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Varaint Recalibrator issues.


I am Krithika S. I am working Human Whole Genome data analysis. I am getting the following only particular chromosome only. I am getting this same error around 6 chromosome.

My command:

Java -jar GenomeAnalysisTK.jar -T VariantRecalibrator -R reference.fasta --resource:dbsnp,known=true,training=false,truth=false,prior=2.0 dbsnp138.vcf --resource:hapmap,known=false,training=true,truth=true,prior=15.0 hapmap.vcf --resource:omni,known=false,training=true,truth=true,prior=12.0 omni.vcf.gz --resource:1000G,known=false,training=true,truth=false,prior=10.0 high_confidence.vcf -an QD -an FS -an DP -an SOR -an ReadPosRankSum -an MQRankSum -an InbreedingCoeff --maxGaussians 4 -mode SNP -allPoly -tranche 100.0 -tranche 99.9 -tranche 99.8 -tranche 99.7 -tranche 99.5 -tranche 99.3 -tranche 99.0 -tranche 98.5 -tranche 98.0 -tranche 97.0 -tranche 95.0 -tranche 90.0 --input chr8.g.vcf.gz -nt 24 -recalFile chr8.recalibrate_SNP.recal -tranchesFile chr8.recalibrate_SNP.tranches -rscriptFile chr8.recalibrate_SNP_plots.R

INFO 14:04:46,776 VariantRecalibratorEngine - Finished iteration 0.
INFO 14:04:46,777 VariantRecalibratorEngine - Convergence after 3 iterations!

ERROR ------------------------------------------------------------------------------------------
ERROR A USER ERROR has occurred (version 3.7-0-gcfedb67):
ERROR This means that one or more arguments or inputs in your command are incorrect.
ERROR The error message below tells you what is the problem.
ERROR If the problem is an invalid argument, please check the online documentation guide
ERROR (or rerun your command with --help) to view allowable command-line arguments for this tool.
ERROR Visit our website and forum for extensive documentation and answers to
ERROR commonly asked questions https://software.broadinstitute.org/gatk
ERROR Please do NOT post this error to the GATK forum unless you have really tried to fix it yourself.
ERROR MESSAGE: NaN LOD value assigned. Clustering with this few variants and these annotations is unsafe. Please consider raising the number of variants used to train the negative model (via --minNumBadVariants 5000, for example)

Please help me to fix this issues

Krithika S


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