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Created 2015-11-25 09:07:33 | Updated | Tags: combinevariants fisherstrand qualbydepth variantannotator
Comments (1)

I am doing variant calling of multiple RNAseq datasets using GATK/3.4.46. For limitation of computational resources, I ran HaplotypeCaller on each dataset separately. Then I ran CombineVaraints to merge all output VCF files using this command

java -Xmx10g -jar GenomeAnalysisTK.jar \ -T CombineVariants \ -R $gatk_ref \ --variant set1.vcf \ --variant set2.vcf \ --variant set3.vcf \ -o combine_output.vcf \ -genotypeMergeOptions UNIQUIFY

Then I tried to run VariantFiltration using thic command

java -Xmx2g -jar $GATK/GenomeAnalysisTK.jar \ -T VariantFiltration \ -R $gatk_ref \ -V combine_output.vcf \ -window 35 -cluster 3 \ -filterName FS -filter "FS > 30.0" \ -filterName QD -filter "QD < 2.0" \ -o $output

Several thousands variants though warning for absence of FS and QD. According to @Sheila advise in http://gatkforums.broadinstitute.org/discussion/2334/undefined-variable-variantfiltration, I ran VariantAnnotator to add these annotations using this command

java -Xmx45g -jar GenomeAnalysisTK.jar \ -R $gatk_ref \ -T VariantAnnotator \ -I input1.bam \ -I input2.bam \ . . -I input57.bam \ -V combine_output.vcf \ -A Coverage \ -A FisherStrand \ -A QualByDepth \ -nt 7 \ -o combine_output_ann.vcf

Then I repeated the VariantFiltration but I have 2 problems: 1) about 2000 variants are still not annotated for FS. All of them are indels and many of them are not homozygous for the ALT allele). Also ~ 40 variants are still not annotated for QD. All of them have multiple ALT alleles 2) The combined VCF record has the QUAL of the first VCF record with a non-MISSING QUAL value. According to my manual calculations, I think VariantAnnotator calculates the QD value by dividing this QUAL value by the AD of samples with a non hom-ref genotype call. This cause many variants to fail the QD filter.

Thank you


Created 2014-09-11 15:52:18 | Updated | Tags: vqsr haplotypecaller qualbydepth genotypegvcfs
Comments (21)

Hey there,

How can it be possible that some of my snps or indels calls miss the QD tag? I'm doing the recommended workflow and I've tested if for both RNAseq (hard filters complains, that's how I saw those tags were missing) and Exome sequencing (VQSR). How can a hard filter applied on QD on a line without actually that tag can be considered to pass that threshold too? I'm seeing a lot more INDELs in RNAseq where this kind of scenario is happening as well.

Here's the command lines that I used :

Forming gvcf files like this (for RNAseq in that case):

java -Djava.io.tmpdir=$LSCRATCH -Xmx46g -jar /home/apps/Logiciels/GATK/3.2-2/GenomeAnalysisTK.jar -l INFO -pairHMM VECTOR_LOGLESS_CACHING -T HaplotypeCaller -I sample.bam -R ~/References/hg19/hg19.fasta --emitRefConfidence GVCF --variant_index_type LINEAR --variant_index_parameter 128000 -o sample.g.vcf --dbsnp ~/References/hg19/dbSNP/dbsnp_138.b37.excluding_sites_after_129.CEU_converted.vcf -recoverDanglingHeads -dontUseSoftClippedBases -stand_call_conf 20.0 -stand_emit_conf 0.0 -mmq 1 -U ALLOW_N_CIGAR_READS

GenotypeGVCF after :

java -Djava.io.tmpdir=$LSCRATCH -Xmx22g -jar /home/apps/Logiciels/GATK/3.2-2/GenomeAnalysisTK.jar -l INFO -T GenotypeGVCFs --dbsnp ~/References/hg19/dbSNP/dbsnp_138.b37.excluding_sites_after_129.CEU_converted.vcf -A QualByDepth -A FisherStrand -o Joint_file/96RNAseq.10092014.STAR.q1.1kb.vcf -R ~/References/hg19/hg19.fasta

VQSR (separated for indels and snvs) or Hard filter at the end :

