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Review
. 2020 Feb 18:2:3.
doi: 10.1186/s42522-020-0010-1. eCollection 2020.

Typing methods based on whole genome sequencing data

Laura Uelze  1 , Josephine Grützke  1 , Maria Borowiak  1 , Jens Andre Hammerl  1 , Katharina Juraschek  1 , Carlus Deneke  1 , Simon H Tausch  1 , Burkhard Malorny  1
Affiliations
Review

Typing methods based on whole genome sequencing data

Laura Uelze et al. One Health Outlook. .

Abstract

Whole genome sequencing (WGS) of foodborne pathogens has become an effective method for investigating the information contained in the genome sequence of bacterial pathogens. In addition, its highly discriminative power enables the comparison of genetic relatedness between bacteria even on a sub-species level. For this reason, WGS is being implemented worldwide and across sectors (human, veterinary, food, and environment) for the investigation of disease outbreaks, source attribution, and improved risk characterization models. In order to extract relevant information from the large quantity and complex data produced by WGS, a host of bioinformatics tools has been developed, allowing users to analyze and interpret sequencing data, starting from simple gene-searches to complex phylogenetic studies. Depending on the research question, the complexity of the dataset and their bioinformatics skill set, users can choose between a great variety of tools for the analysis of WGS data. In this review, we describe the relevant approaches for phylogenomic studies for outbreak studies and give an overview of selected tools for the characterization of foodborne pathogens based on WGS data. Despite the efforts of the last years, harmonization and standardization of typing tools are still urgently needed to allow for an easy comparison of data between laboratories, moving towards a one health worldwide surveillance system for foodborne pathogens.

Keywords: Bioinformatics tools; Comparison; Methods; Typing; Whole genome sequencing.

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Conflict of interest statement

Competing interestsAll authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Wheel of tools and supported methods. Provided methods: Antimicrobial resistance gene detection (AMR), Virulence factor search (Virulence), Serotyping and Phylogeny (highlighted in black/grey) by selected tools (BIGSdb, Bionumerics, CGE, COMPARE, PATRIC, EnteroBase, INNUENDO, IRIDA, NCBI Pathogens, PathogenWatch and SeqSphere). Organisms for which a methodology is supported by a tool are specified. For phylogeny, the underlying methods are mentioned. White fields indicate that functionality is not supported by the respective platform. ML = Maximum Likelihood
See this image and copyright information in PMC

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