diff --git a/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/engine/arguments/GenotypeCalculationArgumentCollection.java b/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/engine/arguments/GenotypeCalculationArgumentCollection.java
index c74a3b751..ed639c951 100644
--- a/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/engine/arguments/GenotypeCalculationArgumentCollection.java
+++ b/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/engine/arguments/GenotypeCalculationArgumentCollection.java
@@ -66,39 +66,23 @@ public class GenotypeCalculationArgumentCollection implements Cloneable{
public static final String MAX_ALTERNATE_ALLELES_SHORT_NAME = "maxAltAlleles";
/**
- * Depending on the value of the --max_alternate_alleles argument, we may genotype only a fraction of the alleles being sent on for genotyping.
- * Using this argument instructs the genotyper to annotate (in the INFO field) the number of alternate alleles that were originally discovered at the site.
+ * Depending on the value of the --max_alternate_alleles argument, we may genotype only a fraction of the alleles
+ * being sent on for genotyping. Using this argument instructs the genotyper to annotate (in the INFO field) the
+ * number of alternate alleles that were originally discovered (but not necessarily genotyped) at the site.
*/
- @Argument(fullName = "annotateNDA", shortName = "nda", doc = "If provided, we will annotate records with the number of alternate alleles that were discovered (but not necessarily genotyped) at a given site", required = false)
+ @Argument(fullName = "annotateNDA", shortName = "nda", doc = "Annotate number of alleles observed", required = false)
public boolean ANNOTATE_NUMBER_OF_ALLELES_DISCOVERED = false;
/**
- * Use the new allele frequency / QUAL score model
+ * This activates a model for calculating QUAL that was introduced in version 3.7 (November 2016). We expect this
+ * model will become the default in future versions.
*/
- @Argument(fullName = "useNewAFCalculator", shortName = "newQual", doc = "If provided, we will use the new AF model instead of the so-called exact model", required = false)
+ @Argument(fullName = "useNewAFCalculator", shortName = "newQual", doc = "Use new AF model instead of the so-called exact model", required = false)
public boolean USE_NEW_AF_CALCULATOR = false;
/**
- * The expected heterozygosity value used to compute prior probability that a locus is non-reference.
- *
- * From the heterozygosity we calculate the probability of N samples being hom-ref at a site as 1 - sum_i_2N (hets / i)
- * where hets is this case is analogous to the parameter theta from population genetics. See https://en.wikipedia.org/wiki/Coalescent_theory for more details.
- *
- * Note that heterozygosity as used here is the population genetics concept. (See http://en.wikipedia.org/wiki/Zygosity#Heterozygosity_in_population_genetics.
- * We also suggest the book "Population Genetics: A Concise Guide" by John H. Gillespie for further details on the theory.) That is, a hets value of 0.001
- * implies that two randomly chosen chromosomes from the population of organisms would differ from each other at a rate of 1 in 1000 bp.
- *
- * The default priors provided for humans (hets = 1e-3)
- *
- * Also note that this quantity has nothing to do with the likelihood of any given sample having a heterozygous genotype,
- * which in the GATK is purely determined by the probability of the observed data P(D | AB) under the model that there
- * may be a AB het genotype. The posterior probability of this AB genotype would use the het prior, but the GATK
- * only uses this posterior probability in determining the prob. that a site is polymorphic. So changing the
- * het parameters only increases the chance that a site will be called non-reference across all samples, but
- * doesn't actually change the output genotype likelihoods at all, as these aren't posterior probabilities at all.
- *
- * The quantity that changes whether the GATK considers the possibility of a het genotype at all is the ploidy,
- * which determines how many chromosomes each individual in the species carries.
+ * The expected heterozygosity value used to compute prior probability that a locus is non-reference. See
+ * https://software.broadinstitute.org/gatk/documentation/article?id=8603 for more details.
