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dc.contributor.authorLi, Yu
dc.contributor.authorXu, Zeling
dc.contributor.authorHan, Wenkai
dc.contributor.authorCao, Huiluo
dc.contributor.authorUmarov, Ramzan
dc.contributor.authorYan, Aixin
dc.contributor.authorFan, Ming
dc.contributor.authorChen, Huan
dc.contributor.authorDuarte, Carlos M.
dc.contributor.authorLi, Lihua
dc.contributor.authorHo, Pak-Leung
dc.contributor.authorGao, Xin
dc.date.accessioned2021-02-10T08:28:26Z
dc.date.available2021-02-10T08:28:26Z
dc.date.issued2021-02-08
dc.date.submitted2020-11-11
dc.identifier.citationLi, Y., Xu, Z., Han, W., Cao, H., Umarov, R., Yan, A., … Gao, X. (2021). HMD-ARG: hierarchical multi-task deep learning for annotating antibiotic resistance genes. Microbiome, 9(1). doi:10.1186/s40168-021-01002-3
dc.identifier.issn2049-2618
dc.identifier.doi10.1186/s40168-021-01002-3
dc.identifier.urihttp://hdl.handle.net/10754/667325
dc.description.abstractAbstract Background The spread of antibiotic resistance has become one of the most urgent threats to global health, which is estimated to cause 700,000 deaths each year globally. Its surrogates, antibiotic resistance genes (ARGs), are highly transmittable between food, water, animal, and human to mitigate the efficacy of antibiotics. Accurately identifying ARGs is thus an indispensable step to understanding the ecology, and transmission of ARGs between environmental and human-associated reservoirs. Unfortunately, the previous computational methods for identifying ARGs are mostly based on sequence alignment, which cannot identify novel ARGs, and their applications are limited by currently incomplete knowledge about ARGs. Results Here, we propose an end-to-end Hierarchical Multi-task Deep learning framework for ARG annotation (HMD-ARG). Taking raw sequence encoding as input, HMD-ARG can identify, without querying against existing sequence databases, multiple ARG properties simultaneously, including if the input protein sequence is an ARG, and if so, what antibiotic family it is resistant to, what resistant mechanism the ARG takes, and if the ARG is an intrinsic one or acquired one. In addition, if the predicted antibiotic family is beta-lactamase, HMD-ARG further predicts the subclass of beta-lactamase that the ARG is resistant to. Comprehensive experiments, including cross-fold validation, third-party dataset validation in human gut microbiota, wet-experimental functional validation, and structural investigation of predicted conserved sites, demonstrate not only the superior performance of our method over the state-of-art methods, but also the effectiveness and robustness of the proposed method. Conclusions We propose a hierarchical multi-task method, HMD-ARG, which is based on deep learning and can provide detailed annotations of ARGs from three important aspects: resistant antibiotic class, resistant mechanism, and gene mobility. We believe that HMD-ARG can serve as a powerful tool to identify antibiotic resistance genes and, therefore mitigate their global threat. Our method and the constructed database are available at http://www.cbrc.kaust.edu.sa/HMDARG/.
dc.description.sponsorshipFigure 1 was created by Heno Hwang, scientific illustrator at King Abdullah University of Science and Technology (KAUST). We thank the review greatly for the thorough and detailed comments, which have improved the quality of the manuscript significantly.
dc.description.sponsorshipThe research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. FCC/1/1976-04, FCC/1/1976-06, FCC/1/1976-17, FCC/1/1976-18, FCC/1/1976-23, FCC/1/1976-25, FCC/1/1976-26, URF/1/3450-01, URF/1/4098-01-01, and REI/1/0018-01-01; National Natural Science Foundation of China (61731008, 61871428); and the Natural Science Foundation of Zhejiang Province of China (LJ19H180001); and CHP-PH-13, Health and Medical Research Fund (HMRF), to Dr. Pak-Leung Ho.
dc.publisherSpringer Science and Business Media LLC
dc.relation.urlhttps://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-021-01002-3
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleHMD-ARG: hierarchical multi-task deep learning for annotating antibiotic resistance genes
dc.typeArticle
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer Science
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentMarine Science Program
dc.contributor.departmentRed Sea Research Center (RSRC)
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.identifier.journalMicrobiome
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Computer Science and Engineering (CSE), The Chinese University of Hong Kong (CUHK), Hong Kong, People’s Republic of China.
dc.contributor.institutionSchool of Biological Sciences, The University of Hong Kong, Hong Kong, People’s Republic of China.
dc.contributor.institutionCarol Yu Center for Infection and Department of Microbiology, The University of Hong Kong, Hong Kong, People’s Republic of China. l 23955, Saudi Arabia.
dc.contributor.institutionInstitute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, People’s Republic of China.
dc.contributor.institutionKey Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province, Zhejiang Institute of Microbiology, Hangzhou, People’s Republic of China.
dc.identifier.volume9
dc.identifier.issue1
kaust.personLi, Yu
kaust.personHan, Wenkai
kaust.personUmarov, Ramzan
kaust.personDuarte, Carlos M.
kaust.personGao, Xin
kaust.grant.numberFCC/1/1976-04
kaust.grant.numberFCC/1/1976-06
kaust.grant.numberFCC/1/1976-17
kaust.grant.numberFCC/1/1976-18
kaust.grant.numberFCC/1/1976-23
kaust.grant.numberFCC/1/1976-25
kaust.grant.numberFCC/1/1976-26
kaust.grant.numberURF/1/3450-01
kaust.grant.numberURF/1/4098-01-01
kaust.grant.numberREI/1/0018-01-01
dc.date.accepted2021-01-08
refterms.dateFOA2021-02-10T08:32:39Z
kaust.acknowledged.supportUnitscientific illustrator at King Abdullah University of Science and Technology (KAUST)
kaust.acknowledged.supportUnitOSR


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This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Except where otherwise noted, this item's license is described as This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.