def consensusForAlignments(refWindow, refSequence, alns, arrowConfig):
"""
Call consensus on this interval---without subdividing the interval
further.
Testable!
Clipping has already been done!
"""
_, refStart, refEnd = refWindow
# 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 alns
if a.spansReferenceRange(refStart, refEnd)
]
assert len(fwdSequences) >= arrowConfig.minPoaCoverage
try:
p = cc.PoaConsensus.FindConsensus(
fwdSequences[:arrowConfig.maxPoaCoverage])
except:
logging.info("%s: POA could not be generated" % (refWindow, ))
return ArrowConsensus.noCallConsensus(arrowConfig.noEvidenceConsensus,
refWindow, refSequence)
ga = cc.Align(refSequence, p.Sequence)
numPoaVariants = ga.Errors()
poaCss = p.Sequence
# Extract reads into ConsensusCore2-compatible objects, and map them into the
# coordinates relative to the POA consensus
mappedReads = [
arrowConfig.extractMappedRead(aln, refStart) for aln in alns
]
queryPositions = cc.TargetToQueryPositions(ga)
mappedReads = [(lifted(queryPositions, mr), snr)
for (mr, snr) in mappedReads]
# Load the mapped reads into the mutation scorer, and iterate
# until convergence.
ai = cc.MultiMolecularIntegrator(
poaCss, cc.IntegratorConfig(arrowConfig.minZScore))
coverage = 0
for (mr, snr) in mappedReads:
if (mr.TemplateEnd <= mr.TemplateStart
or mr.TemplateEnd - mr.TemplateStart < 2 or mr.Length() < 2):
continue
coverage += 1 if ai.AddRead(mr, snr) == cc.AddReadResult_SUCCESS else 0
# TODO(lhepler, dalexander): propagate coverage around somehow
# Iterate until covergence
try:
assert coverage >= arrowConfig.minPoaCoverage, \
"Insufficient coverage (%d) to call consensus (%d)" \
% (coverage, arrowConfig.minPoaCoverage)
_, converged = refineConsensus(ai, arrowConfig)
assert converged, "Arrow did not converge to MLE"
arrowCss = str(ai)
if arrowConfig.computeConfidence:
confidence = consensusConfidence(ai)
else:
confidence = np.zeros(shape=len(arrowCss), dtype=int)
return ArrowConsensus(refWindow, arrowCss, confidence, ai)
except:
traceback = ''.join(format_exception(*sys.exc_info()))
logging.info("%s: %s" % (refWindow, traceback))
return ArrowConsensus.noCallConsensus(arrowConfig.noEvidenceConsensus,
refWindow, refSequence)
def consensusForAlignments(refWindow,
refSequence,
alns,
arrowConfig,
draft=None,
polish=True,
alnsUsed=None):
"""
Call consensus on this interval---without subdividing the interval
further.
Returns an ArrowConsensus object.
Requires that clipping has already been done.
If `draft` is provided, it will serve as the starting
point for polishing. If not, the POA will be used to generate a
draft starting point.
If `polish` is False, the arrow polishing procedure will not be
used, and the draft consensus will be returned.
`alnsUsed` is an output parameter; if not None, it should be an
empty list on entry; on return from this function, the list will
contain the alns objects that were actually used to compute the
consensus (those not filtered out).
"""
_, refStart, refEnd = refWindow
if alnsUsed is not None:
assert alnsUsed == []
if draft is None:
# 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 alns
if a.spansReferenceRange(refStart, refEnd)
]
assert len(fwdSequences) >= arrowConfig.minPoaCoverage
try:
p = poaConsensus(fwdSequences, arrowConfig)
except Exception:
logging.info("%s: POA could not be generated" % (refWindow, ))
return ArrowConsensus.noCallConsensus(
arrowConfig.noEvidenceConsensus, refWindow, refSequence)
draft = p.Sequence
ga = cc.Align(refSequence, draft)
# Extract reads into ConsensusCore2-compatible objects, and map them into the
# coordinates relative to the POA consensus
mappedReads = [
arrowConfig.extractMappedRead(aln, refStart) for aln in alns
]
queryPositions = cc.TargetToQueryPositions(ga)
mappedReads = [lifted(queryPositions, mr) for mr in mappedReads]
# Load the mapped reads into the mutation scorer, and iterate
# until convergence.
ai = cc.Integrator(draft, cc.IntegratorConfig(arrowConfig.minZScore))
coverage = 0
for i, mr in enumerate(mappedReads):
if (mr.TemplateEnd <= mr.TemplateStart
or mr.TemplateEnd - mr.TemplateStart < 2 or mr.Length() < 2):
continue
if not sufficientlyAccurate(mr, draft, arrowConfig.minAccuracy):
tpl = draft[mr.TemplateStart:mr.TemplateEnd]
if mr.Strand == cc.StrandType_FORWARD:
pass
elif mr.Strand == cc.StrandType_REVERSE:
tpl = reverseComplement(tpl)
else:
tpl = "INACTIVE/UNMAPPED"
logging.debug(
"%s: skipping read '%s' due to insufficient accuracy, (poa, read): ('%s', '%s')"
% (refWindow, mr.Name, tpl, mr.Seq))
continue
if ai.AddRead(mr) == cc.State_VALID:
coverage += 1
if alnsUsed is not None:
alnsUsed.append(alns[i])
if coverage < arrowConfig.minPoaCoverage:
logging.info("%s: Inadequate coverage to call consensus" %
(refWindow, ))
return ArrowConsensus.noCallConsensus(arrowConfig.noEvidenceConsensus,
refWindow, refSequence)
if not polish:
confidence = np.zeros(len(draft), dtype=int)
return ArrowConsensus(refWindow, draft, confidence, ai)
# Iterate until covergence
_, converged = refineConsensus(ai, arrowConfig, polishDiploid=False)
if converged:
arrowCss = str(ai)
if arrowConfig.computeConfidence:
confidence = consensusConfidence(ai)
else:
confidence = np.zeros(shape=len(arrowCss), dtype=int)
else:
logging.info("%s: Arrow did not converge to MLE" % (refWindow, ))
return ArrowConsensus.noCallConsensus(arrowConfig.noEvidenceConsensus,
refWindow, refSequence)
if arrowConfig.polishDiploid:
# additional rounds of diploid polishing
_, converged = refineConsensus(ai, arrowConfig, polishDiploid=True)
if converged:
arrowCss = str(ai)
if arrowConfig.computeConfidence:
confidence = consensusConfidence(ai)
else:
confidence = np.zeros(shape=len(arrowCss), dtype=int)
else:
logging.info(
"%s: Arrow (diploid) did not converge to optimal solution" %
(refWindow, ))
return ArrowConsensus(refWindow, arrowCss, confidence, ai)