VQSR

java -Djava.io.tmpdir=$LSCRATCH -Xmx10g -jar /home/apps/Logiciels/GATK/3.2-2/GenomeAnalysisTK.jar -l INFO -T VariantRecalibrator -an QD -an MQRankSum -an ReadPosRankSum -an FS -an MQ -mode SNP -resource:1000G,known=false,training=true,truth=false,prior=10.0 ~/References/hg19/VQSR/1000G_phase1.snps.high_confidence.b37.noGL.converted.vcf -resource:hapmap,known=false,training=true,truth=true,prior=15.0 ~/References/hg19/VQSR/hapmap_3.3.b37.noGL.nochrM.converted.vcf -resource:omni,known=false,training=true,truth=false,prior=12.0 ~/References/hg19/VQSR/1000G_omni2.5.b37.noGL.nochrM.converted.vcf -resource:dbsnp,known=true,training=false,truth=false,prior=6.0 ~/References/hg19/VQSR/dbsnp_138.b37.excluding_sites_after_129.noGL.nochrM.converted.vcf -input snv.vcf -recalFile 96Exomes.HC.TruSeq.snv.RECALIBRATED -tranchesFile 96Exomes.HC.TruSeq.snv.tranches -rscriptFile 96Exomes.HC.TruSeq.snv.plots.R -R ~/References/hg19/hg19.fasta --maxGaussians 4

java -Djava.io.tmpdir=$LSCRATCH -Xmx10g -jar /home/apps/Logiciels/GATK/3.2-2/GenomeAnalysisTK.jar -l INFO -T ApplyRecalibration -ts_filter_level 99.0 -mode SNP -input snv.vcf -recalFile 96Exomes.HC.TruSeq.snv.RECALIBRATED -tranchesFile 96Exomes.HC.TruSeq.snv.tranches -o 96Exomes.HC.TruSeq.snv.recal.vcf -R ~/References/hg19/hg19.fasta

HARD FILTER (RNASeq)

java -Djava.io.tmpdir=$LSCRATCH -Xmx2g -jar /home/apps/Logiciels/GATK/3.1-1/GenomeAnalysisTK.jar -l INFO -T VariantFiltration -R ~/References/hg19/hg19.fasta -V 96RNAseq.STAR.q1.vcf -window 35 -cluster 3 -filterName FS -filter "FS > 30.0" -filterName QD -filter "QD < 2.0" -o 96RNAseq.STAR.q1.FS30.QD2.vcf

Here are some examples for RNAseq :

