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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.

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Obolski, Uri, Gori, Andrea, Lourenço, José, Thompson, Craig, Thompson, Robin, French, Neil, Heyderman, Robert S and Gupta, Sunetra (2019) 'Identifying genes associated with invasive disease in S. pneumoniae by applying a machine learning approach to whole genome sequence typing data.'. Scientific Reports, Vol 9, Issue 1, p. 4049.

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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 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.

Item Type: Article
Subjects: QU Biochemistry > QU 26.5 Informatics. Automatic data processing. Computers
QU Biochemistry > Genetics > QU 470 Genetic structures
QW Microbiology and Immunology > QW 50 Bacteria (General). Bacteriology. Archaea
WC Communicable Diseases > Infection. Bacterial Infections > Bacterial Infections > WC 217 Pneumococcal infections
Faculty: Department: Clinical Sciences & International Health > Clinical Sciences Department
Digital Object Identifer (DOI): https://doi.org/10.1038/s41598-019-40346-7
Depositing User: Stacy Murtagh
Date Deposited: 18 Mar 2019 11:16
Last Modified: 18 Mar 2019 11:16
URI: https://archive.lstmed.ac.uk/id/eprint/10401

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