a modified version of the Methods Development calling pipeline made to work with pacbio data.
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@5225 348d0f76-0448-11de-a6fe-93d51630548a
This commit is contained in:
carneiro
parent
b1e4e1afb6
commit
9c2c5efe35
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import org.broadinstitute.sting.queue.extensions.gatk._
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import org.broadinstitute.sting.queue.QScript
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class pbCalling extends QScript {
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qscript =>
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@Argument(shortName="gatk", doc="gatk jar file", required=true)
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var gatkJarFile: File = _
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@Argument(shortName="outputDir", doc="output directory", required=true)
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var outputDir: String = "./"
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@Argument(shortName="dataset", doc="selects the datasets to run. If not provided, all datasets will be used", required=false)
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var datasets: List[String] = Nil
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@Argument(shortName="noBAQ", doc="turns off BAQ calculation", required=false)
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var noBAQ: Boolean = false
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@Argument(shortName="noMASK", doc="turns off MASK calculation", required=false)
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var noMASK: Boolean = false
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trait UNIVERSAL_GATK_ARGS extends CommandLineGATK { logging_level = "INFO"; jarFile = gatkJarFile; memoryLimit = Some(3); }
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class Target(
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val baseName: String,
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val reference: File,
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val dbsnpFile: String,
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val hapmapFile: String,
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val maskFile: String,
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val bamList: File,
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val goldStandard_VCF: File,
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val intervals: String,
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val titvTarget: Double,
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val isLowpass: Boolean) {
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val name = qscript.outputDir + baseName
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val clusterFile = new File(name + ".clusters")
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val rawVCF = new File(name + ".raw.vcf")
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val filteredVCF = new File(name + ".filtered.vcf")
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val titvRecalibratedVCF = new File(name + ".titv.recalibrated.vcf")
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val titvTranchesFile = new File(name + ".titv.tranches")
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val tsRecalibratedVCF = new File(name + ".ts.recalibrated.vcf")
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val tsTranchesFile = new File(name + ".ts.tranches")
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val cutVCF = new File(name + ".cut.vcf")
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val evalFile = new File(name + ".eval")
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val goldStandardName = qscript.outputDir + "goldStandard/" + baseName
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val goldStandardClusterFile = new File(goldStandardName + ".clusters")
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}
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val hg19 = new File("/seq/references/Homo_sapiens_assembly19/v1/Homo_sapiens_assembly19.fasta")
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val hg18 = new File("/seq/references/Homo_sapiens_assembly18/v0/Homo_sapiens_assembly18.fasta")
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val b36 = new File("/humgen/1kg/reference/human_b36_both.fasta")
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val b37 = new File("/humgen/1kg/reference/human_g1k_v37.fasta")
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val dbSNP_hg18 = "/humgen/gsa-hpprojects/GATK/data/Comparisons/Validated/dbSNP/dbsnp_130_hg18.rod"
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val dbSNP_b36 = "/humgen/gsa-hpprojects/GATK/data/Comparisons/Validated/dbSNP/dbsnp_130_b36.rod"
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val dbSNP_b37 = "/humgen/gsa-hpprojects/GATK/data/Comparisons/Validated/dbSNP/dbsnp_132_b37.leftAligned.vcf"
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val dbSNP_b37_129 = "/humgen/gsa-hpprojects/GATK/data/Comparisons/Validated/dbSNP/dbsnp_129_b37.rod" // Special case for NA12878 collections that can't use 132 because they are part of it.
