Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

© 2014 Elsevier Ltd. For nearly 30 years, 16S rRNA gene sequencing has been a fundamental tool for identification and cataloguing of bacterial diversity, but the diversity at this locus lacks the resolution to distinguish closely related bacteria. Multi-locus sequence typing (MLST) established the utility of a portable, gene-by-gene approach to population analyses, using both allelic and nucleotide sequence data that catalogue variation at seven housekeeping loci; however, it did not provide sufficient discrimination to define all variants of all bacteria. Recent advances in high-throughput next-generation sequencing technologies have permitted whole-genome sequencing of a wide variety of bacterial species and facilitated the development of genome-wide expanded MLST schemes. This chapter describes a flexible, scalable and hierarchical gene-by-gene approach to bacterial classification and population analyses, based on the concept of seven-locus MLST. Furthermore, the approach is both backwards and forwards compatible since 16S rRNA and seven-locus MLST information can be extracted and compared with original Sanger sequence data and the databases employed can accommodate sequence data from any source. The gene-by-gene approach is detailed in a variety of analyses: (i) speciation of Neisseria using nucleotide sequence from a single ribosomal protein locus (rplF); (ii) utilisation of variation at the 53 ribosomal protein gene loci to accurately and unambiguously identify and differentiate among bacterial species; (iii) identification of core genes and their comparison within a bacterial population; and (iv) the use of whole-genome population analysis for high-resolution studies, for example, the identification of potential disease outbreaks.

Original publication

DOI

10.1016/bs.mim.2014.06.001

Type

Journal article

Journal

Methods in Microbiology

Publication Date

01/01/2014

Volume

41

Pages

201 - 219