Obolski, U;
Gori, A;
Lourenco, J;
Thompson, C;
Thompson, R;
French, N;
Heyderman, RS;
(2019)
Identifying genes associated with invasive disease in S. pneumoniae by applying a machine learning approach to whole genome sequence typing data.
Scientific Reports
, 9
, Article 4049. 10.1038/s41598-019-40346-7.
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Abstract
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 fnd 43 genes consistently associated with IPD across three geographically distinct WGS data sets of pneumococcal carriage isolates. Of the genes we identifed as associated with IPD, we fnd 23 genes previously shown to be directly relevant to IPD, as well as 18 uncharacterized genes. We suggest that these uncharacterized genes identifed by us are also likely to be relevant for IPD.
| Type: | Article |
|---|---|
| Title: | Identifying genes associated with invasive disease in S. pneumoniae by applying a machine learning approach to whole genome sequence typing data |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s41598-019-40346-7 |
| Publisher version: | http://doi.org/10.1038/s41598-019-40346-7 |
| Language: | English |
| Additional information: | Copyright © The Author(s) 2019. Open Access This article is licensed under a Creative Commons Attribution 4.0 nternational 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. Te 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/. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Infection and Immunity |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10071547 |
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