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

Cao, Minh Duc, Ganesamoorthy, Devika, Elliott, Alysha G., Zhang, Huihui, Cooper, Matthew A. and Coin, Lachlan J. M. (2016) Streaming algorithms for identification of pathogens and antibiotic resistance potential from real-time MinION (TM) sequencing. Gigascience, 5 1: 32. doi:10.1186/s13742-016-0137-2


Author Cao, Minh Duc
Ganesamoorthy, Devika
Elliott, Alysha G.
Zhang, Huihui
Cooper, Matthew A.
Coin, Lachlan J. M.
Title Streaming algorithms for identification of pathogens and antibiotic resistance potential from real-time MinION (TM) sequencing
Journal name Gigascience   Check publisher's open access policy
ISSN 2047-217X
Publication date 2016-07-01
Year available 2016
Sub-type Article (original research)
DOI 10.1186/s13742-016-0137-2
Open Access Status DOI
Volume 5
Issue 1
Start page 32
Total pages 16
Place of publication London, United Kingdom
Publisher BioMed Central
Language eng
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 and strain information can be obtained within 30 min of sequencing and using about 500 reads, initial drug-resistance profiles within two hours, and complete resistance profiles within 10 h. While strain identification with multi-locus sequence typing required more than 15x coverage to generate confident assignments, our novel gene-presence typing could detect the presence of a known strain with 0.5x coverage. We also show that our pipeline can process over 100 times more data than the current throughput of the MinION on a desktop computer.
Keyword Nanopore sequencing
Real-time analysis
Pathogen identification
Antibiotic resistance
Nanopore Sequencer
Next-Generation
Alignment
Genes
Protein
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID 610246
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Institute for Molecular Bioscience - Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 7 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 11 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Sun, 21 Aug 2016, 10:16:43 EST by System User on behalf of Learning and Research Services (UQ Library)