The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
PointFinder: a novel web tool for WGS-based detection of antimicrobial resistance associated with chromosomal point mutations in bacterial pathogens
Open accessAbstract
Background: Antibiotic resistance is a major health problem, as drugs that were once highly effective no longer cure bacterial infections. WGS has previously been shown to be an alternative method for detecting horizontally acquired antimicrobial resistance genes. However, suitable bioinformatics methods that can provide easily interpretable, accurate and fast results for antimicrobial resistance associated with chromosomal point mutations are still lacking. Methods: Phenotypic antimicrobial susceptibility tests were performed on 150 isolates covering three different bacterial species: Salmonella enterica, Escherichia coli and Campylobacter jejuni. The web-server ResFinder-2.1 was used to identify acquired antimicrobial resistance genes and two methods, the novel PointFinder (using BLAST) and an in-house method (mapping of raw WGS reads), were used to identify chromosomal point mutations. Results were compared with phenotypic antimicrobial susceptibility testing results. Results: A total of 685 different phenotypic tests associated with chromosomal resistance to quinolones, polymyxin, rifampicin, macrolides and tetracyclines resulted in 98.4% concordance. Eleven cases of disagreement between tested and predicted susceptibility were observed: two C. jejuni isolates with phenotypic fluoroquinolone resistance and two with phenotypic erythromycin resistance and five colistin-susceptible E. coli isolates with a detected pmrB V161G mutation when assembled with Velvet, but not when using SPAdes or when mapping the reads. Conclusions: PointFinder proved, with high concordance between phenotypic and predicted antimicrobial susceptibility, to be a user-friendly web tool for detection of chromosomal point mutations associated with antimicrobial resistance.
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SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing
Bankevich, A., Nurk, S., Antipov, D., Gurevich, A. A., Dvorkin, M., Kulikov, A. S., Lesin, V. M., Nikolenko, S. I., Pham, S., Prjibelski, A. D., et al. (2012) · Journal of Computational Biology · doi:10.1089/cmb.2012.0021 · Cited by 25,987
Identification of acquired antimicrobial resistance genes
Zankari, E., Hasman, H., Cosentino, S., Vestergaard, M., Rasmussen, S., Lund, O., Aarestrup, F. M., & Larsen, M. V. (2012) · Journal of Antimicrobial Chemotherapy · doi:10.1093/jac/dks261 · Cited by 4,980
Self-citationThe Comprehensive Antibiotic Resistance Database
McArthur, A. G., Waglechner, N., Nizam, F., Yan, A., Azad, M. A., Baylay, A. J., Bhullar, K., Canova, M. J., De Pascale, G., Ejim, L., et al. (2013) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.00419-13 · Cited by 2,226
ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes
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Joensen, K. G., Scheutz, F., Lund, O., Hasman, H., Kaas, R. S., Nielsen, E. M., & Aarestrup, F. M. (2014) · Journal of Clinical Microbiology · doi:10.1128/jcm.03617-13 · Cited by 1,409
Self-citationEngberg, J., Aarestrup, F. M., Taylor, D. E., Gerner-Smidt, P., & Nachamkin, I. (2001) · Emerging Infectious Diseases · doi:10.3201/eid0701.010104 · Cited by 696
Self-citationJin, D. J., & Gross, C. A. (1988) · Journal of Molecular Biology · doi:10.1016/0022-2836(88)90517-7 · Cited by 683
Mechanisms of quinolone resistance in Escherichia coli and Salmonella: Recent developments
Hopkins, K. L., Davies, R. H., & Threlfall, E. J. (2005) · International Journal of Antimicrobial Agents · doi:10.1016/j.ijantimicag.2005.02.006 · Cited by 557
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Zankari, E., Hasman, H., Kaas, R. S., Seyfarth, A. M., Agerso, Y., Lund, O., Larsen, M. V., & Aarestrup, F. M. (2013) · Journal of Antimicrobial Chemotherapy · doi:10.1093/jac/dks496 · Cited by 325
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Cited by
ResFinder 4.0 for predictions of phenotypes from genotypes
Bortolaia, V., Kaas, R. S., Ruppe, E., Roberts, M. C., Schwarz, S., Cattoir, V., Philippon, A., Allesoe, R. L., Rebelo, A. R., Florensa, A. F., et al. (2020) · Journal of Antimicrobial Chemotherapy · doi:10.1093/jac/dkaa345 · Cited by 3,373
Self-citationFlorensa, A. F., Kaas, R. S., Clausen, P. T. L. C., Aytan-Aktug, D., & Aarestrup, F. M. (2022) · Microbial Genomics · doi:10.1099/mgen.0.000748 · Cited by 695
Self-citationSequencing-based methods and resources to study antimicrobial resistance
Boolchandani, M., D’Souza, A. W., & Dantas, G. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0108-4 · Cited by 522
Using Genomics to Track Global Antimicrobial Resistance
Hendriksen, R. S., Bortolaia, V., Tate, H., Tyson, G. H., Aarestrup, F. M., & McDermott, P. F. (2019) · Frontiers in Public Health · doi:10.3389/fpubh.2019.00242 · Cited by 425
Self-citationGenome-Based Prediction of Bacterial Antibiotic Resistance
Su, M., Satola, S. W., & Read, T. D. (2019) · Journal of Clinical Microbiology · doi:10.1128/jcm.01405-18 · Cited by 369
Metadata
- Authors
- Zankari, E., Allesøe, R., Joensen, K. G., Cavaco, L. M., Lund, O., & Aarestrup, F. M.
- Venue
- Journal of Antimicrobial Chemotherapy
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-07-19
- Volume
- 72
- Issue
- 10
- Pages
- 2764-2768
- ISSN
- 0305-7453 (ISSN-L)
- 1460-2091
- OpenAlex
- W2739323458
- Cited by
- 783
- References
- 33
- FWCI
- 8.04
- Type
- journal article
- Language
- en
- Open access
- hybrid
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