I've generated vcf file using samtools mpileup. I understood all the fields in vcf file, except INFO column.
DP=55;VDB=2.813188e-01;AF1=1;AC1=2;DP4=0,0,15,15;MQ=44;FQ=-117
From this I can interpret DP, MQ and FQ, but what are VDB, AF1, AC1, Dp4?
Can anyone explain to me?
Though the question has already been answered but I would just paste the header of the vcf file that samtools + bcftools generates as it might be useful.
##samtoolsVersion=0.1.18 (r982:295)
##INFO=<ID=DP,Number=1,Type=Integer,Description="Raw read depth">
##INFO=<ID=DP4,Number=4,Type=Integer,Description="# high-quality ref-forward bases, ref-reverse, alt-forward and alt-reverse bases">
##INFO=<ID=MQ,Number=1,Type=Integer,Description="Root-mean-square mapping quality of covering reads">
##INFO=<ID=FQ,Number=1,Type=Float,Description="Phred probability of all samples being the same">
##INFO=<ID=AF1,Number=1,Type=Float,Description="Max-likelihood estimate of the first ALT allele frequency (assuming HWE)">
##INFO=<ID=AC1,Number=1,Type=Float,Description="Max-likelihood estimate of the first ALT allele count (no HWE assumption)">
##INFO=<ID=G3,Number=3,Type=Float,Description="ML estimate of genotype frequencies">
##INFO=<ID=HWE,Number=1,Type=Float,Description="Chi^2 based HWE test P-value based on G3">
##INFO=<ID=CLR,Number=1,Type=Integer,Description="Log ratio of genotype likelihoods with and without the constraint">
##INFO=<ID=UGT,Number=1,Type=String,Description="The most probable unconstrained genotype configuration in the trio">
##INFO=<ID=CGT,Number=1,Type=String,Description="The most probable constrained genotype configuration in the trio">
##INFO=<ID=PV4,Number=4,Type=Float,Description="P-values for strand bias, baseQ bias, mapQ bias and tail distance bias">
##INFO=<ID=INDEL,Number=0,Type=Flag,Description="Indicates that the variant is an INDEL.">
##INFO=<ID=PC2,Number=2,Type=Integer,Description="Phred probability of the nonRef allele frequency in group1 samples being larger (,smaller) than in group2.">
##INFO=<ID=PCHI2,Number=1,Type=Float,Description="Posterior weighted chi^2 P-value for testing the association between group1 and group2 samples.">
##INFO=<ID=QCHI2,Number=1,Type=Integer,Description="Phred scaled PCHI2.">
##INFO=<ID=PR,Number=1,Type=Integer,Description="# permutations yielding a smaller PCHI2.">
##INFO=<ID=VDB,Number=1,Type=Float,Description="Variant Distance Bias">
##INFO=<ID=PASS,Number=0,Type=Flag,Description="Variants that passed filtering (step1) at Samtool level">
##INFO=<ID=COMMON_PASS_FAIL,Number=0,Type=Flag,Description="Variants that passed filtering at Samtool level (step1) but failed at GATK level (step2)">
##INFO=<ID=COMMON_PASS_PASS,Number=0,Type=Flag,Description="Variants that passed filtering at Samtool level (step1)and GATK level (step2)">
##INFO=<ID=DUAL_VARIANT_PASS_FAIL,Number=0,Type=Flag,Description="Position called as both SNP and Indel and post-filtering eliminated one effect based on some criteria">
##INFO=<ID=DUAL_VARIANT_PASS_PASS,Number=0,Type=Flag,Description="Position called as both SNP and Indel and post-filtering approved both of them. Must be used with caution.">
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version 2.3.6
I've changed the category of this post from 'Blog' to 'Question', because it is actually a question and not a blog post. Have a look at the VCF format specifications to have an idea on how to interpret the INFO field. You should also look at how these elements are defined in the header of the VCF file itself.
sorry to ask basic
please explain me following things
AF1: Max-likelihood estimate of the first ALT allele frequency (assuming HWE)
DP4=high-quality ref-forward bases, ref-reverse, alt-forward and alt-reverse bases
VDB=Variant Distance Bias (v2) for filtering splice-site artefacts in RNA-seq data
PV4=P-values for strand bias, baseQ bias, mapQ bias and tail distance bias
Which parts of those don't you understand?
How samtools count values for AF1 Ac1 VDB PV4 DP4?
By "count" do you mean "calculate"? Some of this is described in a PDF from Heng Li. VDB is described schematically in this PDF. You should be able to find anything else with google.
i want know how significant they are when finding SNPs in human for clinical purpose ?
Well, it's probably a good idea to perform some filtering according to them and check if any SNPs of particular interest show any particular bias. That's basically the point of these various metrics.
thanks Devon Ryan !!