Abstract
Pathogens produce effectors that manipulate the host to the benefit of the pathogen. These effectors are often secreted proteins that are upregulated during the early phases of infection. These properties can be used to identify candidate effectors from genomes and transcriptomes of pathogens. Here we describe commonly used bioinformatic approaches that (1) allow identification of genes encoding predicted secreted proteins within a genome and (2) allow the identification of genes encoding predicted secreted proteins that are upregulated at important stages of the life cycle. Other approaches for bioinformatic identification of effector candidates, including OrthoMCL analysis to identify expanded gene families, are also described.
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Acknowledgments
The James Hutton Institute receives funding from the Scottish Government. Adam James Reid was funded by the Wellcome Trust. We would like to thank Hayley Bennett for critical reading of the manuscript.
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Reid, A.J., Jones, J.T. (2014). Bioinformatic Analysis of Expression Data to Identify Effector Candidates. In: Birch, P., Jones, J., Bos, J. (eds) Plant-Pathogen Interactions. Methods in Molecular Biology, vol 1127. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-986-4_2
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DOI: https://doi.org/10.1007/978-1-62703-986-4_2
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