ARG-SHINE: improve antibiotic resistance class prediction by integrating sequence homology, functional information and deep convolutional neural network

Ziye Wang; Shuo Li; Ronghui You; Shanfeng Zhu; Xianghong Jasmine Zhou; Fengzhu Sun

doi:10.1093/nargab/lqab066

ARG-SHINE: improve antibiotic resistance class prediction by integrating sequence homology, functional information and deep convolutional neural network

NAR Genom Bioinform. 2021 Aug 5;3(3):lqab066. doi: 10.1093/nargab/lqab066. eCollection 2021 Sep.

Authors

Ziye Wang¹, Shuo Li², Ronghui You³, Shanfeng Zhu⁴, Xianghong Jasmine Zhou², Fengzhu Sun⁵

Affiliations

¹ School of Mathematical Sciences, Fudan University, Shanghai 200433, China.
² Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA.
³ School of Computer Science, Fudan University, Shanghai 200433, China.
⁴ Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.
⁵ Quantitative and Computational Biology Department, University of Southern California, Los Angeles, CA 90089, USA.

Abstract

Antibiotic resistance in bacteria limits the effect of corresponding antibiotics, and the classification of antibiotic resistance genes (ARGs) is important for the treatment of bacterial infections and for understanding the dynamics of microbial communities. Although several methods have been developed to classify ARGs, none of them work well when the ARGs diverge from those in the reference ARG databases. We develop a novel method, ARG-SHINE, for ARG classification. ARG-SHINE utilizes state-of-the-art learning to rank machine learning approach to ensemble three component methods with different features, including sequence homology, protein domain/family/motif and raw amino acid sequences for the deep convolutional neural network. Compared with other methods, ARG-SHINE achieves better performance on two benchmark datasets in terms of accuracy, macro-average f1-score and weighted-average f1-score. ARG-SHINE is used to classify newly discovered ARGs through functional screening and achieves high prediction accuracy. ARG-SHINE is freely available at https://github.com/ziyewang/ARG_SHINE.