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Hi there,
I am working on exome data for 10 trios and am not an expert in the field. Our vendor provided us the VCF files and I can see that the Filter column consists of three categories 'PASS', 'VQSRTrancheSNP99.90to100.00' and 'VQSRTrancheSNP99.50to99.90' (I did not include info for indels).
My question is, I am not sure if I can consider variants categorized into 'VQSRTrancheSNP99.90to100.00' and 'VQSRTrancheSNP99.50to99.90'?
I checked the following explanation in an article from GATK (https://www.broadinstitute.org/gatk/...rticle?id=1259). However, it would be really helpful if someone can guide me whether I can consider all the three categories or only 'PASS'.
SNP specific recommendations:
For SNPs we used HapMap 3.3 and the Omni 2.5M chip as our truth set. We typically seek to achieve 99.5% sensitivity to the accessible truth sites, but this is by no means universally applicable: you will need to experiment to find out what tranche cutoff is right for your data. Generally speaking, projects involving a higher degree of diversity in terms of world populations can expect to achieve a higher truth sensitivity than projects with a smaller scope.
Thanks in advance.
Best,
I am working on exome data for 10 trios and am not an expert in the field. Our vendor provided us the VCF files and I can see that the Filter column consists of three categories 'PASS', 'VQSRTrancheSNP99.90to100.00' and 'VQSRTrancheSNP99.50to99.90' (I did not include info for indels).
My question is, I am not sure if I can consider variants categorized into 'VQSRTrancheSNP99.90to100.00' and 'VQSRTrancheSNP99.50to99.90'?
I checked the following explanation in an article from GATK (https://www.broadinstitute.org/gatk/...rticle?id=1259). However, it would be really helpful if someone can guide me whether I can consider all the three categories or only 'PASS'.
SNP specific recommendations:
For SNPs we used HapMap 3.3 and the Omni 2.5M chip as our truth set. We typically seek to achieve 99.5% sensitivity to the accessible truth sites, but this is by no means universally applicable: you will need to experiment to find out what tranche cutoff is right for your data. Generally speaking, projects involving a higher degree of diversity in terms of world populations can expect to achieve a higher truth sensitivity than projects with a smaller scope.
Thanks in advance.
Best,