zzh
/
gatk-3.8
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
gatk-3.8/scala/qscript/oneoffs/rpoplin/VQSR_parameterSearch.scala

363 lines
18 KiB
Scala
Raw Normal View History

Another relevant qscript. I use this one to do thousands of variant recalibration jobs to search for optimal parameters. git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@5019 348d0f76-0448-11de-a6fe-93d51630548a
2011-01-20 02:17:32 +08:00
import org.broadinstitute.sting.queue.extensions.gatk._
import org.broadinstitute.sting.queue.extensions.samtools.SamtoolsIndexFunction
import org.broadinstitute.sting.queue.QScript
import org.apache.commons.io.FilenameUtils;
class VQSR_parameterSearch extends QScript {
qscript =>
@Argument(shortName="gatk", doc="gatk jar file", required=true)
var gatkJarFile: File = _
@Argument(shortName="experiment", doc="experiment number", required=true)
var experiment: String = "0000"
@Argument(shortName="outputDir", doc="output directory", required=true)
var outputDir: String = "./"
@Argument(shortName="skipCalling", doc="If true, skip the calling part of the pipeline and only run VQSR on preset, gold standard VCF files", required=false)
var skipCalling: Boolean = false
Walkers can now specify a class extending from Gatherer to merge custom output formats. Add @Gather(MyGatherer.class) to the walker @Output. JavaCommandLineFunctions can now specify the classpath+mainclass as an alternative to specifying a path to an executable jar. JCLF by default pass on the current classpath and only require the mainclass be specified by the developer extending the JCLF, relieving the QScript author from having to explicitly specify the jar. Like the Picard MergeSamFiles, GATK engine by default is now run from the current classpath. The GATK can still be overridden via .jarFile or .javaClasspath. Walkers from the GATK package are now also embedded into the Queue package. Updated AnalyzeCovariates to make it easier to guess the main class, AnalyzeCovariates instead of AnalyzeCovariatesCLP. Removed the GATK jar argument from the example QScripts. Removed one of the most FAQ when getting started with Scala/Queue, the use of Option[_] in QScripts: 1) Fixed mistaken assumption with java enums. In java enums can be null so they don't need nullable wrappers. 2) Added syntactic sugar for Nullable primitives to the QScript trait. Any variable defined as Option[Int] can just be assigned an Int value or None, ex: myFunc.memoryLimit = 3 Removed other unused code. Re-fixed dry run function ordering. Re-ordered the QCommandline companion object so that IntelliJ doesn't complain about missing main methods. git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@5504 348d0f76-0448-11de-a6fe-93d51630548a
2011-03-24 22:03:51 +08:00
trait UNIVERSAL_GATK_ARGS extends CommandLineGATK { logging_level = "INFO"; jarFile = gatkJarFile; memoryLimit = 2; }
Another relevant qscript. I use this one to do thousands of variant recalibration jobs to search for optimal parameters. git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@5019 348d0f76-0448-11de-a6fe-93d51630548a
2011-01-20 02:17:32 +08:00
class Target(val baseName: String, val reference: File, val rodName: String, val bamList: File, val goldStandard_VCF: File, val intervals: String, val titvTarget: Double, val isLowpass: Boolean) {
def name = qscript.outputDir + baseName
def clusterFile = new File(name + ".clusters")
def rawVCF = new File(name + ".raw.vcf")
def filteredVCF = new File(name + ".filtered.vcf")
def goldStandardName = qscript.outputDir + "goldStandard/" + baseName
