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Training data for VariantRecalibrator (Mouse)

freshtuofreshtuo Posts: 4
edited January 2013 in Ask the GATK team

Dear all,

I was calling SNP from Mouse samples using GATK and was in the step of "Variant quality score recalibration". The VariantRecalibrator walker asked me to provide training sets for mouse SNPs. The only SNP data (for the USCS mm9 assembly) I can find right now is the dbSNP. So I tried the run VariantRecalibrator like this:

java -Xmx4g -jar GenomeAnalysisTK.jar
-T VariantRecalibrator
-R Refseq.fa
-input snps.raw.vcf
-resource:dbsnp,known=true,training=false,truth=false,prior=6.0 snp128.vcf
-an QD -an HaplotypeScore -an MQRankSum -an ReadPosRankSum -an FS -an MQ -an InbreedingCoeff
-mode BOTH
-recalFile output.recal
-tranchesFile output.tranches
-rscriptFile output.plots.R

However, the program asked for more:

ERROR MESSAGE: Invalid command line: No training set found! Please provide sets of known polymorphic loci marked with the training=true ROD binding tag. For example, -resource:hapmap,VCF,known=false,training=true,truth=true,prior=12.0 hapmapFile.vcf
ERROR ------------------------------------------------------------------------------------------

I was wondering if I can change the parameters by setting both the training/truth to true:

-resource:dbsnp,known=true,training=true,truth=true,prior=6.0 snp128.vcf

or should I ignore the "-resource" option at the cost of being less accurate?

Any suggestions would be greatly appreciated.

Thanks!

Tuo

Post edited by Geraldine_VdAuwera on

Best Answer

Answers

  • rpoplinrpoplin Posts: 122 ✭✭✭

    Hi there,

    Yeah, the text of the user exception error message is telling you that it needs some set of sites to use as a training set. You can indicate this by marking one of the resources as training=true

    Your proposed parameters make a lot of sense if that is the only database of variants that you have for your organism.

    I hope that helps,

  • I got this error when using the VariantRecalibrator:

    ERROR MESSAGE: Bad input: Error during negative model training. Minimum number of variants to use in training is larger than the whole call set. One can attempt to lower the --minNumBadVariants arugment but this is unsafe.

    What shoild I do?

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