*/
@Argument(fullName = "heterozygosity", shortName = "hets", doc = "Heterozygosity value used to compute prior likelihoods for any locus", required = false)
public Double snpHeterozygosity = HomoSapiensConstants.SNP_HETEROZYGOSITY;
@@ -110,8 +94,8 @@ public class GenotypeCalculationArgumentCollection implements Cloneable{
public double indelHeterozygosity = HomoSapiensConstants.INDEL_HETEROZYGOSITY;
/**
- * The standard deviation of the distribution of alt allele fractions. The above heterozygosity parameters give the
- * *mean* of this distribution; this parameter gives its spread.
+ * The standard deviation of the distribution of alt allele fractions. The above heterozygosity parameters give
+ * the *mean* of this distribution; this parameter gives its spread.
*/
@Argument(fullName = "heterozygosity_stdev", shortName = "heterozygosityStandardDeviation", doc = "Standard deviation of eterozygosity for SNP and indel calling.", required = false)
public double heterozygosityStandardDeviation = 0.01;
@@ -134,10 +118,11 @@ public class GenotypeCalculationArgumentCollection implements Cloneable{
public double STANDARD_CONFIDENCE_FOR_EMITTING = 30.0;
/**
- * If there are more than this number of alternate alleles presented to the genotyper (either through discovery or GENOTYPE_GIVEN_ALLELES),
- * then only this many alleles will be used. Note that genotyping sites with many alternate alleles is both CPU and memory intensive and it
- * scales exponentially based on the number of alternate alleles. Unless there is a good reason to change the default value, we highly recommend
- * that you not play around with this parameter.
+ * If there are more than this number of alternate alleles presented to the genotyper (either through discovery or
+ * GENOTYPE_GIVEN_ALLELES), then only this many alleles will be used. Note that genotyping sites with many
+ * alternate alleles is both CPU and memory intensive and it scales exponentially based on the number of alternate
+ * alleles. Unless there is a good reason to change the default value, we highly recommend that you not play around
+ * with this parameter.
*
* See also {@link #MAX_GENOTYPE_COUNT}.
*/
@@ -146,19 +131,23 @@ public class GenotypeCalculationArgumentCollection implements Cloneable{
public int MAX_ALTERNATE_ALLELES = 6;
/**
- * If there are more than this number of genotypes at a locus presented to the genotyper, then only this many genotypes will be used.
- * The possible genotypes are simply different ways of partitioning alleles given a specific ploidy asumption.
- * Therefore, we remove genotypes from consideration by removing alternate alleles that are the least well supported.
- * The estimate of allele support is based on the ranking of the candidate haplotypes coming out of the graph building step.
- * Note that the reference allele is always kept.
+ * If there are more than this number of genotypes at a locus presented to the genotyper, then only this many
+ * genotypes will be used. This is intended to deal with sites where the combination of high ploidy and high alt
+ * allele count can lead to an explosion in the number of possible genotypes, with extreme adverse effects on
+ * runtime performance.
*
- * Note that genotyping sites with large genotype counts is both CPU and memory intensive.
- * Unless there is a good reason to change the default value, we highly recommend that you not play around with this parameter.
+ * How does it work? The possible genotypes are simply different ways of partitioning alleles given a specific
+ * ploidy assumption. Therefore, we remove genotypes from consideration by removing alternate alleles that are the
+ * least well supported. The estimate of allele support is based on the ranking of the candidate haplotypes coming
+ * out of the graph building step. Note however that the reference allele is always kept.
*
* The maximum number of alternative alleles used in the genotyping step will be the lesser of the two:
* 1. the largest number of alt alleles, given ploidy, that yields a genotype count no higher than {@link #MAX_GENOTYPE_COUNT}
* 2. the value of {@link #MAX_ALTERNATE_ALLELES}
*
+ * As noted above, genotyping sites with large genotype counts is both CPU and memory intensive. Unless you have
+ * a good reason to change the default value, we highly recommend that you not play around with this parameter.
+ *
* See also {@link #MAX_ALTERNATE_ALLELES}.
*/
@Advanced
@@ -175,23 +164,19 @@ public class GenotypeCalculationArgumentCollection implements Cloneable{
public int MAX_NUM_PL_VALUES = AFCalculator.MAX_NUM_PL_VALUES_DEFAULT;
/**
- * By default, the prior specified with the argument --heterozygosity/-hets is used for variant discovery at a particular locus, using an infinite sites model,
- * see e.g. Waterson (1975) or Tajima (1996).
