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Clinical PathoScope: rapid alignment and filtration for accurate pathogen identification in clinical samples using unassembled sequencing data

BACKGROUND: The use of sequencing technologies to investigate the microbiome of a sample can positively impact patient healthcare by providing therapeutic targets for personalized disease treatment. However, these samples contain genomic sequences from various sources that complicate the identificat...

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Autores principales: Byrd, Allyson L, Perez-Rogers, Joseph F, Manimaran, Solaiappan, Castro-Nallar, Eduardo, Toma, Ian, McCaffrey, Tim, Siegel, Marc, Benson, Gary, Crandall, Keith A, Johnson, William Evan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4131054/
https://www.ncbi.nlm.nih.gov/pubmed/25091138
http://dx.doi.org/10.1186/1471-2105-15-262
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author Byrd, Allyson L
Perez-Rogers, Joseph F
Manimaran, Solaiappan
Castro-Nallar, Eduardo
Toma, Ian
McCaffrey, Tim
Siegel, Marc
Benson, Gary
Crandall, Keith A
Johnson, William Evan
author_facet Byrd, Allyson L
Perez-Rogers, Joseph F
Manimaran, Solaiappan
Castro-Nallar, Eduardo
Toma, Ian
McCaffrey, Tim
Siegel, Marc
Benson, Gary
Crandall, Keith A
Johnson, William Evan
author_sort Byrd, Allyson L
collection PubMed
description BACKGROUND: The use of sequencing technologies to investigate the microbiome of a sample can positively impact patient healthcare by providing therapeutic targets for personalized disease treatment. However, these samples contain genomic sequences from various sources that complicate the identification of pathogens. RESULTS: Here we present Clinical PathoScope, a pipeline to rapidly and accurately remove host contamination, isolate microbial reads, and identify potential disease-causing pathogens. We have accomplished three essential tasks in the development of Clinical PathoScope. First, we developed an optimized framework for pathogen identification using a computational subtraction methodology in concordance with read trimming and ambiguous read reassignment. Second, we have demonstrated the ability of our approach to identify multiple pathogens in a single clinical sample, accurately identify pathogens at the subspecies level, and determine the nearest phylogenetic neighbor of novel or highly mutated pathogens using real clinical sequencing data. Finally, we have shown that Clinical PathoScope outperforms previously published pathogen identification methods with regard to computational speed, sensitivity, and specificity. CONCLUSIONS: Clinical PathoScope is the only pathogen identification method currently available that can identify multiple pathogens from mixed samples and distinguish between very closely related species and strains in samples with very few reads per pathogen. Furthermore, Clinical PathoScope does not rely on genome assembly and thus can more rapidly complete the analysis of a clinical sample when compared with current assembly-based methods. Clinical PathoScope is freely available at: http://sourceforge.net/projects/pathoscope/. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2105-15-262) contains supplementary material, which is available to authorized users.
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spelling pubmed-41310542014-08-15 Clinical PathoScope: rapid alignment and filtration for accurate pathogen identification in clinical samples using unassembled sequencing data Byrd, Allyson L Perez-Rogers, Joseph F Manimaran, Solaiappan Castro-Nallar, Eduardo Toma, Ian McCaffrey, Tim Siegel, Marc Benson, Gary Crandall, Keith A Johnson, William Evan BMC Bioinformatics Methodology Article BACKGROUND: The use of sequencing technologies to investigate the microbiome of a sample can positively impact patient healthcare by providing therapeutic targets for personalized disease treatment. However, these samples contain genomic sequences from various sources that complicate the identification of pathogens. RESULTS: Here we present Clinical PathoScope, a pipeline to rapidly and accurately remove host contamination, isolate microbial reads, and identify potential disease-causing pathogens. We have accomplished three essential tasks in the development of Clinical PathoScope. First, we developed an optimized framework for pathogen identification using a computational subtraction methodology in concordance with read trimming and ambiguous read reassignment. Second, we have demonstrated the ability of our approach to identify multiple pathogens in a single clinical sample, accurately identify pathogens at the subspecies level, and determine the nearest phylogenetic neighbor of novel or highly mutated pathogens using real clinical sequencing data. Finally, we have shown that Clinical PathoScope outperforms previously published pathogen identification methods with regard to computational speed, sensitivity, and specificity. CONCLUSIONS: Clinical PathoScope is the only pathogen identification method currently available that can identify multiple pathogens from mixed samples and distinguish between very closely related species and strains in samples with very few reads per pathogen. Furthermore, Clinical PathoScope does not rely on genome assembly and thus can more rapidly complete the analysis of a clinical sample when compared with current assembly-based methods. Clinical PathoScope is freely available at: http://sourceforge.net/projects/pathoscope/. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2105-15-262) contains supplementary material, which is available to authorized users. BioMed Central 2014-08-04 /pmc/articles/PMC4131054/ /pubmed/25091138 http://dx.doi.org/10.1186/1471-2105-15-262 Text en © Byrd et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Byrd, Allyson L
Perez-Rogers, Joseph F
Manimaran, Solaiappan
Castro-Nallar, Eduardo
Toma, Ian
McCaffrey, Tim
Siegel, Marc
Benson, Gary
Crandall, Keith A
Johnson, William Evan
Clinical PathoScope: rapid alignment and filtration for accurate pathogen identification in clinical samples using unassembled sequencing data
title Clinical PathoScope: rapid alignment and filtration for accurate pathogen identification in clinical samples using unassembled sequencing data
title_full Clinical PathoScope: rapid alignment and filtration for accurate pathogen identification in clinical samples using unassembled sequencing data
title_fullStr Clinical PathoScope: rapid alignment and filtration for accurate pathogen identification in clinical samples using unassembled sequencing data
title_full_unstemmed Clinical PathoScope: rapid alignment and filtration for accurate pathogen identification in clinical samples using unassembled sequencing data
title_short Clinical PathoScope: rapid alignment and filtration for accurate pathogen identification in clinical samples using unassembled sequencing data
title_sort clinical pathoscope: rapid alignment and filtration for accurate pathogen identification in clinical samples using unassembled sequencing data
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4131054/
https://www.ncbi.nlm.nih.gov/pubmed/25091138
http://dx.doi.org/10.1186/1471-2105-15-262
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