| [1] |
Macesic N, Uhlemann AC, Peleg AY. 2025. Multidrug-resistant Gram-negative bacterial infections. |
| [2] |
Naghavi M, Vollset SE, Ikuta KS, Swetschinski LR, Gray AP, et al. 2024. Global burden of bacterial antimicrobial resistance 1990–2021: a systematic analysis with forecasts to 2050. |
| [3] |
Larsson DGJ, Flach CF. 2022. Antibiotic resistance in the environment. |
| [4] |
Li L, Li B, Yin X, Xia Y, Yang Y, et al. 2025. Assessing antimicrobial resistance connectivity across One Health sectors. |
| [5] |
Hernando-Amado S, Coque TM, Baquero F, Martínez JL. 2019. Defining and combating antibiotic resistance from One Health and Global Health perspectives. |
| [6] |
Soucy SM, Huang J, Gogarten JP. 2015. Horizontal gene transfer: building the web of life. |
| [7] |
Huang J, Yong H, Huang J, Che Y, Klümper U, et al. 2025. Microbial risks triggered by oral administration of antibiotics in fish aquaculture persist long after the legally mandated antibiotic withdrawal time. |
| [8] |
Martínez JL, Coque TM, Baquero F. 2015. What is a resistance gene? Ranking risk in resistomes. |
| [9] |
Tang A, Zhang J, Huang J, Deng Y, Wang D, et al. 2024. Decrypting the viral community in aerobic activated sludge reactors treating antibiotic production wastewater. |
| [10] |
Zhang J, Tang A, Jin T, Sun D, Guo F, et al. 2024. A panoramic view of the virosphere in three wastewater treatment plants by integrating viral-like particle-concentrated and traditional non-concentrated metagenomic approaches. |
| [11] |
Cen T, Zhang X, Xie S, Li D. 2020. Preservatives accelerate the horizontal transfer of plasmid-mediated antimicrobial resistance genes via differential mechanisms. |
| [12] |
Jiao P, Zhou Y, Zhang X, Jian H, Zhang XX, et al. 2024. Mechanisms of horizontal gene transfer and viral contribution to the fate of intracellular and extracellular antibiotic resistance genes in anaerobic digestion supplemented with conductive materials under ammonia stress. |
| [13] |
Zhou H, Beltrán JF, Brito IL. 2021. Functions predict horizontal gene transfer and the emergence of antibiotic resistance. |
| [14] |
Yao Y, Maddamsetti R, Weiss A, Ha Y, Wang T, et al. 2022. Intra- and interpopulation transposition of mobile genetic elements driven by antibiotic selection. |
| [15] |
Bengtsson-Palme J, Larsson DGJ, Kristiansson E. 2017. Using metagenomics to investigate human and environmental resistomes. |
| [16] |
Li S, Zhang C, Li F, Hua T, Zhou Q, et al. 2021. Technologies towards antibiotic resistance genes (ARGs) removal from aquatic environment: a critical review. |
| [17] |
Nesme J, Cécillon S, Delmont TO, Monier JM, Vogel TM, et al. 2014. Large-scale metagenomic-based study of antibiotic resistance in the environment. |
| [18] |
Pillay S, Calderón-Franco D, Urhan A, Abeel T. 2022. Metagenomic-based surveillance systems for antibiotic resistance in non-clinical settings. |
| [19] |
Li B, Yang Y, Ma L, Ju F, Guo F, et al. 2015. Metagenomic and network analysis reveal wide distribution and co-occurrence of environmental antibiotic resistance genes. |
| [20] |
Beatson SA, Walker MJ. 2014. Tracking antibiotic resistance. |
| [21] |
Rhoads A, Au KF. 2015. PacBio sequencing and its applications. |
| [22] |
Sereika M, Kirkegaard RH, Karst SM, Michaelsen TY, Sørensen EA, et al. 2022. Oxford Nanopore R10.4 long-read sequencing enables the generation of near-finished bacterial genomes from pure cultures and metagenomes without short-read or reference polishing. |
| [23] |
Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, et al. 2014. Binning metagenomic contigs by coverage and composition. |
| [24] |
Li D, Liu CM, Luo R, Sadakane K, Lam TW. 2015. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. |
| [25] |
McCorison CB, Kim T, Donato JJ, LaPara TM. 2025. Proximity-ligation metagenomic sequence analysis reveals that the antibiotic resistome makes significant transitions during municipal wastewater treatment. |
| [26] |
