Accuracy of Different Bioinformatics Methods in Detecting Antibiotic Resistance and Virulence Factors from Staphylococcus aureus Whole-Genome Sequences-Reference-Cited by-同舟云学术

Accuracy of Different Bioinformatics Methods in Detecting Antibiotic Resistance and Virulence Factors from Staphylococcus aureus Whole-Genome Sequences

Published:2018-09 Issue:9 Volume:56 Page:
ISSN:0095-1137
Container-title:Journal of Clinical Microbiology
language:en
Short-container-title:J Clin Microbiol

Author:

Mason Amy¹,Foster Dona¹²,Bradley Phelim³,Golubchik Tanya¹,Doumith Michel⁴,Gordon N. Claire¹^ORCID,Pichon Bruno⁴,Iqbal Zamin³,Staves Peter⁴,Crook Derrick¹⁵⁶²,Walker A. Sarah¹⁶²,Kearns Angela⁴⁶,Peto Tim¹⁶²

Affiliation:

1. Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom

2. NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom

3. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom

4. Staphylococcus Reference Service, National Infection Service, Public Health England, London, United Kingdom

5. National Infection Service, Public Health England, London, United Kingdom

6. The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom

Abstract

In principle, whole-genome sequencing (WGS) can predict phenotypic resistance directly from a genotype, replacing laboratory-based tests. However, the contribution of different bioinformatics methods to genotype-phenotype discrepancies has not been systematically explored to date.

Publisher

American Society for Microbiology

Subject

Microbiology (medical)

Link

https://journals.asm.org/doi/pdf/10.1128/JCM.01815-17

Reference50 articles.

1. Staphylococcus aureusInfections

2. Public Health England. 2016. Annual epidemiological commentary: mandatory MRSA, MSSA and E. coli bacteraemia and C. difficile infection data 2015/16. Public Health England, London, United Kingdom. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/535635/AEC_final.pdf.

3. The Impact of Methicillin Resistance in Staphylococcus aureus Bacteremia on Patient Outcomes: Mortality, Length of Stay, and Hospital Charges

4. Randomized Trial of Rapid Multiplex Polymerase Chain Reaction–Based Blood Culture Identification and Susceptibility Testing

5. A novel universal DNA labeling and amplification system for rapid microarray-based detection of 117 antibiotic resistance genes in Gram-positive bacteria

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