New findings from our paper, "These are not the k-mers you are looking for"

Earlier today, I posted our response to the reviewers' comments on our k-mer counting paper, "These are not the k-mers you are looking for: efficient online k-mer counting using a probabilistic data structure.

A side note -- I was wondering how many public examples there are of the whole paper submission/review/responses to reviewer/revised paper submission cycle? And how many of them weren't legally compelled? (Thanks to our reviewers for letting us post their reviews publicly!)

As you can see, our reviewers were very thorough and we put in a lot of work in the response; the paper is now dramatically better and considerably more solid. In the process we discovered a few new things.

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New factoids from the paper

  1. Digital normalization is remarkably refractory to high k-mer counting false positive rates.

    At the request of the reviewers, we got down to the point where we were running digital normalization on an E. coli data set at a false-positive rate of 98.8%, in 20 MB of RAM, before we saw any ill effects of low-memory diginorm. (See output at tab 111 in our IPython Notebook for details.)

  2. k-mer abundance trimming is incredibly effective at getting rid of errors. See output at tab 109 in our IPython Notebook; filtering by quality scores leaves at least 1000x more unique k-mers in a high coverage data set (and yes, these are certain to be errors). Now that fairly low-memory k-mer trimming approaches are available, we may switch our protocols over to k-mer trimming only (and avoid quality score trimming altogether).

    Addendum: k-mer abundance trimming won't work for low-coverage resequencing experiments, note. But for assembly or high-coverage data sets it might be better than quality score-based trimming approaches.

  3. Turtle is remarkably fast, and KAnalyze is not very fast in our hands.

    You can read the details in our paper, but in our hands, KAnalyze was slower than everything but Tallymer; counter to the claims in their paper, Jellyfish and DSK (as well as KMC, BFCounter, Turtle, and khmer) all beat KAnalyze. See the figure in our IPython Notebook.

    Now, before anyone argues with us about the details, I'd like to point out that our analysis is completely open and remarkably reproducible. We ran everything in the Amazon cloud, with open source and open data, and you can run it too, if you like -- see our tutorial. If you get different results, let us know, m'kay?

    In any case, we believe (but have not verified) that the source of the speed discrepancy in the KAnalyze paper is caused by their use of custom parsing scripts to rewrite other k-mer counters' output into their format. Regardless, our paper almost directly refutes their claims (we'd have to do the benchmarks on their data to refute directly). Any thoughts on what to do about this?

    Addendum: we put a lot of effort into thinking about appropriate benchmarks for our paper, so I feel a strong personal connection to this issue :)

--titus

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