- * This model asserts that the probability of having a population of k variant sites in N chromosomes is proportional to theta/k, for 1=1:N
+ * By default, the prior specified with the argument --heterozygosity/-hets is used for variant discovery at a
+ * particular locus, using an infinite sites model (see e.g. Waterson, 1975 or Tajima, 1996). This model asserts that
+ * the probability of having a population of k variant sites in N chromosomes is proportional to theta/k, for 1=1:N.
+ * However, there are instances where using this prior might not be desirable, e.g. for population studies where prior
+ * might not be appropriate, as for example when the ancestral status of the reference allele is not known.
*
- * There are instances where using this prior might not be desirable, e.g. for population studies where prior might not be appropriate,
- * as for example when the ancestral status of the reference allele is not known.
- * By using this argument, the user can manually specify a list of probabilities for each AC>1 to be used as priors for genotyping,
- * with the following restrictions:
- * a) User must specify 2N values, where N is the number of samples.
- * b) Only diploid calls supported.
- * c) Probability values are specified in Double format, in linear space (not log10 space or Phred-scale).
- * d) No negative values allowed.
- * e) Values will be added and Pr(AC=0) will be 1-sum, so that they sum up to one.
- * f) If user-defined values add to more than one, an error will be produced.
+ * This argument allows you to manually specify a list of probabilities for each AC>1 to be used as
+ * priors for genotyping, with the following restrictions: only diploid calls are supported; you must specify 2 *
+ * N values where N is the number of samples; probability values must be positive and specified in Double format,
+ * in linear space (not log10 space nor Phred-scale); and all values must sume to 1.
*
- * If user wants completely flat priors, then user should specify the same value (=1/(2*N+1)) 2*N times,e.g.
- * -inputPrior 0.33 -inputPrior 0.33
+ * For completely flat priors, specify the same value (=1/(2*N+1)) 2*N times, e.g.
+ * -inputPrior 0.33 -inputPrior 0.33
* for the single-sample diploid case.
*/
@Advanced
@@ -199,9 +184,10 @@ public class GenotypeCalculationArgumentCollection implements Cloneable{
public List The ideal result is a value close to zero, which indicates there is little to no difference. A negative value indicates that the bases supporting the alternate allele have lower quality scores than those supporting the reference allele. Conversely, a positive value indicates that the bases supporting the alternate allele have higher quality scores than those supporting the reference allele. Finding a statistically significant difference either way suggests that the sequencing process may have been biased or affected by an artifact. The value output for this annotation is the u-based z-approximation from the Mann-Whitney-Wilcoxon Rank Sum Test for base qualities (bases supporting REF vs. bases supporting ALT). See the method document on statistical tests for a more detailed explanation of the ranksum test. The value output for this annotation is the u-based z-approximation from the Mann-Whitney-Wilcoxon Rank Sum Test for base qualities (bases supporting REF vs. bases supporting ALT). See the method document on statistical tests for a more detailed explanation of the ranksum test.
- * This annotation tests whether the insert sizes of reads supporting the REF allele and ALT allele are roughly equal.
- * In case of multiple alternate alleles, each alternate allele is considered separately.
+ * This variant-level annotation compares the insert sizes of reads supporting the reference allele with those supporting each alternate allele. To be clear, it does so separately for each alternate allele. The ideal result is a value close to zero, which indicates there is little to no difference. A negative value indicates that the reads supporting the alternate allele are associated with smaller insert sizes than those supporting the reference allele. Conversely, a positive value indicates that reads supporting the alternate allele are associated with larger insert sizes than those supporting the reference allele. Finding a statistically significant difference either way suggests that the sequencing process may have been biased or affected by an artifact.Statistical notes
- * Caveats
*
diff --git a/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/annotator/AS_InsertSizeRankSum.java b/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/annotator/AS_InsertSizeRankSum.java
index 4d7db9ba9..cb9afcfe5 100644
--- a/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/annotator/AS_InsertSizeRankSum.java
+++ b/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/annotator/AS_InsertSizeRankSum.java
@@ -63,13 +63,19 @@ import java.util.List;
/**
* Allele specific Rank Sum Test for insert sizes of REF versus ALT reads
*
- *
See the method document for a more detailed explanation of the rank sum test.
