Question: I tried testing MuSiC (v0.4) but have gotten substantially more significantly mutated genes then expected. For my own pan-cancer dataset it returned thousands of genes, and for a testing set of published Ovarian somatic mutations returned ~350. I used the default settings for these runs using `calc-wig-covg`, `calc-bmr`, and `smg` (so I didn't need the BAM files). I obtained the ovarian MAF file from synapse (https://www.synapse.org/#!Synapse:syn1729383 ), coverage wig files from [firehose](http://gdac.broadinstitute.org/runs/stddata__latest/data/OV/) (recommended on this [post](https://www.biostars.org/p/168558/)), and recommended ROI file ([here](https://github.com/ding-lab/calc-roi-covg/tree/master/data)). Is there anything I'm missing or are there parameter tweaks or changes so MuSiC reports a more reasonable number of significantly mutated genes?

I saw a couple of parameters that might be helpful. One was the `--bmr-groups` option in `genome music bmr calc-bmr`, which appears to group samples into a certain number of similarly mutated groups. Is there a recommended way to set up the number of BMR groups? Another was the `--bmr-modifier-file` option in `genome music smg` as a multiplication factor for the background mutation rate for certain genes. Is there a standard/recommended BMR modifier file?

Answer: MuSiC's SMG test is very sensitive to false-positive mutations, especially recurrent ones like germline calls or alignment artifacts. So use super strict filters on your VCFs [with this tool][1], and annotate with [vcf2maf][2] for `common_variant` tags based on ExAC.

Genes in genomic regions that are somehow protected from somatic alteration, are inevitably mutated in hypermutated tumor types like endometrial, melanoma, colorectal, etc. Since we're testing for "genes that are mutated more often than the background mutation rate (BMR)", such genes show up as significantly mutated genes (SMGs). So another quick fix is to simply exclude hypermutated samples from the MAF, say anything with more than 200 mutations. And use a stricter `--max-fdr` for cancer types with higher overall BMRs. The 20% default was meant for AML, the TCGA cohort with the lowest BMRs.

If your results don't get better with the steps above, then read on below, to understand what's going on under the hood.

MuSiC's `calc-bmr` categorizes BMRs for transitions/transversions, CpG, CG, AT sites, indels, that `smg` will test each gene's categorized MRs against. But we know how these vary a lot between cancer types. There's a mutation spectra from smoking, and another from UV, another associated with altered mismatch repair genes like POLE, etc. So it makes sense to build different BMRs for different subsets of your pan-cancer dataset. A simple solution is to run MuSiC separately per cancer type. Or you could split samples by exposure type (e.g. smoking/UV), histologic subtype (e.g. lobular/ductal), mutation signatures (e.g. [Alexandrov et al.][3]), etc. We have to compromise on having enough mutations to build a decent representative BMR per category/subcohort. There are some arguments in MuSiC you can use to control such compromises. See the documentation for `--bmr-groups` in [calc-bmr][4]. And `--bmr-modifier-file` for the [smg test][5].

P.S. I'm surprised it took 4 years for this question to come up on biostars! I know collaboration can be a bottleneck, but it doesn't hurt to ask. Kudos Collin.

[1]: https://github.com/ucscCancer/fpfilter-tool/blob/master/fpfilter.pl#L272
[2]: https://github.com/mskcc/vcf2maf
[3]: http://www.ncbi.nlm.nih.gov/pubmed/23945592
[4]: http://gmt.genome.wustl.edu/packages/genome-music/genome-music-bmr-calc-bmr.html
[5]: http://gmt.genome.wustl.edu/packages/genome-music/genome-music-smg.html