Hi there,

I have a tumor and a normal BAM file and am preparing to run base recalibration.

I was planning on calling variants on the normal and using that, in addition to dbSNP, as input for recalibration of tumor BAM(s), e.g.:

```
gatk BaseRecalibrator \
  -I tumor.bam \
  -R hg38.fasta \
  --known-sites normal.vcf  \
  --known-sites dbSNP_hg38.vcf  \
  -O tumor_recal.table
```
Before producing the normal VCF however, **it's not clear to me whether I should run base recalibration on the normal BAM**. If this is advised, I had planned using dbSNP as the known sites (for normal), e.g.:

```
gatk BaseRecalibrator \
  -I normal.bam \
  -R hg38.fasta \
  --known-sites dbSNP_hg38.vcf  \
  -O normal_recal.table
```

**Alternatively, I could keep things simple and run base recalibration on both tumor and normal using dbSNP only.**

Is one of these workflows more preferable?
Any clarity here would be much appreciated. Thanks!

The current "best-practice" is to always do BQSR with the latest ([and largest][1]) dbSNP VCF on all samples - tumor or normal, FFPE or blood, etc. Per discussions in [this post][2] and [this post][3], BQSR can benefit slightly if you provide a "bootstrap of known variants" unique to your samples, either somatic/germline variants found in your tumor/normal. However, you are effectively running your primary analysis pipeline twice (which is overkill), potentially amplifying false-positive variants (from your first-pass variant list), and potentially breaking compatibility of your BAMs with secondary analysis pipelines (e.g. downstream false-positive filters that use BQ).

There are also recent arguments against using BQSR at all, and instead flagging false-positives based on [Base Quality drop-off][4] (at the ends of reads, strand-bias, etc.). You can find an [old Perl script here][5] that implements such BQD filters. I also just found this tweet from Geraldine of GATK saying they're thinking of [dropping BQSR from the best-practices][6] - presumably because the high computational-expense of BQSR is not reasonable when the quality of DNA sequencing has improved so much. I would still recommend BQSR when re-analyzing old FASTQs, or when comparing FASTQs from a mix of different sequencers (e.g. HiSeq and NovaSeq).

  [1]: https://www.semanticscholar.org/paper/Effect-of-Database-Size-in-the-Genetic-Variants-Kim-Lee/448b6a85026b3e1a0334d315aebf8ce77269afba
  [2]: https://www.biostars.org/p/104852/
  [3]: https://sites.google.com/a/broadinstitute.org/legacy-gatk-forum-discussions/2017-06-18-2017-01-18/9713-BQSR-with-MuTect2-use-it-or-not-
  [4]: https://support.illumina.com/content/dam/illumina-support/help/Illumina_DRAGEN_Bio_IT_Platform_v3_7_1000000141465/Content/SW/Informatics/Dragen/BaseQualiltyDropoff_fDG.htm
  [5]: https://github.com/genome/fpfilter-tool/blob/master/fpfilter.pl#L123-L133
  [6]: https://twitter.com/vdageraldine/status/1296181178534440963