BQSR Score recalibration for NovaSeq Data
Hi,
I am working with reads generated from NovaSeq 6000 System, where read quality scores are generated by w Real Time Analysis 3 (RTA3) software. In this pipeline, quality scores are binned into 3 read quality scores: 11, 23, and 37
My question is how should BQSR? Can I still safely preform BQSR on this binned data?
Further I am getting some negative covariate values, how should I interpret this? Below is an example.
ReadGroup QualityScore CovariateValue CovariateName EventType EmpiricalQuality Observations Errors
GAATAGGAGC 11 -1 Cycle M 11.0000 4027044 331593.00
GAATAGGAGC 11 -10 Cycle M 12.0000 2988853 179747.00
GAATAGGAGC 11 -100 Cycle M 10.0000 6836097 634522.00
GAATAGGAGC 11 -101 Cycle M 10.0000 7085606 670666.00
GAATAGGAGC 11 -102 Cycle M 10.0000 7328714 702378.00
GAATAGGAGC 11 -103 Cycle M 10.0000 7746444 756268.00
GAATAGGAGC 11 -104 Cycle M 10.0000 7705322 753964.00
GAATAGGAGC 11 -105 Cycle M 10.0000 7677938 747428.00
GAATAGGAGC 11 -106 Cycle M 10.0000 7390798 706122.00
GAATAGGAGC 11 -107 Cycle M 10.0000 7679508 744698.00
GAATAGGAGC 11 -108 Cycle M 10.0000 7897758 774895.00
GAATAGGAGC 11 -109 Cycle M 10.0000 7582387 734061.00
GAATAGGAGC 11 -11 Cycle M 12.0000 2926815 189634.00
GAATAGGAGC 11 -110 Cycle M 10.0000 7808037 760562.00
GAATAGGAGC 11 -111 Cycle M 10.0000 8712375 897978.00
GAATAGGAGC 11 -112 Cycle M 10.0000 8358118 844592.00
GAATAGGAGC 11 -113 Cycle M 10.0000 7690245 745209.00
GAATAGGAGC 11 -114 Cycle M 10.0000 8169472 813646.00
GAATAGGAGC 11 -115 Cycle M 10.0000 8206542 823844.00
GAATAGGAGC 11 -116 Cycle M 10.0000 8211105 823020.00
GAATAGGAGC 11 -117 Cycle M 10.0000 8895160 932012.00
GAATAGGAGC 11 -118 Cycle M 10.0000 8460555 858210.00
GAATAGGAGC 11 -119 Cycle M 10.0000 8316532 831433.00
GAATAGGAGC 11 -12 Cycle M 12.0000 2935154 187220.00
GAATAGGAGC 11 -120 Cycle M 10.0000 8658915 884996.00
GAATAGGAGC 11 -121 Cycle M 10.0000 8676612 892961.00
GAATAGGAGC 11 -122 Cycle M 10.0000 8279606 821672.00
GAATAGGAGC 11 -123 Cycle M 10.0000 8332575 825292.00
GAATAGGAGC 11 -124 Cycle M 10.0000 8359717 831353.00
GAATAGGAGC 11 -125 Cycle M 10.0000 8913775 915782.00
GAATAGGAGC 11 -126 Cycle M 10.0000 9271802 966521.00
GAATAGGAGC 11 -127 Cycle M 10.0000 9644887 1037433.00
GAATAGGAGC 11 -128 Cycle M 10.0000 9293909 967864.00
GAATAGGAGC 11 -129 Cycle M 10.0000 8897379 902030.00
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You should be able to perform BQSR just as fine since we are also using the very same instruments within our production workflows.
If you are still curious about how your data looks like after recalibration you can check that with AnalyzeCovariates tool and check your graphs. They should be similar to this one
Regards.
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