Arango-Argoty G, Garner E, Pruden A, Heath LS, Vikesland P, et al. 2018. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. |
| [27] |
Yin X, Jiang XT, Chai B, Li L, Yang Y, et al. 2018. ARGs-OAP v2.0 with an expanded SARG database and Hidden Markov Models for enhancement characterization and quantification of antibiotic resistance genes in environmental metagenomes. |
| [28] |
dos Santos DFK, Istvan P, Quirino BF, Kruger RH. 2017. Functional metagenomics as a tool for identification of new antibiotic resistance genes from natural environments. |
| [29] |
Zhang AN, Gaston JM, Dai CL, Zhao S, Poyet M, et al. 2021. An omics-based framework for assessing the health risk of antimicrobial resistance genes. |
| [30] |
Shendure J, Balasubramanian S, Church GM, Gilbert W, Rogers J, et al. 2017. DNA sequencing at 40: past, present and future. |
| [31] |
Boolchandani M, D'Souza AW, Dantas G. 2019. Sequencing-based methods and resources to study antimicrobial resistance. |
| [32] |
Hu T, Chitnis N, Monos D, Dinh A. 2021. Next-generation sequencing technologies: an overview. |
| [33] |
Metzker ML. 2010. Sequencing technologies — the next generation. |
| [34] |
Buermans HPJ, den Dunnen JT. 2014. Next generation sequencing technology: advances and applications. |
| [35] |
Foox J, Tighe SW, Nicolet CM, Zook JM, Byrska-Bishop M, et al. 2021. Performance assessment of DNA sequencing platforms in the ABRF Next-Generation Sequencing Study. |
| [36] |
Ari Ş, Arikan M. 2016. Next-generation sequencing: advantages, disadvantages, and future. In Plant Omics: Trends and Applications, eds Hakeem K, Tombuloğlu H, Tombuloğlu G. Cham: Springer. pp. 109–135 doi: 10.1007/978-3-319-31703-8_5 |
| [37] |
Mardis ER. 2008. Next-generation DNA sequencing methods. |
| [38] |
Cuber P, Chooneea D, Geeves C, Salatino S, Creedy TJ, et al. 2023. Comparing the accuracy and efficiency of third generation sequencing technologies, Oxford Nanopore Technologies, and Pacific Biosciences, for DNA barcode sequencing applications. |
| [39] |
Larkin J, Henley RY, Jadhav V, Korlach J, Wanunu M. 2017. Length-independent DNA packing into nanopore zero-mode waveguides for low-input DNA sequencing. |
| [40] |
Eid J, Fehr A, Gray J, Luong K, Lyle J, et al. 2009. Real-time DNA sequencing from single polymerase molecules. |
| [41] |
Petersen LM, Martin IW, Moschetti WE, Kershaw CM, Tsongalis GJ. 2019. Third-generation sequencing in the clinical laboratory: exploring the advantages and challenges of nanopore sequencing. |
| [42] |
Kumar KR, Cowley MJ, Davis RL. 2024. Next-generation sequencing and emerging technologies. |
| [43] |
Liu L, Li Y, Li S, Hu N, He Y, et al. 2012. Comparison of next-generation sequencing systems. |
| [44] |
Chen B, Yuan K, Chen X, Yang Y, Zhang T, et al. 2016. Metagenomic analysis revealing antibiotic resistance genes (ARGs) and their genetic compartments in the Tibetan environment. |
| [45] |
Djordjevic SP, Jarocki VM, Seemann T, Cummins ML, Watt AE, et al. 2024. Genomic surveillance for antimicrobial resistance — a One Health perspective. |
| [46] |
Ma L, Xia Y, Li B, Yang Y, Li LG, et al. 2016. Metagenomic assembly reveals hosts of antibiotic resistance genes and the shared resistome in pig, chicken, and human feces. |
| [47] |
Che Y, Xia Y, Liu L, Li AD, Yang Y, et al. 2019. Mobile antibiotic resistome in wastewater treatment plants revealed by Nanopore metagenomic sequencing. |
| [48] |
Dai D, Brown C, Bürgmann H, Larsson DGJ, Nambi I, et al. 2022. Long-read metagenomic sequencing reveals shifts in associations of antibiotic resistance genes with mobile genetic elements from sewage to activated sludge. |
| [49] |
Nayfach S, Pollard KS. 2016. Toward accurate and quantitative comparative metagenomics. |
| [50] |
Langmead B, Salzberg SL. 2012. Fast gapped-read alignment with Bowtie 2. |
| [51] |
Li H, Durbin R. 2009. Fast and accurate short read alignment with Burrows–Wheeler transform. |
| [52] |
