A new preprint on semi-streaming analysis of large data sets

We just posted a new preprint (well, ok, a few weeks back)! The preprint title is "Crossing the streams: a framework for streaming analysis of short DNA sequencing reads", by Qingpeng Zhang, Sherine Awad, and myself. Note that like our other recent papers, this paper is 100% reproducible, with all source code in github. we haven't finished writing up the instructions yet, but happy to share (see AWS.md for notes).

This paper's major point is that you can use the massive redundancy of deep short-read sequencing to analyze data as it comes in, and that this could easily be integrated into existing k-mer based error correctors and variant callers. Conveniently the algorithm doesn't care what you're sequencing - no assumptions are made about uniformity of coverage, so you can apply the same ol' spectral approaches you use for genomes to transcriptomes, metagenomes, and probably single-cell data. Which is, and let's be frank here, super awesome.

The paper also provides two useful tools, both implemented as part of khmer: one is an efficient approach to k-mer-abundance-based error trimming of short reads, and the other is a streaming approach to looking at per-position error profiles of short-read sequencing experiments.

A colleague, Erich Schwarz, suggested that I more strongly make the point that is really behind this work: we're in for more data. Scads and scads of it. Coming it with ways of efficiently dealing with it at an algorithmic level is important. (We didn't strengthen this point in the posted preprint - the feedback came too late -- but we will hopefully get a chance to stick it in in the revisions.)

The really surprising thing for me is that the general semi-streaming approach has virtually no drawbacks - the results are very similar to the full two-pass offline approaches used currently for error correction etc. Without implementing a huge amount of stuff we had to make this argument transitively, but I think it's solid.

For entertainment, take a look at the error profile in Figure 6. That's from a real data set, published in Nature something or other...

And, finally, dear prospective reviewers: the biggest flaws that I see are these:

  • we chose to make most of our arguments by analyzing real data, and we didn't spend any time developing theory. This is a choice that our lab frequently makes -- to implement effective methods rather than developing the underlying theory -- but it leaves us open for a certain type of criticism.
  • to extend the previous point, the algorithmic performance depends critically on details of the data set. We didn't know how to discuss this and so the discussion is maybe a bit weak. We'd love reviewers to ask pointed questions that we can address in order to shore it up.
  • Repeats! How does all this stuff work with repeats!? I did a lot of work simulating repetitive sequences and couldn't find any place where repeats actually caused problems. My intuition now tells me that repeats are not actually a problem for methods that interrogate De Bruijn graphs using entire reads as an index into the graph, but I'd welcome someone telling me I'm wrong and either telling me where to look, or asking concrete questions that illuminate better directions to take it.

--titus

Comments !

(Please check out the comments policy before commenting.)