{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T13:32:09Z","timestamp":1793194329579,"version":"4.5.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"The Ineos Oxford Institute for antimicrobial research"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Bacterial antimicrobial resistance (AMR) poses a significant public health threat. The increase of both global awareness and affordable whole genome sequencing has yielded an ever-growing collection of bacterial genome sequence datasets and corresponding antibiotic resistance metadata. This enables the use of computational techniques, including machine learning (ML), to predict phenotypes and discover novel AMR-associated variants. With the great variety of resistance mechanisms to interrogate and the number of datasets that can be mined, there is a need to identify where novel AMR marker discovery is most necessary. Multiple databases and annotation pipelines exist to annotate AMR variants known to be associated with resistance to specific antibiotics or antibiotic classes, however, the completeness of these databases varies, and for some antibiotics, even the most complete databases remain insufficient for accurate classification. Here, we build predictive ML models using only those known markers, which we call \u201cminimal models\u201d of resistance. We predict the binary resistance phenotypes of 20 major antimicrobials in the genomically diverse pathogen Klebsiella pneumoniae, allowing us to identify their shortcomings in phenotype prediction, thereby highlighting opportunities for novel marker discovery. We provide a critical review of the differences in annotation tools and databases commonly used in bacterial AMR studies, and outline guidance for the establishment of a standard dataset for the development and benchmarking of ML models of AMR.<\/jats:p>","DOI":"10.1038\/s41598-025-24333-9","type":"journal-article","created":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T13:48:34Z","timestamp":1763473714000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Comparative assessment of annotation tools reveals critical antimicrobial resistance knowledge gaps in Klebsiella pneumoniae"],"prefix":"10.1038","volume":"15","author":[{"given":"Kristina","family":"Kordova","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caitlin","family":"Collins","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julian","family":"Parkhill","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,18]]},"reference":[{"key":"24333_CR1","doi-asserted-by":"publisher","first-page":"707330","DOI":"10.3389\/fmicb.2021.707330","volume":"12","author":"E Avershina","year":"2021","unstructured":"Avershina, E., Shapovalova, V. & Shipulin, G. Fighting antibiotic resistance in Hospital-Acquired infections: current state and emerging technologies in disease Prevention, diagnostics and therapy. Front. Microbiol. 12, 707330. https:\/\/doi.org\/10.3389\/fmicb.2021.707330 (2021).","journal-title":"Front. Microbiol."},{"key":"24333_CR2","doi-asserted-by":"publisher","unstructured":"Feldgarden, M. et al. Validating the AMRFinder Tool and Resistance Gene Database by Using Antimicrobial Resistance Genotype-Phenotype Correlations in a Collection of Isolates. Antimicrob Agents Chemother. 63 (11), e00483-19. https:\/\/doi.org\/10.1128\/AAC.00483-19 (2019). Erratum in: Antimicrob Agents Chemother. 64 (4), e00361-20. https:\/\/doi.org\/10.1128\/AAC.00361-20 (2020).","DOI":"10.1128\/AAC.00483-19"},{"key":"24333_CR3","doi-asserted-by":"publisher","first-page":"122079","DOI":"10.1016\/j.watres.2024.122079","volume":"262","author":"P Jiang","year":"2024","unstructured":"Jiang, P. et al. A rapid approach with machine learning for quantifying the relative burden of antimicrobial resistance in natural aquatic environments. Water Res. 262, 122079. https:\/\/doi.org\/10.1016\/j.watres.2024.122079 (2024). Epub 2024 Jul 10.","journal-title":"Water Res."},{"issue":"12","key":"24333_CR4","doi-asserted-by":"publisher","first-page":"944","DOI":"10.1016\/j.tim.2016.09.007","volume":"24","author":"KL Wyres","year":"2016","unstructured":"Wyres, K. L. & Holt, K. E. Klebsiella pneumoniae population genomics and Antimicrobial-Resistant clones. Trends Microbiol. 24 (12), 944\u2013956. https:\/\/doi.org\/10.1016\/j.tim.2016.09.007 (2016). Epub 2016 Oct 11.","journal-title":"Trends Microbiol."},{"key":"24333_CR5","doi-asserted-by":"publisher","unstructured":"Wyres, K. L. & Holt, K. E. Klebsiella pneumoniae as a key trafficker of drug resistance genes from environmental to clinically important bacteria. Curr Opin Microbiol. ;45:131\u2013139. (2018). https:\/\/doi.org\/10.1016\/j.mib.2018.04.004. Epub 2018 May 1.","DOI":"10.1016\/j.mib.2018.04.004"},{"issue":"D1","key":"24333_CR6","doi-asserted-by":"publisher","first-page":"D690","DOI":"10.1093\/nar\/gkac920","volume":"51","author":"BP Alcock","year":"2023","unstructured":"Alcock, B. P. et al. CARD 2023: expanded curation, support for machine learning, and resistome prediction at the comprehensive antibiotic resistance database. Nucleic Acids Res. 51 (D1), D690\u2013D699. https:\/\/doi.org\/10.1093\/nar\/gkac920 (2023).","journal-title":"Nucleic Acids Res."},{"issue":"12","key":"24333_CR7","doi-asserted-by":"publisher","first-page":"3491","DOI":"10.1093\/jac\/dkaa345","volume":"75","author":"V Bortolaia","year":"2020","unstructured":"Bortolaia, V. et al. ResFinder 4.0 for predictions of phenotypes from genotypes. J. Antimicrob. Chemother. 75 (12), 3491\u20133500. https:\/\/doi.org\/10.1093\/jac\/dkaa345 (2020).","journal-title":"J. Antimicrob. Chemother."},{"key":"24333_CR8","doi-asserted-by":"publisher","unstructured":"Gibson, M. K., Forsberg, K. J. & Dantas, G. Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology. ISME J. ;9(1):207\u2009\u2013\u2009216. (2015). https:\/\/doi.org\/10.1038\/ismej.2014.106. Epub 2014 Jul 8.","DOI":"10.1038\/ismej.2014.106"},{"key":"24333_CR9","doi-asserted-by":"publisher","unstructured":"Liu, B. & Pop, M. ARDB\u2013Antibiotic Resistance Genes Database. Nucleic Acids Res. ;37(Database issue):D443-7. (2009). https:\/\/doi.org\/10.1093\/nar\/gkn656. Epub 2008 Oct 2.","DOI":"10.1093\/nar\/gkn656"},{"issue":"D1","key":"24333_CR10","doi-asserted-by":"publisher","first-page":"D523","DOI":"10.1093\/nar\/gkac1052","volume":"51","author":"UniProt Consortium","year":"2023","unstructured":"UniProt Consortium. UniProt: the universal protein knowledgebase in 2023. Nucleic Acids Res. 51 (D1), D523\u2013D531. https:\/\/doi.org\/10.1093\/nar\/gkac1052 (2023).","journal-title":"Nucleic Acids Res."},{"issue":"1","key":"24333_CR11","doi-asserted-by":"publisher","first-page":"12728","DOI":"10.1038\/s41598-021-91456-0","volume":"11","author":"M Feldgarden","year":"2021","unstructured":"Feldgarden, M. et al. AMRFinderPlus and the reference gene catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. Sci. Rep. 11 (1), 12728. https:\/\/doi.org\/10.1038\/s41598-021-91456-0 (2021).","journal-title":"Sci. Rep."},{"issue":"10","key":"24333_CR12","doi-asserted-by":"publisher","first-page":"1400","DOI":"10.3390\/antibiotics11101400","volume":"11","author":"G Maboni","year":"2022","unstructured":"Maboni, G. et al. Three distinct annotation platforms differ in detection of antimicrobial resistance genes in Long-Read, Short-Read, and hybrid sequences derived from total genomic DNA or from purified plasmid DNA. Antibiot. (Basel). 11 (10), 1400. https:\/\/doi.org\/10.3390\/antibiotics11101400 (2022).","journal-title":"Antibiot. (Basel)"},{"key":"24333_CR13","doi-asserted-by":"publisher","DOI":"10.1186\/s40168-018-0401-z","author":"G Arango-Argoty","year":"2018","unstructured":"Arango-Argoty, G. et al. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome ;6(1):23. doi: https:\/\/doi.org\/10.1186\/s40168-018-0401-z. (2018)."},{"key":"24333_CR14","doi-asserted-by":"publisher","unstructured":"Mendes, I. et al. hAMRonization: enhancing antimicrobial resistance prediction using the PHA4GE AMR detection specification and tooling. bioRxiv 2024.03.07.583950; https:\/\/doi.org\/10.1101\/2024.03.07.583950","DOI":"10.1101\/2024.03.07.583950"},{"key":"24333_CR15","unstructured":"Seemann, T. Abricate, Github https:\/\/github.com\/tseemann\/abricate"},{"issue":"1","key":"24333_CR16","doi-asserted-by":"publisher","first-page":"4188","DOI":"10.1038\/s41467-021-24448-3","volume":"12","author":"MMC Lam","year":"2021","unstructured":"Lam, M. M. C. et al. A genomic surveillance framework and genotyping tool for Klebsiella pneumoniae and its related species complex. Nat. Commun. 12 (1), 4188. https:\/\/doi.org\/10.1038\/s41467-021-24448-3 (2021).","journal-title":"Nat. Commun."},{"key":"24333_CR17","doi-asserted-by":"publisher","unstructured":"Verboven, L., Phelan, J., Heupink, T. H. & Van Rie, A. TBProfiler for automated calling of the association with drug resistance of variants in Mycobacterium tuberculosis. PLoS One. 17 (12), e0279644. https:\/\/doi.org\/10.1371\/journal.pone.0279644 (2022). Erratum in: PLoS One. 18 (10), e0293254. https:\/\/doi.org\/10.1371\/journal.pone.0293254 (2023).","DOI":"10.1371\/journal.pone.0279644"},{"key":"24333_CR18","doi-asserted-by":"publisher","first-page":"191","DOI":"10.12688\/wellcomeopenres.15603.1","volume":"4","author":"M Hunt","year":"2019","unstructured":"Hunt, M. et al. Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with mykrobe. Wellcome Open. Res. 4, 191. https:\/\/doi.org\/10.12688\/wellcomeopenres.15603.1 (2019).","journal-title":"Wellcome Open. Res."},{"issue":"2","key":"24333_CR19","doi-asserted-by":"publisher","first-page":"e000335","DOI":"10.1099\/mgen.0.000335","volume":"6","author":"RM Doyle","year":"2020","unstructured":"Doyle, R. M. et al. Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: an inter-laboratory study. Microb. Genom. 6 (2), e000335. https:\/\/doi.org\/10.1099\/mgen.0.000335 (2020). Epub 2020 Feb 12.","journal-title":"Microb. Genom"},{"issue":"D1","key":"24333_CR20","doi-asserted-by":"publisher","first-page":"D678","DOI":"10.1093\/nar\/gkac1003","volume":"51","author":"RD Olson","year":"2023","unstructured":"Olson, R. D. et al. Introducing the bacterial and viral bioinformatics resource center (BV-BRC): a resource combining PATRIC, IRD and vipr. Nucleic Acids Res. 51 (D1), D678\u2013D689. https:\/\/doi.org\/10.1093\/nar\/gkac1003 (2023).","journal-title":"Nucleic Acids Res."},{"issue":"1","key":"24333_CR21","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1038\/s41598-017-18972-w","volume":"8","author":"M Nguyen","year":"2018","unstructured":"Nguyen, M. et al. Developing an in Silico minimum inhibitory concentration panel test for Klebsiella pneumoniae. Sci. Rep. 8 (1), 421. https:\/\/doi.org\/10.1038\/s41598-017-18972-w (2018).","journal-title":"Sci. Rep."},{"key":"24333_CR22","unstructured":"Lundberg, M. S. Su-In Lee S. A Unified Approach to Interpreting Model Predictions. NeurIPS Proceedings. (2017)."},{"issue":"3","key":"24333_CR23","doi-asserted-by":"publisher","first-page":"001222","DOI":"10.1099\/mgen.0.001222","volume":"10","author":"G Batisti Biffignandi","year":"2024","unstructured":"Batisti Biffignandi, G. et al. Optimising machine learning prediction of minimum inhibitory concentrations in Klebsiella pneumoniae. Microb. Genom. 10 (3), 001222. https:\/\/doi.org\/10.1099\/mgen.0.001222 (2024).","journal-title":"Microb. Genom"},{"issue":"3","key":"24333_CR24","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1016\/S0022-2836(05)80360-2","volume":"215","author":"SF Altschul","year":"1990","unstructured":"Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215 (3), 403\u2013410. https:\/\/doi.org\/10.1016\/S0022-2836(05)80360-2 (1990).","journal-title":"J. Mol. Biol."},{"key":"24333_CR25","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1186\/1471-2105-11-119","volume":"11","author":"D Hyatt","year":"2010","unstructured":"Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119. https:\/\/doi.org\/10.1186\/1471-2105-11-119 (2010).","journal-title":"BMC Bioinform."},{"key":"24333_CR26","doi-asserted-by":"publisher","unstructured":"Panunzi, L. G. SraX: A novel comprehensive resistome analysis tool. Front. Microbiol. 11, 52. https:\/\/doi.org\/10.3389\/fmicb.2020.00052 (2020). Erratum in: Front Microbiol. 11, 594635. https:\/\/doi.org\/10.3389\/fmicb.2020.594635 (2020).","DOI":"10.3389\/fmicb.2020.00052"},{"issue":"2","key":"24333_CR27","doi-asserted-by":"publisher","first-page":"292","DOI":"10.3390\/microorganisms10020292","volume":"10","author":"A Bharat","year":"2022","unstructured":"Bharat, A. et al. Correlation between phenotypic and in Silico detection of antimicrobial resistance in Salmonella enterica in Canada using Staramr. Microorganisms 10 (2), 292. https:\/\/doi.org\/10.3390\/microorganisms10020292 (2022).","journal-title":"Microorganisms"},{"issue":"10","key":"24333_CR28","doi-asserted-by":"publisher","first-page":"e000131","DOI":"10.1099\/mgen.0.000131","volume":"3","author":"M Hunt","year":"2017","unstructured":"Hunt, M. et al. ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads. Microb. Genom. 3 (10), e000131. https:\/\/doi.org\/10.1099\/mgen.0.000131 (2017).","journal-title":"Microb. Genom"},{"issue":"D1","key":"24333_CR29","doi-asserted-by":"publisher","first-page":"D744","DOI":"10.1093\/nar\/gkac1047","volume":"51","author":"N Bonin","year":"2023","unstructured":"Bonin, N. et al. MEGARes and AMR++, v3.0: an updated comprehensive database of antimicrobial resistance determinants and an improved software pipeline for classification using high-throughput sequencing. Nucleic Acids Res. 51 (D1), D744\u2013D752. https:\/\/doi.org\/10.1093\/nar\/gkac1047 (2023).","journal-title":"Nucleic Acids Res."},{"issue":"11","key":"24333_CR30","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1186\/s13073-014-0090-6","volume":"6","author":"M Inouye","year":"2014","unstructured":"Inouye, M. et al. SRST2: rapid genomic surveillance for public health and hospital microbiology labs. Genome Med. 6 (11), 90. https:\/\/doi.org\/10.1186\/s13073-014-0090-6 (2014).","journal-title":"Genome Med."},{"issue":"21","key":"24333_CR31","doi-asserted-by":"publisher","first-page":"3601","DOI":"10.1093\/bioinformatics\/bty387","volume":"34","author":"WPM Rowe","year":"2018","unstructured":"Rowe, W. P. M. & Winn, M. D. Indexed variation graphs for efficient and accurate resistome profiling. Bioinformatics 34 (21), 3601\u20133608. https:\/\/doi.org\/10.1093\/bioinformatics\/bty387 (2018).","journal-title":"Bioinformatics"},{"issue":"9","key":"24333_CR32","doi-asserted-by":"publisher","first-page":"2484","DOI":"10.1093\/jac\/dkw184","volume":"71","author":"PT Clausen","year":"2016","unstructured":"Clausen, P. T., Zankari, E., Aarestrup, F. M. & Lund, O. Benchmarking of methods for identification of antimicrobial resistance genes in bacterial whole genome data. J. Antimicrob. Chemother. 71 (9), 2484\u20132488. https:\/\/doi.org\/10.1093\/jac\/dkw184 (2016). Epub 2016 Jun 30.","journal-title":"J. Antimicrob. Chemother."},{"issue":"1","key":"24333_CR33","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1186\/s40168-019-0670-1","volume":"7","author":"F Berglund","year":"2019","unstructured":"Berglund, F. et al. Identification and reconstruction of novel antibiotic resistance genes from metagenomes. Microbiome 7 (1), 52. https:\/\/doi.org\/10.1186\/s40168-019-0670-1 (2019).","journal-title":"Microbiome"},{"key":"24333_CR34","doi-asserted-by":"publisher","DOI":"10.1128\/mSphere.00050-15","author":"TJ de Man","year":"2016","unstructured":"de Man, T. J. & Limbago, B. M. SSTAR, a Stand-Alone Easy-To-Use Antimicrobial Resistance Gene Predictor. mSphere ;1(1):e00050\u2013e00015. doi: https:\/\/doi.org\/10.1128\/mSphere.00050-15. (2016)."},{"issue":"B","key":"24333_CR35","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1093\/jac\/5.supplement_b.61","volume":"5","author":"JM Hamilton-Miller","year":"1979","unstructured":"Hamilton-Miller, J. M. Mechanisms and distribution of bacterial resistance to diaminopyrimidines and sulphonamides. J. Antimicrob. Chemother. 5 (B), 61\u201373. https:\/\/doi.org\/10.1093\/jac\/5.supplement_b.61 (1979).","journal-title":"J. Antimicrob. Chemother."},{"issue":"11","key":"24333_CR36","doi-asserted-by":"publisher","first-page":"3099","DOI":"10.1093\/jac\/dkaa257","volume":"75","author":"N Mahfouz","year":"2020","unstructured":"Mahfouz, N., Ferreira, I., Beisken, S., von Haeseler, A. & Posch, A. E. Large-scale assessment of antimicrobial resistance marker databases for genetic phenotype prediction: a systematic review. J. Antimicrob. Chemother. 75 (11), 3099\u20133108. https:\/\/doi.org\/10.1093\/jac\/dkaa257 (2020).","journal-title":"J. Antimicrob. Chemother."},{"issue":"8","key":"24333_CR37","doi-asserted-by":"publisher","first-page":"e3002186","DOI":"10.1371\/journal.pbio.3002186","volume":"21","author":"M Saathoff","year":"2023","unstructured":"Saathoff, M. et al. Gene amplifications cause high-level resistance against Albicidin in gram-negative bacteria. PLoS Biol. 21 (8), e3002186. https:\/\/doi.org\/10.1371\/journal.pbio.3002186 (2023).","journal-title":"PLoS Biol."},{"issue":"7","key":"24333_CR38","doi-asserted-by":"publisher","first-page":"e24519","DOI":"10.1002\/jcla.24519","volume":"36","author":"H Chen","year":"2022","unstructured":"Chen, H., Li, N., Wang, F., Wang, L. & Liang, W. Carbapenem antibiotic stress increases blaKPC\u20132 gene relative copy number and bacterial resistance levels of Klebsiella pneumoniae. J. Clin. Lab. Anal. 36 (7), e24519. https:\/\/doi.org\/10.1002\/jcla.24519 (2022). Epub 2022 Jun 19.","journal-title":"J. Clin. Lab. Anal."},{"key":"24333_CR39","doi-asserted-by":"publisher","unstructured":"Zhou, X. et al. Prediction of antimicrobial resistance in Klebsiella pneumoniae using genomic and metagenomic next-generation sequencing data. J Antimicrob Chemother. 79 (10), 2509\u20132517. https:\/\/doi.org\/10.1093\/jac\/dkae248 (2024). Erratum in: J Antimicrob Chemother. 79 (9), 2412. https:\/\/doi.org\/10.1093\/jac\/dkae289 (2024).","DOI":"10.1093\/jac\/dkae248"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-24333-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-24333-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-24333-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T02:03:25Z","timestamp":1763863405000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-24333-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,18]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["24333"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-24333-9","relation":{"references":[{"id-type":"doi","id":"10.1186\/s40168-018-0401-z","asserted-by":"subject"},{"id-type":"doi","id":"10.1128\/mSphere.00050-15","asserted-by":"subject"}]},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,18]]},"assertion":[{"value":"13 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"40495"}}