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val hapmap_hg18 = "/humgen/gsa-hpprojects/GATK/data/Comparisons/Validated/HapMap/3.3/genotypes_r27_nr.hg18_fwd.vcf"
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val hapmap_b36 = "/humgen/gsa-hpprojects/GATK/data/Comparisons/Validated/HapMap/3.3/genotypes_r27_nr.b36_fwd.vcf"
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val hapmap_b37 = "/humgen/gsa-hpprojects/GATK/data/Comparisons/Validated/HapMap/3.3/genotypes_r27_nr.b37_fwd.vcf"
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val indelMask_b36 = "/humgen/1kg/processing/pipeline_test_bams/pilot1.dindel.mask.b36.bed"
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val indelMask_b37 = "/humgen/1kg/processing/pipeline_test_bams/pilot1.dindel.mask.b37.bed"
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// ToDos:
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// reduce the scope of the datasets so the script is more nimble
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// 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
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// create gold standard BAQ'd bam files, no reason to always do it on the fly
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// Analysis to add at the end of the script:
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// auto generation of the cluster plots
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// 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
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// produce Kiran's Venn plots based on comparison between new VCF and gold standard produced VCF
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val lowPass: Boolean = true
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val targetDataSets: Map[String, Target] = Map(
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"HiSeq" -> new Target("NA12878.HiSeq", hg18, dbSNP_hg18, hapmap_hg18,
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"/humgen/gsa-hpprojects/dev/depristo/oneOffProjects/1000GenomesProcessingPaper/wgs.v13/HiSeq.WGS.cleaned.indels.10.mask",
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new File("/humgen/gsa-hpprojects/NA12878Collection/bams/NA12878.HiSeq.WGS.bwa.cleaned.recal.bam"),
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new File("/home/radon01/depristo/work/oneOffProjects/1000GenomesProcessingPaper/wgs.v13/HiSeq.WGS.cleaned.ug.snpfiltered.indelfiltered.vcf"),
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"/humgen/1kg/processing/pipeline_test_bams/whole_genome_chunked.hg18.intervals", 2.07, !lowPass),
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"FIN" -> new Target("FIN", b37, dbSNP_b37, hapmap_b37, indelMask_b37,
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new File("/humgen/1kg/processing/pipeline_test_bams/FIN.79sample.Nov2010.chr20.bam"),
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new File("/humgen/gsa-hpprojects/dev/data/AugChr20Calls_v4_3state/ALL.august.v4.chr20.filtered.vcf"), // ** THIS GOLD STANDARD NEEDS TO BE CORRECTED **
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"/humgen/1kg/processing/pipeline_test_bams/whole_genome_chunked.chr20.hg19.intervals", 2.3, lowPass),
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"WEx" -> new Target("NA12878.WEx", hg18, dbSNP_hg18, hapmap_hg18,
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"/humgen/gsa-hpprojects/dev/depristo/oneOffProjects/1000GenomesProcessingPaper/wgs.v13/GA2.WEx.cleaned.indels.10.mask",
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new File("/humgen/gsa-hpprojects/NA12878Collection/bams/NA12878.WEx.cleaned.recal.bam"),
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new File("/home/radon01/depristo/work/oneOffProjects/1000GenomesProcessingPaper/wgs.v13/GA2.WEx.cleaned.ug.snpfiltered.indelfiltered.vcf"),
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"/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),
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"TGPWExGdA" -> new Target("1000G.WEx.GdA", b37, dbSNP_b37, hapmap_b37, indelMask_b37,
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new File("/humgen/1kg/processing/pipeline_test_bams/Barcoded_1000G_WEx_Reduced_Plate_1.cleaned.list"), // BUGBUG: reduce from 60 to 20 people
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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 **
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"/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),
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"LowPassN60" -> new Target("lowpass.N60", b36, dbSNP_b36, hapmap_b36, indelMask_b36,
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new File("/humgen/1kg/analysis/bamsForDataProcessingPapers/lowpass_b36/lowpass.chr20.cleaned.matefixed.bam"), // the bam list to call from
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new File("/home/radon01/depristo/work/oneOffProjects/VQSRCutByNRS/lowpass.N60.chr20.filtered.vcf"), // the gold standard VCF file to run through the VQSR
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"/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
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"LowPassAugust" -> new Target("ALL.august.v4", b37, dbSNP_b37, hapmap_b37, indelMask_b37, // BUGBUG: kill this, it is too large
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new File("/humgen/1kg/processing/allPopulations_chr20_august_release.cleaned.merged.bams/ALL.cleaned.merged.list"),
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new File("/humgen/gsa-hpprojects/dev/data/AugChr20Calls_v4_3state/ALL.august.v4.chr20.filtered.vcf"),
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"/humgen/1kg/processing/pipeline_test_bams/whole_genome_chunked.chr20.hg19.intervals", 2.3, lowPass),
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"LowPassEUR363Nov" -> new Target("EUR.nov2010", b37, dbSNP_b37, hapmap_b37, indelMask_b37,
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new File("/humgen/1kg/processing/pipeline_test_bams/EUR.363sample.Nov2010.chr20.bam"),
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new File("/humgen/gsa-hpprojects/dev/data/AugChr20Calls_v4_3state/ALL.august.v4.chr20.filtered.vcf"), // ** THIS GOLD STANDARD NEEDS TO BE CORRECTED **
