Abstract
Traditionally, diagnostic tools for plant pathogens were limited to the analysis of purified pathogen isolates subjected to phenotypic characterization and/or PCR-based genotypic analysis. However, these approaches detect only already known pathogenic agents, may not always recognize novel races, and can introduce bias in the results. Recent advances in next-generation sequencing technologies have provided new opportunities to integrate high-resolution genotype data into pathogen surveillance programs. Here, we describe some of the key bioinformatics analysis used in the newly developed “Field Pathogenomics” pathogen surveillance technique. This technique is based on RNA-seq data generated directly form pathogen-infected plant leaf samples collected in the field, providing a unique opportunity to characterize the pathogen population and its host directly in their natural environment. We describe two main analyses: (1) a phylogenetic analysis of the pathogen isolates that have been collected to understand how they are related to each other, and (2) a population structure analysis to provide insight into the genetic substructure within the pathogen population. This provides a high-resolution representation of pathogen population dynamics directly in the field, providing new insights into pathogen biology, population structure, and pathogenesis.
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Acknowledgments
This work was funded by an Industrial Partnership Award (BB/M025519/1) from the Biotechnology Biological Sciences Research Council (BBSRC), the BBSRC Institute Strategic Programme (BB/J004553/1 and BB/P012574/1) and the John Innes Foundation.
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Supplemental File 1
All necessary scripts for this method. The three sub-folders contain all scripts for each one of the main pipelines (SNP _calling, Phylogenetic_Analysis and Population_STRUCTURE ). In addition, five bash scripts that are used in this chapter are also provided: SNP _pipeline .sh, get_consensus.sh, Tree_pipeline .sh, create_snpeff.sh and Structure_pipeline .sh (ZIP 27 kb)
Supplemental File 2
Reference genome of Puccinia striiformis sp. tritici isolate PST-130 in fasta format (FA 63,681 kb)
Supplemental File 3
Annotation of the reference genome of Puccinia striiformis sp. tritici isolate PST-130 in GFF3 format (GFF3 5737 kb)
Supplemental Table 1
Details of the 39 PST-infected field samples used as an example in this chapter (XLSX 46 kb)
Supplemental Table 2
Quantity of reads from the 39 PST-infected samples that aligned to the PST-130 reference genome (XLSX 38 kb)
Supplemental Table 3
Assignment of the 39 PST samples to genetic groups using the software STRUCTURE (XLSX 50 kb)
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Bueno-Sancho, V., Bunting, D.C.E., Yanes, L.J., Yoshida, K., Saunders, D.G.O. (2017). Field Pathogenomics: An Advanced Tool for Wheat Rust Surveillance. 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_2
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DOI: https://doi.org/10.1007/978-1-4939-7249-4_2
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Publisher Name: Humana Press, New York, NY
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Online ISBN: 978-1-4939-7249-4
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