Categorizing 400,000 microbial genome shotgun data sets from the SRA

This is another blog post on MinHash sketches; see also:

A few months ago I was at the Woods Hole MBL Microbial Diversity course, and I ran across Mihai Pop, who was teaching at the STAMPS Microbial Ecology course. Mihai is an old friend who shares my interest in microbial genomes and assembly and other such stuff, and during our conversation he pointed out that there were many unassembled microbial genomes sitting in the Sequence Read Archive.

The NCBI Sequence Read Archive is one of the primary archives for biological sequencing data, but it generally holds only the raw sequencing data; assemblies and analysis products go elsewhere. It's also largely unsearchable by sequence: you can search an individual data set with BLAST, I think, but you can't search multiple data sets (because each data set is large, and the search functionality to handle it doesn't really exist). There have been some attempts to make it searchable, including most notably Brad Solomon and Carl Kingsford's Sequence Bloom Tree paper (also on biorxiv, and see my review), but it's still not straightforward.

Back to Mihai - Mihai told me that there were several hundred thousand microbial WGS samples in the SRA for which assemblies were not readily available. That got me kind of interested, and -- combined with my interest in indexing all the microbial genomes for MinHash searching -- led to... well, read on!

How do you dance lightly across the surface of 400,000 data sets?

tl;dr? To avoid downloading all the things, we're sipping from the beginning of each SRA data set only.

The main problem we faced in looking at the SRA is that whole genome shotgun data sets are individually rather large (typically at least 500 MB to 1 GB), and we have no special access to the SRA, so we were looking at a 200-400 PB download. Luiz Irber found that NCBI seems to throttle downloads to about 100 Mbps, so we calculated that grabbing the 400k samples would significantly extend his PhD.

But, not only is the data volume quite large, the samples themselves are mildly problematic: they're not assembled or error trimmed, so we had to develop a way to error trim them in order to minimize spurious k-mer presence.

We tackled these problems in several ways:

  • Luiz implemented a distributed system to grab SRA samples and compute MinHash sketch signatures on them with sourmash; he then ran this 50x across Rackspace, Google Compute Engine, and the MSU High Performance Compute cluster (see blog post);

    To quote, "Just to keep track: we are posting Celery tasks from a Rackspace server to Amazon SQS, running workers inside Docker managed by Kubernetes on GCP, putting results on Amazon S3 and finally reading the results on Rackspace and then posting it to IPFS."

    This meant we were no longer dependent on a single node, or even on a single compute solution. w00t!

  • We needed a way to quickly and efficiently error trim the WGS samples. In MinHash land, this means walking through reads and finding "true" k-mers based on their abundance in the read data set.

    Thanks to khmer, we already have ways of doing this on a low-memory streaming basis, so we started with that (using

  • Because whole-genome shotgun data is generally pretty high coverage, we guessed that we could get away with computing signatures on only a small subset of the data. After all, if you have 100x coverage sample, and you only need 5x coverage to build a MinHash signature, then you only need to look at 5% of the data!

    The fastq-dump program has a streaming output mode, and both khmer and sourmash support streaming I/O, so we could do all this computing progressively. The question was, how do we know when to stop?

    Our first attempt was to grab the first million reads from each sample, and then abundance-trim them, and MinHash them. Luiz calculated that (with about 50 workers going over the holiday break) this would take about 3 weeks to run on the 400,000 samples.

    Fortunately, due to a bug in my categorization code, we thought that this approach wasn't working. I say "fortunately" because in attempting to fix the wrong problem, we came across a much better solution :).

    For mark 2 of streaming, some basic experimentation suggested that we could get a decent match when searching a sample against known microbial genomes with only about 20% of the genome. For E. coli, this is about 1m bases, which is about 1m k-mers.

    So I whipped together a program called syrah that reads FASTA/FASTQ sequences and outputs high-abundance regions of the sequences until it has seen 1m k-mers. Then it exits, terminating the stream.

    This is nice and simple to use with fastq-dump and sourmash --

    fastq-dump -A {sra_id} -Z | syrah | \
       sourmash compute -k 21 --dna - -o {output} --name {sra_id}

    and when Luiz tested it out we found that it was 3-4x faster than our previous approach, because it tended to terminate much earlier in the stream and hence downloaded less data. (See the final command here.)

At this point we were down to an estimated 5 days for computing about 400,000 sourmash signatures on the microbial genomes section of the SRA. That was fast enough even for grad students in a hurry :).

Categorizing 400,000 sourmash signatures... quickly!

tl;dr? We sped up the sourmash Sequence Bloom Tree search functionality, like, a lot.

Now we had the signatures! Done, right? We just categorize 'em all! How long can that take!?

Well, no. It turns out when operating at this scale even the small things take too much time!

We knew from browsing the SRA metadata that most of the samples were likely to be strain variants of human pathogens, which are very well represented in the microbial RefSeq. Conveniently, we already had prepared those for search. So my initial approach to looking at the signatures was to compare them to the 52,000 microbial RefSeq genomes, and screen out those that could be identified at k=21 as something known. This would leave us with the cool and interesting unknown/unidentifiable SRA samples.

