The DOI matches its Crossref record; 165 of the 165 references it cites were found in a registry or an index.
Whole-Genome Sequencing in Outbreak Analysis
Open accessAbstract
SUMMARY: In addition to the ever-present concern of medical professionals about epidemics of infectious diseases, the relative ease of access and low cost of obtaining, producing, and disseminating pathogenic organisms or biological toxins mean that bioterrorism activity should also be considered when facing a disease outbreak. Utilization of whole-genome sequencing (WGS) in outbreak analysis facilitates the rapid and accurate identification of virulence factors of the pathogen and can be used to identify the path of disease transmission within a population and provide information on the probable source. Molecular tools such as WGS are being refined and advanced at a rapid pace to provide robust and higher-resolution methods for identifying, comparing, and classifying pathogenic organisms. If these methods of pathogen characterization are properly applied, they will enable an improved public health response whether a disease outbreak was initiated by natural events or by accidental or deliberate human activity. The current application of next-generation sequencing (NGS) technology to microbial WGS and microbial forensics is reviewed.
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Metadata
- Authors
- Gilchrist, C. A., Turner, S. D., Riley, M. F., Petri, W. A., & Hewlett, E. L.
- Venue
- Clinical Microbiology Reviews
- Publisher
- American Society for Microbiology
- Published
- 2015-07
- Volume
- 28
- Issue
- 3
- Pages
- 541-563
- ISSN
- 0893-8512 (ISSN-L)
- 1098-6618
- OpenAlex
- W2121402826
- Cited by
- 288
- References
- 165
- FWCI
- 8.23
- Type
- journal article
- Language
- en
- Open access
- green
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- Gilchrist, C. A., Turner, S. D., Riley, M. F., Petri, W. A., & Hewlett, E. L. (2015). Whole-Genome Sequencing in Outbreak Analysis. Clinical Microbiology Reviews. https://doi.org/10.1128/cmr.00075-13
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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.
