceFilename, alnReader)
else:
referenceTable = None
for refWindow in options.referenceWindows:
refId = refWindow.refId
refName = alnReader.referenceInfo(refWindow.refId).FullName
refLength = alnReader.referenceInfo(refWindow.refId).Length
refWindow = refWindow._replace(refId=refId)
refWindow = makeDisplayWindow(refLength, options.width, refWindow)
if options.rowNumbers != None:
alns = alnReader[options.rowNumbers]
else:
alns = readsInWindow(alnReader, refWindow, options.depth,
minMapQV=options.minMapQV, strategy=options.sorting)
print windowToGffString(Window(refName, refWindow.start, refWindow.end))
if options.oneAtATime:
formatIndividualAlignments(alnReader, refWindow, alns)
else:
formatWindow(alnReader, refWindow, alns,
referenceTable, options.aligned, options.color,
options.realign, options.consensus)
print
def mainGff(options):
reader = GffReader(options.inputGff)
cmpH5Fname, referenceFname = extractCmpH5AndReferenceFromGff(reader)
# Allow overriding
cmpH5Fname = options.inputCmpH5 or cmpH5Fname
referenceFname = options.referenceFilename or referenceFname
assert cmpH5Fname
assert referenceFname
cmpH5 = CmpH5Reader(cmpH5Fname)
referenceTable = loadReferences(referenceFname, cmpH5)
for gffRecord in reader:
referenceSeq = gffRecord.get("reference", "-")
variantSeq = gffRecord.get("variantSeq", "-")
variantConfidence = gffRecord.confidence
variantSummary = "(%s > %s)" % (referenceSeq, variantSeq)
print gffRecord.type, gffRecord.seqid, gffRecord.start, gffRecord.end, \
variantSummary, variantConfidence
refId = cmpH5.referenceInfo(gffRecord.seqid).ID
refWindow = Window(refId,
gffRecord.start - 10,
gffRecord.end + 10)
rowNumbers = readsInWindow(cmpH5, refWindow, options.depth,
minMapQV=options.minMapQV, strategy=options.sorting)
formatWindow(cmpH5, refWindow, rowNumbers, referenceTable,
aligned=(gffRecord.type != "insertion"),
consensus=options.consensus)
print
def consensus(alnReader, refWindow, referenceTable, alns):
# identify the enlarged interval [-5, +5]
refName = alnReader.referenceInfo(refWindow.refId).FullName
refLength = len(referenceTable[refName].sequence)
eWindow = enlargedReferenceWindow(refWindow, refLength, overlap)
refSeqInEnlargedWindow = referenceTable[refName].sequence[eWindow.start:eWindow.end]
# find 3-spanned intervals in the enlarged interval
# call css for each interval
subConsensi = []
tStart = [ a.tStart for a in alns ]
tEnd = [ a.tEnd for a in alns ]
coveredIntervals = w.kSpannedIntervals(eWindow, K, tStart, tEnd)
holes = w.holes(eWindow, coveredIntervals)
for interval in sorted(coveredIntervals + holes):
subWin = subWindow(eWindow, interval)
#print subWin
intStart, intEnd = interval
intRefSeq = refSeqInEnlargedWindow[intStart-eWindow.start:
intEnd-eWindow.start]
css_ = Consensus.nAsConsensus(subWin, intRefSeq)
if interval in coveredIntervals:
alns = readsInWindow(alnReader, subWin,
depthLimit=100,
minMapQV=quiverConfig.minMapQV,
strategy="longest")
clippedAlns = [ aln.clippedTo(*interval) for aln in alns ]
goodAlns = q.utils.filterAlns(subWin, clippedAlns, quiverConfig)
if len(goodAlns) >= K:
css_ = q.utils.consensusForAlignments(subWin,
intRefSeq,
goodAlns,
quiverConfig)
subConsensi.append(css_)
# join subconsensus objects
css = join(subConsensi)
# align css back to refWindow, and clip
ga = cc.Align(refSeqInEnlargedWindow, css.sequence)
targetPositions = cc.TargetToQueryPositions(ga)
cssStart = targetPositions[refWindow.start-eWindow.start]
cssEnd = targetPositions[refWindow.end-eWindow.start]
cssSequence = css.sequence[cssStart:cssEnd]
cssQv = css.confidence[cssStart:cssEnd]
consensusObj = Consensus(refWindow,
cssSequence,
cssQv)
return consensusObj
def mainGff(options):
reader = GffReader(options.inputGff)
alnsFname, referenceFname = extractCmpH5AndReferenceFromGff(reader)
# Allow overriding
alnsFname = options.inputCmpH5 or alnsFname
referenceFname = options.referenceFilename or referenceFname
assert os.path.isfile(alnsFname)