Uritskiy GV, DiRuggiero J, Taylor J. 2018. MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. |
| [53] |
Qiu Z, Yuan L, Lian CA, Lin B, Chen J, et al. 2024. BASALT refines binning from metagenomic data and increases resolution of genome-resolved metagenomic analysis. |
| [54] |
Kang DD, Li F, Kirton E, Thomas A, Egan R, et al. 2019. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. |
| [55] |
Wu YW, Simmons BA, Singer SW. 2016. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. |
| [56] |
Zankari E, Hasman H, Cosentino S, Vestergaard M, Rasmussen S, et al. 2012. Identification of acquired antimicrobial resistance genes. |
| [57] |
Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, et al. 2009. BLAST+: architecture and applications. |
| [58] |
McArthur AG, Waglechner N, Nizam F, Yan A, Azad MA, et al. 2013. The comprehensive antibiotic resistance database. |
| [59] |
Alcock BP, Raphenya AR, Lau TTY, Tsang KK, Bouchard M, et al. 2020. CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. |
| [60] |
Yang Y, Jiang X, Chai B, Ma L, Li B, et al. 2016. ARGs-OAP: online analysis pipeline for antibiotic resistance genes detection from metagenomic data using an integrated structured ARG-database. |
| [61] |
Jia B, Raphenya AR, Alcock B, Waglechner N, Guo P, et al. 2017. CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. |
| [62] |
Feldgarden M, Brover V, Haft DH, Prasad AB, Slotta DJ, et al. 2019. Validating the AMRFinder tool and resistance gene database by using antimicrobial resistance genotype-phenotype correlations in a collection of isolates. |
| [63] |
Liu B, Pop M. 2009. ARDB—antibiotic resistance genes database. |
| [64] |
Yang Y, Li B, Ju F, Zhang T. 2013. Exploring variation of antibiotic resistance genes in activated sludge over a four-year period through a metagenomic approach. |
| [65] |
Mao X, Yin X, Yang Y, Che Y, Xu X, et al. 2024. Standardization in global environmental antibiotic resistance genes (ARGs) surveillance. |
| [66] |
Yin X, Chen X, Jiang XT, Yang Y, Li B, et al. 2023. Toward a universal unit for quantification of antibiotic resistance genes in environmental samples. |
| [67] |
Li B, Li X, Yan T. 2021. A quantitative metagenomic sequencing approach for high-throughput gene quantification and demonstration with antibiotic resistance genes. |
| [68] |
Ju F, Beck K, Yin X, Maccagnan A, McArdell CS, et al. 2019. Wastewater treatment plant resistomes are shaped by bacterial composition, genetic exchange, and upregulated expression in the effluent microbiomes. |
| [69] |
Satinsky BM, Gifford SM, Crump BC, Moran MA. 2013. Use of internal standards for quantitative metatranscriptome and metagenome analysis. In Methods in Enzymology. Volume 531. Amsterdam, the Netherlands: Elsevier. pp. 237–250 doi: 10.1016/B978-0-12-407863-5.00012-5 |
| [70] |
Shi X, Yang Y, Wang C, Xu X, Mao X, et al. 2025. Microbial risk assessment across multiple environments based on metagenomic absolute quantification with cellular internal standards. |
| [71] |
Wang X, Zhang H, Yu S, Li D, Gillings MR, et al. 2024. Inter-plasmid transfer of antibiotic resistance genes accelerates antibiotic resistance in bacterial pathogens. |
| [72] |
Carattoli A, Zankari E, Garcìa-Fernandez A, Larsen MV, Lund O, et al. 2014. In silico detection and typing of plasmids using PlasmidFinder and plasmid multilocus sequence typing. |
| [73] |
Krawczyk PS, Lipinski L, Dziembowski A. 2018. PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures. |
| [74] |
Zhou F, Xu Y. 2010. cBar: a computer program to distinguish plasmid-derived from chromosome-derived sequence fragments in metagenomics data. |
| [75] |
Antipov D, Hartwick N, Shen M, Raiko M, Lapidus A, et al. 2016. plasmidSPAdes: assembling plasmids from whole genome sequencing data. |
| [76] |
Rozov R, Brown Kav A, Bogumil D, Shterzer N, Halperin E, et al. 2017. Recycler: an algorithm for detecting plasmids from de novo assembly graphs. |
| [77] |
