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David R. Greig

Comparison of single nucleotide variants identified by Illumina and Oxford Nanopore technologies in the context of a potential outbreak of Shiga Toxin producing E.coli

About David R. Greig

David graduated from the University of Bedfordshire with a BSc in Biomedical Science in 2014 before completing a MSc in Biomedical Science, specialising in Medical Microbiology, from Ulster University in 2015. He then joined the Gastrointestinal Bacteria Reference Unit (GBRU) at Public Health England in London, for the laboratory typing of gastrointestinal pathogens, before moving to the bioinformatics team where he performed data analysis on whole genome sequencing data. David is currently working as a bioinformatician at Public Health England and is a part-time PhD student with the University of Edinburgh, Roslin Institute studying the use of Oxford Nanopore sequencing technologies for the investigation of outbreaks of Shiga-toxin producing Escherichia coli in humans. 


Short-read sequencing platforms have been adopted by public health agencies for infectious disease surveillance worldwide and have proved to be a robust and accurate method for quantifying relatedness between bacterial genomes. However, this approach offers less flexibility for urgent, small scale sequencing that is often required during public health emergencies. In contrast, Oxford Nanopore Technologies offers a range of rapid real-time sequencing platforms, although at this time it has been suggested that lower read accuracy compared to other sequencing technologies might be problematic for variant identification. We compared Illumina and Oxford Nanopore sequencing data of two isolates of Shiga toxin producing Escherichia coli to assess the utility of nanopore technologies for urgent, small scale sequencing.  We investigated whether the same single nucleotide variants were identified by the two sequencing technologies and whether inference of relatedness was consistent.  We show that with optimised variant calling using nanopore sequencing data alone, it is possible to rapidly determine whether or not two cases of were likely to be epidemiologically linked.

David Greig

David Greig

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