var goldStandardClusterFile: File = new File("")
var gaussian: Int = 1
var shrinkage: Double = 1.0
var dirichlet: Double = 1.0
var backoff: Double = 1.0
var qualCutoff: Int = 1
var std: Double = 1.0
var useQD: Int = 1
var useSB: Int = 1
var useHS: Int = 1
var useHRUN: Int = 1
var useMQRST: Int = 1
var useBQRST: Int = 1
var useGC: Int = 1
var useMQ: Int = 1
var useSumGL: Int = 1
var trainOmni: Int = 1
}
val hg18 = new File("/seq/references/Homo_sapiens_assembly18/v0/Homo_sapiens_assembly18.fasta")
val b36 = new File("/humgen/1kg/reference/human_b36_both.fasta")
val b37 = new File("/humgen/1kg/reference/human_g1k_v37.fasta")
// ToDos:
// reduce the scope of the datasets so the script is more nimble
// figure out how to give names to all the Queue-LSF logs (other than Q-1931@node1434-24.out) so that it is easier to find logs for certain steps
// create gold standard BAQ'd bam files, no reason to always do it on the fly
// Analysis to add at the end of the script:
// auto generation of the cluster plots
// spike in NA12878 to the exomes and to the lowpass, analysis of how much of her variants are being recovered compared to single sample exome or HiSeq calls
// produce Kiran's Venn plots based on comparison between new VCF and gold standard produced VCF
// Define the target datasets here
def lowPass = true
val HiSeq = new Target("NA12878.HiSeq", hg18, "hg18", // BUGBUG: cut down to chr1
new File("/humgen/gsa-hpprojects/NA12878Collection/bams/NA12878.HiSeq.WGS.bwa.cleaned.recal.bam"),
new File("/home/radon01/depristo/work/oneOffProjects/1000GenomesProcessingPaper/wgs.v13/HiSeq.WGS.cleaned.ug.snpfiltered.indelfiltered.vcf"),
"/humgen/1kg/processing/pipeline_test_bams/whole_genome_chunked.hg18.intervals", 2.07, !lowPass)
val WEx = new Target("NA12878.WEx", hg18, "hg18",
new File("/humgen/gsa-hpprojects/NA12878Collection/bams/NA12878.WEx.cleaned.recal.bam"),
new File("/home/radon01/depristo/work/oneOffProjects/1000GenomesProcessingPaper/wgs.v13/GA2.WEx.cleaned.ug.snpfiltered.indelfiltered.vcf"),
"/seq/references/HybSelOligos/whole_exome_agilent_1.1_refseq_plus_3_boosters/whole_exome_agilent_1.1_refseq_plus_3_boosters.targets.interval_list", 2.6, !lowPass)
val LowPassN60 = new Target("lowpass.N60", b36, "b36", // which reference the data is aligned to
new File("/humgen/1kg/analysis/bamsForDataProcessingPapers/lowpass_b36/lowpass.chr20.cleaned.matefixed.bam"), // the bam list to call from
new File("/home/radon01/depristo/work/oneOffProjects/VQSRCutByNRS/lowpass.N60.chr20.filtered.vcf"), // the gold standard VCF file to run through the VQSR
"/humgen/1kg/processing/pipeline_test_bams/whole_genome_chunked.chr20.b36.intervals", 2.3, lowPass) // chunked interval list to use with Queue's scatter/gather functionality
val LowPassAugust = new Target("ALL.august.v4", b37, "b37", // BUGBUG: kill this, it is too large
new File("/humgen/1kg/processing/allPopulations_chr20_august_release.cleaned.merged.bams/ALL.cleaned.merged.list"),
new File("/humgen/gsa-hpprojects/dev/data/AugChr20Calls_v4_3state/ALL.august.v4.chr20.filtered.vcf"),
"/humgen/1kg/processing/pipeline_test_bams/whole_genome_chunked.chr20.hg19.intervals", 2.3, lowPass)
val LowPassEUR363Nov = new Target("EUR.nov2010", b37, "b37",
new File("/humgen/1kg/processing/pipeline_test_bams/EUR.363sample.Nov2010.chr20.bam"),
new File("/humgen/gsa-hpprojects/dev/rpoplin/haplotypeScore/sting_dev_oldQD_hs10/logs/EUR.nov.filtered.vcf"), // ** THIS GOLD STANDARD NEEDS TO BE CORRECTED **
"/humgen/1kg/processing/pipeline_test_bams/whole_genome_chunked.chr20.hg19.intervals", 2.3, lowPass)
val LowPassFIN79Nov = new Target("FIN.nov2010", b37, "b37",
