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package org.broadinstitute.gatk.tools.walkers.genotyper;
import htsjdk.samtools.SAMUtils;
import org.broadinstitute.gatk.tools.walkers.genotyper.afcalc.ExactACset;
import org.broadinstitute.gatk.utils.BaseUtils;
import org.broadinstitute.gatk.utils.MathUtils;
import org.broadinstitute.gatk.utils.exceptions.ReviewedGATKException;
import org.broadinstitute.gatk.utils.exceptions.UserException;
import org.broadinstitute.gatk.utils.pileup.PileupElement;
import org.broadinstitute.gatk.utils.pileup.ReadBackedPileup;
import org.broadinstitute.gatk.utils.pileup.ReadBackedPileupImpl;
import htsjdk.variant.variantcontext.Allele;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.List;
import static java.lang.Math.log10;
import static java.lang.Math.pow;
/**
* Stable, error checking version of the pool genotyper. Useful for calculating the likelihoods, priors,
* and posteriors given a pile of bases and quality scores
*
*/
public class GeneralPloidySNPGenotypeLikelihoods extends GeneralPloidyGenotypeLikelihoods/* implements Cloneable*/ {
final List<Allele> myAlleles;
final int[] alleleIndices;
final boolean useBAQedPileup;
final byte refByte;
int mbq;
//final double[] PofDGivenBase;
protected static final double[][][] qualLikelihoodCache;
/**
* Create a new GenotypeLikelhoods object with given priors and PCR error rate for each pool genotype
* @param alleles Alleles associated with this likelihood object
* @param logLikelihoods Likelihoods (can be null if no likelihoods known)
* @param ploidy Ploidy of sample (# of chromosomes)
* @param perLaneErrorModels error model objects for each lane
* @param useBQAedPileup Use BAQed pileup
* @param ignoreLaneInformation If true, lane info is ignored
*/
public GeneralPloidySNPGenotypeLikelihoods(final List<Allele> alleles, final double[] logLikelihoods, final int ploidy,
final HashMap<String, ErrorModel> perLaneErrorModels, final boolean useBQAedPileup, final boolean ignoreLaneInformation) {
super(alleles, logLikelihoods, ploidy, perLaneErrorModels, ignoreLaneInformation);
this.useBAQedPileup = useBQAedPileup;
myAlleles = new ArrayList<Allele>(alleles);
Allele refAllele = alleles.get(0);
//sanity check: by construction, first allele should ALWAYS be the reference alleles
if (!refAllele.isReference())
throw new ReviewedGATKException("BUG: First allele in list passed to GeneralPloidySNPGenotypeLikelihoods should be reference!");
refByte = refAllele.getBases()[0]; // by construction, first allele in list is always ref!
if (myAlleles.size() < BaseUtils.BASES.length) {
// likelihood only defined for subset of possible alleles. Fill then with other alleles to have all possible ones,
for (byte b : BaseUtils.BASES) {
// if base is not included in myAlleles, add new allele
boolean isRef = (b==refByte);
if (!myAlleles.contains(Allele.create(b,isRef)))
myAlleles.add(Allele.create(b,isRef));
}
}
// compute permutation vector to figure out mapping from indices to bases
int idx = 0;
alleleIndices = new int[myAlleles.size()];
for (byte b : BaseUtils.BASES) {
boolean isRef = (b==refByte);
alleleIndices[idx++] = myAlleles.indexOf(Allele.create(b,isRef));
}
}
// -------------------------------------------------------------------------------------
//
// add() routines. These are the workhorse routines for calculating the overall genotype
// likelihoods given observed bases and reads. Includes high-level operators all the
// way down to single base and qual functions.
//
// -------------------------------------------------------------------------------------
public int add(ReadBackedPileup pileup, UnifiedArgumentCollection UAC) {
mbq = UAC.MIN_BASE_QUALTY_SCORE; // record for later use
return add(pileup, true, true, mbq);
}
/**
* Updates likelihoods and posteriors to reflect the additional observations contained within the
* read-based pileup up by calling add(observedBase, qualityScore) for each base / qual in the
* pileup
*
* @param pileup read pileup
* @param ignoreBadBases should we ignore bad bases?
