 |
English
|
正體中文
|
简体中文
|
Items with full text/Total items : 12145/12927 (94%)
Visitors : 848456
Online Users : 1067
|
|
|
Loading...
|
Please use this identifier to cite or link to this item:
http://ir.nhri.org.tw/handle/3990099045/15833
|
| Title: | ARGNet: Using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences |
| Authors: | Pei, Y;Shum, MHH;Liao, YS;Leung, VW;Gong, YN;Smith, DK;Yin, XL;Guan, Y;Luo, RB;Zhang, T;Lam, TTY |
| Contributors: | National Institute of Infectious Diseases and Vaccinology |
| Abstract: | Background Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identification of ARGs, particularly from high-throughput sequencing data of the specimens, is the state-of-the-art method for comprehensively monitoring their spread and evolution. Current computational methods to identify ARGs mainly rely on alignment-based sequence similarities with known ARGs. Such approaches are limited by choice of reference databases and may potentially miss novel ARGs. The similarity thresholds are usually simple and could not accommodate variations across different gene families and regions. It is also difficult to scale up when sequence data are increasing.Results In this study, we developed ARGNet, a deep neural network that incorporates an unsupervised learning autoencoder model to identify ARGs and a multiclass classification convolutional neural network to classify ARGs that do not depend on sequence alignment. This approach enables a more efficient discovery of both known and novel ARGs. ARGNet accepts both amino acid and nucleotide sequences of variable lengths, from partial (30-50 aa; 100-150 nt) sequences to full-length protein or genes, allowing its application in both target sequencing and metagenomic sequencing. Our performance evaluation showed that ARGNet outperformed other deep learning models including DeepARG and HMD-ARG in most of the application scenarios especially quasi-negative test and the analysis of prediction consistency with phylogenetic tree. ARGNet has a reduced inference runtime by up to 57% relative to DeepARG.Conclusions ARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet, with an online service provided at https://ARGNet.hku.hk. 7nR91jX7ySRMRPZrE5tsTy Video AbstractConclusions ARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet, with an online service provided at https://ARGNet.hku.hk. 7nR91jX7ySRMRPZrE5tsTy Video Abstract |
| Date: | 2024-05-09 |
| Relation: | Microbiome. 2024 May 09;12:Article number 84. |
| Link to: | http://dx.doi.org/10.1186/s40168-024-01805-0 |
| JIF/Ranking 2023: | http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=2049-2618&DestApp=IC2JCR |
| Cited Times(WOS): | https://www.webofscience.com/wos/woscc/full-record/WOS:001217611200002 |
| Cited Times(Scopus): | https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85192569064 |
| Appears in Collections: | [其他] 期刊論文
|
Files in This Item:
| File |
Description |
Size | Format | |
|---|
| ISI001217611200002.pdf | | 4375Kb | Adobe PDF | 64 | View/Open |
|
All items in NHRI are protected by copyright, with all rights reserved.
|
