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MOST: a modified MLST typing tool based on short read sequencing

  • Rediat Tewolde
  • , Timothy Dallman
  • , Ulf Schaefer
  • , Carmen L Sheppard
  • , Philip Ashton
  • , Bruno Pichon
  • , Matthew Ellington
  • , Craig Swift
  • , Jonathan Green
  • , Anthony Underwood
  • Infectious Disease Informatics Unit
  • extern
  • Respiratory and Vaccine Preventable Bacteria Reference Unit
  • Gastrointestinal Bacteria Reference Unit
  • Antimicrobial Resistance and Healthcare Associated Infection Unit

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Multilocus sequence typing (MLST) is an effective method to describe bacterial populations. Conventionally, MLST involves Polymerase Chain Reaction (PCR) amplification of housekeeping genes followed by Sanger DNA sequencing. Public Health England (PHE) is in the process of replacing the conventional MLST methodology with a method based on short read sequence data derived from Whole Genome Sequencing (WGS). This paper reports the comparison of the reliability of MLST results derived from WGS data, comparing mapping and assembly-based approaches to conventional methods using 323 bacterial genomes of diverse species. The sensitivity of the two WGS based methods were further investigated with 26 mixed and 29 low coverage genomic data sets from Salmonella enteridis and Streptococcus pneumoniae. Of the 323 samples, 92.9% (n = 300), 97.5% (n = 315) and 99.7% (n = 322) full MLST profiles were derived by the conventional method, assembly- and mapping-based approaches, respectively. The concordance between samples that were typed by conventional (92.9%) and both WGS methods was 100%. From the 55 mixed and low coverage genomes, 89.1% (n = 49) and 67.3% (n = 37) full MLST profiles were derived from the mapping and assembly based approaches, respectively. In conclusion, deriving MLST from WGS data is more sensitive than the conventional method. When comparing WGS based methods, the mapping based approach was the most sensitive. In addition, the mapping based approach described here derives quality metrics, which are difficult to determine quantitatively using conventional and WGS-assembly based approaches.

Original languageEnglish
Pages (from-to)e2308
JournalPeerJ
Volume4
DOIs
Publication statusPublished - 2016
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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