Thoughts on my Salmon review

I was one of the reviewers of the Salmon paper by Patro et al., 2017, Salmon provides fast and bias-aware quantification of transcript expression, and I posted my review in large part because of Lior Pachter's blog post levying charges of intellectual theft and dishonest against the Salmon authors. More recently, the Salmon authors posted a strong rebuttal that is worth reading.

I posted my review verbatim, without any editing, and with the charge for the review laid out by the journal. Part of my goal was to provide some insight into why I was enthusiastic about Salmon, and why I reviewed it positively for publication.

Looking back on my review, I have a few thoughts - especially in light of Lior's hit piece and Rob et al's response.

First, it is always a pleasure reviewing software papers when you have used the tool for ages. My lab uses Salmon, trains people in running Salmon, and passes around blog posts and papers discussing Salmon results. I think we had been using Salmon for at least a year when I was asked to review the paper. So I was already pretty confident that Salmon performed well in general and I did not need to try to run it, evaluate it, etc.

Second, my review hit on a number of high points about Salmon: generally, that it was an important new tool of interest and that it was a significant improvement to the tried-and-tested Kallisto technique. More specifically, I felt that the GC bias correction was likely to be critical in the future (see esp the isoform switching observation!), and that the open-source license was an important aspect of the Salmon software. To my mind this meant that it warranted publication, a case I made succinctly but tried to make strongly.

Third, my review missed at least one very important aspect, which was the streaming nature of the Salmon implementation. The rebuttal covers this nicely...

Last, my review uses the term "pseudoalignment" and not "quasimapping." The distinction between these two terms is part of the brouhaha; but to my mind the basic idea of finding equivalence classes for reading mapping is pretty similar no matter what you call it, and it was clear (both from the citations to Kallisto and the discussion in the paper) that the core concept had been published as part of the Kallisto work. I don't think it matters what it's called - it's a good idea, I'm glad that there are multiple implementations that agree well with each other, and I think it was adequately, appropriately, and generously cited in the Salmon paper.

So, anyway, there are my thoughts. Yours welcome below!

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