MEGARes: an antimicrobial resistance database for high throughput sequencing

By Steven M. Lakin, C. Dean, N. Noyes, Adam Dettenwanger, A. S. Ross, E. Doster, P. Rovira, Z. Abdo, Kenneth L. Jones, Jaime Ruiz and 3 more. Nucleic Acids Res., 2016

325 citations recorded by the source corpus.

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.

Subfields: Molecular Biology, Molecular Medicine

View Paper: https://www.semanticscholar.org/paper/67eda9755cdc702a2b3f5dd7b218b5d65e288588

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In Molecular Biology

Citations in the corpus

CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database (2019, methodology): “Similarly, CARD engages in data harmonization with other AMR resources including the NCBI National Database of Antibiotic Resistant Organisms and the Pathogen Detection Reference Gene catalog (22) and AMR research tools such as MEGARes and AMR++ (17).”
Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment (2021, methodology): “To further evaluate and validate performance, the DeepARG-LS model was applied to all the ARG sequences in the MEGARes database (Lakin et al., 2017).”
ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads (2017, background): “ARIBA can easily be provided with custom reference sequence-sets, and includes support for a number of public databases: ARGANNOT [6], CARD [10], MEGARes [11], and ResFinder [5].”
MEGARes 2.0: a database for classification of antimicrobial drug, biocide and metal resistance determinants in metagenomic sequence data (2019, methodology): “Extended descriptions of each step of the pipeline have been previously reported (20) and updated documentation is available on the MEGARes website (http://megares.”