Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains

Abstract Background The genus Xanthomonas has long been considered to consist predominantly of plant pathogens, but over the last decade there has been an increasing number of reports on non-pathogenic and endophytic members. As Xanthomonas species are prevalent pathogens on a wide variety of import...

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Autores principales:	Dennie te Molder, Wasin Poncheewin, Peter J. Schaap, Jasper J. Koehorst
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Pathogenicity Protein domains Machine learning Xanthomonas Biotechnology TP248.13-248.65 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/95ed90908a38453e8622ba0e6b17c599
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spelling	oai:doaj.org-article:95ed90908a38453e8622ba0e6b17c5992021-11-28T12:23:04ZMachine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains10.1186/s12864-021-08093-01471-2164https://doaj.org/article/95ed90908a38453e8622ba0e6b17c5992021-11-01T00:00:00Zhttps://doi.org/10.1186/s12864-021-08093-0https://doaj.org/toc/1471-2164Abstract Background The genus Xanthomonas has long been considered to consist predominantly of plant pathogens, but over the last decade there has been an increasing number of reports on non-pathogenic and endophytic members. As Xanthomonas species are prevalent pathogens on a wide variety of important crops around the world, there is a need to distinguish between these plant-associated phenotypes. To date a large number of Xanthomonas genomes have been sequenced, which enables the application of machine learning (ML) approaches on the genome content to predict this phenotype. Until now such approaches to the pathogenomics of Xanthomonas strains have been hampered by the fragmentation of information regarding pathogenicity of individual strains over many studies. Unification of this information into a single resource was therefore considered to be an essential step. Results Mining of 39 papers considering both plant-associated phenotypes, allowed for a phenotypic classification of 578 Xanthomonas strains. For 65 plant-pathogenic and 53 non-pathogenic strains the corresponding genomes were available and de novo annotated for the presence of Pfam protein domains used as features to train and compare three ML classification algorithms; CART, Lasso and Random Forest. Conclusion The literature resource in combination with recursive feature extraction used in the ML classification algorithms provided further insights into the virulence enabling factors, but also highlighted domains linked to traits not present in pathogenic strains.Dennie te MolderWasin PoncheewinPeter J. SchaapJasper J. KoehorstBMCarticlePathogenicityProtein domainsMachine learningXanthomonasBiotechnologyTP248.13-248.65GeneticsQH426-470ENBMC Genomics, Vol 22, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Pathogenicity Protein domains Machine learning Xanthomonas Biotechnology TP248.13-248.65 Genetics QH426-470
spellingShingle	Pathogenicity Protein domains Machine learning Xanthomonas Biotechnology TP248.13-248.65 Genetics QH426-470 Dennie te Molder Wasin Poncheewin Peter J. Schaap Jasper J. Koehorst Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains
description	Abstract Background The genus Xanthomonas has long been considered to consist predominantly of plant pathogens, but over the last decade there has been an increasing number of reports on non-pathogenic and endophytic members. As Xanthomonas species are prevalent pathogens on a wide variety of important crops around the world, there is a need to distinguish between these plant-associated phenotypes. To date a large number of Xanthomonas genomes have been sequenced, which enables the application of machine learning (ML) approaches on the genome content to predict this phenotype. Until now such approaches to the pathogenomics of Xanthomonas strains have been hampered by the fragmentation of information regarding pathogenicity of individual strains over many studies. Unification of this information into a single resource was therefore considered to be an essential step. Results Mining of 39 papers considering both plant-associated phenotypes, allowed for a phenotypic classification of 578 Xanthomonas strains. For 65 plant-pathogenic and 53 non-pathogenic strains the corresponding genomes were available and de novo annotated for the presence of Pfam protein domains used as features to train and compare three ML classification algorithms; CART, Lasso and Random Forest. Conclusion The literature resource in combination with recursive feature extraction used in the ML classification algorithms provided further insights into the virulence enabling factors, but also highlighted domains linked to traits not present in pathogenic strains.
format	article
author	Dennie te Molder Wasin Poncheewin Peter J. Schaap Jasper J. Koehorst
author_facet	Dennie te Molder Wasin Poncheewin Peter J. Schaap Jasper J. Koehorst
author_sort	Dennie te Molder
title	Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains
title_short	Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains
title_full	Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains
title_fullStr	Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains
title_full_unstemmed	Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains
title_sort	machine learning approaches to predict the plant-associated phenotype of xanthomonas strains
publisher	BMC
publishDate	2021
url	https://doaj.org/article/95ed90908a38453e8622ba0e6b17c599
work_keys_str_mv	AT dennietemolder machinelearningapproachestopredicttheplantassociatedphenotypeofxanthomonasstrains AT wasinponcheewin machinelearningapproachestopredicttheplantassociatedphenotypeofxanthomonasstrains AT peterjschaap machinelearningapproachestopredicttheplantassociatedphenotypeofxanthomonasstrains AT jasperjkoehorst machinelearningapproachestopredicttheplantassociatedphenotypeofxanthomonasstrains
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Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains

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