Whole genome sequencing options for bacterial strain typing and epidemiologic analysis based on single nucleotide polymorphism versus gene-by-gene–based approaches. (April 2018)

• Record Type: Journal Article
• Authors: Schürch, A.C.; Arredondo-Alonso, S.; Willems, R.J.L.; Goering, R.V.
• Is Part Of: Clinical microbiology and infection. Volume 24:Number 4(2018)
• Page Start: 350
• Page End: 354
• Publication Date: 2018-04
• Subjects: Accessory genome -- cgMLST -- Core genome -- Pan-genome -- Relatedness threshold -- SNP -- Whole genome sequencing; Medical microbiology -- Periodicals; Diagnostic microbiology -- Periodicals; Communicable diseases -- Periodicals; Infection -- Periodicals; 616.01
• DOI: 10.1016/j.cmi.2017.12.016
• Languages: English
• ISSNs: 1198-743X

Abstract

Abstract: Background: Whole genome sequence (WGS)-based strain typing finds increasing use in the epidemiologic analysis of bacterial pathogens in both public health as well as more localized infection control settings. Aims: This minireview describes methodologic approaches that have been explored for WGS-based epidemiologic analysis and considers the challenges and pitfalls of data interpretation. Sources: Personal collection of relevant publications. Content: When applying WGS to study the molecular epidemiology of bacterial pathogens, genomic variability between strains is translated into measures of distance by determining single nucleotide polymorphisms in core genome alignments or by indexing allelic variation in hundreds to thousands of core genes, assigning types to unique allelic profiles. Interpreting isolate relatedness from these distances is highly organism specific, and attempts to establish species-specific cutoffs are unlikely to be generally applicable. In cases where single nucleotide polymorphism or core gene typing do not provide the resolution necessary for accurate assessment of the epidemiology of bacterial pathogens, inclusion of accessory gene or plasmid sequences may provide the additional required discrimination. Implications: As with all epidemiologic analysis, realizing the full potential of the revolutionary advances in WGS-based approaches requires understanding and dealing with issues related to the fundamental steps of data generation and interpretation.