Automated extraction of typing information for bacterial pathogens from whole genome sequence data: Neisseria meningitidis as an exemplar - Jolley, Keith A. (2013)
Whole genome sequence (WGS) data are becoming a major means of characterising samples of bacterial pathogens. These data have the advantage of providing detailed information on the genotypes and likely phenotypes of aetiological agents, enabling the relationships of samples from potential disease outbreaks to be established precisely. However, the generation of increasing quantities of sequence data does not, in itself, resolve the problems that a wide variety of microbiological typing methods have addressed over the last 100 years or so; indeed, the provision of very high volumes of unstructured data can confuse rather than resolve these issues. Here we review the nascent field of the storage of WGS data for clinical application and show how curated sequence-based typing schemes on websites such as PubMLST.org, accumulated over the past 14 years or so, has generated an infrastructure that can be used to exploit WGS for bacterial typing efficiently. We review the tools that have been implemented within the PubMLST.org website to extract clinically useful, strain characterisation information which can be provided to physicians and public health scientists and officials in a timely, concise and understandable way. These data can be used to inform medical decisions such as how to treat a patient, whether to institute public health action, and what action might be appropriate. The information is compatible both with previous sequence-based typing data and also with data that can be obtained in the absence of WGS data, for example by real-time PCR tests, providing a flexible infrastructure for WGS-based clinical microbiology.