import org.broadinstitute.sting.queue.extensions.picard.PicardBamJarFunction 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 trait UNIVERSAL_GATK_ARGS extends CommandLineGATK { logging_level = "INFO"; jarFile = gatkJarFile; memoryLimit = Some(2); } 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) this.qual = Some(t.qualCutoff) this.std = Some(t.std) this.mG = Some(t.gaussian) this.ignoreFilter ++= FiltersToIgnore this.dirichlet = Some(t.dirichlet) this.shrinkage = Some(t.shrinkage) } // 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") this.target_titv = Some(t.titvTarget) this.backOff = Some(t.backoff) } // 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) { this.sm = Some(org.broadinstitute.sting.gatk.walkers.variantrecalibration.VariantRecalibrator.SelectionMetricType.TRUTH_SENSITIVITY) 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) } }