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 bene ts such as rapid diagnosis of bacterial infection and identi cation 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 pro le identi cation. 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 minutes of sequencing and using about 500 reads, initial drug-resistance pro les within two hours, and complete resistance pro les within 10 hours. While strain identi cation with multi-locus sequence typing required more than 15x coverage to generate con dent 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.

Keywords:

• Nanopore sequencing
• Real-time analysis
• Pathogen identi cation
• Antibiotic resistance

Read the peer-reviewed publication(s):

Cao, M. D., Ganesamoorthy, D., Elliott, A. G., Zhang, H., Cooper, M. A., & Coin, L. J. M. (2016). Streaming algorithms for identification of pathogens and antibiotic resistance potential from real-time MinIONTM sequencing. GigaScience, 5(1). doi:10.1186/s13742-016-0137-2

Additional information:

https://github.com/mdcao/npAnalysis

https://github.com/mdcao/japsa

Accessions (data included in GigaDB):

BioProject: PRJEB14532