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"/humgen/1kg/processing/pipeline_test_bams/whole_genome_chunked.chr20.hg19.intervals", 2.3, lowPass),
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"WExTrio" -> new Target("NA12878Trio.WEx", b37, dbSNP_b37_129, hapmap_b37, indelMask_b37,
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new File("/humgen/gsa-hpprojects/NA12878Collection/bams/CEUTrio.HiSeq.WEx.bwa.cleaned.recal.bam"),
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new File("/humgen/gsa-scr1/carneiro/prj/trio/snps/NA12878Trio.WEx.filtered.vcf"),
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"/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),
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"pacbio" -> new Target("pacbio", b37, dbSNP_b37_129, hapmap_b37, indelMask_b37,
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new File("/humgen/gsa-scr1/carneiro/prj/pacbio/data/pacbio.recal.bam"),
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new File("/humgen/gsa-scr1/carneiro/prj/pacbio/analisys/snps/amplicon/pacbio.filtered.vcf"),
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"/humgen/gsa-scr1/carneiro/prj/pacbio/data/pacbio.hg19.intervals", 1.8, !lowPass),
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"pb200" -> new Target("pb200", b37, dbSNP_b37_129, hapmap_b37, indelMask_b37,
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new File("/humgen/gsa-scr1/carneiro/prj/pacbio/data/pb200.recal.bam"),
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new File("/humgen/gsa-scr1/carneiro/prj/pacbio/analisys/snps/amplicon/pb200.filtered.vcf"),
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"/humgen/gsa-scr1/carneiro/prj/pacbio/data/pb200.hg19.intervals", 1.8, !lowPass),
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"pb2k" -> new Target("pb2k", b37, dbSNP_b37_129, hapmap_b37, indelMask_b37,
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new File("/humgen/gsa-scr1/carneiro/prj/pacbio/data/pb2k.recal.bam"),
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new File("/humgen/gsa-scr1/carneiro/prj/pacbio/analisys/snps/amplicon/pb2k.filtered.vcf"),
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"/humgen/gsa-scr1/carneiro/prj/pacbio/data/pb2k.hg19.intervals", 1.8, !lowPass)
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)
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def script = {
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// Selects the datasets in the -dataset argument and adds them to targets.
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var targets: List[Target] = List()
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if (!datasets.isEmpty)
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for (ds <- datasets)
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targets ::= targetDataSets(ds) // Could check if ds was mispelled, but this way an exception will be thrown, maybe it's better this way?
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else // If -dataset is not specified, all datasets are used.
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for (targetDS <- targetDataSets.valuesIterator) // for Scala 2.7 or older, use targetDataSets.values
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targets ::= targetDS
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val goldStandard = true
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for (target <- targets) {
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add(new UnifiedGenotyper(target))
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add(new VariantFiltration(target))
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add(new GenerateVariantClusters(target, !goldStandard))
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// add(new VariantRecalibratorTiTv(target, !goldStandard))
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add(new VariantRecalibratorNRS(target, !goldStandard))
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add(new VariantCut(target))
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add(new VariantEvaluation(target))
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}
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}
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def bai(bam: File) = new File(bam + ".bai")
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val FiltersToIgnore = List("DPFilter", "ABFilter", "ESPStandard", "QualByDepth", "StrandBias", "HomopolymerRun")
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// 1.) Call SNPs with UG
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class UnifiedGenotyper(t: Target) extends org.broadinstitute.sting.queue.extensions.gatk.UnifiedGenotyper with UNIVERSAL_GATK_ARGS {
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this.reference_sequence = t.reference
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this.intervalsString ++= List(t.intervals)
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this.scatterCount = 63 // the smallest interval list has 63 intervals, one for each Mb on chr20
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this.dcov = Some( if ( t.isLowpass ) { 50 } else { 250 } )
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this.stand_call_conf = Some( if ( t.isLowpass ) { 4.0 } else { 30.0 } )
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this.stand_emit_conf = Some( if ( t.isLowpass ) { 4.0 } else { 30.0 } )
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this.input_file :+= t.bamList
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this.out = t.rawVCF
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this.baq = Some( if (noBAQ) {org.broadinstitute.sting.utils.baq.BAQ.CalculationMode.OFF} else {org.broadinstitute.sting.utils.baq.BAQ.CalculationMode.RECALCULATE})
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this.analysisName = t.name + "_UG"
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if (t.dbsnpFile.endsWith(".rod"))
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this.DBSNP = new File(t.dbsnpFile)
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else if (t.dbsnpFile.endsWith(".vcf"))
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this.rodBind :+= RodBind("dbsnp", "VCF", t.dbsnpFile)
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// Ridiculous workaround to get pacbio data to run.. never commit this!