assert os.path.isfile(referenceFname)
alnReader = AlignmentSet(alnsFname, referenceFastaFname=referenceFname)
if options.fofn is not None:
alnReader.attach(options.fofn)
referenceTable = loadReferences(referenceFname, alnReader)
for i, gffRecord in enumerate(reader):
referenceSeq = gffRecord.get("reference", "-")
variantSeq = gffRecord.get("variantSeq", "-")
variantConfidence = gffRecord.confidence
variantSummary = "(%s > %s)" % (referenceSeq, variantSeq)
print gffRecord.type, gffRecord.seqid, gffRecord.start, gffRecord.end, \
variantSummary, variantConfidence
refId = gffRecord.seqid
refLength = alnReader.referenceInfo(gffRecord.seqid).Length
refWindow = makeDisplayWindow(refLength, options.width,
Window(refId,
gffRecord.start-10,
gffRecord.end+10))
if "rows" in gffRecord.attributes:
alns = alnReader[map(int, gffRecord.rows.split(","))]
else:
alns = readsInWindow(alnReader, refWindow, options.depth,
minMapQV=options.minMapQV, strategy=options.sorting)
formatWindow(alnReader, refWindow, alns, referenceTable,
aligned=(gffRecord.type != "insertion"),
consensus=options.consensus,
useColor=options.color,
doRealign=options.realign)
if options.pulseRecognizer:
# CSV output for pulse recognizer
print
csvFname = "variant-" + str(i) + ".csv"
dumpVariantCsv(csvFname, alnReader, alns, gffRecord)
formatVariantCsvLink(csvFname)
print
def mainCmpH5(options):
cmpH5 = CmpH5Reader(options.inputCmpH5)
refId = cmpH5.referenceInfo(options.referenceWindow.refId).ID
refWindow = options.referenceWindow._replace(refId=refId)
if options.rowNumbers != None:
rowNumbers = options.rowNumbers
else:
rowNumbers = readsInWindow(cmpH5, refWindow, options.depth,
minMapQV=options.minMapQV, strategy=options.sorting)
if options.referenceFilename:
referenceTable = loadReferences(options.referenceFilename, cmpH5)
else:
referenceTable = None
formatWindow(cmpH5, refWindow, rowNumbers,
referenceTable, options.aligned, options.color,
options.consensus)
print
def run_real_quiver(cmpH5, quiverConfig, interval, depthLimit, refSeq, refWindow, seedConsensus):
intStart, intEnd = interval
subWin = subWindow(refWindow, interval)
windowRefSeq = refSeq[intStart:intEnd]
rows = readsInWindow(cmpH5, subWin,
depthLimit = depthLimit,
minMapQV = quiverConfig.minMapQV,
strategy = "longest",
stratum = None,
barcode = None)
spanningRows = [row for row in rows if cmpH5[row].spansReferenceRange(intStart, intEnd) ]
alns = cmpH5[spanningRows]
clippedAlns_ = [ aln.clippedTo(*interval) for aln in alns ]
clippedAlns__ = [ aln for aln in clippedAlns_ if aln.alignedLength <= 120]
clippedAlns = filterAlns(subWin, clippedAlns__, quiverConfig)
consensus = consensusForAlignmentsDisregardPOA(subWin, windowRefSeq, clippedAlns, quiverConfig, "A"*100)
print(str(consensus.sequence))
def getReads(cmpH5,
reference,
interval,
paddedTemplateWidth,
depthLimit,
real_quiver=False):
minMapQV = 10
minPoaCoverage = 3
maxPoaCoverage = 11
mutationSeparation = 10
mutationNeighborhood = 20
maxIterations = 20
refineDinucleotideRepeats = True
noEvidenceConsensus = "nocall"
computeConfidence = True
readStumpinessThreshold = 0.1
refId = [x for x in reference.enumerateIds()][0]
refSeq = reference.byId[refId].sequence
refWindow = (refId, 0, reference.byId[refId].length)
intStart, intEnd = interval
subWin = subWindow(refWindow, interval)
windowRefSeq = refSeq[intStart:intEnd]
rows = readsInWindow(cmpH5,
subWin,
depthLimit=depthLimit,
minMapQV=minMapQV,
strategy="longest",
stratum=None,
barcode=None)
#print([cmpH5[row].alignedLength for row in rows if cmpH5[row].spansReferenceRange(intStart, intEnd)])
spanningRows = [
row for row in rows
if cmpH5[row].spansReferenceRange(intStart, intEnd)
]
alns = cmpH5[spanningRows]
clippedAlns_ = [aln.clippedTo(*interval) for aln in alns]
clippedAlns__ = [
aln for aln in clippedAlns_
if aln.alignedLength <= paddedTemplateWidth - 7
]
clippedAlns = filterAlns(subWin, clippedAlns__, readStumpinessThreshold)
