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Identifying genes associated with invasive disease in S. pneumoniae by applying a machine learning approach to whole genome sequence typing data

Streptococcus pneumoniae, a normal commensal of the upper respiratory tract, is a major public health concern, responsible for substantial global morbidity and mortality due to pneumonia, meningitis and sepsis. Why some pneumococci invade the bloodstream or CSF (so-called invasive pneumococcal disea...

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Autores principales: Obolski, Uri, Gori, Andrea, Lourenço, José, Thompson, Craig, Thompson, Robin, French, Neil, Heyderman, Robert S., Gupta, Sunetra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6411942/
https://www.ncbi.nlm.nih.gov/pubmed/30858412
http://dx.doi.org/10.1038/s41598-019-40346-7
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author Obolski, Uri
Gori, Andrea
Lourenço, José
Thompson, Craig
Thompson, Robin
French, Neil
Heyderman, Robert S.
Gupta, Sunetra
author_facet Obolski, Uri
Gori, Andrea
Lourenço, José
Thompson, Craig
Thompson, Robin
French, Neil
Heyderman, Robert S.
Gupta, Sunetra
author_sort Obolski, Uri
collection PubMed
description Streptococcus pneumoniae, a normal commensal of the upper respiratory tract, is a major public health concern, responsible for substantial global morbidity and mortality due to pneumonia, meningitis and sepsis. Why some pneumococci invade the bloodstream or CSF (so-called invasive pneumococcal disease; IPD) is uncertain. In this study we identify genes associated with IPD. We transform whole genome sequence (WGS) data into a sequence typing scheme, while avoiding the caveat of using an arbitrary genome as a reference by substituting it with a constructed pangenome. We then employ a random forest machine-learning algorithm on the transformed data, and find 43 genes consistently associated with IPD across three geographically distinct WGS data sets of pneumococcal carriage isolates. Of the genes we identified as associated with IPD, we find 23 genes previously shown to be directly relevant to IPD, as well as 18 uncharacterized genes. We suggest that these uncharacterized genes identified by us are also likely to be relevant for IPD.
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spelling pubmed-64119422019-03-13 Identifying genes associated with invasive disease in S. pneumoniae by applying a machine learning approach to whole genome sequence typing data Obolski, Uri Gori, Andrea Lourenço, José Thompson, Craig Thompson, Robin French, Neil Heyderman, Robert S. Gupta, Sunetra Sci Rep Article Streptococcus pneumoniae, a normal commensal of the upper respiratory tract, is a major public health concern, responsible for substantial global morbidity and mortality due to pneumonia, meningitis and sepsis. Why some pneumococci invade the bloodstream or CSF (so-called invasive pneumococcal disease; IPD) is uncertain. In this study we identify genes associated with IPD. We transform whole genome sequence (WGS) data into a sequence typing scheme, while avoiding the caveat of using an arbitrary genome as a reference by substituting it with a constructed pangenome. We then employ a random forest machine-learning algorithm on the transformed data, and find 43 genes consistently associated with IPD across three geographically distinct WGS data sets of pneumococcal carriage isolates. Of the genes we identified as associated with IPD, we find 23 genes previously shown to be directly relevant to IPD, as well as 18 uncharacterized genes. We suggest that these uncharacterized genes identified by us are also likely to be relevant for IPD. Nature Publishing Group UK 2019-03-11 /pmc/articles/PMC6411942/ /pubmed/30858412 http://dx.doi.org/10.1038/s41598-019-40346-7 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Obolski, Uri
Gori, Andrea
Lourenço, José
Thompson, Craig
Thompson, Robin
French, Neil
Heyderman, Robert S.
Gupta, Sunetra
Identifying genes associated with invasive disease in S. pneumoniae by applying a machine learning approach to whole genome sequence typing data
title Identifying genes associated with invasive disease in S. pneumoniae by applying a machine learning approach to whole genome sequence typing data
title_full Identifying genes associated with invasive disease in S. pneumoniae by applying a machine learning approach to whole genome sequence typing data
title_fullStr Identifying genes associated with invasive disease in S. pneumoniae by applying a machine learning approach to whole genome sequence typing data
title_full_unstemmed Identifying genes associated with invasive disease in S. pneumoniae by applying a machine learning approach to whole genome sequence typing data
title_short Identifying genes associated with invasive disease in S. pneumoniae by applying a machine learning approach to whole genome sequence typing data
title_sort identifying genes associated with invasive disease in s. pneumoniae by applying a machine learning approach to whole genome sequence typing data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6411942/
https://www.ncbi.nlm.nih.gov/pubmed/30858412
http://dx.doi.org/10.1038/s41598-019-40346-7
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