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Microb Genom. 2016 Aug 25;2(8):e000075. doi: 10.1099/mgen.0.000075. eCollection 2016 Aug.

Bayesian identification of bacterial strains from sequencing data.

Author information

1
1​Helsinki Institute for Information Technology, Department of Computer Science, University of Helsinki, Helsinki, Finland.
2
2​German Centre for Cardiovascular Research DZHK, Klaus Tschira Institute for Integrative Computational Cardiology and Department of Internal Medicine III, University of Heidelberg, Germany.
3
3​Department of Biology and Biochemistry, University of Bath, UK.
4
4​Institute of Life Sciences, College of Medicine, Swansea University, UK.
5
5​Helsinki Institute for Information Technology, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
6
6​Department of Biostatistics, University of Oslo, Norway.

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.

KEYWORDS:

pathogenic bacteria; probabilistic modelling; staphylococcus aureus; strain identification

PMID:
28348870
PMCID:
PMC5320594
DOI:
10.1099/mgen.0.000075
[Indexed for MEDLINE]
Free PMC Article

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