+ *The value output for this annotation is the u-based z-approximation from the Mann-Whitney-Wilcoxon Rank Sum Test for insert sizes (insert sizes of reads supporting REF vs. insert sizes of reads supporting ALT). See the method document on statistical tests for a more detailed explanation of the ranksum test.
+ * + *This annotation can be used to evaluate confidence in a variant call and could be used as a covariate for variant recalibration (VQSR). Finding a statistically significant difference in quality either way suggests that the sequencing and/or mapping process may have been biased or affected by an artifact. In practice, we only filter out low negative values when evaluating variant quality because the idea is to filter out variants for which the quality of the data supporting the alternate allele is comparatively low. The reverse case, where it is the quality of data supporting the reference allele that is lower (resulting in positive ranksum scores), is not really informative for filtering variants.
* *The value output for this annotation is the u-based z-approximation from the Mann-Whitney-Wilcoxon Rank Sum Test for mapping qualities of the read's mate See the method document on statistical tests for a more detailed explanation of the ranksum test.
+ *The value output for this annotation is the u-based z-approximation from the Mann-Whitney-Wilcoxon Rank Sum Test for mapping qualities of the read's mate See the method document on statistical tests for a more detailed explanation of the ranksum test.
* * *Finding a statistically significant difference in quality either way suggests that the sequencing and/or mapping process may have been biased or affected by an artifact. In practice, we only filter out low negative values when evaluating variant quality because the idea is to filter out variants for which the quality of the data supporting the alternate allele is comparatively low. The reverse case, where it is the quality of data supporting the reference allele that is lower (resulting in positive ranksum scores), is not really informative for filtering variants. * *
The value output for this annotation is the u-based z-approximation from the Mann-Whitney-Wilcoxon Rank Sum Test for mapping qualities (MAPQ of reads supporting REF vs. MAPQ of reads supporting ALT). See the method document on statistical tests for a more detailed explanation of the ranksum test.
+ *The value output for this annotation is the u-based z-approximation from the Mann-Whitney-Wilcoxon Rank Sum Test for mapping qualities (MAPQ of reads supporting REF vs. MAPQ of reads supporting ALT). See the method document on statistical tests for a more detailed explanation of the ranksum test.
* *This annotation can be used to evaluate confidence in a variant call and is a recommended covariate for variant recalibration (VQSR). Finding a statistically significant difference in relative position either way suggests that the sequencing process may have been biased or affected by an artifact. In practice, we only filter out low negative values when evaluating variant quality because the idea is to filter out variants for which the quality of the data supporting the alternate allele is comparatively low. The reverse case, where it is the quality of data supporting the reference allele that is lower (resulting in positive ranksum scores), is not really informative for filtering variants.
* *The value output for this annotation is the u-based z-approximation from the Mann-Whitney-Wilcoxon Rank Sum Test for site position within reads (position within reads supporting REF vs. position within reads supporting ALT). See the method document on statistical tests for a more detailed explanation of the ranksum test.
+ *The value output for this annotation is the u-based z-approximation from the Mann-Whitney-Wilcoxon Rank Sum Test for site position within reads (position within reads supporting REF vs. position within reads supporting ALT). See the method document on statistical tests for a more detailed explanation of the ranksum test.
* *The ideal result is a value close to zero, which indicates there is little to no difference. A negative value indicates that the bases supporting the alternate allele have lower quality scores than those supporting the reference allele. Conversely, a positive value indicates that the bases supporting the alternate allele have higher quality scores than those supporting the reference allele. Finding a statistically significant difference either way suggests that the sequencing process may have been biased or affected by an artifact.
* *The value output for this annotation is the u-based z-approximation from the Mann-Whitney-Wilcoxon Rank Sum Test for base qualities (bases supporting REF vs. bases supporting ALT). See the method document on statistical tests for a more detailed explanation of the ranksum test.
