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

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

Sammanfattning

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.
Originalspråkengelska
TidskriftMicrobial Genomics
Volym2
Utgåva8
Antal sidor9
ISSN2057-5858
DOI
StatusPublicerad - 25 aug 2016
MoE-publikationstypA1 Tidskriftsartikel-refererad

Vetenskapsgrenar

  • 113 Data- och informationsvetenskap
  • 1183 Växtbiologi, mikrobiologi, virologi

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