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

Abstract 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 me...

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Autores principales:	Sean J. Buckley, Robert J. Harvey, Zack Shan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a233ddd7968e4ca58c712652c6293b0a
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spelling	oai:doaj.org-article:a233ddd7968e4ca58c712652c6293b0a2021-12-02T17:39:53ZApplication of the random forest algorithm to Streptococcus pyogenes response regulator allele variation: from machine learning to evolutionary models10.1038/s41598-021-91941-62045-2322https://doaj.org/article/a233ddd7968e4ca58c712652c6293b0a2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91941-6https://doaj.org/toc/2045-2322Abstract 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.Sean J. BuckleyRobert J. HarveyZack ShanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sean J. Buckley Robert J. Harvey Zack Shan Application of the random forest algorithm to Streptococcus pyogenes response regulator allele variation: from machine learning to evolutionary models
description	Abstract 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.
format	article
author	Sean J. Buckley Robert J. Harvey Zack Shan
author_facet	Sean J. Buckley Robert J. Harvey Zack Shan
author_sort	Sean J. Buckley
title	Application of the random forest algorithm to Streptococcus pyogenes response regulator allele variation: from machine learning to evolutionary models
title_short	Application of the random forest algorithm to Streptococcus pyogenes response regulator allele variation: from machine learning to evolutionary models
title_full	Application of the random forest algorithm to Streptococcus pyogenes response regulator allele variation: from machine learning to evolutionary models
title_fullStr	Application of the random forest algorithm to Streptococcus pyogenes response regulator allele variation: from machine learning to evolutionary models
title_full_unstemmed	Application of the random forest algorithm to Streptococcus pyogenes response regulator allele variation: from machine learning to evolutionary models
title_sort	application of the random forest algorithm to streptococcus pyogenes response regulator allele variation: from machine learning to evolutionary models
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a233ddd7968e4ca58c712652c6293b0a
work_keys_str_mv	AT seanjbuckley applicationoftherandomforestalgorithmtostreptococcuspyogenesresponseregulatorallelevariationfrommachinelearningtoevolutionarymodels AT robertjharvey applicationoftherandomforestalgorithmtostreptococcuspyogenesresponseregulatorallelevariationfrommachinelearningtoevolutionarymodels AT zackshan applicationoftherandomforestalgorithmtostreptococcuspyogenesresponseregulatorallelevariationfrommachinelearningtoevolutionarymodels
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Application of the random forest algorithm to Streptococcus pyogenes response regulator allele variation: from machine learning to evolutionary models

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