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Computational Methods for Predicting Effectors in Rust Pathogens.

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Computational Methods for Predicting Effectors in Rust Pathogens.

Auteurs : Jana Sperschneider [Australie] ; Peter N. Dodds [Australie] ; Jennifer M. Taylor [Australie] ; Sébastien Duplessis [France]

Source :

Methods in molecular biology (Clifton, N.J.) [ 1940-6029 ] ; 2017.

RBID : pubmed:28856642

Abstract

Lower costs and improved sequencing technologies have led to a large number of high-quality rust pathogen genomes and deeper characterization of gene expression profiles during early and late infection stages. However, the set of secreted proteins expressed during infection is too large for experimental investigations and contains not only effectors but also proteins that play a role in niche colonization or in fighting off competing microbes. Therefore, accurate computational prediction is essential for identifying high-priority rust effector candidates from secretomes.

DOI: 10.1007/978-1-4939-7249-4_7
PubMed: 28856642

Affiliations:

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pubmed:28856642

Le document en format XML

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Computational Methods for Predicting Effectors in Rust Pathogens.

Computational Methods for Predicting Effectors in Rust Pathogens.

Source :

Abstract

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Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

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Pour générer des pages wiki