+ *The value output for this annotation is the u-based z-approximation from the Mann-Whitney-Wilcoxon Rank Sum Test for base qualities (bases supporting REF vs. bases supporting ALT). See the method document on statistical tests for a more detailed explanation of the ranksum test.
* *This variant-level annotation tests whether the data supporting the reference allele shows more or less base clipping (hard clips) than those supporting the alternate allele. The ideal result is a value close to zero, which indicates there is little to no difference. A negative value indicates that the reads supporting the alternate allele have more hard-clipped bases than those supporting the reference allele. Conversely, a positive value indicates that the reads supporting the alternate allele have fewer hard-clipped bases than those supporting the reference allele. Finding a statistically significant difference either way suggests that the sequencing and/or mapping process may have been biased or affected by an artifact.
* *The value output for this annotation is the u-based z-approximation from the Mann-Whitney-Wilcoxon Rank Sum Test applied to base clips (number of hard-clipped bases on reads supporting REF vs. number of hard-clipped bases on reads supporting ALT). See the method document on statistical tests for a more detailed explanation of the ranksum test.
+ *The value output for this annotation is the u-based z-approximation from the Mann-Whitney-Wilcoxon Rank Sum Test applied to base clips (number of hard-clipped bases on reads supporting REF vs. number of hard-clipped bases on reads supporting ALT). See the method document on statistical tests for a more detailed explanation of the ranksum test.
* *The clipping rank sum test cannot be calculated for sites without a mixture of reads showing both the reference and alternate alleles.
diff --git a/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/annotator/FisherStrand.java b/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/annotator/FisherStrand.java index d737f3d8b..5cfddd1da 100644 --- a/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/annotator/FisherStrand.java +++ b/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/annotator/FisherStrand.java @@ -75,7 +75,7 @@ import java.util.*; *The output is a Phred-scaled p-value. The higher the output value, the more likely there is to be bias. More bias is indicative of false positive calls.
* *See the method document on statistical tests for a more detailed explanation of this application of Fisher's Exact Test.
+ *See the method document on statistical tests for a more detailed explanation of this application of Fisher's Exact Test.
* *This annotation estimates whether there is evidence of inbreeding in a population. The higher the score, the higher the chance that there is inbreeding.
* *The calculation is a continuous generalization of the Hardy-Weinberg test for disequilibrium that works well with limited coverage per sample. The output is the F statistic from running the HW test for disequilibrium with PL values. See the method document on statistical tests for a more detailed explanation of this statistical test.
+ *The calculation is a continuous generalization of the Hardy-Weinberg test for disequilibrium that works well with limited coverage per sample. The output is the F statistic from running the HW test for disequilibrium with PL values. See the method document on statistical tests for a more detailed explanation of this statistical test.
* *This variant-level annotation compares the likelihoods of reads to their best haplotype match, between reads that support the reference allele and those that support the alternate allele. The ideal result is a value close to zero, which indicates there is little to no difference. A negative value indicates that the reads supporting the alternate allele have lower likelihoods to their best haplotype match than those supporting the reference allele. Conversely, a positive value indicates that the reads supporting the alternate allele have higher likelihoods to their best haplotype match than those supporting the reference allele. Finding a statistically significant difference either way suggests that the sequencing and/or mapping process may have been biased or affected by an artifact.
* *The value output for this annotation is the u-based z-approximation from the Mann-Whitney-Wilcoxon Rank Sum Test for per-read likelihoods to the best haplotype match (likelihoods of reads supporting REF vs. likelihoods of reads supporting ALT). See the method document on statistical tests for a more detailed explanation of the ranksum test.
+ *The value output for this annotation is the u-based z-approximation from the Mann-Whitney-Wilcoxon Rank Sum Test for per-read likelihoods to the best haplotype match (likelihoods of reads supporting REF vs. likelihoods of reads supporting ALT). See the method document on statistical tests for a more detailed explanation of the ranksum test.
