DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data.
DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data.
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DOI：
10.1186/s40168-018-0401-z
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发表时间：
2018-02-01
期刊：
影响因子：
15.5
通讯作者：
Zhang L
中科院分区：
文献类型：
--
作者：
Arango-Argoty G;Garner E;Pruden A;Heath LS;Vikesland P;Zhang L
对抗生素耐药性增加的日益关切要求扩大和全面的全球监测。推进环境介质监测方法（例如，废水、农业废弃物、食物和水）的研究，尤其需要用于确定新型抗生素耐药基因（ARG）的潜在资源、基因交换的热点以及ARG传播和人类暴露的途径。下一代测序现在能够直接访问和分析总宏基因组DNA库，其中ARG通常基于对现有数据库的序列搜索的“最佳命中”来识别或预测。不幸的是，这种方法产生了很高的假阴性率。为了解决这些限制，我们在这里提出了一种深度学习方法，考虑到使用所有已知类别的ARG创建的相异性矩阵。分别针对短读段序列和全基因长度序列构建了两个深度学习模型DeepARG-SS和DeepARG-LS。对30种抗生素耐药性类别的深度学习模型的评估表明，DeepARG模型可以以高精度（> 0.97）和召回率（> 0.90）预测ARG。该模型显示出优于典型的最佳命中方法的优势，产生了持续较低的假阴性率，从而提高了整体召回率（> 0.9）。随着越来越多的数据可用于代表性不足的ARG类别，由于底层神经网络的性质，预计DeepARG模型的性能将进一步增强。我们新开发的ARG数据库DeepARG-DB包含了高度可信的ARG预测和广泛的人工检查，大大扩展了当前的ARG库。相对于当前的生物信息学实践，这里开发的深度学习模型提供了更准确的抗菌素耐药性注释。DeepARG不需要严格的截止值，这使得能够识别更广泛的ARG多样性。DeepARG模型和数据库可以作为命令行版本和Web服务在http://bench.cs.vt.edu/deeparg上提供。本文的在线版本（10.1186/s40168-018-0401-z）包含补充材料，可供授权用户使用。
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. The online version of this article (10.1186/s40168-018-0401-z) contains supplementary material, which is available to authorized users.
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影响因子：
15.5
作者：
Berglund F;Marathe NP;Österlund T;Bengtsson-Palme J;Kotsakis S;Flach CF;Larsson DGJ;Kristiansson E
通讯作者：
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通讯作者：
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48
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Buggenthin F;Buettner F;Hoppe PS;Endele M;Kroiss M;Strasser M;Schwarzfischer M;Loeffler D;Kokkaliaris KD;Hilsenbeck O;Schroeder T;Theis FJ;Marr C
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Marr C
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作者：
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通讯作者：
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