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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
general
Identifying genes associated with invasive disease in S. pneumoniae by applying a machine learning approach to whole genome sequence typing data
Scientific Reports, Volume 9, No. 1, Article 4049, Year 2019
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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. © 2019, The Author(s).
Authors & Co-Authors
Obolski, Uri
United Kingdom, Oxford
University of Oxford
Gori, Andrea
United Kingdom, London
University College London
Lourenço, José M.L.
United Kingdom, Oxford
University of Oxford
Thompson, Craig Peter
United Kingdom, Oxford
University of Oxford
Thompson, Robin N.
United Kingdom, Oxford
University of Oxford
French, N.
United Kingdom, Liverpool
Liverpool School of Tropical Medicine
Heyderman, Robert Simon
United Kingdom, London
University College London
Gupta, Sunetra
United Kingdom, Oxford
University of Oxford
Statistics
Citations: 13
Authors: 8
Affiliations: 3
Identifiers
Doi:
10.1038/s41598-019-40346-7
ISSN:
20452322