* *The read position rank sum test can not be calculated for sites without a mixture of reads showing both the reference and alternate alleles.
diff --git a/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/annotator/MappingQualityRankSumTest.java b/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/annotator/MappingQualityRankSumTest.java index 7a8554eea..6fd308e5c 100644 --- a/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/annotator/MappingQualityRankSumTest.java +++ b/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/annotator/MappingQualityRankSumTest.java @@ -68,7 +68,7 @@ import java.util.*; *This annotation can be used to evaluate confidence in a variant call and is a recommended covariate for variant recalibration (VQSR). Finding a statistically significant difference in quality either way suggests that the sequencing and/or mapping process may have been biased or affected by an artifact. In practice, we only filter out low negative values when evaluating variant quality because the idea is to filter out variants for which the quality of the data supporting the alternate allele is comparatively low. The reverse case, where it is the quality of data supporting the reference allele that is lower (resulting in positive ranksum scores), is not really informative for filtering variants. * *
The value output for this annotation is the u-based z-approximation from the Mann-Whitney-Wilcoxon Rank Sum Test for mapping qualities (MAPQ of reads supporting REF vs. MAPQ of reads supporting ALT). See the method document on statistical tests for a more detailed explanation of the ranksum test.
+ *The value output for this annotation is the u-based z-approximation from the Mann-Whitney-Wilcoxon Rank Sum Test for mapping qualities (MAPQ of reads supporting REF vs. MAPQ of reads supporting ALT). See the method document on statistical tests for a more detailed explanation of the ranksum test.
* *This annotation can be used to evaluate confidence in a variant call and is a recommended covariate for variant recalibration (VQSR). Finding a statistically significant difference in relative position either way suggests that the sequencing process may have been biased or affected by an artifact. In practice, we only filter out low negative values when evaluating variant quality because the idea is to filter out variants for which the quality of the data supporting the alternate allele is comparatively low. The reverse case, where it is the quality of data supporting the reference allele that is lower (resulting in positive ranksum scores), is not really informative for filtering variants.
* *The value output for this annotation is the u-based z-approximation from the Mann-Whitney-Wilcoxon Rank Sum Test for site position within reads (position within reads supporting REF vs. position within reads supporting ALT). See the method document on statistical tests for a more detailed explanation of the ranksum test.
+ *The value output for this annotation is the u-based z-approximation from the Mann-Whitney-Wilcoxon Rank Sum Test for site position within reads (position within reads supporting REF vs. position within reads supporting ALT). See the method document on statistical tests for a more detailed explanation of the ranksum test.
* *and its inverse:
* - *| + strand | - strand | |
| REF; | X[0][0] | X[0][1] |
| ALT; | X[1][0] | X[1][1] |
The sum R + 1/R is used to detect a difference in strand bias for REF and for ALT (the sum makes it symmetric). A high value is indicative of large difference where one entry is very small compared to the others. A scale factor of refRatio/altRatio where
* - * $$ refRatio = \frac{max(X[0][0], X[0][1])}{min(X[0][0], X[0][1} $$ + * $$ refRatio = \frac{min(X[0][0], X[0][1])}{max(X[0][0], X[0][1} $$ * *and
* - * $$ altRatio = \frac{max(X[1][0], X[1][1])}{min(X[1][0], X[1][1]} $$ + * $$ altRatio = \frac{min(X[1][0], X[1][1])}{max(X[1][0], X[1][1]} $$ * *ensures that the annotation value is large only.
* - *See the method document on statistical tests for a more detailed explanation of this statistical test.
- * ** The name SOR is not entirely appropriate because the implementation was changed somewhere between the start of development and release of this annotation. Now SOR isn't really an odds ratio anymore. The goal was to separate certain cases of data without penalizing variants that occur at the ends of exons because they tend to only be covered by reads in one direction (depending on which end of the exon they're on), so if a variant has 10 ref reads in the + direction, 1 ref read in the - direction, 9 alt reads in the + direction and 2 alt reads in the - direction, it's actually not strand biased, but the FS score is pretty bad. The implementation that resulted derived in part from empirically testing some read count tables of various sizes with various ratios and deciding from there.
diff --git a/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/cancer/contamination/ContEst.java b/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/cancer/contamination/ContEst.java index a0639181f..4e33b4fa3 100755 --- a/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/cancer/contamination/ContEst.java +++ b/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/cancer/contamination/ContEst.java @@ -197,29 +197,29 @@ public class ContEst extends RodWalker