chr1 6711349 . G A 79.10 PASS BaseQRankSum=-1.369e+00;ClippingRankSum=1.00;DP=635;FS=1.871;MLEAC=1;MLEAF=5.495e-03;MQ=60.00;MQ0=0;MQRankSum=-4.560e-01;ReadPosRankSum=-1.187e+00 GT:AD:DP:GQ:PL 0/0:8,0:8:24:0,24,280 ./.:0,0:0 0/0:9,0:9:21:0,21,248 0/0:7,0:7:21:0,21,196 0/0:7,0:7:21:0,21,226 0/0:8,0:8:21:0,21,227 0/0:8,0:8:21:0,21,253 0/0:7,0:7:21:0,21,218 0/0:9,0:9:27:0,27,282 1/1:0,0:5:15:137,15,0 0/0:2,0:2:6:0,6,47 0/0:28,0:28:78:0,78,860 0/0:7,0:7:21:0,21,252 0/0:2,0:2:6:0,6,49 0/0:5,0:5:12:0,12,152 0/0:3,0:3:6:0,6,90 0/0:4,0:4:12:0,12,126 0/0:9,0:9:21:0,21,315 0/0:7,0:7:21:0,21,256 0/0:7,0:7:21:0,21,160 0/0:8,0:8:21:0,21,298 0/0:20,0:20:60:0,60,605 0/0:2,0:2:6:0,6,49 0/0:2,0:2:6:0,6,67 0/0:2,0:2:6:0,6,71 0/0:14,0:14:20:0,20,390 0/0:7,0:7:21:0,21,223 0/0:7,0:7:21:0,21,221 0/0:4,0:4:12:0,12,134 0/0:2,0:2:6:0,6,54 ./.:0,0:0 0/0:4,0:4:9:0,9,118 0/0:8,0:8:21:0,21,243 0/0:6,0:6:15:0,15,143 0/0:8,0:8:21:0,21,244 0/0:7,0:7:21:0,21,192 0/0:2,0:2:6:0,6,54 0/0:13,0:13:27:0,27,359 0/0:8,0:8:21:0,21,245 0/0:7,0:7:21:0,21,218 0/0:12,0:12:36:0,36,354 0/0:8,0:8:21:0,21,315 0/0:7,0:7:21:0,21,215 0/0:2,0:2:6:0,6,49 0/0:10,0:10:24:0,24,301 0/0:7,0:7:21:0,21,208 0/0:7,0:7:21:0,21,199 0/0:2,0:2:6:0,6,47 0/0:3,0:3:9:0,9,87 0/0:2,0:2:6:0,6,73 0/0:7,0:7:21:0,21,210 0/0:8,0:8:22:0,22,268 0/0:7,0:7:21:0,21,184 0/0:7,0:7:21:0,21,213 0/0:5,0:5:9:0,9,135 0/0:7,0:7:21:0,21,200 0/0:4,0:4:12:0,12,118 0/0:7,0:7:21:0,21,232 0/0:7,0:7:21:0,21,232 0/0:7,0:7:21:0,21,217 0/0:8,0:8:21:0,21,255 0/0:9,0:9:24:0,24,314 0/0:8,0:8:21:0,21,221 0/0:9,0:9:24:0,24,276 0/0:9,0:9:21:0,21,285 0/0:3,0:3:6:0,6,90 0/0:2,0:2:6:0,6,57 0/0:13,0:13:20:0,20,385 0/0:2,0:2:6:0,6,48 0/0:11,0:11:27:0,27,317 0/0:8,0:8:21:0,21,315 0/0:9,0:9:24:0,24,284 0/0:7,0:7:21:0,21,228 0/0:14,0:14:33:0,33,446 0/0:2,0:2:6:0,6,64 0/0:2,0:2:6:0,6,72 0/0:7,0:7:21:0,21,258 0/0:10,0:10:27:0,27,348 0/0:7,0:7:21:0,21,219 0/0:9,0:9:21:0,21,289 0/0:20,0:20:57:0,57,855 0/0:4,0:4:12:0,12,146 0/0:7,0:7:21:0,21,205 0/0:12,0:14:36:0,36,1030 0/0:3,0:3:6:0,6,87 0/0:2,0:2:6:0,6,60 0/0:7,0:7:21:0,21,226 0/0:7,0:7:21:0,21,229 0/0:8,0:8:21:0,21,265 0/0:4,0:4:6:0,6,90 ./.:0,0:0 0/0:7,0:7:21:0,21,229 0/0:2,0:2:6:0,6,59 0/0:2,0:2:6:0,6,56 chr1 7992047 . T C 45.83 SnpCluster BaseQRankSum=1.03;ClippingRankSum=0.00;DP=98;FS=0.000;MLEAC=1;MLEAF=0.014;MQ=60.00;MQ0=0;MQRankSum=-1.026e+00;ReadPosRankSum=-1.026e+00 GT:AD:DP:GQ:PL ./.:0,0:0 ./.:0,0:0 ./.:0,0:0 ./.:0,0:0 ./.:0,0:0 0/0:2,0:2:6:0,6,70 0/0:2,0:2:6:0,6,45 0/0:3,0:3:6:0,6,87 0/0:2,0:2:6:0,6,52 ./.:0,0:0 ./.:0,0:0 ./.:1,0:1 ./.:0,0:0 0/0:2,0:2:6:0,6,55 0/0:2,0:2:6:0,6,49 ./.:0,0:0 ./.:0,0:0 0/0:2,0:2:6:0,6,61 0/0:2,0:2:6:0,6,49 ./.:0,0:0 ./.:0,0:0 0/0:3,0:3:6:0,6,90 ./.:0,0:0 ./.:0,0:0 0/0:2,0:2:6:0,6,52 ./.:0,0:0 ./.:0,0:0 0/0:2,0:2:6:0,6,49 0/0:2,0:2:6:0,6,69 ./.:0,0:0 ./.:0,0:0 ./.:0,0:0 ./.:0,0:0 ./.:0,0:0 ./.:0,0:0 0/0:2,0:2:6:0,6,49 0/0:2,0:2:6:0,6,64 ./.:0,0:0 0/0:2,0:2:6:0,6,37 ./.:0,0:0 0/0:2,0:2:6:0,6,67 ./.:0,0:0 ./.:0,0:0 0/0:2,0:2:6:0,6,49 0/0:2,0:2:6:0,6,68 ./.:0,0:0 ./.:0,0:0 ./.:0,0:0 ./.:0,0:0 ./.:0,0:0 ./.:0,0:0 0/0:2,0:2:6:0,6,49 0/0:11,0:11:24:0,24,360 ./.:0,0:0 ./.:0,0:0 0/0:2,0:2:6:0,6,49 0/0:2,0:2:6:0,6,68 0/0:2,0:2:6:0,6,50 ./.:0,0:0 ./.:0,0:0 ./.:0,0:0 ./.:0,0:0 ./.:0,0:0 0/0:2,0:2:6:0,6,50 0/0:3,0:3:6:0,6,90 ./.:0,0:0 ./.:0,0:0 ./.:0,0:0 0/0:2,0:4:6:0,6,50 ./.:0,0:0 ./.:0,0:0 ./.:0,0:0 0/0:7,0:7:21:0,21,231 0/0:2,0:2:6:0,6,64 ./.:0,0:0 0/0:2,0:2:6:0,6,63 0/0:2,0:2:6:0,6,70 ./.:0,0:0 0/0:6,0:6:15:0,15,148 ./.:0,0:0 ./.:0,0:0 1/1:0,0:2:6:90,6,0 ./.:0,0:0 0/0:2,0:2:6:0,6,63 0/0:2,0:2:6:0,6,74 ./.:0,0:0 ./.:0,0:0 ./.:0,0:0 ./.:0,0:0 0/0:2,0:2:6:0,6,58 0/0:2,0:2:6:0,6,71 ./.:0,0:0 ./.:0,0:0 0/0:2,0:2:6:0,6,49