* @param capBaseQualsAtMappingQual should we cap a base's quality by its read's mapping quality?
* @param minBaseQual the minimum base quality at which to consider a base valid
* @return the number of good bases found in the pileup
*/
public int add(ReadBackedPileup pileup, boolean ignoreBadBases, boolean capBaseQualsAtMappingQual, int minBaseQual) {
int n = 0;
if ( useBAQedPileup )
pileup = createBAQedPileup( pileup );
if (!hasReferenceSampleData) {
return add(pileup, ignoreBadBases, capBaseQualsAtMappingQual, minBaseQual, null);
}
for (String laneID : perLaneErrorModels.keySet() ) {
// get pileup for this lane
ReadBackedPileup perLanePileup;
if (ignoreLaneInformation)
perLanePileup = pileup;
else
perLanePileup = pileup.getPileupForLane(laneID);
if (perLanePileup == null || perLanePileup.isEmpty())
continue;
ErrorModel errorModel = perLaneErrorModels.get(laneID);
n += add(perLanePileup, ignoreBadBases, capBaseQualsAtMappingQual, minBaseQual, errorModel);
if (ignoreLaneInformation)
break;
}
return n;
}
/**
* Calculates the pool's probability for all possible allele counts for all bases. Calculation is based on the error model
* generated by the reference sample on the same lane. The probability is given by :
*
* Pr(ac=jA,jC,jG,jT| pool, errorModel) = sum_over_all_Qs ( Pr(ac=jA,jC,jG,jT) * Pr(errorModel_q) *
* [jA*(1-eq)/2n + eq/3*(jc+jg+jt)/2N)^nA * jC*(1-eq)/2n + eq/3*(ja+jg+jt)/2N)^nC *
* jG*(1-eq)/2n + eq/3*(jc+ja+jt)/2N)^nG * jT*(1-eq)/2n + eq/3*(jc+jg+ja)/2N)^nT
*
* log Pr(ac=jA,jC,jG,jT| pool, errorModel) = logsum( Pr(ac=jA,jC,jG,jT) * Pr(errorModel_q) *
* [jA*(1-eq)/2n + eq/3*(jc+jg+jt)/2N)^nA * jC*(1-eq)/2n + eq/3*(ja+jg+jt)/2N)^nC *
* jG*(1-eq)/2n + eq/3*(jc+ja+jt)/2N)^nG * jT*(1-eq)/2n + eq/3*(jc+jg+ja)/2N)^nT)
* = logsum(logPr(ac=jA,jC,jG,jT) + log(Pr(error_Model(q)
* )) + nA*log(jA/2N(1-eq)+eq/3*(2N-jA)/2N) + nC*log(jC/2N(1-eq)+eq/3*(2N-jC)/2N)
* + log(jG/2N(1-eq)+eq/3*(2N-jG)/2N) + log(jT/2N(1-eq)+eq/3*(2N-jT)/2N)
*
* Let Q(j,k) = log(j/2N*(1-e[k]) + (2N-j)/2N*e[k]/3)
*
* Then logPr(ac=jA,jC,jG,jT|D,errorModel) = logPR(ac=Ja,jC,jG,jT) + logsum_k( logPr (errorModel[k],
* nA*Q(jA,k) + nC*Q(jC,k) + nG*Q(jG,k) + nT*Q(jT,k))
*
* If pileup data comes from several error models (because lanes can have different error models),
* Pr(Ac=j|D,E1,E2) = sum(Pr(AC1=j1|D,E1,E2) * Pr(AC2=j-j2|D,E1,E2))
* = sum(Pr(AC1=j1|D,E1)*Pr(AC2=j-j1|D,E2)) from j=0..2N
*
* So, for each lane, build error model and combine lanes.
