MEGARes: an antimicrobial resistance database for high throughput sequencing. Issue Volume 45:Issue D1(2017) (24th November 2016)

• Record Type: Journal Article
• Authors: Lakin, Steven M.; Dean, Chris; Noyes, Noelle R.; Dettenwanger, Adam; Ross, Anne Spencer; Doster, Enrique; Rovira, Pablo; Abdo, Zaid; Jones, Kenneth L.; Ruiz, Jaime; Belk, Keith E.; Morley, Paul S.; Boucher, Christina
• Is Part Of: Nucleic acids research. Volume 45:Issue D1(2017)
• Page Start: D574
• Page End: D580
• Publication Date: 2016-11-24
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkw1009
• Languages: English
• ISSNs: 0305-1048

Abstract

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.