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this.assume_single_sample_reads = "NA12878"
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this.deletions = Some(0.5)
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this.mbq = Some(10)
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}
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// 2.) Filter SNPs
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class VariantFiltration(t: Target) extends org.broadinstitute.sting.queue.extensions.gatk.VariantFiltration with UNIVERSAL_GATK_ARGS {
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this.reference_sequence = t.reference
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this.intervalsString ++= List(t.intervals)
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this.scatterCount = 10
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this.variantVCF = t.rawVCF
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this.out = t.filteredVCF
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this.filterName ++= List("HARD_TO_VALIDATE")
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this.filterExpression ++= List("\"MQ0 >= 4 && (MQ0 / (1.0 * DP)) > 0.1\"")
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this.analysisName = t.name + "_VF"
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if (!noMASK) {
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this.rodBind :+= RodBind("mask", "Bed", t.maskFile)
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this.maskName = "InDel"
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}
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}
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// 3.) VQSR part1 Generate Gaussian clusters based on truth sites
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class GenerateVariantClusters(t: Target, goldStandard: Boolean) extends org.broadinstitute.sting.queue.extensions.gatk.GenerateVariantClusters with UNIVERSAL_GATK_ARGS {
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val name: String = if ( goldStandard ) { t.goldStandardName } else { t.name }
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this.reference_sequence = t.reference
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this.rodBind :+= RodBind("hapmap", "VCF", t.hapmapFile)
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if( t.hapmapFile.contains("b37") )
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this.rodBind :+= RodBind("1kg", "VCF", "/humgen/gsa-hpprojects/GATK/data/Comparisons/Unvalidated/1kg_pilot1_projectCalls/ALL.low_coverage.2010_07.hg19.vcf")
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this.rodBind :+= RodBind("input", "VCF", if ( goldStandard ) { t.goldStandard_VCF } else { t.filteredVCF } )
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this.clusterFile = if ( goldStandard ) { t.goldStandardClusterFile } else { t.clusterFile }
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this.use_annotation ++= List("QD", "SB", "HaplotypeScore", "HRun")
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this.analysisName = name + "_GVC"
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this.intervalsString ++= List(t.intervals)
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this.qual = Some(350) // clustering parameters to be updated soon pending new experimentation results
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this.std = Some(3.5)
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this.mG = Some(10)
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this.ignoreFilter ++= FiltersToIgnore
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if (t.dbsnpFile.endsWith(".rod"))
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this.DBSNP = new File(t.dbsnpFile)
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else if (t.dbsnpFile.endsWith(".vcf"))
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this.rodBind :+= RodBind("dbsnp", "VCF", t.dbsnpFile)
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}
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// 4.) VQSR part2 Calculate new LOD for all input SNPs by evaluating the Gaussian clusters
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class VariantRecalibratorBase(t: Target, goldStandard: Boolean) extends org.broadinstitute.sting.queue.extensions.gatk.VariantRecalibrator with UNIVERSAL_GATK_ARGS {
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val name: String = if ( goldStandard ) { t.goldStandardName } else { t.name }
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this.reference_sequence = t.reference
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if( t.hapmapFile.contains("b37") )
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this.rodBind :+= RodBind("1kg", "VCF", "/humgen/gsa-hpprojects/GATK/data/Comparisons/Unvalidated/1kg_pilot1_projectCalls/ALL.low_coverage.2010_07.hg19.vcf")
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this.rodBind :+= RodBind("hapmap", "VCF", t.hapmapFile)
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this.rodBind :+= RodBind("truth", "VCF", t.hapmapFile)
