Application of the random forest algorithm to Streptococcus pyogenes response regulator allele variation: from machine learning to evolutionary models

Sean J Buckley; Robert J Harvey; Zack Shan

doi:10.1038/s41598-021-91941-6

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Application of the random forest algorithm to Streptococcus pyogenes response regulator allele variation: from machine learning to evolutionary models

Journal article

Open access

Peer reviewed

Application of the random forest algorithm to Streptococcus pyogenes response regulator allele variation: from machine learning to evolutionary models

Sean J Buckley, Robert J Harvey and Zack Shan

Scientific Reports, Vol.11, pp.1-14

2021

DOI: https://doi.org/10.1038/s41598-021-91941-6

Appears in Thompson Institute Research Collection

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Abstract

Scientific data

Genome informatics

Gene regulation

Data processing

Prokaryote

Evolutionary genetics

Predictive markers

Other Collaborations

Thompson Institute Special Collection

Group A Streptococcus (GAS) is a globally significant bacterial pathogen. The GAS genotyping gold standard characterises the nucleotide variation of emm, which encodes a surface-exposed protein that is recombinogenic and under immune-based selection pressure. Within a supervised learning methodology, we tested three random forest (RF) algorithms (Guided, Ordinary, and Regularized) and 53 GAS response regulator (RR) allele types to infer six genomic traits (emm-type, emm-subtype, tissue and country of sample, clinical outcomes, and isolate invasiveness). The Guided, Ordinary, and Regularized RF classifiers inferred the emm-type with accuracies of 96.7%, 95.7%, and 95.2%, using ten, three, and four RR alleles in the feature set, respectively. Notably, we inferred the emm-type with 93.7% accuracy using only mga2 and lrp. We demonstrated a utility for inferring emm-subtype (89.9%), country (88.6%), invasiveness (84.7%), but not clinical (56.9%), or tissue (56.4%), which is consistent with the complexity of GAS pathophysiology. We identified a novel cell wall-spanning domain (SF5), and proposed evolutionary pathways depicting the ‘contrariwise’ and ‘likewise’ chimeric deletion-fusion of emm and enn. We identified an intermediate strain, which provides evidence of the time-dependent excision of mga regulon genes. Overall, our workflow advances the understanding of the GAS mga regulon and its plasticity.

Details

Title: Application of the random forest algorithm to Streptococcus pyogenes response regulator allele variation: from machine learning to evolutionary models
Authors: Sean J Buckley (Author) - University of the Sunshine Coast, Queensland, School of Health and Behavioural Sciences - Legacy
Robert J Harvey (Author) - University of the Sunshine Coast, Queensland, School of Health and Behavioural Sciences - Legacy
Zack Shan (Author) - University of the Sunshine Coast, Queensland, Thompson Institute
Publication details: Scientific Reports, Vol.11, pp.1-14
Publisher: Nature Publishing Group
Date published: 2021
DOI: 10.1038/s41598-021-91941-6
ISSN: 2045-2322
Copyright note: © The Author(s) 2021. 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
Organisation Unit: School of Health; University of the Sunshine Coast, Queensland; Thompson Institute; School of Science, Technology and Engineering; Centre for Bioinnovation; School of Health and Behavioural Sciences - Legacy
Language: English
Record Identifier: 99545808302621
Output Type: Journal article

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