For Exome Seq now : chr2 111878571 . C T 93.21 PASS DP=634;FS=0.000;MLEAC=1;MLEAF=5.319e-03;MQ=60.00;MQ0=0;VQSLOD=14.19;culprit=MQ GT:AD:DP:GQ:PL 0/0:8,0:8:24:0,24,243 0/0:4,0:4:9:0,9,135 0/0:7,0:7:18:0,18,270 0/0:7,0:7:21:0,21,230 0/0:16,0:16:48:0,48,542 0/0:8,0:8:21:0,21,315 0/0:6,0:6:18:0,18,186 0/0:5,0:5:15:0,15,168 0/0:6,0:6:15:0,15,225 0/0:10,0:10:30:0,30,333 0/0:7,0:7:21:0,21,239 0/0:6,0:6:18:0,18,202 0/0:6,0:6:15:0,15,225 0/0:7,0:7:21:0,21,225 0/0:8,0:8:24:0,24,272 0/0:5,0:5:15:0,15,168 1/1:0,0:13:13:147,13,0 0/0:2,0:2:6:0,6,73 0/0:8,0:8:24:0,24,256 0/0:14,0:14:4:0,4,437 0/0:3,0:3:9:0,9,85 0/0:4,0:4:12:0,12,159 0/0:7,0:7:21:0,21,238 0/0:5,0:5:15:0,15,195 0/0:7,0:7:15:0,15,225 0/0:12,0:12:36:0,36,414 0/0:4,0:4:12:0,12,156 0/0:7,0:7:0:0,0,190 0/0:2,0:2:6:0,6,64 0/0:7,0:7:21:0,21,242 0/0:7,0:7:21:0,21,234 0/0:8,0:8:24:0,24,267 0/0:7,0:7:21:0,21,245 0/0:7,0:7:21:0,21,261 0/0:6,0:6:18:0,18,204 0/0:8,0:8:24:0,24,302 0/0:5,0:5:15:0,15,172 0/0:9,0:9:24:0,24,360 0/0:18,0:18:51:0,51,649 0/0:5,0:5:15:0,15,176 0/0:2,0:2:6:0,6,70 0/0:14,0:14:33:0,33,495 0/0:4,0:4:9:0,9,135 0/0:8,0:8:21:0,21,315 0/0:4,0:4:12:0,12,149 0/0:4,0:4:6:0,6,90 0/0:10,0:10:27:0,27,405 0/0:3,0:3:6:0,6,90 0/0:4,0:4:12:0,12,133 0/0:14,0:14:6:0,6,431 0/0:4,0:4:12:0,12,151 0/0:5,0:5:15:0,15,163 0/0:3,0:3:9:0,9,106 0/0:7,0:7:21:0,21,237 0/0:7,0:7:21:0,21,268 0/0:8,0:8:21:0,21,315 0/0:2,0:2:6:0,6,68 ./.:0,0:0 0/0:3,0:3:9:0,9,103 0/0:7,0:7:21:0,21,230 0/0:3,0:3:6:0,6,90 0/0:9,0:9:26:0,26,277 0/0:7,0:7:21:0,21,236 0/0:5,0:5:15:0,15,170 ./.:1,0:1 0/0:15,0:15:45:0,45,653 0/0:8,0:8:24:0,24,304 0/0:6,0:6:15:0,15,225 0/0:3,0:3:9:0,9,103 0/0:2,0:2:6:0,6,79 0/0:7,0:7:21:0,21,241 0/0:4,0:4:12:0,12,134 0/0:3,0:3:6:0,6,90 0/0:5,0:5:15:0,15,159 0/0:4,0:4:12:0,12,136 0/0:5,0:5:12:0,12,180 0/0:11,0:11:21:0,21,315 0/0:13,0:13:39:0,39,501 0/0:3,0:3:9:0,9,103 0/0:8,0:8:24:0,24,257 0/0:2,0:2:6:0,6,73 0/0:8,0:8:24:0,24,280 0/0:4,0:4:12:0,12,144 0/0:4,0:4:9:0,9,135 0/0:8,0:8:24:0,24,298 0/0:4,0:4:12:0,12,129 0/0:5,0:5:15:0,15,184 0/0:2,0:2:6:0,6,62 0/0:2,0:2:6:0,6,65 0/0:9,0:9:27:0,27,337 0/0:7,0:7:21:0,21,230 0/0:7,0:7:21:0,21,239 0/0:5,0:5:0:0,0,113 0/0:11,0:11:33:0,33,369 0/0:7,0:7:21:0,21,248 0/0:10,0:10:30:0,30,395

