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DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data

Overview of attention for article published in Microbiome, February 2018
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

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Title
DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data
Published in
Microbiome, February 2018
DOI 10.1186/s40168-018-0401-z
Pubmed ID
Authors

Gustavo Arango-Argoty, Emily Garner, Amy Pruden, Lenwood S. Heath, Peter Vikesland, Liqing Zhang

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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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 868 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 868 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 161 19%
Researcher 122 14%
Student > Master 109 13%
Student > Bachelor 62 7%
Other 38 4%
Other 138 16%
Unknown 238 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 166 19%
Agricultural and Biological Sciences 129 15%
Computer Science 67 8%
Immunology and Microbiology 49 6%
Engineering 39 4%
Other 134 15%
Unknown 284 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 84. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 June 2023.
All research outputs
#510,177
of 25,529,543 outputs
Outputs from Microbiome
#127
of 1,775 outputs
Outputs of similar age
#12,048
of 449,770 outputs
Outputs of similar age from Microbiome
#5
of 52 outputs
Altmetric has tracked 25,529,543 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,775 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 38.0. This one has done particularly well, scoring higher than 92% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 449,770 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.