Presented at London Calling 2016

Included in antibiotic_resistance, nanopore themes

Preprint Link (reviewed version):N/A
Preprint Link (updated version):
Research area: bioinformatics

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

Created on 9th December 2015

This paper has been published in GigaScience.

Note: this is an old submission, see here for the current submission.

Minh Duc Cao; Devika Ganesamoorthy; Alysha Elliott; Huihui Zhang; Matthew Cooper ; Lachlan James Murray Coin

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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