# Compute the POA consensus, which is our initial guess, and
# should typically be > 99.5% accurate
fwdSequences = [
a.read(orientation="genomic", aligned=False) for a in clippedAlns
]
p = cc.PoaConsensus.FindConsensus(fwdSequences[:maxPoaCoverage])
template = p.Sequence()
tmplSeq = np.zeros((paddedTemplateWidth), dtype=np.uint8)
tmplOrds = map(ord, template)
tmplSeq[:len(tmplOrds)] = tmplOrds
#read pos y, read x
readSeqs = np.zeros((paddedTemplateWidth, len(clippedAlns)),
dtype=np.uint8)
for i in xrange(len(clippedAlns)):
alnOrds = map(ord, fwdSequences[i])
readSeqs[:len(alnOrds), i] = alnOrds
#uint8
#metric z, read pos y, read x
qvInfo = np.zeros((8, paddedTemplateWidth, len(clippedAlns)),
dtype=np.uint8)
for i in xrange(len(clippedAlns)):
qvInfo[0, :clippedAlns[i].readLength,
i] = clippedAlns[i].InsertionQV(orientation="genomic",
aligned=False)
qvInfo[1, :clippedAlns[i].readLength,
i] = clippedAlns[i].MergeQV(orientation="genomic",
aligned=False)
qvInfo[2, :clippedAlns[i].readLength,
i] = clippedAlns[i].DeletionQV(orientation="genomic",
aligned=False)
qvInfo[3, :clippedAlns[i].readLength,
i] = clippedAlns[i].DeletionTag(orientation="genomic",
aligned=False)
qvInfo[4, :clippedAlns[i].readLength,
i] = clippedAlns[i].SubstitutionQV(orientation="genomic",
aligned=False)
if real_quiver:
return template, len(tmplOrds), fwdSequences, qvInfo
else:
return tmplSeq, len(tmplOrds), readSeqs, qvInfo
def getReads(cmpH5, reference, interval, paddedTemplateWidth, depthLimit, real_quiver=False):
minMapQV = 10
minPoaCoverage = 3
maxPoaCoverage = 11
mutationSeparation = 10
mutationNeighborhood = 20
maxIterations = 20
refineDinucleotideRepeats = True
noEvidenceConsensus = "nocall"
computeConfidence = True
readStumpinessThreshold = 0.1
refId = [x for x in reference.enumerateIds()][0]
refSeq = reference.byId[refId].sequence
refWindow = (refId, 0, reference.byId[refId].length)
intStart, intEnd = interval
subWin = subWindow(refWindow, interval)
windowRefSeq = refSeq[intStart:intEnd]
rows = readsInWindow(cmpH5, subWin,
depthLimit = depthLimit,
minMapQV = minMapQV,
strategy = "longest",
stratum = None,
barcode = None)
#print([cmpH5[row].alignedLength for row in rows if cmpH5[row].spansReferenceRange(intStart, intEnd)])
spanningRows = [row for row in rows if cmpH5[row].spansReferenceRange(intStart, intEnd) ]
alns = cmpH5[spanningRows]
clippedAlns_ = [ aln.clippedTo(*interval) for aln in alns ]
clippedAlns__ = [ aln for aln in clippedAlns_ if aln.alignedLength <= paddedTemplateWidth - 7]
clippedAlns = filterAlns(subWin, clippedAlns__, readStumpinessThreshold)
# Compute the POA consensus, which is our initial guess, and
# should typically be > 99.5% accurate
fwdSequences = [ a.read(orientation="genomic", aligned=False)
for a in clippedAlns]
p = cc.PoaConsensus.FindConsensus(fwdSequences[:maxPoaCoverage])
template = p.Sequence()
tmplSeq = np.zeros((paddedTemplateWidth), dtype=np.uint8)
tmplOrds = map(ord, template)
tmplSeq[:len(tmplOrds)] = tmplOrds
#read pos y, read x
readSeqs = np.zeros((paddedTemplateWidth, len(clippedAlns)), dtype=np.uint8)
for i in xrange(len(clippedAlns)):
alnOrds = map(ord, fwdSequences[i])
readSeqs[:len(alnOrds), i] = alnOrds
#uint8
#metric z, read pos y, read x
qvInfo = np.zeros((8, paddedTemplateWidth, len(clippedAlns)), dtype=np.uint8)
for i in xrange(len(clippedAlns)):
qvInfo[0, :clippedAlns[i].readLength, i] = clippedAlns[i].InsertionQV(orientation="genomic", aligned=False)
qvInfo[1, :clippedAlns[i].readLength, i] = clippedAlns[i].MergeQV(orientation="genomic", aligned=False)
qvInfo[2, :clippedAlns[i].readLength, i] = clippedAlns[i].DeletionQV(orientation="genomic", aligned=False)
qvInfo[3, :clippedAlns[i].readLength, i] = clippedAlns[i].DeletionTag(orientation="genomic", aligned=False)
qvInfo[4, :clippedAlns[i].readLength, i] = clippedAlns[i].SubstitutionQV(orientation="genomic", aligned=False)
if real_quiver:
return template, len(tmplOrds), fwdSequences, qvInfo
else:
return tmplSeq, len(tmplOrds), readSeqs, qvInfo
