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Computational Methods for Predicting Effectors in Rust Pathogens

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.
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https://hal.univ-lorraine.fr/hal-02989876
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Submitted on : Thursday, November 5, 2020 - 12:27:10 PM
Last modification on : Tuesday, November 24, 2020 - 11:34:03 AM

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Jana Sperschneider, Peter Dodds, Jennifer Taylor, Sébastien Duplessis. Computational Methods for Predicting Effectors in Rust Pathogens. News (National Library of Medicine), Bethesda, Md. : The Library, 2017, pp.73-83. ⟨10.1007/978-1-4939-7249-4_7⟩. ⟨hal-02989876⟩

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