PT  - JOURNAL ARTICLE
AU  - Minh Duc Cao
AU  - Devika Ganesamoorthy
AU  - Alysha G. Elliott
AU  - Huihui Zhang
AU  - Matthew A. Cooper
AU  - Lachlan Coin
TI  - Streaming algorithms for identification of pathogens and antibiotic resistance potential from real-time MinION<sup>TM</sup> sequencing
AID  - 10.1101/019356
DP  - 2015 Jan 01
TA  - bioRxiv
PG  - 019356
4099  - http://biorxiv.org/content/early/2015/12/09/019356.short
4100  - http://biorxiv.org/content/early/2015/12/09/019356.full
AB  - 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.