new File("/humgen/1kg/processing/pipeline_test_bams/FIN.79sample.Nov2010.chr20.bam"),
new File("/broad/shptmp/rpoplin/pipeline_newHS7/FIN.nov2010.filtered.vcf"), // ** THIS GOLD STANDARD NEEDS TO BE CORRECTED **
"/humgen/1kg/processing/pipeline_test_bams/whole_genome_chunked.chr20.hg19.intervals", 2.3, lowPass)
val TGPWExGdA = new Target("1000G.WEx.GdA", b37, "b37",
new File("/humgen/1kg/processing/pipeline_test_bams/Barcoded_1000G_WEx_Reduced_Plate_1.cleaned.list"), // BUGBUG: reduce from 60 to 20 people
new File("/humgen/gsa-scr1/delangel/NewUG/calls/AugustRelease.filtered_Q50_QD5.0_SB0.0.allSamples.SNPs_hg19.WEx_UG_newUG_MQC.vcf"), // ** THIS GOLD STANDARD NEEDS TO BE CORRECTED **
"/seq/references/HybSelOligos/whole_exome_agilent_1.1_refseq_plus_3_boosters/whole_exome_agilent_1.1_refseq_plus_3_boosters.Homo_sapiens_assembly19.targets.interval_list", 2.6, !lowPass)
//val targets = List(HiSeq, WEx, LowPassN60, LowPassAugust, LowPassEUR363Nov, LowPassFIN79Nov, TGPWExGdA)
val targets = List(LowPassEUR363Nov)
def script = {
def goldStandard = true
var gaussianList = List(6)
var shrinkageList = List(0.0001)
var dirichletList = List(1000.0)
var backoffList = List(1.3)
var qualCutoffList = List(100)
var stdList = List(4.5)
var useQDList = List(1)
var useSBList = List(1)
var useHSList = List(1)
var useHRUNList = List(1)
var useMQRSTList = List(0)
var useBQRSTList = List(0)
var useGCList = List(0)
var useMQList = List(0)
var useSumGLList = List(0)
var trainOmniList = List(1)
if(experiment == "0000") {
gaussianList = List(6,16)
trainOmniList = List(0,1)
useMQRSTList = List(0,1)
}
if(experiment == "0001") {
gaussianList = List(6, 16)
shrinkageList = List(0.0001, 0.01)
dirichletList = List(0.001, 1000.0)
backoffList = List(0.7, 1.0, 1.3)
useQDList = List(0,1)
useSBList = List(0,1)
useHSList = List(0,1)
useHRUNList = List(0,1)
useMQRSTList = List(0,1)
useBQRSTList = List(0,1)
useSumGLList = List(0,1)
trainOmniList = List(0,1)
}
if(experiment == "0002") {
gaussianList = List(2, 10, 50)
stdList = List(2.0, 4.5, 8.5)
dirichletList = List(0.0001, 0.01)
backoffList = List(0.5, 0.6, 0.9)
useQDList = List(1)
useSBList = List(0,1)
useHSList = List(0,1)
useHRUNList = List(0)
useMQRSTList = List(0,1)
useBQRSTList = List(0)
useSumGLList = List(0,1)
useGCList = List(0,1)
useMQList = List(0,1)
trainOmniList = List(0,1)
}
if(experiment == "0003") {
qualCutoffList = List(5, 40, 100, 400)
shrinkageList = List(0.0001, 0.001, 0.1)
dirichletList = List(0.0001, 0.001, 0.01)
useQDList = List(1)
useSBList = List(0,1)
useHSList = List(1)
useHRUNList = List(0)
useMQRSTList = List(0,1)
useBQRSTList = List(0,1)
useGCList = List(0,1)
useMQList = List(0,1)
useSumGLList = List(0,1)
trainOmniList = List(0,1)
}
if(experiment == "0004") {
gaussianList = List(5, 25)
shrinkageList = List(0.01, 1.0, 100.0)
dirichletList = List(0.001, 10.0, 1000.0)
backoffList = List(0.6, 1.0, 1.4)
useQDList = List(1)
useSBList = List(1)
useHSList = List(0,1)
useHRUNList = List(0,1)
useMQRSTList = List(0,1)
useBQRSTList = List(0,1)
useGCList = List(0,1)
useMQList = List(0,1)
}
if(experiment == "0005") {
gaussianList = List(4,50,100)
shrinkageList = List(0.0001, 10.0)
dirichletList = List(0.0001, 0.001)
backoffList = List(0.2, 0.3, 0.6)
stdList = List(0.5, 1.0, 10.0)
useQDList = List(1)
useSBList = List(1)
useHSList = List(1)
useHRUNList = List(0,1)
useMQRSTList = List(0,1)
useBQRSTList = List(0,1)
useGCList = List(0,1)
useMQList = List(0)
trainOmniList = List(0,1)
}
for (target <- targets) {
for(gaussian: Int <- gaussianList) {
for(shrinkage: Double <- shrinkageList) {
