Fighting fungal pathogens with big data: new computational approaches for effector discovery and crop disease management

Fungal plant-pathogens are a major contributor to crop yield loss. In this thesis we developed state of the art computational methods to predict molecular determinants of pathogen virulence. We have also performed the first population genetic and pan-genomic analysis of a major wheat pathogen in WA,...

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Main Author: Jones, Darcy Adam Bain
Format: Thesis
Published: Curtin University 2021
Online Access:http://hdl.handle.net/20.500.11937/85748
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author Jones, Darcy Adam Bain
author_facet Jones, Darcy Adam Bain
author_sort Jones, Darcy Adam Bain
building Curtin Institutional Repository
collection Online Access
description Fungal plant-pathogens are a major contributor to crop yield loss. In this thesis we developed state of the art computational methods to predict molecular determinants of pathogen virulence. We have also performed the first population genetic and pan-genomic analysis of a major wheat pathogen in WA, Parastagonospora nodorum. This thesis has provided a valuable suite of methods and insights into plant pathogen genetics, ranging from the molecular level to the population level.
first_indexed 2025-11-14T11:24:37Z
format Thesis
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T11:24:37Z
publishDate 2021
publisher Curtin University
recordtype eprints
repository_type Digital Repository
spelling curtin-20.500.11937-857482021-09-29T00:15:34Z Fighting fungal pathogens with big data: new computational approaches for effector discovery and crop disease management Jones, Darcy Adam Bain Fungal plant-pathogens are a major contributor to crop yield loss. In this thesis we developed state of the art computational methods to predict molecular determinants of pathogen virulence. We have also performed the first population genetic and pan-genomic analysis of a major wheat pathogen in WA, Parastagonospora nodorum. This thesis has provided a valuable suite of methods and insights into plant pathogen genetics, ranging from the molecular level to the population level. 2021 Thesis http://hdl.handle.net/20.500.11937/85748 Curtin University fulltext
spellingShingle Jones, Darcy Adam Bain
Fighting fungal pathogens with big data: new computational approaches for effector discovery and crop disease management
title Fighting fungal pathogens with big data: new computational approaches for effector discovery and crop disease management
title_full Fighting fungal pathogens with big data: new computational approaches for effector discovery and crop disease management
title_fullStr Fighting fungal pathogens with big data: new computational approaches for effector discovery and crop disease management
title_full_unstemmed Fighting fungal pathogens with big data: new computational approaches for effector discovery and crop disease management
title_short Fighting fungal pathogens with big data: new computational approaches for effector discovery and crop disease management
title_sort fighting fungal pathogens with big data: new computational approaches for effector discovery and crop disease management
url http://hdl.handle.net/20.500.11937/85748