MEGARes: an antimicrobial resistance database for high throughput sequencing

Steven M Lakin; Chris Dean; Noelle R Noyes; Adam Dettenwanger; Anne Spencer Ross; Enrique Doster; Pablo Rovira; Zaid Abdo; Kenneth L Jones; Jaime Ruiz; Keith E Belk; Paul S Morley; Christina Boucher

doi:10.1093/nar/gkw1009

MEGARes: an antimicrobial resistance database for high throughput sequencing

Nucleic Acids Res. 2017 Jan 4;45(D1):D574-D580. doi: 10.1093/nar/gkw1009. Epub 2016 Nov 28.

Authors

Affiliations

¹ Department of Clinical Sciences, Colorado State University, Fort Collins, CO 80523, USA.
² Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO 80523, USA.
³ Department of Computer Science, Colorado State University, Fort Collins, CO 80523, USA.
⁴ Department of Animal Sciences, Colorado State University, Fort Collins, CO 80523, USA.
⁵ Department of Biochemistry and Molecular Genetics, University of Colorado Denver School of Medicine, Aurora, CO 80045, USA.
⁶ Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA.
⁷ Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA cboucher@cise.ufl.edu.

Abstract

Antimicrobial resistance has become an imminent concern for public health. As methods for detection and characterization of antimicrobial resistance move from targeted culture and polymerase chain reaction to high throughput metagenomics, appropriate resources for the analysis of large-scale data are required. Currently, antimicrobial resistance databases are tailored to smaller-scale, functional profiling of genes using highly descriptive annotations. Such characteristics do not facilitate the analysis of large-scale, ecological sequence datasets such as those produced with the use of metagenomics for surveillance. In order to overcome these limitations, we present MEGARes (https://megares.meglab.org), a hand-curated antimicrobial resistance database and annotation structure that provides a foundation for the development of high throughput acyclical classifiers and hierarchical statistical analysis of big data. MEGARes can be browsed as a stand-alone resource through the website or can be easily integrated into sequence analysis pipelines through download. Also via the website, we provide documentation for AmrPlusPlus, a user-friendly Galaxy pipeline for the analysis of high throughput sequencing data that is pre-packaged for use with the MEGARes database.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Computational Biology / methods
Databases, Genetic*
Drug Resistance, Microbial*
High-Throughput Nucleotide Sequencing*
Metagenome
Metagenomics / methods
Web Browser

Grants and funding

T32 OD012201/OD/NIH HHS/United States