Bayesian identification of bacterial strains from sequencing data

Aravind Sankar, Brandon Michael Malone, Sion C. Bayliss, Ben Pascoe, Guillaume Méric, Matthew D. Hitchings, Samuel K. Sheppard, Edward J. Feil, Jukka Ilmari Corander, Antti Juho Henrikki Honkela

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Rapidly assaying the diversity of a bacterial species present in a sample obtained from a hospital patient or an environmental source has become possible after recent technological advances in DNA sequencing. For several applications it is important to accurately identify the presence and estimate relative abundances of the target organisms from short sequence reads obtained from a sample. This task is particularly challenging when the set of interest includes very closely related organisms, such as different strains of pathogenic bacteria, which can vary considerably in terms of virulence, resistance and spread. Using advanced Bayesian statistical modelling and computation techniques we introduce a novel pipeline for bacterial identification that is shown to outperform the currently leading pipeline for this purpose. Our approach enables fast and accurate sequence-based identification of bacterial strains while using only modest computational resources. Hence it provides a useful tool for a wide spectrum of applications, including rapid clinical diagnostics to distinguish among closely related strains causing nosocomial infections. The software implementation is available at https://github.com/PROBIC/BIB.
Original languageEnglish
JournalMicrobial Genomics
Volume2
Issue number8
Number of pages9
ISSN2057-5858
DOIs
Publication statusPublished - 25 Aug 2016
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 113 Computer and information sciences
  • 1183 Plant biology, microbiology, virology

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