The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis
Open accessAbstract
The rise of antibiotic-resistant bacteria has led to an urgent need for rapid detection of drug resistance in clinical samples, and improvements in global surveillance. Here we show how de Bruijn graph representation of bacterial diversity can be used to identify species and resistance profiles of clinical isolates. We implement this method for Staphylococcus aureus and Mycobacterium tuberculosis in a software package (‘Mykrobe predictor’) that takes raw sequence data as input, and generates a clinician-friendly report within 3 minutes on a laptop. For S . aureus , the error rates of our method are comparable to gold-standard phenotypic methods, with sensitivity/specificity of 99.1%/99.6% across 12 antibiotics (using an independent validation set, n =470). For M . tuberculosis , our method predicts resistance with sensitivity/specificity of 82.6%/98.5% (independent validation set, n =1,609); sensitivity is lower here, probably because of limited understanding of the underlying genetic mechanisms. We give evidence that minor alleles improve detection of extremely drug-resistant strains, and demonstrate feasibility of the use of emerging single-molecule nanopore sequencing techniques for these purposes.
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Cites
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
Lina, G., Piemont, Y., Godail-Gamot, F., Bes, M., Peter, M.-O., Gauduchon, V., Vandenesch, F., & Etienne, J. (1999) · Clinical Infectious Diseases · doi:10.1086/313461 · Cited by 2,489
Metagenomic microbial community profiling using unique clade-specific marker genes
Segata, N., Waldron, L., Ballarini, A., Narasimhan, V., Jousson, O., & Huttenhower, C. (2012) · Nature Methods · doi:10.1038/nmeth.2066 · Cited by 1,776
Cited by
Pai, M., Behr, M. A., Dowdy, D., Dheda, K., Divangahi, M., Boehme, C. C., Ginsberg, A., Swaminathan, S., Spigelman, M., Getahun, H., et al. (2016) · Nature Reviews Disease Primers · doi:10.1038/nrdp.2016.76 · Cited by 1,270
Multidrug evolutionary strategies to reverse antibiotic resistance
Baym, M., Stone, L. K., & Kishony, R. (2016) · Science · doi:10.1126/science.aad3292 · Cited by 710
Ellington, M. J., Ekelund, O., Aarestrup, F. M., Canton, R., Doumith, M., Giske, C., Grundman, H., Hasman, H., Holden, M. T. G., Hopkins, K. L., et al. (2017) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2016.11.012 · Cited by 603
Self-citationPhelan, J. E., O’Sullivan, D. M., Machado, D., Ramos, J., Oppong, Y. E. A., Campino, S., O’Grady, J., McNerney, R., Hibberd, M. L., Viveiros, M., et al. (2019) · Genome Medicine · doi:10.1186/s13073-019-0650-x · Cited by 518
Mechanisms and clinical relevance of bacterial heteroresistance
Andersson, D. I., Nicoloff, H., & Hjort, K. (2019) · Nature Reviews Microbiology · doi:10.1038/s41579-019-0218-1 · Cited by 513
Metadata
- Authors
- Bradley, P., Gordon, N. C., Walker, T. M., Dunn, L., Heys, S., Huang, B., Earle, S., Pankhurst, L. J., Anson, L., de Cesare, M., Piazza, P., Votintseva, A. A., Golubchik, T., Wilson, D. J., Wyllie, D. H., Diel, R., Niemann, S., Feuerriegel, S., Kohl, T. A., Ismail, N., Omar, S. V., Smith, E. G., Buck, D., McVean, G., Walker, A. S., Peto, T. E. A., Crook, D. W., & Iqbal, Z.
- Venue
- Nature Communications
- Published
- 2015-12-21
- Volume
- 6
- Issue
- 1
- Pages
- 10063-10063
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2236299009
- Cited by
- 556
- References
- 3
- FWCI
- 45.05
- Type
- journal article
- Language
- en
- Open access
- gold
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
