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Bayesian identification of bacterial strains from sequencing data

Aravind Sankar, Sion C. Bayliss, Edward J. Feil, Jukka Corander, Ben Pascoe Orcid Logo, Guillaume Méric, Antti Honkela, Matthew Hitchings Orcid Logo, Brandon Malone, Samuel K. Sheppard

Microbial Genomics, Volume: 2, Issue: 8

Swansea University Authors: Ben Pascoe Orcid Logo, Matthew Hitchings Orcid Logo

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DOI (Published version): 10.1099/mgen.0.000075

Abstract

Rapidly assaying the diversity of a bacterial species present in a sample obtained from a hospital patient or an evironmental source has become possible after recent technological advances in DNA sequencing. For several applications it is important to accurately identify the presence and estimate re...

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Published in: Microbial Genomics
ISSN: 2057-5858 2057-5858
Published: 2016
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URI: https://cronfa.swan.ac.uk/Record/cronfa26788
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first_indexed 2016-03-22T02:04:01Z
last_indexed 2018-10-08T14:06:42Z
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spelling 2018-10-08T12:37:15.8610708 v2 26788 2016-03-21 Bayesian identification of bacterial strains from sequencing data 4660c0eb7e6bfd796cd749ae713ea558 0000-0001-6376-5121 Ben Pascoe Ben Pascoe true false be98847c72c14a731c4a6b7bc02b3bcf 0000-0002-5527-4709 Matthew Hitchings Matthew Hitchings true false 2016-03-21 PMSC Rapidly assaying the diversity of a bacterial species present in a sample obtained from a hospital patient or an evironmental 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 this https URL Journal Article Microbial Genomics 2 8 2057-5858 2057-5858 31 12 2016 2016-12-31 10.1099/mgen.0.000075 COLLEGE NANME Medicine COLLEGE CODE PMSC Swansea University 2018-10-08T12:37:15.8610708 2016-03-21T08:44:44.6105154 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Aravind Sankar 1 Sion C. Bayliss 2 Edward J. Feil 3 Jukka Corander 4 Ben Pascoe 0000-0001-6376-5121 5 Guillaume Méric 6 Antti Honkela 7 Matthew Hitchings 0000-0002-5527-4709 8 Brandon Malone 9 Samuel K. Sheppard 10
title Bayesian identification of bacterial strains from sequencing data
spellingShingle Bayesian identification of bacterial strains from sequencing data
Ben Pascoe
Matthew Hitchings
title_short Bayesian identification of bacterial strains from sequencing data
title_full Bayesian identification of bacterial strains from sequencing data
title_fullStr Bayesian identification of bacterial strains from sequencing data
title_full_unstemmed Bayesian identification of bacterial strains from sequencing data
title_sort Bayesian identification of bacterial strains from sequencing data
author_id_str_mv 4660c0eb7e6bfd796cd749ae713ea558
be98847c72c14a731c4a6b7bc02b3bcf
author_id_fullname_str_mv 4660c0eb7e6bfd796cd749ae713ea558_***_Ben Pascoe
be98847c72c14a731c4a6b7bc02b3bcf_***_Matthew Hitchings
author Ben Pascoe
Matthew Hitchings
author2 Aravind Sankar
Sion C. Bayliss
Edward J. Feil
Jukka Corander
Ben Pascoe
Guillaume Méric
Antti Honkela
Matthew Hitchings
Brandon Malone
Samuel K. Sheppard
format Journal article
container_title Microbial Genomics
container_volume 2
container_issue 8
publishDate 2016
institution Swansea University
issn 2057-5858
2057-5858
doi_str_mv 10.1099/mgen.0.000075
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
document_store_str 0
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description Rapidly assaying the diversity of a bacterial species present in a sample obtained from a hospital patient or an evironmental 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 this https URL
published_date 2016-12-31T03:32:15Z
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score 11.013731

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