Journal article

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

MD Cao, D Ganesamoorthy, AG Elliott, H Zhang, MA Cooper, LJM Coin

Gigascience | OXFORD UNIV PRESS | Published : 2016

Open access

Abstract

The recently introduced Oxford Nanopore MinION platform generates DNA sequence data in real-time. This has great potential to shorten the sample-to-results time and is likely to have benefits such as rapid diagnosis of bacterial infection and identification of drug resistance. However, there are few tools available for streaming analysis of real-time sequencing data. Here, we present a framework for streaming analysis of MinION real-time sequence data, together with probabilistic streaming algorithms for species typing, strain typing and antibiotic resistance profile identification. Using four culture isolate samples, as well as a mixed-species sample, we demonstrate that bacterial species a..

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University of Melbourne Researchers

Grants

Awarded by National Health and Medical Research Council


Funding Acknowledgements

We thank Ilias Karaiskos and Helen Giamarellou (6th Dept. of Internal Medicine, Hygeia General Hospital, Athens, Greece) for providing the clinical K. pneumoniae isolate. MAC is an NHMRC Principal Research Fellow (APP1059354). LC is an ARC Future Fellow (FT110100972). The research is supported by funding from the Institute for Molecular Bioscience Centre for Superbug Solutions (610246).