private VariantCallContext estimateReferenceConfidence(
VariantContext vc,
Map<String, AlignmentContext> contexts,
double theta,
boolean ignoreCoveredSamples,
double initialPofRef) {
if (contexts == null) return null;
double P_of_ref = initialPofRef;
// for each sample that we haven't examined yet
for (String sample : samples) {
boolean isCovered = contexts.containsKey(sample);
if (ignoreCoveredSamples && isCovered) continue;
int depth = 0;
if (isCovered) {
depth = contexts.get(sample).getBasePileup().depthOfCoverage();
}
P_of_ref *= 1.0 - (theta / 2.0) * getRefBinomialProb(depth);
}
return new VariantCallContext(
vc,
QualityUtils.phredScaleErrorRate(1.0 - P_of_ref) >= UAC.STANDARD_CONFIDENCE_FOR_CALLING,
false);
}
private double scoreReadAgainstHaplotype(
final PileupElement p, final int contextSize, final Haplotype haplotype, final int locus) {
double expected = 0.0;
double mismatches = 0.0;
// What's the expected mismatch rate under the model that this read is actually sampled from
// this haplotype? Let's assume the consensus base c is a random choice one of A, C, G, or T,
// and that
// the observed base is actually from a c with an error rate e. Since e is the rate at which
// we'd
// see a miscalled c, the expected mismatch rate is really e. So the expected number of
// mismatches
// is just sum_i e_i for i from 1..n for n sites
//
// Now, what's the probabilistic sum of mismatches? Suppose that the base b is equal to c.
// Well, it could
// actually be a miscall in a matching direction, which would happen at a e / 3 rate. If b !=
// c, then
// the chance that it is actually a mismatch is 1 - e, since any of the other 3 options would be
// a mismatch.
// so the probability-weighted mismatch rate is sum_i ( matched ? e_i / 3 : 1 - e_i ) for i = 1
// ... n
final byte[] haplotypeBases = haplotype.getBases();
final GATKSAMRecord read = p.getRead();
byte[] readBases = read.getReadBases();
readBases =
AlignmentUtils.readToAlignmentByteArray(
p.getRead().getCigar(), readBases); // Adjust the read bases based on the Cigar string
byte[] readQuals = read.getBaseQualities();
readQuals =
AlignmentUtils.readToAlignmentByteArray(
p.getRead().getCigar(),
readQuals); // Shift the location of the qual scores based on the Cigar string
int readOffsetFromPileup = p.getOffset();
readOffsetFromPileup =
AlignmentUtils.calcAlignmentByteArrayOffset(
p.getRead().getCigar(), p, read.getAlignmentStart(), locus);
final int baseOffsetStart = readOffsetFromPileup - (contextSize - 1) / 2;
for (int i = 0; i < contextSize; i++) {
final int baseOffset = i + baseOffsetStart;
if (baseOffset < 0) {
continue;
}
if (baseOffset >= readBases.length) {
break;
}
final byte haplotypeBase = haplotypeBases[i];
final byte readBase = readBases[baseOffset];
final boolean matched =
(readBase == haplotypeBase || haplotypeBase == (byte) REGEXP_WILDCARD);
byte qual = readQuals[baseOffset];
if (qual == PileupElement.DELETION_BASE) {
qual = PileupElement.DELETION_QUAL;
} // calcAlignmentByteArrayOffset fills the readQuals array with DELETION_BASE at deletions
qual = (byte) Math.min((int) qual, p.getMappingQual());
if (((int) qual)
>= 5) { // quals less than 5 are used as codes and don't have actual probabilistic meaning
// behind them
final double e = QualityUtils.qualToErrorProb(qual);
expected += e;
mismatches += matched ? e : 1.0 - e / 3.0;
}
// a more sophisticated calculation would include the reference quality, but it's nice to
// actually penalize
// the mismatching of poorly determined regions of the consensus
}
return mismatches - expected;
}
protected boolean confidentlyCalled(double conf, double PofF) {
return conf >= UAC.STANDARD_CONFIDENCE_FOR_CALLING
|| (UAC.GenotypingMode
== GenotypeLikelihoodsCalculationModel.GENOTYPING_MODE.GENOTYPE_GIVEN_ALLELES
&& QualityUtils.phredScaleErrorRate(PofF) >= UAC.STANDARD_CONFIDENCE_FOR_CALLING);
}
