Hi all, I would like to know which best practices are available for BQRS only and, before that, I need some detail on how BQRS works. In particular: suppose I have exome data for multiple samples and a set of intervals that covers all exome baits. One way to perform BQRS is to run the BaseRecalibrator walker on the whole file, using all BAM files available and have a single covariate table; I run PrintReads on each BAM file using the covariate table. Another way, is to run in the very same way feeding with the file containing intervals. This speeds things up because the walker doesn't have to check the whole genome space. Another way, faster, is to run a single BaseRecalibrator process for each interval. This results in a number of covariate tables equal to the number of intervals. I then run the same number of PrintReads and merge the results. If the latter would be enough, I know I can run really fast, but I'm afraid I may get some biased covariate table. I may apply the same procedure on whole genome, choosing the proper set of intervals
Dear all, I have a set of 48 exomes which were analysed according to the best practices (using GATK-2.2-3 and HaplotypeCaller). According to the VQRS I have this first level of "uncertainty":
##FILTER=<ID=VQSRTrancheBOTH90.00to99.00,Description="Truth sensitivity tranche level for BOTH model at VQS Lod: -1.3455 <= x < 2.62">
that sets filter=PASS for variants with VQSLOD >= 2.62. I also have an external validation of some SNPs, 3 out of 20 have a VQSLOD lower than 2.62 (1.24, .1.37 and 1.69). Now the question: should I trust the validation and set the filter to, say, VQSLOD >= 1.2 or keep the GATK filter? What is your experience about this?