* To store model, can do
* for jA=0:2N
* for jC = 0:2N-jA
* for jG = 0:2N-jA-jC
* for jT = 0:2N-jA-jC-jG
* Q(jA,jC,jG,jT)
* for k = minSiteQual:maxSiteQual
* likelihood(jA,jC,jG,jT) = logsum(logPr (errorModel[k],nA*Q(jA,k) + nC*Q(jC,k) + nG*Q(jG,k) + nT*Q(jT,k))
*
*
*
* where: nA,nC,nG,nT = counts of bases observed in pileup.
*
*
* @param pileup Base pileup
* @param ignoreBadBases Whether to ignore bad bases
* @param capBaseQualsAtMappingQual Cap base at mapping qual
* @param minBaseQual Minimum base quality to consider
* @param errorModel Site error model
* @return Number of bases added - only good bases actually added to GLs are counted.
*/
private int add(ReadBackedPileup pileup, boolean ignoreBadBases, boolean capBaseQualsAtMappingQual, int minBaseQual, ErrorModel errorModel) {
// Number of [A C G T]'s in pileup, in that order
List<Integer> numSeenBases = new ArrayList<Integer>(BaseUtils.BASES.length);
for (byte b: BaseUtils.BASES)
numSeenBases.add(0);
int nGoodBases = 0;
// count number of elements in pileup
for (PileupElement elt : pileup) {
byte obsBase = elt.getBase();
byte qual = qualToUse(elt, ignoreBadBases, capBaseQualsAtMappingQual, minBaseQual);
if ( qual == 0 )
continue;
int idx = 0;
for (byte base:BaseUtils.BASES) {
int cnt = numSeenBases.get(idx);
numSeenBases.set(idx++,cnt + (base == obsBase?1:0));
}
nGoodBases++;
}
if (VERBOSE)
System.out.format("numSeenBases: %d %d %d %d\n",numSeenBases.get(0),numSeenBases.get(1),numSeenBases.get(2),numSeenBases.get(3));
computeLikelihoods(errorModel, myAlleles, numSeenBases, pileup);
return nGoodBases;
}
/**
* Compute likelihood of current conformation
*
* @param ACset Count to compute
* @param errorModel Site-specific error model object
* @param alleleList List of alleles
* @param numObservations Number of observations for each allele in alleleList
*/
public void getLikelihoodOfConformation(final ExactACset ACset,
final ErrorModel errorModel,
final List<Allele> alleleList,
final List<Integer> numObservations,
final ReadBackedPileup pileup) {
final int[] currentCnt = Arrays.copyOf(ACset.getACcounts().getCounts(), BaseUtils.BASES.length);
final int[] ac = new int[BaseUtils.BASES.length];
for (int k=0; k < BaseUtils.BASES.length; k++ )
ac[k] = currentCnt[alleleIndices[k]];
double p1 = 0.0;
if (!hasReferenceSampleData) {
// no error model: loop through pileup to compute likelihoods just on base qualities
// In this case, vector numObservations is not used directly for GL computation
for (final PileupElement elt : pileup) {
final byte obsBase = elt.getBase();
final byte qual = qualToUse(elt, true, true, mbq);
if ( qual == 0 )
continue;
final double acc[] = new double[ACset.getACcounts().getCounts().length];
for (int k=0; k < acc.length; k++ )
acc[k] = qualLikelihoodCache[BaseUtils.simpleBaseToBaseIndex(alleleList.get(k).getBases()[0])][BaseUtils.simpleBaseToBaseIndex(obsBase)][qual] +
MathUtils.Log10Cache.get(ACset.getACcounts().getCounts()[k]) - LOG10_PLOIDY;
p1 += MathUtils.log10sumLog10(acc);
}
}
else {
final int minQ = errorModel.getMinSignificantQualityScore();
final int maxQ = errorModel.getMaxSignificantQualityScore();
final double[] acVec = new double[maxQ - minQ + 1];
final int nA = numObservations.get(0);
final int nC = numObservations.get(1);
final int nG = numObservations.get(2);
final int nT = numObservations.get(3);