Thanks for your help.


Created 2013-05-07 16:55:34 | Updated | Tags: qualbydepth variantfiltration
Comments (2)

Hello,

I am hoping to perform hard filtering on some variants from a sequencing project where, unfortunately, I do not have information from enough samples for VQSR. I was planning to filter on the QD value, but it seems to be very low for variants that seem reasonable. Example:

chr7    55249063 .       G       A       225     PASS
AC=1;AC1=1;AF=0.500;AF1=0.5;AN=2;BaseQRankSum=1.307;DP=4582;DP4=937,935,1299,1316;Dels=0.00;FQ=225;FS=0.323;
HaplotypeScore=390.2899;MQ=59.95;MQ0=0;MQRankSum=-1.910;PV4=0.81,1,1,1;QD=0.05;ReadPosRankSum=4.848;VDB=0.0003
GT:AD:GQ:PL     0/1:1917,2657:99:255,0,255

This variant is shown in IGV in the attached file- it looks to be a true positive, but because of the high depth, QD is very low. Based on the QD documentation, it looks as QD simply cannot be used to filter high-coverage data, since the value is QUAL/unfiltered depth.

Is there an alternative annotation that expresses the same measure, since QD is recommended in all the hard filtering documentation? Would GQ be a good substitute?

Your help is much appreciated!


Created 2012-11-05 08:12:48 | Updated | Tags: unifiedgenotyper qualbydepth variantannotator error
Comments (14)

I had annotated raw indel file (given by UnifiedGenotyper), 1000G_omni2.5.b37.sites.vcf and hapmap_3.3.b37.sites.vcf with all possible annotations including QD (QualByDepth) using VariantAnnotator. However, i got an error when i tried to run VariantRecalibrator. It was complaing that QD has not been found in training variant. Is QD important annotation for indel filtering. Can it be ignored ?

P.S. - i did not use sample bam file while annotating training data set.

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INFO  15:11:55,999 RMDTrackBuilder - Loading Tribble index from disk for file NCBI_dbsnp_for_GATK.vcf
INFO  15:12:21,650 TraversalEngine -  chr1:128346793        1.98e+07   30.0 s        1.5 s      4.1%        12.1 m    11.6 m
INFO  15:12:51,650 TraversalEngine -  chr9:130658800        5.26e+07   60.0 s        1.1 s     53.9%       111.2 s    51.2 s
INFO  15:13:13,618 VariantDataManager - QD:      mean = NaN      standard deviation = NaN
INFO  15:13:16,417 GATKRunReport - Uploaded run statistics report to AWS S3
##### ERROR ------------------------------------------------------------------------------------------
##### ERROR A USER ERROR has occurred (version 2.1-13-g1706365):
##### ERROR The invalid arguments or inputs must be corrected before the GATK can proceed
##### ERROR Please do not post this error to the GATK forum
##### ERROR
##### ERROR See the documentation (rerun with -h) for this tool to view allowable command-line arguments.
##### ERROR Visit our website and forum for extensive documentation and answers to
##### ERROR commonly asked questions http://www.broadinstitute.org/gatk
##### ERROR
##### ERROR MESSAGE: Bad input: Values for QD annotation not detected for ANY training variant in the input callset. VariantAnnotator may be used to add these annotations. See http://www.broadinstitute.org/gsa/wiki/index.php/VariantAnnotator
##### ERROR ------------------------------------------------------------------------------------------