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Variant recalibration issue
I am feeling a bit lost and I think I need an external opinion so here I am. Actually, I have a cohort of 25 samples of non-human whole genome sequencing data (21 HiSeq + 4 MiSeq), I also have a dbSNP-like vcf file which I made myself based on the SNP data publicly available from multiple studies on different strains belonging to the organism I am dealing with. The issue is that first the dbSNP-like vcf file is lacking to indel data and second the validation degree of confidence of that SNP call set is not known so it has to be assumed as low. Knowing that, I think I should operate roughly as follow:
1- VariantFiltration on my raw_whole_cohort.vcf (GenotypeGVCFs output) with stringent enough parameters to generate both SNP call set (hapmap_like.vcf) and Indel call set (mills_like.vcf) that both might be assumed as at high degree of confidence in the next steps.
2- VariantRecalibrator -mode SNP -resource:hapmap,known=false,training=true,truth=true,prior=15.0 hapmap_like.vcf -resource:dbsnp,known=true,training=false,truth=false,prior=2.0 dbSNP-like.vcf
3- VariantRecalibrator -mode INDEL -resource:mills,known=true,training=true,truth=true,prior=12.0 mills_like.vcf
4- ApplyRecalibration -input raw_whole_cohort.vcf -mode SNP -o whole_cohort_snp_recalibrated_indel_raw.vcf
5- ApplyRecalibration -input whole_cohort_snp_recalibrated_indel_raw.vcf -mode INDEL -o whole_cohort_snp_recalibrated_indel_recalibrated.vcf
Hence, I would like to ask where am I wrong (short version). Can I practically do the above steps ? and is it quite rational to unroll it that way ? (long version)
Thanks for help.