The DOI matches its Crossref record; 114 of the 114 references it cites were found in a registry or an index.
Genome-Based Prediction of Bacterial Antibiotic Resistance
Open accessAbstract
Clinical microbiology has long relied on growing bacteria in culture to determine antimicrobial susceptibility profiles, but the use of whole-genome sequencing for antibiotic susceptibility testing (WGS-AST) is now a powerful alternative. This review discusses the technologies that made this possible and presents results from recent studies to predict resistance based on genome sequences.
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Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads
Wick, R. R., Judd, L. M., Gorrie, C. L., & Holt, K. E. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005595 · Cited by 8,653
Biofilms: Survival Mechanisms of Clinically Relevant Microorganisms
Donlan, R. M., & Costerton, J. W. (2002) · Clinical Microbiology Reviews · doi:10.1128/cmr.15.2.167-193.2002 · Cited by 6,644
Base-Calling of Automated Sequencer Traces Using Phred. II. Error Probabilities
Ewing, B., & Green, P. (1998) · Genome Research · doi:10.1101/gr.8.3.186 · Cited by 5,439
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
Coming of age: ten years of next-generation sequencing technologies
Goodwin, S., McPherson, J. D., & McCombie, W. R. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.49 · Cited by 4,352
CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database
Jia, B., Raphenya, A. R., Alcock, B., Waglechner, N., Guo, P., Tsang, K. K., Lago, B. A., Dave, B. M., Pereira, S., Sharma, A. N., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1004 · Cited by 2,661
The 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
Shotgun metagenomics, from sampling to analysis
Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J., & Segata, N. (2017) · Nature Biotechnology · doi:10.1038/nbt.3935 · Cited by 2,202
PacBio Sequencing and its Applications
Rhoads, A., & Au, K. F. (2015) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2015.08.002 · Cited by 2,135
Cited by
Antibiotic Resistance: One Health One World Outlook
Aslam, B., Khurshid, M., Arshad, M. I., Muzammil, S., Rasool, M., Yasmeen, N., Shah, T., Chaudhry, T. H., Rasool, M. H., Shahid, A., et al. (2021) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2021.771510 · Cited by 609
Peiffer-Smadja, N., Rawson, T. M., Ahmad, R., Buchard, A., Georgiou, P., Lescure, F.-X., Birgand, G., & Holmes, A. H. (2020) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2019.09.009 · Cited by 561
Surveillance of antimicrobial resistance in low- and middle-income countries: a scattered picture
Iskandar, K., Molinier, L., Hallit, S., Sartelli, M., Hardcastle, T. C., Haque, M., Lugova, H., Dhingra, S., Sharma, P., Islam, S., et al. (2021) · Antimicrobial Resistance & Infection Control · doi:10.1186/s13756-021-00931-w · Cited by 479
Innovative and rapid antimicrobial susceptibility testing systems
van Belkum, A., Burnham, C.-A. D., Rossen, J. W. A., Mallard, F., Rochas, O., & Dunne, W. M. (2020) · Nature Reviews Microbiology · doi:10.1038/s41579-020-0327-x · Cited by 355
A fast impedance-based antimicrobial susceptibility test
Spencer, D. C., Paton, T. F., Mulroney, K. T., Inglis, T. J. J., Sutton, J. M., & Morgan, H. (2020) · Nature Communications · doi:10.1038/s41467-020-18902-x · Cited by 183
Metadata
- Authors
- Su, M., Satola, S. W., & Read, T. D.
- Venue
- Journal of Clinical Microbiology
- Publisher
- American Society for Microbiology
- Published
- 2019-03
- Volume
- 57
- Issue
- 3
- ISSN
- 0095-1137 (ISSN-L)
- 1098-660X
- OpenAlex
- W2899068429
- Cited by
- 369
- References
- 114
- FWCI
- 22.16
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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