Last week our pre-print on nanopore sequencing came online at bioRxiv. Nanopore sequencing is a relatively new sequencing technology that is starting to come of age. As part of this process we last year started playing with the ONT MinION sequencer. This post summarizes a bit of the background behind the pre-print.
Previously I covered the London Calling 2015 event where a lot of progress on the development of the MinION was showcased. We were keen to find out how the MinION could contribute to our daily lab work, but also to see what new ground can be covered with this new sequencing technology.
One of the aspects colleagues in the lab are working on is the dissemination of antibiotic resistance genes, as a major healthcare challenge is the emergence of pathogens that are resistant against antibiotics. Therefor we thought of combining the MinION with antibiotic resistance gene profiling. More specifically; coupling functional metagenomic selections with nanopore sequencing.
Previous work in this field, for example by Justin O’Grady and colleagues, showed the use of the MinION [$] to identify the structure and chromosomal insertion site of a bacterial antibiotic resistance island in Salmonella Typhi.
Instead of going after single isolates, we set out the map the antibiotic resistance genes that are present in the gut (resistome) of a hospitalized patient. The resistome can influence the outcome of antibiotic treatment and it is therefor highly interesting to get insights in this complex network. Through a collaboration under the EvoTAR programma with Willem van Schaik of the University of Utrecht we had a clinical fecal sample available of an ICU patient, which we used in the experiments.
Key in the whole experimental setup to capture the resistome is the use of functional metagenomic selections. In contrast to culturing individual microorganisms directly from a fecal sample, metagenomic DNA is extracted from the sample. This metagenomic DNA is subsequently sheared, ligated and transformed in E. coli and finally plated out on solid agar containing various antibiotics. Only E. coli cells that harbor a metagenomic DNA fragment that encodes for an antibiotic resistant phenotype can survive. With these functional metagenomic selections in hand, the complexity of the resistome can be rapidly mapped.
And this is were the MinION comes in. Although other sequencing technologies, such as the Illumina and the PacBio platform, are available, they do not provide both long reads and low capital requirements.
— Eric van der Helm (@EricvdHelm) February 3, 2016
After some initial failed attempts to get the MinION sequencer running in our lab, we started to see >100 Mbase runs in October last year. Also PoreCamp last December in Birmingham provided, on top of a great experience and nice people, some useful data (next week a new round of PoreCamp takes place).
In order to analyze the sequencing data that Metrichor generates we developed the poreFUME pipeline, which automates the process of barcode demultiplexing, error correction (using nanocorrect) and antibiotic resistance gene annotation (using CARD). The poreFUMe software is available on Github as a python script. The subsequent analysis is as well available on Github in a Jupyter notebook.
In order to benchmark the nanopore sequencing data we also Sanger and PacBio sequenced the sample. From these results we could achieve a >97% sequence accuracy and we were able to identify all the 26 antibiotic resistance genes in both the Pacbio and nanopore set.
Since the whole workflow can be performed relatively quickly, it would be really interesting to move these techniques to the next stage and do in-situ resistome profiling. Especially integrating Matt Loose’s read-until functionally could open up new avenues. Furthermore these experiments were done with the R7 chemistry, however it seems that the new R9 chemistry is able to deliver even higher accuracies and faster turn-around.
Update 2016-11-01: Added the ENA link to the raw data