Open preprint reviews by Mick Watson

Streaming algorithms for identification of pathogens and antibiotic resistance potential from real-time MinIONTM sequencing

Minh Duc Cao, Devika Ganesamoorthy, Alysha Elliott, Huihui Zhang, Matthew Cooper and Lachlan Coin

Cao et al present streaming algorithms for the identification of pathogens and antibiotic resistance genes using "real time" MinION sequencing. The paper has some interesting points in terms of proof-of-principle, though there is a lot of work needed before this could be implemented in practice.


A major point of discussion needs to be the fact that base-calling, via the cloud-based base caller Metrichor, is a major bottleneck to the pipeline and can add hours to the process. By using pre-basecalled data the authors bypass this issue, however in a real setting, the online base-calling would be an issue. Rapid matching on "sqiggle data" (e.g. http://biorxiv.org/content/early/2016/02/03/038760) could be discussed as a potential solution.

I don't think the "pipeline" itself is described in sufficient detail and I would suggest a flowchart showing information flow through the pipeline, including software names/versions. I am also unsure the pipeline is genuinely an example of "streaming", which usually refers to the fact that data are not written to disk, simply piped from one process to another. However, the FAST5 files are written to disk by metrichor, sequence data extracted using npReader and (I assume) written to disk, picked up by BWA and then the output of BWA is streamed to other processes. The authors may or may not be aware, but there is an API to MinKNOW that allows genuine streaming of data from the MinION device. Again, these points should be discussed.

The authors state:

"We developed a novel strain typing method to identify the bacterial strain from the MinION sequence reads based on patterns of gene presence and absence."

I would like to know how this differs from metagenomic profilers such as Kraken (and many others), and indeed why the authors couldn't use one of these existing pipelines.

Finally, though the results are interesting, the conclusions are limited as the authors use pure cultures and I would be very interested to see how the platform performs on genuine clinical samples. The authors should also be aware of Phelim et al (http://www.nature.com/ncomms/2015/151221/ncomms10063/full/ncomms10063.html) which has a section on use of MinION for AMR typing.

show less