for(dirichlet: Double <- dirichletList) {
for(backoff: Double <- backoffList) {
for(qualCutoff: Int <- qualCutoffList) {
for(std: Double <- stdList) {
for(useQD: Int <- useQDList ) {
for(useSB: Int <- useSBList ) {
for(useHS: Int <- useHSList ) {
for(useHRUN: Int <- useHRUNList ) {
for(useMQRST: Int <- useMQRSTList ) {
for(useBQRST: Int <- useBQRSTList ) {
for(useGC: Int <- useGCList ) {
for(useMQ: Int <- useMQList ) {
for(useSumGL: Int <- useSumGLList ) {
for(trainOmni: Int <- trainOmniList) {
target.gaussian = gaussian
target.shrinkage = shrinkage
target.dirichlet = dirichlet
target.backoff = backoff
target.qualCutoff = qualCutoff
target.std = std
target.useQD = useQD
target.useSB = useSB
target.useHS = useHS
target.useHRUN = useHRUN
target.useMQRST = useMQRST
target.useBQRST = useBQRST
target.useGC = useGC
target.useMQ = useMQ
target.useSumGL = useSumGL
target.trainOmni = trainOmni
val clustersName: String = "%s_%d_%.4f_%.4f_%.1f_%d_%.1f_%d%d%d%d%d%d%d%d%d_%d.clusters".format(target.name, target.gaussian, target.shrinkage, target.dirichlet, target.backoff, target.qualCutoff, target.std, target.useQD, target.useSB, target.useHS, target.useHRUN, target.useMQRST, target.useBQRST, target.useGC, target.useMQ, target.useSumGL, target.trainOmni)
target.goldStandardClusterFile = new File(clustersName)
add(new GenerateVariantClusters(target, goldStandard))
add(new VariantRecalibratorTiTv(target, goldStandard))
add(new VariantRecalibratorNRS(target, goldStandard))
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
def bai(bam: File) = new File(bam + ".bai")
val FiltersToIgnore = List("DPFilter", "ABFilter", "ESPStandard", "QualByDepth", "StrandBias", "HomopolymerRun")
// 3.) VQSR part1 Generate Gaussian clusters based on truth sites
class GenerateVariantClusters(t: Target, goldStandard: Boolean) extends org.broadinstitute.sting.queue.extensions.gatk.GenerateVariantClusters with UNIVERSAL_GATK_ARGS {
val name: String = if ( goldStandard ) { t.goldStandardName } else { t.name }
this.reference_sequence = t.reference
this.DBSNP = new File("/humgen/gsa-hpprojects/GATK/data/dbsnp_129_" + t.rodName + ".rod")
this.rodBind :+= RodBind("hapmap", "VCF", "/humgen/gsa-hpprojects/GATK/data/Comparisons/Validated/HapMap/3.2/genotypes_r27_nr." + t.rodName + "_fwd.vcf")
if(t.trainOmni == 0) {
this.rodBind :+= RodBind("1kg", "VCF", "/humgen/gsa-hpprojects/GATK/data/Comparisons/Unvalidated/1kg_pilot1_projectCalls/ALL.low_coverage.2010_07.hg19.vcf")
this.rodBind :+= RodBind("truth", "VCF", "/humgen/gsa-hpprojects/GATK/data/Comparisons/Validated/HapMap/3.2/genotypes_r27_nr." + t.rodName + "_fwd.vcf")
} else {
this.rodBind :+= RodBind("1kg", "VCF", "/humgen/gsa-hpprojects/GATK/data/Comparisons/Validated/Omni2.5_chip/764samples.deduped.b37.annot.vcf")
this.rodBind :+= RodBind("truth", "VCF", "/humgen/gsa-hpprojects/GATK/data/Comparisons/Validated/Omni2.5_chip/764samples.deduped.b37.annot.vcf")
}
this.rodBind :+= RodBind("input", "VCF", if ( goldStandard ) { t.goldStandard_VCF } else { t.filteredVCF } )
this.clusterFile = if ( goldStandard ) { t.goldStandardClusterFile } else { t.clusterFile }
//this.use_annotation ++= List("QD", "SB", "HaplotypeScore", "HRun")
if(t.useQD == 1) {
this.use_annotation ++= List("QD")
}
if(t.useSB == 1) {
this.use_annotation ++= List("SB")
}
if(t.useHS == 1) {
this.use_annotation ++= List("HaplotypeScore1")
}
if(t.useHRUN == 1) {
this.use_annotation ++= List("HRun")
}
if(t.useMQRST == 1) {
this.use_annotation ++= List("MQRankSum")
}
if(t.useBQRST == 1) {
this.use_annotation ++= List("BaseQRankSum")
}
if(t.useGC == 1) {
this.use_annotation ++= List("GC")