I implemented a new sourmash subcommand, categorize, that took in a list (or a directory) full of sourmash signatures and searched them individually against a Sequence Bloom Tree of signatures. The output was a CSV file of categorized signatures, with each entry containing the best match to a given signature against the entire SBT.

The command looks like this:

sourmash categorize --csv categories.csv \
   -k 21 --dna --traverse-directory syrah microbes.sbt.json

and the default threshold is 8%, which is just above random background.

This worked great! It took about 1-3 seconds per genome. For 400,000 signatures that would take... 14 days. Sigh. Even if we parallelized that it was annoyingly slow.

So I dug into the source code and found that the problem was our YAML signature format, which was slow as a dog. When searching the SBT, each leaf node was stored in YAML and loading this was consuming something like 80% of the time.

My first solution was to cache all the signatures, which worked great but consumed about a GB of RAM. Now we could search each signature in about half a second.

In the meantime, Laurent Gautier had discovered the same problem in his work and he came along and reimplemented signature storage in JSON, which was 10-20x faster and was a way better permanent solution. So now we have JSON as the default sourmash signature format, huzzah!

At this point I could categorize about 200,000 signatures in 1 day on an AWS m4.xlarge, when running 8 categorize tasks in parallel (on a single machine). That was fast enough for me.

It's worth noting that we explicitly opted for separating the signature creation from the categorization, because (a) the signatures themselves are valuable, and (b) we were sure the signature generation code was reasonably bug free but we didn't know how much iteration we would have to do on the categorization code. If you're interested in calculating and categorizing signatures directly from streaming FASTQ, see sourmash watch. But Buyer Beware ;).

Results! What are the results?!

For 361,077 SRA samples, we cannot identify 8707 against the 52,000 RefSeq microbial genomes. That's about 2.4%.

Most of the 340,000+ samples are human pathogens. I can do a breakdown later, but it's all E. coli, staph, tuberculosis, etc.

From the 8707 unidentified, I randomly chose and downloaded 34 entire samples. I ran them all through the MEGAHIT assembler, and 27 of them assembled (the rest looked like PacBio, which MEGAHIT doesn't assemble). Of the 27, 20 could not be identified against the RefSeq genomes. This suggests that about 60% of the 8707 samples (5200 or so) are samples that are (a) Illumina sequence, (b) assemble-able, and (c) not identifiable.

You can download the signatures here - the .tar.gz file is about 1 GB in size.

You can get the CSV of categorized samples here (it's about 5 MB, .csv.gz).

What next?

Well, there are a few directions --

  • we have about 350,000 SRA samples identified based on sequence content now. We should cross-check that against the SRA metadata to see where the metadata is wrong or incomplete.
  • we could do bulk strain analyses of a variety of human pathogens at this point, if we wanted.
  • we can pursue the uncategorized/uncategorizable samples too, of course! There are a few strategies we can try here but I think the best strategy boils down to assembling them, annotating them, and then using protein-based comparisons to identify nearest known microbes. I'm thinking of trying phylosift. (See Twitter conversation 1 and Twitter conversation 2.)
  • we should cross-compare uncategorized samples!

At this point I'm not 100% sure what we'll do next - we have some other fish to fry in the sourmash project first, I think - but we'll see. Suggestions welcome!

A few points based partly on reactions to the Twitter conversations (1) and (2) about what to do --

  • mash/MinHash comparisons aren't going to give us anything interesting, most likely; that's what's leading to our list of uncategorizables, after all.
  • I'm skeptical that nucleotide level comparisons of any kind (except perhaps of SSU/16s genes) will get us anywhere.
  • functional analysis seems secondary to figuring out what branch of bacteria they are, but maybe I'm just guilty of name-ism here. Regardless, if we were to do any functional analysis for e.g. metabolism, I'd want to do it on all of 'em, not just the identified ones.

Backing up -- why would you want to do any of this?

No, I'm not into doing this just for the sake of doing it ;). Here's some of my (our) motivations:

  • It would be nice to make the entire SRA content searchable. This is particularly important for non-model genomic/transcriptomic/metagenomic folk who are looking for resources.
  • I think a bunch of the tooling we're building around sourmash is going to be broadly useful for lots of people who are sequencing lots of microbes.
  • Being able to scale sourmash to hundreds of thousands (and millions and eventually billions) of samples is going to be, like, super useful.
  • More generally, this is infrastructure to support data-intensive biology, and I think this is important. Conveniently the Moore Foundation has funded me to develop stuff like this.
  • I'm hoping I can tempt the grey (access restricted, etc.) databases into indexing their (meta)genomes and transcriptomes and making the signatures available for search. See e.g. "MinHash signatures as ways to find samples, and collaborators?".

Also, I'm starting to talk to some databases about getting local access to do this to their data. If you are at, or know of, a public database that would like to cooperate with this kind of activity, let's chat --


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