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Bayesian identication of bacterial strains from sequencing data

Aravind Sankar, Brandon Malone, Sion Bayliss, Ben Pascoe Orcid Logo, Guillaume Meric, Matthew Hitchings, Samuel Sheppard, Edward Feil, Jukka Corander, Antti Honkela

arXiv.org

Swansea University Author: Ben Pascoe Orcid Logo

Abstract

Rapidly assaying the diversity a bacterial species present in a population obtained from for instance 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...

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Published in: arXiv.org
Published: 2015
Online Access: http://arxiv.org/abs/1511.06546
URI: https://cronfa.swan.ac.uk/Record/cronfa23991
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first_indexed 2015-10-27T05:30:01Z
last_indexed 2018-02-09T05:03:18Z
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spelling 2015-12-23T23:33:22.0233400 v2 23991 2015-10-26 Bayesian identication of bacterial strains from sequencing data 4660c0eb7e6bfd796cd749ae713ea558 0000-0001-6376-5121 Ben Pascoe Ben Pascoe true false 2015-10-26 PMSC Rapidly assaying the diversity a bacterial species present in a population obtained from for instance 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 for 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 method 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 made freely available as an open source package. Journal Article arXiv.org 30 11 2015 2015-11-30 http://arxiv.org/abs/1511.06546 COLLEGE NANME Medicine COLLEGE CODE PMSC Swansea University 2015-12-23T23:33:22.0233400 2015-10-26T22:43:50.8190971 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Aravind Sankar 1 Brandon Malone 2 Sion Bayliss 3 Ben Pascoe 0000-0001-6376-5121 4 Guillaume Meric 5 Matthew Hitchings 6 Samuel Sheppard 7 Edward Feil 8 Jukka Corander 9 Antti Honkela 10
title Bayesian identication of bacterial strains from sequencing data
spellingShingle Bayesian identication of bacterial strains from sequencing data
Ben Pascoe
title_short Bayesian identication of bacterial strains from sequencing data
title_full Bayesian identication of bacterial strains from sequencing data
title_fullStr Bayesian identication of bacterial strains from sequencing data
title_full_unstemmed Bayesian identication of bacterial strains from sequencing data
title_sort Bayesian identication of bacterial strains from sequencing data
author_id_str_mv 4660c0eb7e6bfd796cd749ae713ea558
author_id_fullname_str_mv 4660c0eb7e6bfd796cd749ae713ea558_***_Ben Pascoe
author Ben Pascoe
author2 Aravind Sankar
Brandon Malone
Sion Bayliss
Ben Pascoe
Guillaume Meric
Matthew Hitchings
Samuel Sheppard
Edward Feil
Jukka Corander
Antti Honkela
format Journal article
container_title arXiv.org
publishDate 2015
institution Swansea University
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
url http://arxiv.org/abs/1511.06546
document_store_str 0
active_str 0
description Rapidly assaying the diversity a bacterial species present in a population obtained from for instance 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 for 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 method 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 made freely available as an open source package.
published_date 2015-11-30T03:28:23Z
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score 11.013731

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