Journal
JOURNAL OF CLINICAL MICROBIOLOGY
Volume 57, Issue 3, Pages -Publisher
AMER SOC MICROBIOLOGY
DOI: 10.1128/JCM.01405-18
Keywords
antibiotic resistance; genome-based prediction
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Funding
- National Institute of Allergy and Infectious Diseases (NIAID) [AI121860]
- Antimicrobial Resistance and Therapeutic Discovery Training Program - NIAID T32 award [AI106699-05]
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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. We examine differences between calling antibiotic resistance profiles by the simple presence or absence of previously known genes and single-nucleotide polymorphisms (SNPs) against approaches that deploy machine learning and statistical models. Often, the limitations to genome-based prediction arise from limitations of accuracy of culture-based AST in addition to an incomplete knowledge of the genetic basis of resistance. However, we need to maintain phenotypic testing even as genome-based prediction becomes more widespread to ensure that the results do not diverge over time. We argue that standardization of WGS-AST by challenge with consistently phenotyped strain sets of defined genetic diversity is necessary to compare the efficacy of methods of prediction of antibiotic resistance based on genome sequences.
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