- * Using the tranche file generated by the previous step the ApplyRecalibration walker looks at each variant's VQSLOD value + * This tool performs the second pass in a two-stage process called VQSR; the first pass is performed by the + * VariantRecalibrator 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. + *
+ * + *+ * Using the tranche file and recalibration table generated by the previous step, the ApplyRecalibration tool looks at each variant's VQSLOD value * and decides which tranche it falls in. Variants in tranches that fall below the specified truth sensitivity filter level - * have their filter field annotated with its tranche level. This will result in a call set that simultaneously is filtered - * to the desired level but also has the information necessary to pull out more variants for a higher sensitivity but a - * slightly lower quality level. + * have their FILTER field annotated with the corresponding tranche level. This will result in a call set that is filtered + * to the desired level but retains the information necessary to increase sensitivity if needed.
+ * + *To be clear, please note that by "filtered", we mean that variants failing the requested tranche cutoff are marked + * as filtered in the output VCF; they are not discarded.
+ * + *VQSR is probably the hardest part of the Best Practices to get right, so be sure to read the + * method documentation, + * parameter recommendations and + * tutorial to really understand what these + * tools and how to use them for best results on your own data.
* *- * The input raw variants to be recalibrated. - *
- * The recalibration table file in VCF format that was generated by the VariantRecalibrator walker. - *
- * The tranches file that was generated by the VariantRecalibrator walker. + *
- * A recalibrated VCF file in which each variant is annotated with its VQSLOD and filtered if the score is below the desired quality level. + *
* java -Xmx3g -jar GenomeAnalysisTK.jar \ * -T ApplyRecalibration \ @@ -108,6 +124,16 @@ import java.util.*; * -o path/to/output.recalibrated.filtered.vcf ** + *
- * This walker is the first pass in a two-stage processing step. This walker is designed to be used in conjunction with the ApplyRecalibration walker. + * This tool performs the first pass in a two-stage process called VQSR; the second pass is performed by the + * ApplyRecalibration 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. *
* ** The purpose of the variant recalibrator 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 (QD, MQ, HaplotypeScore, and ReadPosRankSum, for example) and the probability that a SNP is a true genetic + * between SNP call annotations (such as QD, MQ, and ReadPosRankSum, for example) and the probability that a SNP is a true genetic * variant versus a sequencing or data processing artifact. This model is determined adaptively based on "true sites" provided - * as input, typically HapMap 3 sites and those sites found to be polymorphic on the Omni 2.5M SNP chip array. This adaptive + * as input, typically HapMap 3 sites and those sites found to be polymorphic on the Omni 2.5M SNP chip array (in humans). This adaptive * error model can then be applied to both known and novel variation discovered in the call set of interest to evaluate the * 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 ratio of being a true variant versus being false under the trained Gaussian mixture model. *
* + *VQSR is probably the hardest part of the Best Practices to get right, so be sure to read the + * method documentation, + * parameter recommendations and + * tutorial to really understand what these + * tools and how to use them for best results on your own data.
+ * *- * The input raw variants to be recalibrated. - *
- * Known, truth, and training sets to be used by the algorithm. How these various sets are used is described below. + *
- * A recalibration table file in VCF format that is used by the ApplyRecalibration walker. - *
- * A tranches file which shows various metrics of the recalibration callset as a function of making several slices through the data. + *
* java -Xmx4g -jar GenomeAnalysisTK.jar \ * -T VariantRecalibrator \ @@ -118,22 +128,25 @@ import java.util.*; * -input NA12878.HiSeq.WGS.bwa.cleaned.raw.subset.b37.vcf \ * -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 \ - * -an QD -an HaplotypeScore -an MQRankSum -an ReadPosRankSum -an FS -an MQ -an InbreedingCoeff \ + * -an QD -an MQ -an MQRankSum -an ReadPosRankSum -an FS -an SOR -an InbreedingCoeff \ * -mode SNP \ * -recalFile path/to/output.recal \ * -tranchesFile path/to/output.tranches \ * -rscriptFile path/to/output.plots.R ** - *