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Computational Methods for Predicting Effectors in Rust Pathogens

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Wheat Rust Diseases

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1659))

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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Author information

Authors and Affiliations

  1. Centre for Environmental and Life Sciences, CSIRO Agriculture and Food, Underwood Avenue, Floreat, WA, Australia

    Jana Sperschneider

  2. Black Mountain Laboratories, CSIRO Agriculture and Food, Canberra, ACT, Australia

    Peter N. Dodds & Jennifer M. Taylor

  3. INRA, Unité Mixte de Recherche INRA/Université de Lorraine 1136 Interactions Arbres-Microorganismes, INRA Centre Grand Est - Nancy, Champenoux, France

    Sébastien Duplessis

Authors
  1. Jana Sperschneider
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  2. Peter N. Dodds
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  3. Jennifer M. Taylor
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  4. Sébastien Duplessis
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Corresponding author

Correspondence to Jana Sperschneider .

Editor information

Editors and Affiliations

  1. Research School of Biology, Australian National University, Canberra, Aust Capital Terr, Australia

    Sambasivam Periyannan

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Sperschneider, J., Dodds, P.N., Taylor, J.M., Duplessis, S. (2017). Computational Methods for Predicting Effectors in Rust Pathogens. In: Periyannan, S. (eds) Wheat Rust Diseases. Methods in Molecular Biology, vol 1659. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7249-4_7

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  • DOI: https://doi.org/10.1007/978-1-4939-7249-4_7

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7248-7

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