A review of "Large-Scale Search of Transcriptomic Read Sets with Sequence Bloom Trees"

(This is a review of Large-Scale Search of Transcriptomic Read Sets with Sequence Bloom Trees, Solomon and Kingsford, 2015.)

In this paper, Solomon and Kingsford present Sequence Bloom Trees (SBTs). SBT provides an efficient method for indexing multiple sequencing datasets and finding in which datasets a query sequence is present.

The new method is based on using multiple Bloom filters and organizing them in a binary tree, where leaves represent specific datasets and internal nodes contain all the k-mers present in their subtrees. A query starts by breaking the sequence into a set of k-mers and checking if they are present in the node Bloom filter at a specific threshold. If yes then the query is repeated for children nodes, but if it isn't the subtree is pruned and search proceeds on other nodes. If all searches are pruned before reaching a leaf then the sequence is not present in any dataset. They prove the false positive rate for a k-mer can be quite higher than traditional applications of Bloom filters, since they are interested in finding if the whole set of k-mers is over a threshold. This leads to very small data structures that remain capable of approximating the correct answer.

Compared to alternative software (like SRA-BLAST or STAR) it has both decreased runtime and memory consumption, and it also can be used as a filter to make these tools faster.

Overall review

The paper is well written, clear, mostly expert in the area (but see below), and lays out the approach and tool well.

The approach is novel within bioinformatics, as far as we know. More, we think it's a tremendously important approach; it's by far the most succinct representation of large data sets we've seen (and Bloom filters are notoriously efficient), and it permits efficient indexing, storage of indices, and queries of indices.

A strange omission is the work that has been done by our group and others with Bloom filters. Pell et al., 2012 (pmid 22847406), showed that implicit De Bruijn graphs could be stored in Bloom filters in exactly the way the authors are doing here; work by Chikhi and Rizk, 2013 (pmid 24040893) implemented exact De Bruijn graphs efficiently using Bloom filters; and Salikhov et al, 2014 (pmid 24565280) further used Cascading Bloom filters. Our group has also used the median k-mer abundance (which, in a Bloom filter, equals median k-mer presence) to estimate read presence and coverage in a very similar way to Solomon and Kingsford (Brown et al., 2012, "digital normalization"). We also showed experimentally that this is very robust to high false positive rates (Zhang et al., 2014, pmid 25062443, buried in the back).

There are three points to make here --

  1. Previous work has been done connecting Bloom filters and k-mer storage, in ways that seem to be ignored by this paper; the authors should cite some of this literature. Given citation space limitations, this doesn't need to be exhaustive, but either Salikhov or Pell seems particularly relevant.
  2. The connection between Bloom filters and implicit De Bruijn graphs should be explicitly made in the paper, as it's a powerful theoretical connection.
  3. All of our previous result support the conclusions reached in this paper, and this paper makes the false-positive robustness argument much more strongly, which is a nice conclusion!


We have found that users are often very confused about how to pick the size of Bloom filters. My sense here is that the RRR compression means that very large Bloom filters will be stored efficiently, so you might as well start big, because there's no way to do progressive size increases on the Bloom filter; do the authors agree with that conclusion, or am I missing something?

One possible writing improvement is to add another level under the leaves in Supp Fig 1 to make it clear that traditional alignment or other alternatives are required, since SBT only finds if the query is present in the dataset (but not where). The speed comparisons in the paper could be qualified a bit more to make it clear that this is only for basic search, although some of us think it's already clear enough so it's advice, not a requested or required change.

However, there is a solid point to be made that (in our opinion) the true value of the SBT approach is not necessarily in speeding up the overall process (3.5x speedup) but in doing the search in very low memory across an index that can be distributed independently of the data.

page 16, Theorem 2 says the probability that ... is nearly 0 when FPR is << theta, fraction threshold. But next in the example, theta is 0.5 and FPR is also 0.5, so here the FPR is NOT << theta, as in Theorem 2. How to conclude that "by Theorem 2, we will be unlikely to observe > theta fraction of false positive kmers in the filter."?

Software and tool publication

Bioinformatics paper checklist (http://ivory.idyll.org/blog/blog-review-criteria-for-bioinfo.html):

The software is directly available for download: Yes, https://github.com/Kingsford-Group/bloomtree

The software license lets readers download and run it: The license is not specified; this needs to be fixed. But 'bloomtree' makes use of several GPL toolkits.

The software source code is available to readers: Yes, https://github.com/Kingsford-Group/bloomtree

We successfully downloaded and ran the software.

The data for replication is available for download: Yes, public data from SRA; it's listed on supp materials, but could be added to the tool site too.

The data format is either standard, or straightforward, or documented. Yes

Other comments:


  • we strongly recommend that a lab-independent URL be used as the official URL for the software (e.g. the github page, instead of the CMU page). Lab Web sites tend to fall out of date or otherwise decay.
  • One of the big drawbacks to Bloom filters is that they are fixed in size. Guidance on choosing Bloom filter size would be welcome. One way to do this is to use an efficient method to calculate cardinality, and khmer has a BSD- licensed implementation of the HyperLogLog cardinality counter that they'd be welcome to copy wholesale.


C. Titus Brown

Luiz Irber

Qingpeng Zhang

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