Search Engine for Antimicrobial Resistance: A Cloud Compatible Pipeline and Web Interface for Rapidly Detecting Antimicrobial Resistance Genes Directly from Sequence Data.

Will Rowe,Gareth Pearce,David Verner-Jeffreys,Duncan Maskell,Jim J Ryan,Craig Baker-Austin,Kate S Baker,Willem Van Schaik

doi:10.1371/journal.pone.0133492

Abstract

BackgroundAntimicrobial resistance remains a growing and significant concern in human and veterinary medicine. Current laboratory methods for the detection and surveillance of antimicrobial resistant bacteria are limited in their effectiveness and scope. With the rapidly developing field of whole genome sequencing beginning to be utilised in clinical practice, the ability to interrogate sequencing data quickly and easily for the presence of antimicrobial resistance genes will become increasingly important and useful for informing clinical decisions. Additionally, use of such tools will provide insight into the dynamics of antimicrobial resistance genes in metagenomic samples such as those used in environmental monitoring.ResultsHere we present the Search Engine for Antimicrobial Resistance (SEAR), a pipeline and web interface for detection of horizontally acquired antimicrobial resistance genes in raw sequencing data. The pipeline provides gene information, abundance estimation and the reconstructed sequence of antimicrobial resistance genes; it also provides web links to additional information on each gene. The pipeline utilises clustering and read mapping to annotate full-length genes relative to a user-defined database. It also uses local alignment of annotated genes to a range of online databases to provide additional information. We demonstrate SEAR’s application in the detection and abundance estimation of antimicrobial resistance genes in two novel environmental metagenomes, 32 human faecal microbiome datasets and 126 clinical isolates of Shigella sonnei.ConclusionsWe have developed a pipeline that contributes to the improved capacity for antimicrobial resistance detection afforded by next generation sequencing technologies, allowing for rapid detection of antimicrobial resistance genes directly from sequencing data. SEAR uses raw sequencing data via an intuitive interface so can be run rapidly without requiring advanced bioinformatic skills or resources. Finally, we show that SEAR is effective in detecting antimicrobial resistance genes in metagenomic and isolate sequencing data from both environmental metagenomes and sequencing data from clinical isolates.

Highlights

The global threat of antimicrobial resistance is growing at an alarming rate; infections that were once treatable constitute public health crises [1]
We demonstrate Search Engine for Antimicrobial Resistance (SEAR)’s application in the detection and abundance estimation of antimicrobial resistance genes in two novel environmental metagenomes, 32 human faecal microbiome datasets and 126 clinical isolates of Shigella sonnei
We have developed a pipeline that contributes to the improved capacity for antimicrobial resistance detection afforded by generation sequencing technologies, allowing for rapid detection of antimicrobial resistance genes directly from sequencing data

Summary

Introduction

The global threat of antimicrobial resistance is growing at an alarming rate; infections that were once treatable constitute public health crises [1]. The detection of Mycobacterium tuberculosis requires inoculated isolation media to be incubated for several days in order to diagnose infection and additional time for phenotypic characterisation of antimicrobial resistance [14] This highlights the potential for developing more efficient diagnostic tests and the utilisation of AMD technologies to create more rapid alternatives for ARG detection. With the rapidly developing field of whole genome sequencing beginning to be utilised in clinical practice, the ability to interrogate sequencing data quickly and for the presence of antimicrobial resistance genes will become increasingly important and useful for informing clinical decisions Use of such tools will provide insight into the dynamics of antimicrobial resistance genes in metagenomic samples such as those used in environmental monitoring

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Discussion

Conclusion