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Created 2014-02-20 15:30:32 | Updated | Tags: vqsr snpcalling
Comments (2)

Hi - I have a question on how best to do VQSR on my samples. One of the readgroups for my individuals are from genomic DNA and have very even coverage (around 10x) while the remaining 4-5 readgroups in the individuals are from Whole Genome Amplified (WGA) DNA. The WGA readgruops have very uneven coverage ranging from 0 to over a 1000 with a mean of around 30x (see attached image, blue is wga and turquoise is genomic, y-axis is depth and x-axis is sliding windows along a chromosome). So I have WGA and genomic libs for each individual and their coverage distributions are very different.

We tested different SNP calling (Unified Genotyper) and VSQR strategies and at the moment we think a strategy where we call and vqsr the genomic and wga libs separately and then combine them in the end works best. However I am interested on what the GATK team would have done in such a case. The reason we are doing it separately is that we think the vqsr on the combined libs would not be wise since there is such difference in the depth (and strand bias) between the WGA and genomic readgroups. If there was a way in the VQSR step to incorporate read group difference and include it in the algortihm it could maybe solve such a problem - but as far as I can see there is no such thing (we used the ReadGroupblacklist option when calling the RGs separately) - but for VQSR there is not a "include read group effects" kind of option. Or does it intrinsically include read group information in the machine learning step? By the way - we did the BQSR so the qualities would have been adjusted according to readgroup effects. But still there does seem to be a noticeable difference between the VQSR results we get from WGA vs genomic read groups (for instance WGA readgroups have consistently lower Hz than genomic readgroups calls - which we think is due to strand bias). From the VQSR plots it is clear that many SNPs are excluded in the WGA RGs due to strand bias and DP - however the bias is still visible after VQSR.

Sorry for the elaborate explanation - however - my question is how the GATK team would have handled SNPcalling and VQSR if the RG depth vary that much as in the attached image case.


Created 2014-02-20 15:27:51 | Updated | Tags: vqsr snpcalling
Comments (0)

Hi - I have a question on how best to do VQSR on my samples. One of the readgroups for my individuals are from genomic DNA and have very even coverage (around 10x) while the remaining 4-5 readgroups in the individuals are from Whole Genome Amplified (WGA) DNA. The WGA readgruops have very uneven coverage ranging from 0 to over a 1000 with a mean of around 30x (see attached image, blue is wga and turquoise is genomic, y-axis is depth and x-axis is sliding windows along a chromosome). So I have WGA and genomic libs for each individual and their coverage distributions are very different.

We tested different SNP calling (Unified Genotyper) and VSQR strategies and at the moment we think a strategy where we call and vqsr the genomic and wga libs separately and then combine them in the end works best. However I am interested on what the GATK team would have done in such a case. The reason we are doing it separately is that we think the vqsr on the combined libs would not be wise since there is such difference in the depth (and strand bias) between the WGA and genomic readgroups. If there was a way in the VQSR step to incorporate read group difference and include it in the algortihm it could maybe solve such a problem - but as far as I can see there is no such thing (we used the ReadGroupblacklist option when calling the RGs separately) - but for VQSR there is not a "include read group effects" kind of option. Or does it intrinsically include read group information in the machine learning step? By the way - we did the BQSR so the qualities would have been adjusted according to readgroup effects. But still there does seem to be a noticeable difference between the VQSR results we get from WGA vs genomic read groups (for instance WGA readgroups have consistently lower Hz than genomic readgroups calls - which we think is due to strand bias). From the VQSR plots it is clear that many SNPs are excluded in the WGA RGs due to strand bias and DP - however the bias is still visible after VQSR.

Sorry for the elaborate explanation - however - my question is how the GATK team would have handled SNPcalling and VQSR if the RG depth vary that much as in the attached image case.


Created 2013-10-25 11:00:08 | Updated | Tags: multi-sample snpcalling
Comments (1)

Hi,

I have performed multisample SNP calling and also single sample SNP calling for 8 individuals. Some of the variants detected by multi-sample SNP calling were absent in variants detected by single sample snp calling.

For example, Multisample SNP calling output:

CHROM POS ID REF ALT QUAL FILTER INFO FORMAT BC210 BC212 BC214 BC217 BC219 BC226 BC229 BC230

chr1 389 . T G 178.50 . AC=6;AF=0.375;AN=16;BaseQRankSum=-0.775;DP=202;Dels=0.00;FS=26.344;HaplotypeScore=0.7725;MLEAC=6;MLEAF=0.375;MQ=12.37;MQ0=75;MQRankSum=-4.220;QD=1.08;ReadPosRankSum=0.073 GT:ADP:GQ:PL 0/1:21,21:42:35:35,0,116 0/0:11,7:18:9:0,9,68 0/1:13,5:18:18:18,0,58 0/1:8,14:21:39:39,0,59 0/1:16,16:32:59:66,0,59 0/1:14,7:20:43:43,0,85 0/1:10,20:29:19:21,0,19 0/0:9,9:18:6:0,6,44

Output from Individual SNP calling but merged into a single VCF file:

CHROM POS ID REF ALT QUAL FILTER INFO FORMAT BC210 BC212 BC214 BC217 BC219 BC226 BC229 BC230

chr1 389 . T G 37.77 . AC=1;AF=0.500;AN=2;BaseQRankSum=-0.189;DP=33;Dels=0.00;FS=3.979;HaplotypeScore=2.9800;MLEAC=1;MLEAF=0.500;MQ0=12;MQ=12.44;MQRankSum=-1.474;QD=1.14;ReadPosRankSum=-0.794;SF=5 GT:GQP:PL:AD . . . . 0/1:59:32:66,0,59:16,16 . . .

Here we can observe that the variant is present in only 1 individual from single sample SNP calling, whereas it is present in all the individuals with multi-sample SNP calling.

Could someone comment, which of these would be reliable results?