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this.rodBind :+= RodBind("input", "VCF", if ( goldStandard ) { t.goldStandard_VCF } else { t.filteredVCF } )
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this.clusterFile = if ( goldStandard ) { t.goldStandardClusterFile } else { t.clusterFile }
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this.analysisName = name + "_VR"
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this.intervalsString ++= List(t.intervals)
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this.ignoreFilter ++= FiltersToIgnore
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this.ignoreFilter ++= List("HARD_TO_VALIDATE")
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this.target_titv = Some(t.titvTarget)
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if (t.dbsnpFile.endsWith(".rod"))
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this.DBSNP = new File(t.dbsnpFile)
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else if (t.dbsnpFile.endsWith(".vcf"))
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this.rodBind :+= RodBind("dbsnp", "VCF", t.dbsnpFile)
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}
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// 4a.) Choose VQSR tranches based on novel ti/tv
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class VariantRecalibratorTiTv(t: Target, goldStandard: Boolean) extends VariantRecalibratorBase(t, goldStandard) {
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this.tranche ++= List("0.1", "1.0", "10.0", "100.0")
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this.out = t.titvRecalibratedVCF
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this.tranchesFile = t.titvTranchesFile
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}
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// 4b.) Choose VQSR tranches based on sensitivity to truth set
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class VariantRecalibratorNRS(t: Target, goldStandard: Boolean) extends VariantRecalibratorBase(t, goldStandard) {
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this.sm = Some(org.broadinstitute.sting.gatk.walkers.variantrecalibration.VariantRecalibrator.SelectionMetricType.TRUTH_SENSITIVITY)
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this.tranche ++= List("0.1", "1.0", "10.0", "100.0")
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this.out = t.tsRecalibratedVCF
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this.priorDBSNP = Some(2.0)
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this.priorHapMap = Some(2.0)
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this.prior1KG = Some(2.0)
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this.tranchesFile = t.tsTranchesFile
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}
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// 5.) Variant Cut (OPTIONAL!) filter out the variants marked by recalibration to the 99% tranche
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class VariantCut(t: Target) extends org.broadinstitute.sting.queue.extensions.gatk.ApplyVariantCuts with UNIVERSAL_GATK_ARGS {
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this.reference_sequence = t.reference
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this.rodBind :+= RodBind("input", "VCF", t.tsRecalibratedVCF )
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this.analysisName = t.name + "_VC"
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this.intervalsString ++= List(t.intervals)
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this.out = t.cutVCF
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this.tranchesFile = t.tsTranchesFile
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this.fdr_filter_level = Some(1.0)
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if (t.dbsnpFile.endsWith(".rod"))
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this.DBSNP = new File(t.dbsnpFile)
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else if (t.dbsnpFile.endsWith(".vcf"))
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this.rodBind :+= RodBind("dbsnp", "VCF", t.dbsnpFile)
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}
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// 6.) Variant Evaluation (OPTIONAL!) based on the sensitivity recalibrated vcf
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class VariantEvaluation(t: Target) extends VariantEval with UNIVERSAL_GATK_ARGS {
|
||||
val name: String = t.name
|
||||
this.reference_sequence = t.reference
|
||||
this.rodBind :+= RodBind("comphapmap", "VCF", t.hapmapFile)
|
||||
this.rodBind :+= RodBind("eval", "VCF", t.cutVCF)
|
||||
this.analysisName = name + "_VE"
|
||||
this.intervalsString ++= List(t.intervals)
|
||||
this.EV ++= List("GenotypeConcordance")
|
||||
this.out = t.evalFile
|
||||
// Ridiculous workaround to get pacbio data to run.. never commit this!
|
||||
this.sample ++= List("NA12878")
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue