Skip to main content
SpringerLink
Log in

Web-Based Tools Validation for Antimicrobial Resistance Prediction: An Empirical Comparative Analysis

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Antimicrobial resistance (AMR) is a serious threat to global public health, necessitating rapid and precise diagnostic tools. The prevalence of novel antibiotic resistance genes (ARGs) has increased due to microbial sequencing, resulting in the need to extract vital information from vast amounts of data. Although many AMR prediction tools exist, only a few are accurate and scalable. We examined 20 widely used AMR prediction tools and chose 4 web-based tools for antimicrobial resistance surveillance over standalone software due to their easy accessibility, portability, and centralized data management, eliminating the need for complex installation and maintenance. CGE (Center for Genomic Epidemiology) provides bioinformatics tools and promotes open data sharing. At the same time, CARD (Comprehensive Antibiotic Resistance Database) is a valuable resource for antibiotic resistance gene information, collectively contributing to our understanding and management of antibiotic resistance. We highlighted web-based AMR prediction tools and performed a case study using the Pseudomonas aeruginosa complete plasmid sequence (CPS) to identify strengths and weaknesses in the system. Our study explored four web-based antibiotic resistance gene prediction tools: ResFinder, KmerResistance, ResFinderFG, and RGI. ResFinder excelled at finding acquired antimicrobial resistance genes as well as maintaining a database up to date. KmerResistance identified resistance genes using k-mer analysis. esFinderFG offered a unique perspective, excelling in detecting a broad range of resistant phenotypes, due to its inclusion of sequences discovered through functional metagenomics. RGI was versatile in detecting a wide range of resistance genes and provided extensive resistance mechanism information. Researchers must understand the capabilities and trade-offs of these tools to make well-informed choices for efficient resistance gene identification and surveillance as the antibiotic resistance landscape evolves.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

The data presented in this study are available in the supplementary files.

References

  1. Aslam B, Wang W, Arshad MI, et al. Antibiotic resistance: a rundown of a global crisis. Infect Drug Resist. 2018;11:1645–58. https://doi.org/10.2147/IDR.S173867. (Epub ahead of print).

    Article  Google Scholar 

  2. Uluseker C, Kaster KM, Thorsen K, et al. A review on occurrence and spread of antibiotic resistance in wastewaters and in wastewater treatment plants: mechanisms and perspectives. Front Microbiol. 2021. https://doi.org/10.3389/fmicb.2021.717809. (Epub ahead of print).

    Article  Google Scholar 

  3. Baran A, Kwiatkowska A, Potocki L. Antibiotics and bacterial resistance—a short story of an endless arms race. Int J Mol Sci. 2023. https://doi.org/10.3390/ijms24065777. (Epub ahead of print).

    Article  Google Scholar 

  4. Pagès JM, Amaral L. Mechanisms of drug efflux and strategies to combat them: challenging the efflux pump of gram-negative bacteria. Biochim Biophys Acta Prot Proteom. 2009. https://doi.org/10.1016/j.bbapap.2008.12.011. (Epub ahead of print).

    Article  Google Scholar 

  5. Hutchings M, Truman A, Wilkinson B. Antibiotics: past, present and future. Curr Opin Microbiol. 2019. https://doi.org/10.1016/j.mib.2019.10.008. (Epub ahead of print).

    Article  Google Scholar 

  6. Matlock A, Garcia JA, Moussavi K, et al. Advances in novel antibiotics to treat multidrug-resistant gram-negative bacterial infections. Intern Emerg Med. 2021. https://doi.org/10.1007/s11739-021-02749-1.

    Article  Google Scholar 

  7. Ferri M, Ranucci E, Romagnoli P, et al. Antimicrobial resistance: a global emerging threat to public health systems. Crit Rev Food Sci Nutr. 2017. https://doi.org/10.1080/10408398.2015.1077192. (Epub ahead of print).

    Article  Google Scholar 

  8. Matthiessen LE, Hald T, Vigre H. System mapping of antimicrobial resistance to combat a rising global health crisis. Front Public Health. 2022;10:816943. https://doi.org/10.3389/fpubh.2022.816943.

    Article  Google Scholar 

  9. Jasovský D, Littmann J, Zorzet A, et al. Antimicrobial resistance—a threat to the world’s sustainable development. Upsala J Med Sci. 2016. https://doi.org/10.1080/03009734.2016.1195900. (Epub ahead of print).

    Article  Google Scholar 

  10. Zhang AN, Gaston JM, Dai CL, et al. An omics-based framework for assessing the health risk of antimicrobial resistance genes. Nat Commun. 2021. https://doi.org/10.1038/s41467-021-25096-3. (Epub ahead of print).

    Article  Google Scholar 

  11. Lerminiaux NA, Cameron ADS. Horizontal transfer of antibiotic resistance genes in clinical environments. Can J Microbiol. 2019. https://doi.org/10.1139/cjm-2018-0275. (Epub ahead of print).

    Article  Google Scholar 

  12. Zankari E, Hasman H, Cosentino S, et al. Identification of acquired antimicrobial resistance genes. J Antimicrob Chemother. 2012. https://doi.org/10.1093/jac/dks261. (Epub ahead of print).

    Article  Google Scholar 

  13. Okoye EL, Kemakolam C, Ugwuoji ET, et al. Multidrug resistance tracing by plasmid profile analysis and the curing of bacteria from different clinical specimens. Adv Gut Microb Res. 2022. https://doi.org/10.1155/2022/3170342. (Epub ahead of print).

    Article  Google Scholar 

  14. Liu YY, Chen S, Burrus V, et al. Editorial: Globally or regionally spread of epidemic plasmids carrying clinically important resistance genes: epidemiology, molecular mechanism, and drivers. Front Microbiol. 2021. https://doi.org/10.3389/fmicb.2021.822802. (Epub ahead of print).

    Article  Google Scholar 

  15. Svara F, Rankin DJ. The evolution of plasmid-carried antibiotic resistance. BMC Evol Biol. 2011. https://doi.org/10.1186/1471-2148-11-130. (Epub ahead of print).

    Article  Google Scholar 

  16. Gajic I, Kabic J, Kekic D, et al. Antimicrobial susceptibility testing: a comprehensive review of currently used methods. Antibiotics. 2022. https://doi.org/10.3390/antibiotics11040427. (Epub ahead of print).

    Article  Google Scholar 

  17. Vincent AT, Derome N, Boyle B, et al. Next-generation sequencing (NGS) in the microbiological world: how to make the most of your money. J Microbiol Methods. 2017. https://doi.org/10.1016/j.mimet.2016.02.016. (Epub ahead of print).

    Article  Google Scholar 

  18. Motro Y, Moran-Gilad J. Next-generation sequencing applications in clinical bacteriology. Biomol Detect Quantif. 2017. https://doi.org/10.1016/j.bdq.2017.10.002. (Epub ahead of print).

    Article  Google Scholar 

  19. Nayak DSK, Mahapatra S, Swarnkar T. Gene selection and enrichment for microarray data—a comparative network based approach. Adv Intell Syst Comput. 2018. https://doi.org/10.1007/978-981-10-6875-1_41. (Epub ahead of print).

    Article  Google Scholar 

  20. Khan ZA, Siddiqui MF, Park S. Current and emerging methods of antibiotic susceptibility testing. Diagnostics. 2019. https://doi.org/10.3390/diagnostics9020049. (Epub ahead of print).

    Article  Google Scholar 

  21. Mahfouz N, Ferreira I, Beisken S, et al. Large-scale assessment of antimicrobial resistance marker databases for genetic phenotype prediction: a systematic review. J Antimicrob Chemother. 2020. https://doi.org/10.1093/jac/dkaa257. (Epub ahead of print).

    Article  Google Scholar 

  22. Hendriksen RS, Bortolaia V, Tate H, et al. Using genomics to track global antimicrobial resistance. Front Public Health. 2019. https://doi.org/10.3389/fpubh.2019.00242. (Epub ahead of print).

    Article  Google Scholar 

  23. Seoane A, Bou G. Bioinformatics approaches to the study of antimicrobial resistance. Revista Espanola de Quimioterapia. 2021. https://doi.org/10.37201/req/s01.04.2021. (Epub ahead of print).

    Article  Google Scholar 

  24. Gürpınar Ö, Köseoğlu-Eser Ö. Pseudomonas aeruginosa resistome and epidemic high-risk clones. KLIMIK Derg. 2022. https://doi.org/10.36519/kd.2022.4002. (Epub ahead of print).

    Article  Google Scholar 

  25. Gao J, Wei X, Yin L, et al. Emergence and transfer of plasmid-harbored rmtB in a clinical multidrug-resistant pseudomonas aeruginosa strain. Microorganisms. 2022. https://doi.org/10.3390/microorganisms10091818. (Epub ahead of print).

    Article  Google Scholar 

  26. Patil S, Chen X, Dong S, et al. Resistance genomics and molecular epidemiology of high-risk clones of ESBL-producing Pseudomonas aeruginosa in young children. Front Cell Infect Microbiol. 2023. https://doi.org/10.3389/fcimb.2023.1168096. (Epub ahead of print).

    Article  Google Scholar 

  27. Perron GG, Inglis RF, Pennings PS, et al. Fighting microbial drug resistance: a primer on the role of evolutionary biology in public health. Evol Appl. 2015. https://doi.org/10.1111/eva.12254. (Epub ahead of print).

    Article  Google Scholar 

  28. Mu X, Li X, Yin Z, et al. Abundant diversity of accessory genetic elements and associated antimicrobial resistance genes in Pseudomonas aeruginosa isolates from a single Chinese hospital. Ann Clin Microbiol Antimicrob. 2023. https://doi.org/10.1186/s12941-023-00600-3. (Epub ahead of print).

    Article  Google Scholar 

  29. Sanya DRA, Onésime D, Vizzarro G, et al. Recent advances in therapeutic targets identification and development of treatment strategies towards Pseudomonas aeruginosa infections. BMC Microbiol. 2023. https://doi.org/10.1186/s12866-023-02832-x. (Epub ahead of print).

    Article  Google Scholar 

  30. Ahmed OB. Detection of antibiotic resistance genes in Pseudomonas aeruginosa by whole genome sequencing. Infect Drug Resist. 2022. https://doi.org/10.2147/IDR.S389959. (Epub ahead of print).

    Article  Google Scholar 

  31. Feldgarden M, Brover V, Haft DH, 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. 2019. https://doi.org/10.1128/AAC.00483-19. (Epub ahead of print).

    Article  Google Scholar 

  32. Köser CU, Ellington MJ, Peacock SJ. Whole-genome sequencing to control antimicrobial resistance. Trends Genet. 2014. https://doi.org/10.1016/j.tig.2014.07.003. (Epub ahead of print).

    Article  Google Scholar 

  33. Romaniuk K, Styczynski M, Decewicz P, et al. Diversity and horizontal transfer of antarctic Pseudomonas spp. plasmids. Genes (Basel). 2019. https://doi.org/10.3390/genes10110850. (Epub ahead of print).

    Article  Google Scholar 

  34. Gupta SK, Padmanabhan BR, Diene SM, et al. ARG-annot, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrob Agents Chemother. 2014. https://doi.org/10.1128/AAC.01310-13. (Epub ahead of print).

    Article  Google Scholar 

  35. Hunt M, Mather AE, Sánchez-Busó L, et al. ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads. Microb Genom. 2017. https://doi.org/10.1099/mgen.0.000131. (Epub ahead of print).

    Article  Google Scholar 

  36. Arango-Argoty G, Garner E, Pruden A, et al. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome. 2018. https://doi.org/10.1186/s40168-018-0401-z. (Epub ahead of print).

    Article  Google Scholar 

  37. Rowe WPM, Winn MD. Indexed variation graphs for efficient and accurate resistome profiling. Bioinformatics. 2018. https://doi.org/10.1093/bioinformatics/bty387. (Epub ahead of print).

    Article  Google Scholar 

  38. Clausen PTLC, Zankari E, Aarestrup FM, et al. Benchmarking of methods for identification of antimicrobial resistance genes in bacterial whole genome data. J Antimicrob Chemother. 2016. https://doi.org/10.1093/jac/dkw184. (Epub ahead of print).

    Article  Google Scholar 

  39. Feldgarden M, Brover V, Haft DH, et al. Using the NCBI AMRFinder tool to determine antimicrobial resistance genotype-phenotype correlations within a collection of NARMS isolates. bioRxiv. 2019. https://doi.org/10.1101/550707. (Epub ahead of print).

    Article  Google Scholar 

  40. Gschwind R, Ugarcina Perovic S, Weiss M, et al. ResFinderFG v2.0: a database of antibiotic resistance genes obtained by functional metagenomics. Nucleic Acids Res. 2023. https://doi.org/10.1093/nar/gkad384. (Epub ahead of print).

    Article  Google Scholar 

  41. Rowe W, Baker KS, Verner-Jeffreys D, et al. Search engine for antimicrobial resistance: a cloud compatible pipeline and web interface for rapidly detecting antimicrobial resistance genes directly from sequence data. PLoS ONE. 2015. https://doi.org/10.1371/journal.pone.0133492. (Epub ahead of print).

    Article  Google Scholar 

  42. Kaminski J, Gibson MK, Franzosa EA, et al. High-specificity targeted functional profiling in microbial communities with ShortBRED. PLoS Comput Biol. 2015. https://doi.org/10.1371/journal.pcbi.1004557. (Epub ahead of print).

    Article  Google Scholar 

  43. Inouye M, Dashnow H, Raven LA, et al. SRST2: rapid genomic surveillance for public health and hospital microbiology labs. Genome Med. 2014. https://doi.org/10.1186/s13073-014-0090-6. (Epub ahead of print).

    Article  Google Scholar 

  44. de Man TJB, Limbago BM. SSTAR, a stand-alone easy-to-use antimicrobial resistance gene predictor. mSphere. 2016. https://doi.org/10.1128/msphere.00050-15. (Epub ahead of print).

    Article  Google Scholar 

  45. Chiu JKH, Ong RTH. ARGDIT: a validation and integration toolkit for antimicrobial resistance gene databases. Bioinformatics. 2019. https://doi.org/10.1093/bioinformatics/bty987. (Epub ahead of print).

    Article  Google Scholar 

  46. Hasman H, Clausen PTLC, Kaya H, et al. LRE-Finder, a web tool for detection of the 23S rRNA mutations and the optrA, cfr, cfr(B) and poxtA genes encoding linezolid resistance in enterococci from whole-genome sequences. J Antimicrob Chemother. 2019. https://doi.org/10.1093/jac/dkz092. (Epub ahead of print).

    Article  Google Scholar 

  47. Matthews TC, Bristow FR, Griffiths EJ, et al. The integrated rapid infectious disease analysis (IRIDA) platform. bioRxiv. 2018. https://doi.org/10.1101/381830. (Epub ahead of print).

    Article  Google Scholar 

  48. Chowdhury AS, Call DR, Broschat SL. PARGT: a software tool for predicting antimicrobial resistance in bacteria. Sci Rep. 2020. https://doi.org/10.1038/s41598-020-67949-9. (Epub ahead of print).

    Article  Google Scholar 

  49. Li Y, Xu Z, Han W, et al. HMD-ARG: hierarchical multi-task deep learning for annotating antibiotic resistance genes. Microbiome. 2021. https://doi.org/10.1186/s40168-021-01002-3. (Epub ahead of print).

    Article  Google Scholar 

  50. Ren Y, Chakraborty T, Doijad S, et al. Prediction of antimicrobial resistance based on whole-genome sequencing and machine learning. Bioinformatics. 2022. https://doi.org/10.1093/bioinformatics/btab681. (Epub ahead of print).

    Article  Google Scholar 

  51. Thomsen MCF, Ahrenfeldt J, Cisneros JLB, et al. A bacterial analysis platform: an integrated system for analysing bacterial whole genome sequencing data for clinical diagnostics and surveillance. PLoS ONE. 2016. https://doi.org/10.1371/journal.pone.0157718. (Epub ahead of print).

    Article  Google Scholar 

  52. Gardy JL, Loman NJ. Towards a genomics-informed, real-time, global pathogen surveillance system. Nat Rev Genet. 2018. https://doi.org/10.1038/nrg.2017.88. (Epub ahead of print).

    Article  Google Scholar 

  53. Gilchrist CA, Turner SD, Riley MF, et al. Whole-genome sequencing in outbreak analysis. Clin Microbiol Rev. 2015. https://doi.org/10.1128/CMR.00075-13. (Epub ahead of print).

    Article  Google Scholar 

  54. Alcock BP, Raphenya AR, Lau TTY, et al. CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res. 2020. https://doi.org/10.1093/nar/gkz935. (Epub ahead of print).

    Article  Google Scholar 

  55. Bortolaia V, Kaas RS, Ruppe E, et al. ResFinder 4.0 for predictions of phenotypes from genotypes. J Antimicrob Chemother. 2020. https://doi.org/10.1093/jac/dkaa345. (Epub ahead of print).

    Article  Google Scholar 

  56. Zankari E, Hasman H, Kaas RS, et al. Genotyping using whole-genome sequencing is a realistic alternative to surveillance based on phenotypic antimicrobial susceptibility testing. J Antimicrob Chemother. 2013. https://doi.org/10.1093/jac/dks496. (Epub ahead of print).

    Article  Google Scholar 

  57. Partridge SR, Collis CM, Hall RM. Class 1 integron containing a new gene cassette, aadA10, associated with Tn1404 from R151. Antimicrob Agents Chemother. 2002. https://doi.org/10.1128/AAC.46.8.2400-2408.2002. (Epub ahead of print).

    Article  Google Scholar 

  58. Sabbagh P, Rajabnia M, Maali A, et al. Integron and its role in antimicrobial resistance: a literature review on some bacterial pathogens. Iran J Basic Med Sci. 2021. https://doi.org/10.22038/ijbms.2020.48905.11208. (Epub ahead of print).

    Article  Google Scholar 

  59. Tauch A, Schlüter A, Bischoff N, et al. The 79,370-bp conjugative plasmid pB4 consists of an IncP-1β backbone loaded with a chromate resistance transposon, the strA-strB streptomycin resistance gene pair, the oxacillinase gene blaNPS-1, and a tripartite antibiotic efflux system of the resistance-nodulation-division family. Mol Genet Genom. 2003. https://doi.org/10.1007/s00438-002-0785-z. (Epub ahead of print).

    Article  Google Scholar 

  60. Subedi D, Vijay AK, Kohli GS, et al. Nucleotide sequence analysis of NPS-1 β-lactamase and a novel integron (In1427)-carrying transposon in an MDR Pseudomonas aeruginosa keratitis strain. J Antimicrob Chemother. 2018. https://doi.org/10.1093/jac/dky073. (Epub ahead of print).

    Article  Google Scholar 

  61. Wang J, Xu T, Ying J, et al. PAU-1, a novel plasmid-encoded ambler class a β-lactamase identified in a clinical Pseudomonas aeruginosa isolate. Infect Drug Resist. 2019. https://doi.org/10.2147/IDR.S225288. (Epub ahead of print).

    Article  Google Scholar 

  62. Haghighi S, Goli HR. High prevalence of blaVEB, blaGES and blaPER genes in beta-lactam resistant clinical isolates of Pseudomonas aeruginosa. AIMS Microbiol. 2022;8:153–66.

    Article  Google Scholar 

  63. Blake KS, Choi JH, Dantas G. Approaches for characterizing and tracking hospital-associated multidrug-resistant bacteria. Cell Mol Life Sci. 2021. https://doi.org/10.1007/s00018-020-03717-2. (Epub ahead of print).

    Article  Google Scholar 

  64. Majiduddin FK, Materon IC, Palzkill TG. Molecular analysis of beta-lactamase structure and function. Int J Med Microbiol. 2002. https://doi.org/10.1078/1438-4221-00198. (Epub ahead of print).

    Article  Google Scholar 

  65. Lee K, Kim DW, Cha CJ. Overview of bioinformatic methods for analysis of antibiotic resistome from genome and metagenome data. J Microbiol. 2021. https://doi.org/10.1007/s12275-021-0652-4. (Epub ahead of print).

    Article  Google Scholar 

  66. Poirel L, Nordmann P. Acquired carbapenem-hydrolyzing beta-lactamases and their genetic support. Curr Pharm Biotechnol. 2005. https://doi.org/10.2174/1389201023378427. (Epub ahead of print).

    Article  Google Scholar 

  67. Overmeyer AJ, Prentice E, Brink A, et al. The genomic characterization of carbapenem-resistant Serratia marcescens at a tertiary hospital in South Africa. JAC Antimicrob Resist. 2023. https://doi.org/10.1093/jacamr/dlad089. (Epub ahead of print).

    Article  Google Scholar 

Download references

Acknowledgements

The author acknowledges and thanks the Center for Genomic Epidemiology and Comprehensive Antibiotic Resistance Database for providing web tools.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Center for Biotechnology, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, 751003, India

    Sweta Padma Routray & Swayamprabha Sahoo

  2. Department of Computer Science and Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, 751030, India

    Debasish Swapnesh Kumar Nayak

  3. Department of Computer Science and Bioscience, Faculty of Technology, Marwadi University, Rajkot, Gujarat, 360003, India

    Sejal Shah

  4. Department of Computer Application, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, 751030, India

    Tripti Swarnkar

Authors
  1. Sweta Padma Routray
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Swayamprabha Sahoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Debasish Swapnesh Kumar Nayak
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sejal Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tripti Swarnkar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tripti Swarnkar.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Diverse Applications in Computing, Analytics and Networks” guest edited by Archana Mantri and Sagar Juneja.

Supplementary Information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Routray, S.P., Sahoo, S., Nayak, D.S.K. et al. Web-Based Tools Validation for Antimicrobial Resistance Prediction: An Empirical Comparative Analysis. SN COMPUT. SCI. 5, 147 (2024). https://doi.org/10.1007/s42979-023-02460-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-02460-2

Keywords

Search

Navigation