Bayesian identification of bacterial strains from sequencing data

Show full item record



Permalink

http://hdl.handle.net/10138/231150

Citation

Sankar , A , Malone , B M , Bayliss , S C , Pascoe , B , Méric , G , Hitchings , M D , Sheppard , S K , Feil , E J , Corander , J I & Honkela , A J H 2016 , ' Bayesian identification of bacterial strains from sequencing data ' , Microbial Genomics , vol. 2 , no. 8 . https://doi.org/10.1099/mgen.0.000075

Title: Bayesian identification of bacterial strains from sequencing data
Author: Sankar, Aravind; Malone, Brandon Michael; Bayliss, Sion C.; Pascoe, Ben; Méric, Guillaume; Hitchings, Matthew D.; Sheppard, Samuel K.; Feil, Edward J.; Corander, Jukka Ilmari; Honkela, Antti Juho Henrikki
Contributor: University of Helsinki, Helsinki Institute for Information Technology
University of Helsinki, Department of Computer Science
University of Helsinki, Department of Mathematics and Statistics
University of Helsinki, Helsinki Institute for Information Technology
Date: 2016-08-25
Language: eng
Number of pages: 9
Belongs to series: Microbial Genomics
ISSN: 2057-5858
URI: http://hdl.handle.net/10138/231150
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.
Subject: 113 Computer and information sciences
1183 Plant biology, microbiology, virology
Rights:


Files in this item

Total number of downloads: Loading...

Files Size Format View
mgen000075.pdf 450.2Kb PDF View/Open

This item appears in the following Collection(s)

Show full item record