Abstract
The ability to generate high-quality sequence data in a public health laboratory enables the identification of pathogenic strains, the determination of relatedness among outbreak strains, and the analysis of genetic information regarding virulence and antimicrobial-resistance genes. However, the analysis of whole-genome sequence data depends on bioinformatic analysis tools and processes. Many public health laboratories do not have the bioinformatic capabilities to analyze the data generated from sequencing and therefore are unable to take full advantage of the power of whole-genome sequencing. The goal of this perspective is to provide a guide for laboratories to understand the bioinformatic analyses that are needed to interpret whole-genome sequence data and how these in silico analyses can be implemented in a public health laboratory setting easily, affordably, and, in some cases, without the need for intensive computing resources and infrastructure.
Highlights
The ability to generate high-quality sequence data in a public health laboratory enables the identification of pathogenic strains, the determination of relatedness among outbreak strains, and the analysis of genetic information regarding virulence and antimicrobial-resistance genes
The PulseNet program is aiming to replace pulse-field gel electrophoresis (PFGE) with whole-genome sequencing (WGS) by 2018. This trajectory resembles the path taken in the study of human genetics, in which genetic mapping based on restriction fragment length polymorphism was replaced by quasi-complete information obtained by high-throughput genomic sequencing
We describe the bioinformatic pipeline implemented at the Utah Public Health Laboratory (UPHL) to analyze the WGS data
Summary
The ability to generate high-quality sequence data in a public health laboratory enables the identification of pathogenic strains, the determination of relatedness among outbreak strains, and the analysis of genetic information regarding virulence and antimicrobial-resistance genes. More PHLs are adopting NGS and WGS, only a small number of these laboratories have the ability to perform the bioinformatic analyses needed to take full advantage of the data they are generating. Web-based tools are available for PHLs that are looking to participate in WGS data analysis but are not ready to perform analyses in-house.
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