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Clarify VQSR inputs documentation

This commit is contained in:
Geraldine Van der Auwera 2016-03-11 12:47:13 -05:00
parent eeccc95a7a
commit c81d5b898e
1 changed files with 21 additions and 14 deletions
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35
protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/variantrecalibration/VariantRecalibrator.java
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@ -88,15 +88,7 @@ import Jama.Matrix;
* Build a recalibration model to score variant quality for filtering purposes
*
* <p>
* This tool performs the first pass in a two-stage process called VQSR; the second pass is performed by the
* <a href='https://www.broadinstitute.org/gatk/guide/tooldocs/org_broadinstitute_gatk_tools_walkers_variantrecalibration_ApplyRecalibration.php'>ApplyRecalibration</a> tool.
* In brief, the first pass consists of creating a Gaussian mixture model by looking at the distribution of annotation
* values over a high quality subset of the input call set, and then scoring all input variants according to the model.
* The second pass consists of filtering variants based on score cutoffs identified in the first pass.
*</p>
*
* <p>
* The purpose of the variant recalibrator is to assign a well-calibrated probability to each variant call in a call set.
* The purpose of variant recalibration is to assign a well-calibrated probability to each variant call in a call set.
* You can then create highly accurate call sets by filtering based on this single estimate for the accuracy of each call.
* The approach taken by variant quality score recalibration is to develop a continuous, covarying estimate of the relationship
* between SNP call annotations (such as QD, MQ, and ReadPosRankSum, for example) and the probability that a SNP is a true genetic
@ -106,6 +98,14 @@ import Jama.Matrix;
* probability that each call is real. The score that gets added to the INFO field of each variant is called the VQSLOD. It is
* the log odds of being a true variant versus being false under the trained Gaussian mixture model.
* </p>
*
* <p>
* This tool performs the first pass in a two-stage process called VQSR; the second pass is performed by the
* <a href='https://www.broadinstitute.org/gatk/guide/tooldocs/org_broadinstitute_gatk_tools_walkers_variantrecalibration_ApplyRecalibration.php'>ApplyRecalibration</a> tool.
* In brief, the first pass consists of creating a Gaussian mixture model by looking at the distribution of annotation
* values over a high quality subset of the input call set, and then scoring all input variants according to the model.
* The second pass consists of filtering variants based on score cutoffs identified in the first pass.
*</p>
*
* <p>VQSR is probably the hardest part of the Best Practices to get right, so be sure to read the
* <a href='https://www.broadinstitute.org/gatk/guide/article?id=39'>method documentation</a>,
@ -115,7 +115,10 @@ import Jama.Matrix;
*
* <h3>Inputs</h3>
* <ul>
* <li>The input raw variants to be recalibrated.</li>
* <li>The input raw variants to be recalibrated. These variant calls must be annotated with the annotations that will be used for modeling.
* If the calls come from multiple samples, they must have been obtained by joint calling the samples, either directly (running HaplotypeCaller
* on all samples together) or via the GVCF workflow (HaplotypeCaller with -ERC GVCF per-sample then GenotypeGVCFs on the resulting gVCFs)
* which is more scalable.</li>
* <li>Known, truth, and training sets to be used by the algorithm. See the method documentation for more details.</li>
* </ul>
*
@ -135,7 +138,7 @@ import Jama.Matrix;
* -resource:hapmap,known=false,training=true,truth=true,prior=15.0 hapmap_3.3.b37.sites.vcf \
* -resource:omni,known=false,training=true,truth=false,prior=12.0 1000G_omni2.5.b37.sites.vcf \
* -resource:1000G,known=false,training=true,truth=false,prior=10.0 1000G_phase1.snps.high_confidence.vcf
* -resource:dbsnp,known=true,training=false,truth=false,prior=6.0 dbsnp_135.b37.vcf \
* -resource:dbsnp,known=true,training=false,truth=false,prior=2.0 dbsnp_135.b37.vcf \
* -an QD -an MQ -an MQRankSum -an ReadPosRankSum -an FS -an SOR -an InbreedingCoeff \
* -mode SNP \
* -recalFile output.recal \
@ -153,7 +156,7 @@ import Jama.Matrix;
* -resource:hapmap,known=false,training=true,truth=true,prior=15.0 hapmap_3.3.b37.sites.vcf \
* -resource:omni,known=false,training=true,truth=false,prior=12.0 1000G_omni2.5.b37.sites.vcf \
* -resource:1000G,known=false,training=true,truth=false,prior=10.0 1000G_phase1.snps.high_confidence.vcf
* -resource:dbsnp,known=true,training=false,truth=false,prior=6.0 dbsnp_135.b37.vcf \
* -resource:dbsnp,known=true,training=false,truth=false,prior=2.0 dbsnp_135.b37.vcf \
* -an QD -an MQ -an MQRankSum -an ReadPosRankSum -an FS -an SOR -an InbreedingCoeff \
* -mode SNP \
* -recalFile output.AS.recal \
@ -191,9 +194,13 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
// Inputs
/////////////////////////////
/**
* These calls should be unfiltered and annotated with the error covariates that are intended to be used for modeling.
* These variant calls must be annotated with the annotations that will be used for modeling. If the calls come from multiple samples,
* they must have been obtained by joint calling the samples, either directly (running HaplotypeCaller on all samples together) or
* via the GVCF workflow (HaplotypeCaller with -ERC GVCF per-sample then GenotypeGVCFs on the resulting gVCFs) which is more scalable.
* Note that the ability to pass multiple input files is only intended to facilitate scatter-gather parallelism (to enable e.g. running on VCFs
* generated per-chromosome), not to combine different callsets. The variant calls in the separate input files should not overlap.
*/
@Input(fullName="input", shortName = "input", doc="The raw input variants to be recalibrated", required=true)
@Input(fullName="input", shortName = "input", doc="One or more VCFs of raw input variants to be recalibrated", required=true)
public List<RodBindingCollection<VariantContext>> inputCollections;
final private List<RodBinding<VariantContext>> input = new ArrayList<>();