An automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant pathogens

Abstract Fungal plant-pathogens promote infection of their hosts through the release of ‘effectors’—a broad class of cytotoxic or virulence-promoting molecules. Effectors may be recognised by resistance or sensitivity receptors in the host, which can determine disease outcomes. Accurate prediction o...

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Autores principales:	Darcy A. B. Jones, Lina Rozano, Johannes W. Debler, Ricardo L. Mancera, Paula M. Moolhuijzen, James K. Hane
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/26d7ba9b2c6f4195baf24e63da4dc3b7
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spelling	oai:doaj.org-article:26d7ba9b2c6f4195baf24e63da4dc3b72021-12-02T18:01:41ZAn automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant pathogens10.1038/s41598-021-99363-02045-2322https://doaj.org/article/26d7ba9b2c6f4195baf24e63da4dc3b72021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-99363-0https://doaj.org/toc/2045-2322Abstract Fungal plant-pathogens promote infection of their hosts through the release of ‘effectors’—a broad class of cytotoxic or virulence-promoting molecules. Effectors may be recognised by resistance or sensitivity receptors in the host, which can determine disease outcomes. Accurate prediction of effectors remains a major challenge in plant pathology, but if achieved will facilitate rapid improvements to host disease resistance. This study presents a novel tool and pipeline for the ranking of predicted effector candidates—Predector—which interfaces with multiple software tools and methods, aggregates disparate features that are relevant to fungal effector proteins, and applies a pairwise learning to rank approach. Predector outperformed a typical combination of secretion and effector prediction methods in terms of ranking performance when applied to a curated set of confirmed effectors derived from multiple species. We present Predector ( https://github.com/ccdmb/predector ) as a useful tool for the ranking of predicted effector candidates, which also aggregates and reports additional supporting information relevant to effector and secretome prediction in a simple, efficient, and reproducible manner.Darcy A. B. JonesLina RozanoJohannes W. DeblerRicardo L. ManceraPaula M. MoolhuijzenJames K. HaneNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Darcy A. B. Jones Lina Rozano Johannes W. Debler Ricardo L. Mancera Paula M. Moolhuijzen James K. Hane An automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant pathogens
description	Abstract Fungal plant-pathogens promote infection of their hosts through the release of ‘effectors’—a broad class of cytotoxic or virulence-promoting molecules. Effectors may be recognised by resistance or sensitivity receptors in the host, which can determine disease outcomes. Accurate prediction of effectors remains a major challenge in plant pathology, but if achieved will facilitate rapid improvements to host disease resistance. This study presents a novel tool and pipeline for the ranking of predicted effector candidates—Predector—which interfaces with multiple software tools and methods, aggregates disparate features that are relevant to fungal effector proteins, and applies a pairwise learning to rank approach. Predector outperformed a typical combination of secretion and effector prediction methods in terms of ranking performance when applied to a curated set of confirmed effectors derived from multiple species. We present Predector ( https://github.com/ccdmb/predector ) as a useful tool for the ranking of predicted effector candidates, which also aggregates and reports additional supporting information relevant to effector and secretome prediction in a simple, efficient, and reproducible manner.
format	article
author	Darcy A. B. Jones Lina Rozano Johannes W. Debler Ricardo L. Mancera Paula M. Moolhuijzen James K. Hane
author_facet	Darcy A. B. Jones Lina Rozano Johannes W. Debler Ricardo L. Mancera Paula M. Moolhuijzen James K. Hane
author_sort	Darcy A. B. Jones
title	An automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant pathogens
title_short	An automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant pathogens
title_full	An automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant pathogens
title_fullStr	An automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant pathogens
title_full_unstemmed	An automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant pathogens
title_sort	automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant pathogens
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/26d7ba9b2c6f4195baf24e63da4dc3b7
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An automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant pathogens

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