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

Minh Duc Cao,Alysha G Elliott,Huihui Zhang,Devika Ganesamoorthy,Lachlan J M Coin,Matthew A Cooper

doi:10.1186/s13742-016-0137-2

Minh Duc Cao, Alysha G Elliott + Show 4 more

Open Access

https://doi.org/10.1186/s13742-016-0137-2

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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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13742-016-0137-2) contains supplementary material, which is available to authorized users.

Highlights

Parallel, short-read sequencing has profoundly transformed genomics research [1, 2] and has become the dominant technology for sequencing DNA
We developed a number of auxiliary programs to facilitate setting up a real-time pipeline to analyse MinION sequencing data
We developed a method to carry out multi-locus sequence typing (MLST) using MinION sequence data

Summary

Introduction

Short-read sequencing has profoundly transformed genomics research [1, 2] and has become the dominant technology for sequencing DNA. Sequence analysis algorithms have been designed to make inference on a complete sequencing data set. The key innovation of this device is that it measures changes in electrical current as single-stranded DNA passes through the nanopore and uses the signal to determine the nucleotide sequence of the DNA strand [6, 7]. These sequence data can be retrieved and analysed as they are generated, providing the opportunity to obtain answers in the shortest possible time. Real-time sequencing has many potential applications, especially in time-critical areas such as rapid clinical diagnosis

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