Suggested pipelines for finding somatic mutations using RNAseq in normal and tumor cells

Question

[Sharon]

Hi All

I am looking for pipelines to find somatic mutations in tumor and normal cells.

Should I use GATK pipeline for RNAseq variant calling here (https://gatkforums.broadinstitute.org/gatk/discussion/3891/calling-variants-in-rnaseq); apply it on each normal cell then apply it each on tumor cell then find the mutations in tumor cells that are not found in normal cells?

Thanks

[WouterDeCoster]

I don't believe that RNA-seq is an appropriate technology for your application.

[Sharon]

But there is a variant calling recommended for RNAseq I think ?

[WouterDeCoster]

Yes, but just because you can doesn't mean you should. That pipeline is also intended for germline variants, not for somatic AFAIK.

[Sharon]

I mean there is interest in finding somatic mutations from RNAseq, there are publications on that:

http://www.sciencedirect.com/science/article/pii/S0888754316300210

[Kevin Blighe]

I'm not sure that this would be my first choice as an experimental set-up, i.e., to call variants from RNA-seq data. The idea appears to be about saving money for cash-strapped research groups. However, sacrificing quality in the sake of money-saving invariably runs to disaster later down the line, as we see time and time again in various facets of life.

[775056648]

Have you solve the question? I also meet the same problem recently.

Answer 1

[Kristoffer Vitting-Seerup]

I would be VERY careful with such analysis. According to this paper which systematically compared DNA and RNA sequencing:

• 65% of DNA based mutations were not found in RNA (Low sensitivity)
• 92% of RNA based mutations were not detected using DNA (very high false discovery rate)
• RNASeq typically only have power to detect 33% of DNA mutations

Answer 2

[igor]

Variants from RNA-seq is a controversial topic. Although there are arguments against (as other have already mentioned), it's also not entirely unreasonable. There is an interesting statement from Coudray et al (where they also make some pipeline recommendations):

The overlap between RNA-seq and WES was small in all samples, but interestingly, the overlap increased with increasing significance of the variants. An average of only 6.60% of WES variants retained by MuTect2 (PASS) were also present in RNA-seq, while 15.9% of WES variants from coding regions and 17.2% of functional mutations were common to RNA-seq (Fig. 2D). Coverage differences between RNA-seq and WES could partially explain the phenomenon. A previous study indeed found that ∼71% of RNA-seq variants fell outside the WES capture boundaries (O’Brien et al., 2015). Moreover, they showed that a high proportion of RNA-seq-only variants were missed by WES because of their low allele fraction (AF).

Their conclusion:

Our work suggests that since the majority of studies on cancer-driving mutations used WES-only, they are likely to have missed some key driver mutations that might be found using complementary RNA-seq datasets from the same tumors.