Siguier P, Pérochon J, Lestrade L, Mahillon J, Chandler M. 2006. ISfinder: the reference centre for bacterial insertion sequences. |
| [78] |
Xie Z, Tang H. 2017. ISEScan: automated identification of insertion sequence elements in prokaryotic genomes. |
| [79] |
Treepong P, Guyeux C, Meunier A, Couchoud C, Hocquet D, et al. 2018. panISa: ab initio detection of insertion sequences in bacterial genomes from short read sequence data. |
| [80] |
Néron B, Littner E, Haudiquet M, Perrin A, Cury J, et al. 2022. IntegronFinder 2.0: identification and analysis of integrons across bacteria, with a focus on antibiotic resistance in Klebsiella. |
| [81] |
Guo J, Bolduc B, Zayed AA, Varsani A, Dominguez-Huerta G, et al. 2021. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. |
| [82] |
Ren J, Song K, Deng C, Ahlgren NA, Fuhrman JA, et al. 2020. Identifying viruses from metagenomic data using deep learning. |
| [83] |
Ren J, Ahlgren NA, Lu YY, Fuhrman JA, Sun F. 2017. VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data. |
| [84] |
Camargo AP, Roux S, Schulz F, Babinski M, Xu Y, et al. 2024. Identification of mobile genetic elements with geNomad. |
| [85] |
Nayfach S, Camargo AP, Schulz F, Eloe-Fadrosh E, Roux S, et al. 2021. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. |
| [86] |
Kieft K, Zhou Z, Anantharaman K. 2020. VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. |
| [87] |
Rice EW, Wang P, Smith AL, Stadler LB. 2020. Determining hosts of antibiotic resistance genes: a review of methodological advances. |
| [88] |
Garcia-Armisen T, Anzil A, Cornelis P, Chevreuil M, Servais P. 2013. Identification of antimicrobial resistant bacteria in rivers: insights into the cultivation bias. |
| [89] |
Zhu YG, Zhao Y, Li B, Huang CL, Zhang SY, et al. 2017. Continental-scale pollution of estuaries with antibiotic resistance genes. |
| [90] |
Spencer SJ, Tamminen MV, Preheim SP, Guo MT, Briggs AW, et al. 2016. Massively parallel sequencing of single cells by epicPCR links functional genes with phylogenetic markers. |
| [91] |
Liu S, Dai S, Deng Y, Li J, Zhang Y, et al. 2025. Long-read epicPCR enhances species-level host identification of clinically relevant antibiotic resistance genes in environmental microbial communities. |
| [92] |
Lou EG, Fu Y, Wang Q, Treangen TJ, Stadler LB. 2024. Sensitivity and consistency of long-and short-read metagenomics and epicPCR for the detection of antibiotic resistance genes and their bacterial hosts in wastewater. |
| [93] |
Zhao R, Yu K, Zhang J, Zhang G, Huang J, et al. 2020. Deciphering the mobility and bacterial hosts of antibiotic resistance genes under antibiotic selection pressure by metagenomic assembly and binning approaches. |
| [94] |
Liang J, Mao G, Yin X, Ma L, Liu L, et al. 2020. Identification and quantification of bacterial genomes carrying antibiotic resistance genes and virulence factor genes for aquatic microbiological risk assessment. |
| [95] |
Belton JM, McCord RP, Gibcus JH, Naumova N, Zhan Y, et al. 2012. Hi–C: a comprehensive technique to capture the conformation of genomes. |
| [96] |
Stalder T, Press MO, Sullivan S, Liachko I, Top EM. 2019. Linking the resistome and plasmidome to the microbiome. |
| [97] |
Wang Y, Yu Z, Ding P, Lu J, Klümper U, et al. 2022. Non-antibiotic pharmaceuticals promote conjugative plasmid transfer at a community-wide level. |
| [98] |
Li HZ, Yang K, Liao H, Lassen SB, Su JQ, et al. 2022. Active antibiotic resistome in soils unraveled by single-cell isotope probing and targeted metagenomics. |
| [99] |
Zhang Z, Zhang Q, Wang T, Xu N, Lu T, et al. 2022. Assessment of global health risk of antibiotic resistance genes. |
| [100] |
Oh M, Pruden A, Chen C, Heath LS, Xia K, et al. 2018. MetaCompare: a computational pipeline for prioritizing environmental resistome risk. |
| [101] |
Rumi MA, Oh M, Davis BC, Brown CL, Juvekar A, et al. 2024. MetaCompare 2.0: differential ranking of ecological and human health resistome risks. |