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Lineage calling can identify antibiotic resistant clones within minutes

View ORCID ProfileKarel Břinda, Alanna Callendrello, Lauren Cowley, Themoula Charalampous, Robyn S Lee, Derek R MacFadden, Gregory Kucherov, Justin O’Grady, Michael Baym, William P Hanage
doi: https://doi.org/10.1101/403204
Karel Břinda
1Center for Communicable Disease Dynamic, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA
2Department of Biomedical Informatics, Harvard Medical School, Boston, USA
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  • ORCID record for Karel Břinda
Alanna Callendrello
1Center for Communicable Disease Dynamic, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA
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Lauren Cowley
1Center for Communicable Disease Dynamic, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA
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Themoula Charalampous
3University of East Anglia, Norwich Research Park, Norwich, UK
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Robyn S Lee
1Center for Communicable Disease Dynamic, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA
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Derek R MacFadden
1Center for Communicable Disease Dynamic, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA
4Division of Infectious Diseases, Department of Medicine, University of Toronto, Canada
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Gregory Kucherov
5CNRS/LIGM Université Paris-Est, Marne-la-Vallée, France
6Skolkovo Institute of Science and Technology, Moscow, Russia
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Justin O’Grady
7Quadram Institute Bioscience, Norwich Research Park, Norwich, UK
3University of East Anglia, Norwich Research Park, Norwich, UK
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Michael Baym
2Department of Biomedical Informatics, Harvard Medical School, Boston, USA
8Laboratory of Systems Pharmacology, Harvard Medical School, Boston, USA
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William P Hanage
1Center for Communicable Disease Dynamic, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA
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Introductory Paragraph

Surveillance of circulating drug resistant bacteria is essential for healthcare providers to deliver effective empiric antibiotic therapy. However, the results of surveillance may not be available on a timescale that is optimal for guiding patient treatment. Here we present a method for inferring characteristics of an unknown bacterial sample by identifying the presence of sequence variation across the genome that is linked to a phenotype of interest, in this case drug resistance. We demonstrate an implementation of this principle using sequence k-mer content, matched to a database of known genomes. We show this technique can be applied to data from an Oxford Nanopore device in real time and is capable of identifying the presence of a known resistant strain in 5 minutes, even from a complex metagenomic sample. This flexible approach has wide application to pathogen surveillance and may be used to greatly accelerate diagnoses of resistant infections.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted August 29, 2018.
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Lineage calling can identify antibiotic resistant clones within minutes
Karel Břinda, Alanna Callendrello, Lauren Cowley, Themoula Charalampous, Robyn S Lee, Derek R MacFadden, Gregory Kucherov, Justin O’Grady, Michael Baym, William P Hanage
bioRxiv 403204; doi: https://doi.org/10.1101/403204
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Lineage calling can identify antibiotic resistant clones within minutes
Karel Břinda, Alanna Callendrello, Lauren Cowley, Themoula Charalampous, Robyn S Lee, Derek R MacFadden, Gregory Kucherov, Justin O’Grady, Michael Baym, William P Hanage
bioRxiv 403204; doi: https://doi.org/10.1101/403204

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