}
if(t.useMQ == 1) {
this.use_annotation ++= List("MQ")
}
if(t.useSumGL == 1) {
this.use_annotation ++= List("sumGLbyD+")
}
if( t.useQD==0 && t.useSB==0 && t.useHS==0 && t.useHRUN==0 && t.useMQRST==0 && t.useBQRST==0 && t.useGC==0 && t.useMQ==0 && t.useSumGL==0) {
this.use_annotation ++= List("MQ","QD","DP")
}
this.analysisName = name + "_GVC"
this.intervalsString ++= List(t.intervals)
Walkers can now specify a class extending from Gatherer to merge custom output formats. Add @Gather(MyGatherer.class) to the walker @Output. JavaCommandLineFunctions can now specify the classpath+mainclass as an alternative to specifying a path to an executable jar. JCLF by default pass on the current classpath and only require the mainclass be specified by the developer extending the JCLF, relieving the QScript author from having to explicitly specify the jar. Like the Picard MergeSamFiles, GATK engine by default is now run from the current classpath. The GATK can still be overridden via .jarFile or .javaClasspath. Walkers from the GATK package are now also embedded into the Queue package. Updated AnalyzeCovariates to make it easier to guess the main class, AnalyzeCovariates instead of AnalyzeCovariatesCLP. Removed the GATK jar argument from the example QScripts. Removed one of the most FAQ when getting started with Scala/Queue, the use of Option[_] in QScripts: 1) Fixed mistaken assumption with java enums. In java enums can be null so they don't need nullable wrappers. 2) Added syntactic sugar for Nullable primitives to the QScript trait. Any variable defined as Option[Int] can just be assigned an Int value or None, ex: myFunc.memoryLimit = 3 Removed other unused code. Re-fixed dry run function ordering. Re-ordered the QCommandline companion object so that IntelliJ doesn't complain about missing main methods. git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@5504 348d0f76-0448-11de-a6fe-93d51630548a
2011-03-24 22:03:51 +08:00
this.qual = t.qualCutoff
this.std = t.std
this.mG = t.gaussian
Another relevant qscript. I use this one to do thousands of variant recalibration jobs to search for optimal parameters. git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@5019 348d0f76-0448-11de-a6fe-93d51630548a
2011-01-20 02:17:32 +08:00
this.ignoreFilter ++= FiltersToIgnore
Walkers can now specify a class extending from Gatherer to merge custom output formats. Add @Gather(MyGatherer.class) to the walker @Output. JavaCommandLineFunctions can now specify the classpath+mainclass as an alternative to specifying a path to an executable jar. JCLF by default pass on the current classpath and only require the mainclass be specified by the developer extending the JCLF, relieving the QScript author from having to explicitly specify the jar. Like the Picard MergeSamFiles, GATK engine by default is now run from the current classpath. The GATK can still be overridden via .jarFile or .javaClasspath. Walkers from the GATK package are now also embedded into the Queue package. Updated AnalyzeCovariates to make it easier to guess the main class, AnalyzeCovariates instead of AnalyzeCovariatesCLP. Removed the GATK jar argument from the example QScripts. Removed one of the most FAQ when getting started with Scala/Queue, the use of Option[_] in QScripts: 1) Fixed mistaken assumption with java enums. In java enums can be null so they don't need nullable wrappers. 2) Added syntactic sugar for Nullable primitives to the QScript trait. Any variable defined as Option[Int] can just be assigned an Int value or None, ex: myFunc.memoryLimit = 3 Removed other unused code. Re-fixed dry run function ordering. Re-ordered the QCommandline companion object so that IntelliJ doesn't complain about missing main methods. git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@5504 348d0f76-0448-11de-a6fe-93d51630548a
