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Improved prediction of fungal effector proteins from secretomes with EffectorP 2.0
Open accessAbstract
Summary: 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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Sella, L., Gazzetti, K., Faoro, F., Odorizzi, S., D'Ovidio, R., Schäfer, W., & Favaron, F. (2013) · Plant Physiology and Biochemistry · doi:10.1016/j.plaphy.2012.12.008 · Cited by 74
Sperschneider, J., Williams, A., Hane, J. K., Singh, K. B., & Taylor, J. M. (2015) · Frontiers in Plant Science · doi:10.3389/fpls.2015.01168 · Cited by 72
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Transposable Element Dynamics among Asymbiotic and Ectomycorrhizal Amanita Fungi
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A molecular perspective: biology of the emerging pathogen Batrachochytrium dendrobatidis
Rosenblum, E., Fisher, M. C., James, T., Stajich, J., Longcore, J., Gentry, L., & Poorten, T. J. (2009) · Diseases of Aquatic Organisms · doi:10.3354/dao02179 · Cited by 35
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Metadata
- Authors
- Sperschneider, J., Dodds, P. N., Gardiner, D. M., Singh, K. B., & Taylor, J. M.
- Venue
- Molecular Plant Pathology
- Publisher
- Wiley
- Published
- 2018-05-11
- Volume
- 19
- Issue
- 9
- Pages
- 2094-2110
- ISSN
- 1364-3703 (ISSN-L)
- 1464-6722
- OpenAlex
- W2787339353
- Cited by
- 403
- References
- 95
- FWCI
- 17.19
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
