Bioinformatic prediction of plant–pathogenicity effector proteins of fungi
© 2018. Effector proteins are important virulence factors of fungal plant pathogens and their prediction largely relies on bioinformatic methods. In this review we outline the current methods for the prediction of fungal plant pathogenicity effector proteins. Some fungal effectors have been characte...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/66269 |
| _version_ | 1848761280728399872 |
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| author | Jones, Darcy Bertazzoni, Stefania Turo, Chala Syme, Robert Hane, James |
| author_facet | Jones, Darcy Bertazzoni, Stefania Turo, Chala Syme, Robert Hane, James |
| author_sort | Jones, Darcy |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2018. Effector proteins are important virulence factors of fungal plant pathogens and their prediction largely relies on bioinformatic methods. In this review we outline the current methods for the prediction of fungal plant pathogenicity effector proteins. Some fungal effectors have been characterised and are represented by conserved motifs or in sequence repositories, however most fungal effectors do not generally exhibit high conservation of amino acid sequence. Therefore various predictive methods have been developed around: general properties, structure, position in the genomic landscape, and detection of mutations including repeat-induced point mutations and positive selection. A combinatorial approach incorporating several of these methods is often employed and candidates can be prioritised by either ranked scores or hierarchical clustering. |
| first_indexed | 2025-11-14T10:29:10Z |
| format | Journal Article |
| id | curtin-20.500.11937-66269 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:29:10Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-662692018-07-11T03:05:27Z Bioinformatic prediction of plant–pathogenicity effector proteins of fungi Jones, Darcy Bertazzoni, Stefania Turo, Chala Syme, Robert Hane, James © 2018. Effector proteins are important virulence factors of fungal plant pathogens and their prediction largely relies on bioinformatic methods. In this review we outline the current methods for the prediction of fungal plant pathogenicity effector proteins. Some fungal effectors have been characterised and are represented by conserved motifs or in sequence repositories, however most fungal effectors do not generally exhibit high conservation of amino acid sequence. Therefore various predictive methods have been developed around: general properties, structure, position in the genomic landscape, and detection of mutations including repeat-induced point mutations and positive selection. A combinatorial approach incorporating several of these methods is often employed and candidates can be prioritised by either ranked scores or hierarchical clustering. 2018 Journal Article http://hdl.handle.net/20.500.11937/66269 10.1016/j.mib.2018.01.017 restricted |
| spellingShingle | Jones, Darcy Bertazzoni, Stefania Turo, Chala Syme, Robert Hane, James Bioinformatic prediction of plant–pathogenicity effector proteins of fungi |
| title | Bioinformatic prediction of plant–pathogenicity effector proteins of fungi |
| title_full | Bioinformatic prediction of plant–pathogenicity effector proteins of fungi |
| title_fullStr | Bioinformatic prediction of plant–pathogenicity effector proteins of fungi |
| title_full_unstemmed | Bioinformatic prediction of plant–pathogenicity effector proteins of fungi |
| title_short | Bioinformatic prediction of plant–pathogenicity effector proteins of fungi |
| title_sort | bioinformatic prediction of plant–pathogenicity effector proteins of fungi |
| url | http://hdl.handle.net/20.500.11937/66269 |