experiment: target enrichment and sequencing using Illumina platform
raw VCF file from UnifiedGenotyper -> Variantrecalibrator
i got the following error, any potential explanation? Thanks
"
##### 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 --minNumBadV ariants arugment but this is unsafe.
"
script used:
java -Xmx4g -jar GenomeAnalysisTK.jar -T VariantRecalibrator -T \
-mode BOTH -nt 4 \
-R hg19_all_MT.fasta \
-input two.final.vcf \
-resource:hapmap,known=false,training=true,truth=true,prior=15.0 hapmap_3.3.b37.sites.fy_left.vcf \
-resourcemni,known=false,training=true,truth=false,prior=12.0 1000G_omni2.5.b37.site.fy.vcf \
-resource:dbsnp,known=true,training=false,truth=false,prior=8.0 dbsnp137_sort_fy_left.vcf \
-recalFile two.final.vcf.reca \
-tranchesFile two.final.vcf.tranches \
-rscriptFile two.final.vcf.R \
-tranche 100.0 -tranche 99.9 -tranche 99.0 -tranche 90.0 -tranche 85.0 -tranche 80.0 \
-an QD -an HaplotypeScore -an MQRankSum -an ReadPosRankSum -an MQ -an FS -an HRun
raw VCF file from UnifiedGenotyper -> Variantrecalibrator
i got the following error, any potential explanation? Thanks
"
##### 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 --minNumBadV ariants arugment but this is unsafe.
"
script used:
java -Xmx4g -jar GenomeAnalysisTK.jar -T VariantRecalibrator -T \
-mode BOTH -nt 4 \
-R hg19_all_MT.fasta \
-input two.final.vcf \
-resource:hapmap,known=false,training=true,truth=true,prior=15.0 hapmap_3.3.b37.sites.fy_left.vcf \
-resourcemni,known=false,training=true,truth=false,prior=12.0 1000G_omni2.5.b37.site.fy.vcf \
-resource:dbsnp,known=true,training=false,truth=false,prior=8.0 dbsnp137_sort_fy_left.vcf \
-recalFile two.final.vcf.reca \
-tranchesFile two.final.vcf.tranches \
-rscriptFile two.final.vcf.R \
-tranche 100.0 -tranche 99.9 -tranche 99.0 -tranche 90.0 -tranche 85.0 -tranche 80.0 \
-an QD -an HaplotypeScore -an MQRankSum -an ReadPosRankSum -an MQ -an FS -an HRun