Some aspects of BLASTing long-read data

Quick note to explain some of the differences we’ve observed working with long-read data (MinION, PacBio) for sample ID via BLAST. I’ll publish a proper paper on this, but for now:

  • Long reads aren’t just a bit longer than Illumina data, but two, three, four or possibly even five orders of magnitude longer (up to 10^6 already, vs 10^2). This is mathematically obvious, but extremely important…
  • … the massive length means lots of the yield is in comparatively few reads. This makes yield stats based on numbers of reads positively useless for comparison with NGS. Also…
  • Any given long read contains significantly more information than a short one does. Most obviously the genomics facilities of the world have focused on their potential for improving genome assembly contiguity and repeat spanning (as well as using synteny to spot rearrangements etc) but we’ve also shown (Parker et al, submitted) that whole coding loci can be directly recovered from single reads and used in phylogenomics without assembly and annotation. This makes sense (a ~kb long read can easily span a whole gene, also ~kb in scale) but it certainly wasn’t initially obvious, and given error rates, etc, it’s surprising it actually works.
  • Sample ID using BLAST actually works very differently. In particular, the normal ‘rubbish in, rubbish out’ rule is inverted. In other words, nanopore reads (for the time being) may be long, but inevitably contain errors. However, this length means that assuming BLAST database sequences are approximately as long/contiguous, Nanopore queries tend to either match database targets correctly, with very long alignments (hundreds/thousands of identities), or not at all.

This last point is the most important. What it means is that, for a read, interpreting the match is simple – you’ll either have a very long alignment to a target, or you won’t. Even when a read has regions of identity to more than one species, the correct read has a much longer cumulative alignment length overall for the correct one. This is the main result of our paper.

The second implication is that, as it has been put to me, for nanopore reads to be any good for an ID, you have to have a genomic database. While this is true in the narrow sense, our current work (and again, this is partly in our paper, and partly in preparation) shows that in fact all that matters is for the length distribution in the reference database to be similar to the query nanopore one. In particular, we’ve demonstrated that a rapid nanopore sequencing run, with no assembly, can itself serve as a perfectly good reference for future sample ID. This has important implications for sample ID but as I said, more on that later 😉

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