ArticleMolecular plant pathology2018
Improved prediction of fungal effector proteins from secretomes with EffectorP 2.0.
Article in Molecular plant pathology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 204 papers.
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204 citing papers in PubMed, 431 citations in OpenAlex.
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- Exploring effector candidates in Rhynchosporium commune: insights into their expression dynamics during barley infection.Scientific reports · 2025Article
- Whole genome sequencing and annotation of Pseudocercospora abelmoschi, a causal agent of black leaf mould of okra.World journal of microbiology & biotechnology · 2025Article
- Developing a crop- wild-reservoir pathogen system to understand pathogen evolution and emergence.eLife · 2025Article
- Evolutionary genomics reveals variation in structure and genetic content implicated in virulence and lifestyle in the genus Gaeumannomyces.BMC genomics · 2025Article
- Effectors of plants pathogenic fungi and fungal like microbes: a comprehensive review on mechanisms, roles, and host interactions.Frontiers in plant science · 2025Review
- Repeat-induced point mutations driving Parastagonospora nodorum genomic diversity are balanced by selection against non-synonymous mutations.Communications biology · 2024Article
- Use of the Puccinia sorghi haustorial transcriptome to identify and characterize AvrRp1-D recognized by the maize Rp1-D resistance protein.PLoS pathogens · 2024Article
- An array of Zymoseptoria tritici effectors suppress plant immune responses.Molecular plant pathology · 2024Article
144 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant-pathogenic fungi secrete effector proteins to facilitate infection. We describe extensive improvements to EffectorP, the first machine learning classifier for fungal effector prediction. EffectorP 2.0 is now trained on a larger set of effectors and utilizes a different approach based on an ensemble of classifiers trained on different subsets of negative data, offering different views on classification. EffectorP 2.0 achieves an accuracy of 89%, compared with 82% for EffectorP 1.0 and 59.8% for a small size classifier. Important features for effector prediction appear to be protein size, protein net charge as well as the amino acids serine and cysteine. EffectorP 2.0 decreases the number of predicted effectors in secretomes of fungal plant symbionts and saprophytes by 40% when compared with EffectorP 1.0. However, EffectorP 1.0 retains value, and combining EffectorP 1.0 and 2.0 results in a stringent classifier with a low false positive rate of 9%. EffectorP 2.0 predicts significant enrichments of effectors in 12 of 13 sets of infection-induced proteins from diverse fungal pathogens, whereas a small cysteine-rich classifier detects enrichment in only seven of 13. EffectorP 2.0 will fast track the prioritization of high-confidence effector candidates for functional validation and aid in improving our understanding of effector biology. EffectorP 2.0 is available at http://effectorp.csiro.au.
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