2011-03-24 22:03:51 +08:00
this.dirichlet = t.dirichlet
this.shrinkage = t.shrinkage
Another relevant qscript. I use this one to do thousands of variant recalibration jobs to search for optimal parameters. git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@5019 348d0f76-0448-11de-a6fe-93d51630548a
2011-01-20 02:17:32 +08:00
}
// 4.) VQSR part2 Calculate new LOD for all input SNPs by evaluating the Gaussian clusters
class VariantRecalibratorBase(t: Target, goldStandard: Boolean) extends org.broadinstitute.sting.queue.extensions.gatk.VariantRecalibrator with UNIVERSAL_GATK_ARGS {
val name: String = if ( goldStandard ) { t.goldStandardName } else { t.name }
this.reference_sequence = t.reference
this.DBSNP = new File("/humgen/gsa-hpprojects/GATK/data/dbsnp_129_" + t.rodName + ".rod")
this.rodBind :+= RodBind("hapmap", "VCF", "/humgen/gsa-hpprojects/GATK/data/Comparisons/Validated/HapMap/3.2/genotypes_r27_nr." + t.rodName + "_fwd.vcf")
if(t.trainOmni == 0) {
this.rodBind :+= RodBind("1kg", "VCF", "/humgen/gsa-hpprojects/GATK/data/Comparisons/Unvalidated/1kg_pilot1_projectCalls/ALL.low_coverage.2010_07.hg19.vcf")
this.rodBind :+= RodBind("truth", "VCF", "/humgen/gsa-hpprojects/GATK/data/Comparisons/Validated/HapMap/3.2/genotypes_r27_nr." + t.rodName + "_fwd.vcf")
} else {
this.rodBind :+= RodBind("1kg", "VCF", "/humgen/gsa-hpprojects/GATK/data/Comparisons/Validated/Omni2.5_chip/764samples.deduped.b37.annot.vcf")
this.rodBind :+= RodBind("truth", "VCF", "/humgen/gsa-hpprojects/GATK/data/Comparisons/Validated/Omni2.5_chip/764samples.deduped.b37.annot.vcf")
}
this.rodBind :+= RodBind("input", "VCF", if ( goldStandard ) { t.goldStandard_VCF } else { t.filteredVCF } )
this.clusterFile = if ( goldStandard ) { t.goldStandardClusterFile } else { t.clusterFile }
this.analysisName = name + "_VR"
this.intervalsString ++= List(t.intervals)
this.ignoreFilter ++= FiltersToIgnore
this.ignoreFilter ++= List("HARD_TO_VALIDATE")
Walkers can now specify a class extending from Gatherer to merge custom output formats. Add @Gather(MyGatherer.class) to the walker @Output. JavaCommandLineFunctions can now specify the classpath+mainclass as an alternative to specifying a path to an executable jar. JCLF by default pass on the current classpath and only require the mainclass be specified by the developer extending the JCLF, relieving the QScript author from having to explicitly specify the jar. Like the Picard MergeSamFiles, GATK engine by default is now run from the current classpath. The GATK can still be overridden via .jarFile or .javaClasspath. Walkers from the GATK package are now also embedded into the Queue package. Updated AnalyzeCovariates to make it easier to guess the main class, AnalyzeCovariates instead of AnalyzeCovariatesCLP. Removed the GATK jar argument from the example QScripts. Removed one of the most FAQ when getting started with Scala/Queue, the use of Option[_] in QScripts: 1) Fixed mistaken assumption with java enums. In java enums can be null so they don't need nullable wrappers. 2) Added syntactic sugar for Nullable primitives to the QScript trait. Any variable defined as Option[Int] can just be assigned an Int value or None, ex: myFunc.memoryLimit = 3 Removed other unused code. Re-fixed dry run function ordering. Re-ordered the QCommandline companion object so that IntelliJ doesn't complain about missing main methods. git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@5504 348d0f76-0448-11de-a6fe-93d51630548a
