def scoresForPosition(ai, pos):
muts = allSingleBaseMutations(str(ai), positions=[pos])
noMutScore = [0] * ai.NumReads()
mutScores_ = [ ai.ReadLLs(mut)
for mut in muts ]
mutScores = np.column_stack([noMutScore] + mutScores_).astype(np.float32)
return mutScores
def variantsFromConsensus(refWindow, refSequenceInWindow, cssSequenceInWindow,
cssQvInWindow=None, siteCoverage=None, aligner="affine",
ai=None):
"""
Compare the consensus and the reference in this window, returning
a list of variants.
Uses the integrator to identify heterozygous variants.
"""
assert (cssQvInWindow is None) == (siteCoverage is None) # Both or none
refId, refStart, refEnd = refWindow
if ai is not None:
#
# Hunting diploid variants:
# 1. find confident heterozygous sites;
# 2. build a "diploid consensus" using IUPAC encoding
# for het sites; mark cssQv accordingly
# 3. align diploid consensus to reference
# 4. extract and decorate variants
#
assert str(ai) == cssSequenceInWindow
iupacMutations = [] # List of (Mutation, confidence)
for pos in xrange(0, ai.Length()):
ds = cc.IsSiteHeterozygous(scoresForPosition(ai, pos), 40)
if ds:
muts = [None] + list(allSingleBaseMutations(cssSequenceInWindow, positions=[pos]))
mut0 = muts[ds.Allele0]
mut1 = muts[ds.Allele1]
cssBase = cssSequenceInWindow[pos]
packedMut = packMuts(cssBase, mut0, mut1)
iupacMutations.append((packedMut, 40))
# Create diploidCss by applying mutations, meanwhile updating the
# confidence vector accordingly.
diploidCss = cc.ApplyMutations([pair[0] for pair in iupacMutations],
cssSequenceInWindow)
diploidQv = list(cssQvInWindow) if cssQvInWindow is not None else None
runningLengthDiff = 0
for (mut, conf) in iupacMutations:
start = mut.Start() + runningLengthDiff
end = mut.End() + runningLengthDiff
diploidQv[start:end] = [conf]
assert len(diploidCss) == len(diploidQv)
cssSequenceInWindow = diploidCss
cssQvInWindow = diploidQv
vars = variantsFromAlignment(refWindow,
refSequenceInWindow, cssSequenceInWindow,
cssQvInWindow, siteCoverage)
return vars
