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

Minh Duc Cao, Devika Ganesamoorthy, Alysha G. Elliott, Huihui Zhang, Matthew A. Cooper, Lachlan Coin
doi: https://doi.org/10.1101/019356
Minh Duc Cao
1Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, St Lucia, QLD 4072 Brisbane, Australia
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Devika Ganesamoorthy
1Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, St Lucia, QLD 4072 Brisbane, Australia
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Alysha G. Elliott
1Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, St Lucia, QLD 4072 Brisbane, Australia
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Huihui Zhang
1Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, St Lucia, QLD 4072 Brisbane, Australia
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Matthew A. Cooper
1Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, St Lucia, QLD 4072 Brisbane, Australia
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Lachlan Coin
1Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, St Lucia, QLD 4072 Brisbane, Australia
2Department of Genomics of Common Disease, Imperial College London, London W12 0NN UK
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  • For correspondence: l.coin@imb.uq.edu.au
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Abstract

The recently introduced Oxford Nanopore MinION platform generates DNA sequence data in real-time. This opens immense potential to shorten the sample-to-results time and is likely to lead to enormous benefits in rapid diagnosis of bacterial infection and identification of drug resistance. However, there are very 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 three culture isolate samples as well as a mixed sample, we demonstrate that bacterial species and strain information can be obtained within 30 minutes of sequencing and using about 500 reads, initial drug-resistance profiles within two hours, and complete resistance profiles within 10 hours. We also show that our pipeline can process over 100 times more data than the current throughput of the MinION on a desktop computer.

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Posted December 09, 2015.
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Streaming algorithms for identification of pathogens and antibiotic resistance potential from real-time MinIONTM sequencing
Minh Duc Cao, Devika Ganesamoorthy, Alysha G. Elliott, Huihui Zhang, Matthew A. Cooper, Lachlan Coin
bioRxiv 019356; doi: https://doi.org/10.1101/019356
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Streaming algorithms for identification of pathogens and antibiotic resistance potential from real-time MinIONTM sequencing
Minh Duc Cao, Devika Ganesamoorthy, Alysha G. Elliott, Huihui Zhang, Matthew A. Cooper, Lachlan Coin
bioRxiv 019356; doi: https://doi.org/10.1101/019356

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