2011-03-24 22:03:51 +08:00
this.target_titv = t.titvTarget
this.backOff = t.backoff
Another relevant qscript. I use this one to do thousands of variant recalibration jobs to search for optimal parameters. git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@5019 348d0f76-0448-11de-a6fe-93d51630548a
2011-01-20 02:17:32 +08:00
}
// 4a.) Choose VQSR tranches based on novel ti/tv
class VariantRecalibratorTiTv(t: Target, goldStandard: Boolean) extends VariantRecalibratorBase(t, goldStandard) {
this.tranche ++= List("1.0")
this.out = new File("/dev/null")
val tranchesName: String = "%s_%d_%.4f_%.4f_%.1f_%d_%.1f_%d%d%d%d%d%d%d%d%d_%d.titv.tranches".format(this.name, t.gaussian, t.shrinkage, t.dirichlet, t.backoff, t.qualCutoff, t.std, t.useQD, t.useSB, t.useHS, t.useHRUN, t.useMQRST, t.useBQRST, t.useGC, t.useMQ, t.useSumGL, t.trainOmni)
this.tranchesFile = new File(tranchesName)
}
// 4b.) Choose VQSR tranches based on sensitivity to truth set
class VariantRecalibratorNRS(t: Target, goldStandard: Boolean) extends VariantRecalibratorBase(t, goldStandard) {
Walkers can now specify a class extending from Gatherer to merge custom output formats. Add @Gather(MyGatherer.class) to the walker @Output. JavaCommandLineFunctions can now specify the classpath+mainclass as an alternative to specifying a path to an executable jar. JCLF by default pass on the current classpath and only require the mainclass be specified by the developer extending the JCLF, relieving the QScript author from having to explicitly specify the jar. Like the Picard MergeSamFiles, GATK engine by default is now run from the current classpath. The GATK can still be overridden via .jarFile or .javaClasspath. Walkers from the GATK package are now also embedded into the Queue package. Updated AnalyzeCovariates to make it easier to guess the main class, AnalyzeCovariates instead of AnalyzeCovariatesCLP. Removed the GATK jar argument from the example QScripts. Removed one of the most FAQ when getting started with Scala/Queue, the use of Option[_] in QScripts: 1) Fixed mistaken assumption with java enums. In java enums can be null so they don't need nullable wrappers. 2) Added syntactic sugar for Nullable primitives to the QScript trait. Any variable defined as Option[Int] can just be assigned an Int value or None, ex: myFunc.memoryLimit = 3 Removed other unused code. Re-fixed dry run function ordering. Re-ordered the QCommandline companion object so that IntelliJ doesn't complain about missing main methods. git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@5504 348d0f76-0448-11de-a6fe-93d51630548a
2011-03-24 22:03:51 +08:00
this.sm = org.broadinstitute.sting.gatk.walkers.variantrecalibration.VariantRecalibrator.SelectionMetricType.TRUTH_SENSITIVITY
Another relevant qscript. I use this one to do thousands of variant recalibration jobs to search for optimal parameters. git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@5019 348d0f76-0448-11de-a6fe-93d51630548a
2011-01-20 02:17:32 +08:00
if(t.trainOmni == 0 ) {
this.tranche ++= List("1.0")
} else {
this.tranche ++= List("2.5")
}
this.out = new File("/dev/null")
val tranchesName: String = "%s_%d_%.4f_%.4f_%.1f_%d_%.1f_%d%d%d%d%d%d%d%d%d_%d.ts.tranches".format(this.name, t.gaussian, t.shrinkage, t.dirichlet, t.backoff, t.qualCutoff, t.std, t.useQD, t.useSB, t.useHS, t.useHRUN, t.useMQRST, t.useBQRST, t.useGC, t.useMQ, t.useSumGL, t.trainOmni)
this.tranchesFile = new File(tranchesName)
}
}