for (int k=minQ; k<=maxQ; k++)
acVec[k-minQ] = nA*logMismatchProbabilityArray[ac[0]][k] +
nC*logMismatchProbabilityArray[ac[1]][k] +
nG*logMismatchProbabilityArray[ac[2]][k] +
nT*logMismatchProbabilityArray[ac[3]][k];
p1 = MathUtils.logDotProduct(errorModel.getErrorModelVector().getProbabilityVector(minQ,maxQ), acVec);
}
ACset.getLog10Likelihoods()[0] = p1;
/* System.out.println(Arrays.toString(ACset.ACcounts.getCounts())+" "+String.valueOf(p1));
System.out.println(Arrays.toString(errorModel.getErrorModelVector().getProbabilityVector(minQ,maxQ)));
*/
}
public ReadBackedPileup createBAQedPileup( final ReadBackedPileup pileup ) {
final List<PileupElement> BAQedElements = new ArrayList<PileupElement>();
for( final PileupElement PE : pileup ) {
final PileupElement newPE = new SNPGenotypeLikelihoodsCalculationModel.BAQedPileupElement( PE );
BAQedElements.add( newPE );
}
return new ReadBackedPileupImpl( pileup.getLocation(), BAQedElements );
}
/**
* Helper function that returns the phred-scaled base quality score we should use for calculating
* likelihoods for a pileup element. May return 0 to indicate that the observation is bad, and may
* cap the quality score by the mapping quality of the read itself.
*
* @param p Pileup element
* @param ignoreBadBases Flag to ignore bad bases
* @param capBaseQualsAtMappingQual Whether to cap base Q at mapping quality
* @param minBaseQual Min qual to use
* @return New phred-scaled base quality
*/
private static byte qualToUse(PileupElement p, boolean ignoreBadBases, boolean capBaseQualsAtMappingQual, int minBaseQual) {
if ( ignoreBadBases && !BaseUtils.isRegularBase( p.getBase() ) )
return 0;
byte qual = p.getQual();
if ( qual > SAMUtils.MAX_PHRED_SCORE )
throw new UserException.MalformedBAM(p.getRead(), String.format("the maximum allowed quality score is %d, but a quality of %d was observed in read %s. Perhaps your BAM incorrectly encodes the quality scores in Sanger format; see http://en.wikipedia.org/wiki/FASTQ_format for more details", SAMUtils.MAX_PHRED_SCORE, qual, p.getRead().getReadName()));
if ( capBaseQualsAtMappingQual )
qual = (byte)Math.min((int)qual, p.getMappingQual());
if ( (int)qual < minBaseQual )
qual = (byte)0;
return qual;
}
static {
qualLikelihoodCache = new double[BaseUtils.BASES.length][BaseUtils.BASES.length][1+SAMUtils.MAX_PHRED_SCORE];
for (byte j=0; j <= SAMUtils.MAX_PHRED_SCORE; j++) {
for (byte b1:BaseUtils.BASES) {
for (byte b2:BaseUtils.BASES) {
qualLikelihoodCache[BaseUtils.simpleBaseToBaseIndex(b1)][BaseUtils.simpleBaseToBaseIndex(b2)][j] = log10PofObservingBaseGivenChromosome(b1,b2,j);
}
}
}
}
/**
*
* @param observedBase observed base
* @param chromBase target base
* @param qual base quality
* @return log10 likelihood
*/
private static double log10PofObservingBaseGivenChromosome(byte observedBase, byte chromBase, byte qual) {
final double log10_3 = log10(3.0);
double logP;
if ( observedBase == chromBase ) {
// the base is consistent with the chromosome -- it's 1 - e
//logP = oneMinusData[qual];
double e = pow(10, (qual / -10.0));
logP = log10(1.0 - e);
} else {
// the base is inconsistent with the chromosome -- it's e * P(chromBase | observedBase is an error)
logP = qual / -10.0 + (-log10_3);
}
//System.out.printf("%c %c %d => %f%n", observedBase, chromBase, qual, logP);
return logP;
}
}