DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data.
Gustavo Arango-Argoty,Emily Garner,Amy Pruden,Lenwood S. Heath,Peter J. Vikesland,Liqing Zhang +5 more
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TLDR
The deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice, and DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs.Abstract:
Growing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the “best hits” of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Evaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models’ performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories. The deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The DeepARG models and database are available as a command line version and as a Web service at http://bench.cs.vt.edu/deeparg
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Drug combinations: a strategy to extend the life of antibiotics in the 21st century.
Mike Tyers,Gerard D. Wright +1 more
TL;DR: A theoretical and practical framework for the development of effective antibiotic combinations is outlined and a productive strategy to address the widespread emergence of antibiotic-resistant strains is proposed.
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Compositional and functional differences of the mucosal microbiota along the intestine of healthy individuals
Stefania Vaga,Sunjae Lee,Boyang Ji,Anna Andreasson,Anna Andreasson,Anna Andreasson,Nicholas J. Talley,Lars Agréus,Gholamreza Bidkhori,Petia Kovatcheva-Datchary,Petia Kovatcheva-Datchary,Jun-Seok Park,Doheon Lee,Gordon Proctor,Stanislav Dusko Ehrlich,Jens Nielsen,Lars Engstrand,Saeed Shoaie,Saeed Shoaie +18 more
TL;DR: This work used shotgun metagenomics of mucosal biopsies to explore the microbial communities’ compositions of terminal ileum and large intestine in 5 healthy individuals, and details which species are involved with the tryptophan/indole pathway and the antimicrobial resistance biogeography along the intestine.
Journal ArticleDOI
Antibiotic resistance in wastewater treatment plants: Tackling the black box.
Célia M. Manaia,Jaqueline Rocha,Nazareno Scaccia,Roberto B. M. Marano,Elena Radu,Francesco Biancullo,Francisco Cerqueira,Gianuario Fortunato,Iakovos C. Iakovides,Ian Zammit,Ioannis D. Kampouris,Ivone Vaz-Moreira,Ivone Vaz-Moreira,Olga C. Nunes +13 more
TL;DR: The paper aims at contributing to explore how ARB&ARGs behave in UWTPs having in mind that each plant is a unique system that will probably need a specific procedure to maximize ARB &ARGs removal.
Journal ArticleDOI
Sequencing-based methods and resources to study antimicrobial resistance.
TL;DR: Focusing on sequence-based discovery of antibiotic resistance genes, this Review discusses computational strategies and resources for resistance gene identification in genomic and metagenomic samples, including recent deep-learning approaches.
Journal ArticleDOI
Using Genomics to Track Global Antimicrobial Resistance.
Rene S. Hendriksen,Valeria Bortolaia,Heather Tate,Gregory H. Tyson,Frank Møller Aarestrup,Patrick F. McDermott +5 more
TL;DR: A scientific literature review is conducted and a description of examples of available tools and databases for antimicrobial resistance (AMR) detection and future perspectives and recommendations are presented.
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