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. 2020 Jan 8;48(D1):D561-D569.
doi: 10.1093/nar/gkz1010.

MEGARes 2.0: a database for classification of antimicrobial drug, biocide and metal resistance determinants in metagenomic sequence data

Enrique Doster  1   2   3 , Steven M Lakin  3 , Christopher J Dean  2 , Cory Wolfe  4 , Jared G Young  1 , Christina Boucher  5 , Keith E Belk  6 , Noelle R Noyes  2 , Paul S Morley  1
Affiliations

MEGARes 2.0: a database for classification of antimicrobial drug, biocide and metal resistance determinants in metagenomic sequence data

Enrique Doster et al. Nucleic Acids Res. .

Abstract

Antimicrobial resistance (AMR) is a threat to global public health and the identification of genetic determinants of AMR is a critical component to epidemiological investigations. High-throughput sequencing (HTS) provides opportunities for investigation of AMR across all microbial genomes in a sample (i.e. the metagenome). Previously, we presented MEGARes, a hand-curated AMR database and annotation structure developed to facilitate the analysis of AMR within metagenomic samples (i.e. the resistome). Along with MEGARes, we released AmrPlusPlus, a bioinformatics pipeline that interfaces with MEGARes to identify and quantify AMR gene accessions contained within a metagenomic sequence dataset. Here, we present MEGARes 2.0 (https://megares.meglab.org), which incorporates previously published resistance sequences for antimicrobial drugs, while also expanding to include published sequences for metal and biocide resistance determinants. In MEGARes 2.0, the nodes of the acyclic hierarchical ontology include four antimicrobial compound types, 57 classes, 220 mechanisms of resistance, and 1,345 gene groups that classify the 7,868 accessions. In addition, we present an updated version of AmrPlusPlus (AMR ++ version 2.0), which improves accuracy of classifications, as well as expanding scalability and usability.

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Figures

Figure 1.
Figure 1.
Diagram representing nodes in the five levels of the acyclic hierarchical ontology for MEGARes 2.0 (compound type, classes, mechanisms of resistance and gene groups that classify accessions).
Figure 2.
Figure 2.
Overview of AMR++ 2.0 pipeline. Input fastq files are trimmed and aligned to host genome(s) before being analyzed for resistome and microbiome content using MEGARes and Kraken2 databases, respectively. Relevant resistome output can be piped into the Resistance Gene Identifier for secondary confirmation of specific resistance-conferring SNPs. Output content is indicated by red boxes.
See this image and copyright information in PMC

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