Thoughts on Sequence Bloom Trees

We just submitted our review of the paper Large-Scale Search of Transcriptomic Read Sets with Sequence Bloom Trees., by Brad Solomon and Carl Kingsford.

The paper outlines a fairly simple and straightforward way to query massive amounts of sequence data (5 TB of mRNAseq!) in very small disk (~70 GB) and memory (~under a GB), fairly quickly (~2.5 days).

The short version is that I think this is an incredibly powerful approach that I am now planning to build on for our own Moore DDD project.

The review was a lot of fun; it's right up our alley, and we've thought a lot about related (but somewhat distinct) issues. Here are some extended comments that I thought were probably not appropriate for the official review because they're somewhat forward looking.


First, we did the review in approved Open Science style. Since Brad and Carl did us the favor of using GitHub (source code) and bioRxiv (preprint), and I sign my reviews, there need be no mystery in this process. Therefore I am posting our review publicly with only minor edits. Moreover, I filed three issues on GitHub (#1, #2, and #4) and submitted two pull requests (#3 and #5), both of which were merged.

Second, because we work in this area and are very interested in it, I put together both a demo of their software (see 2015-sbt-demo) and also did a simple reimplementation in khmer (see 2015-khmer-sequence-bloom-trees) to make sure that their software worked, and that I thoroughly understood the basic issues involved.

Note that we are unable to use their implementation as licensed, as it is under the GPL and would contaminate our source tree, which is under BSD :(.

Third, I have a lot of suggestions and thoughts! For example,

  • The use of RRR compression is awesome and is something we should look into for khmer (dib-lab/khmer#1074).
  • We get a 20% performance increase from a simple optimization applied to our k-mer lookups that could apply to SBTs -- see just-merged dib-lab/khmer#862, by Camille Scott.
  • The authors might also be interested in making use of our HyperLogLog implementation for k-mer cardinality counting, which could help users choose the right size for their Bloom filters.
  • Streaming diginorm/semi-streaming in general (see Crossing the streams) could be a very useful pre-filter for building SBTs. My guess is that with k-mer prefiltering a la digital normalization, there would be no loss to sensitivity but a substantial decrease in memory requirements.
  • It would be really interesting to brainstorm about how far this can be taken. We have reasonably strong evidence & intuition that you can do straight abundance estimation directly off of counting Bloom filters, and it doesn't seem like a stretch to say, "hey, let's store EVERYTHING in an sequence Bloom tree analog and do comparative expression analysis that way!" We don't have an immediate use case for this ourselves, but I'm sure one will present itself...
  • Qingpeng Zhang and I immediately started talking about how to apply this to metagenomics, and the main worry is that the method seems to depend on low diversity of true k-mers. This can partly be mitigated by diginorm and/or semi-streaming k-mer abundance trimming, but ultimately things are going to scale at best with the true size of the De Bruijn graph. It will be interesting to see how this plays out.

--titus

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