The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Effectors as Tools in Disease Resistance Breeding Against Biotrophic, Hemibiotrophic, and Necrotrophic Plant Pathogens
Open accessAbstract
One of most important challenges in plant breeding is improving resistance to the plethora of pathogens that threaten our crops. The ever-growing world population, changing pathogen populations, and fungicide resistance issues have increased the urgency of this task. In addition to a vital inflow of novel resistance sources into breeding programs, the functional characterization and deployment of resistance also needs improvement. Therefore, plant breeders need to adopt new strategies and techniques. In modern resistance breeding, effectors are emerging as tools to accelerate and improve the identification, functional characterization, and deployment of resistance genes. Since genome-wide catalogues of effectors have become available for various pathogens, including biotrophs as well as necrotrophs, effector-assisted breeding has been shown to be successful for various crops. “Effectoromics” has contributed to classical resistance breeding as well as for genetically modified approaches. Here, we present an overview of how effector-assisted breeding and deployment is being exploited for various pathosystems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Jones, J. D. G., & Dangl, J. L. (2006) · Nature · doi:10.1038/nature05286 · Cited by 13,177
TAL effectors: customizable proteins for DNA targeting
Bogdanove, A. J., & Voytas, D. F. (2011) · Science · doi:10.1126/science.1204094 · Cited by 984
Cited by
A review of wheat diseases—a field perspective
Figueroa, M., Hammond‐Kosack, K. E., & Solomon, P. S. (2018) · Molecular Plant Pathology · doi:10.1111/mpp.12618 · Cited by 718
Toruño, T. Y., Stergiopoulos, I., & Coaker, G. (2016) · Annual Review of Phytopathology · doi:10.1146/annurev-phyto-080615-100204 · Cited by 665
Disease Resistance Mechanisms in Plants
Andersen, E. J., Ali, S., Byamukama, E., Yen, Y., & Nepal, M. P. (2018) · Genes · doi:10.3390/genes9070339 · Cited by 434
Improved prediction of fungal effector proteins from secretomes with EffectorP 2.0
Sperschneider, J., Dodds, P. N., Gardiner, D. M., Singh, K. B., & Taylor, J. M. (2018) · Molecular Plant Pathology · doi:10.1111/mpp.12682 · Cited by 403
Genetic Engineering for Disease Resistance in Plants: Recent Progress and Future Perspectives
Dong, O. X., & Ronald, P. C. (2019) · Plant Physiology · doi:10.1104/pp.18.01224 · Cited by 309
Metadata
- Authors
- Vleeshouwers, V. G. A. A., & Oliver, R. P.
- Venue
- Molecular Plant-Microbe Interactions®
- Publisher
- Scientific Societies
- Published
- 2014-03
- Volume
- 27
- Issue
- 3
- Pages
- 196-206
- ISSN
- 0894-0282 (ISSN-L)
- 1943-7706
- OpenAlex
- W2098669490
- Cited by
- 389
- References
- 2
- FWCI
- 38.92
- Type
- journal article
- Language
- en
- Open access
- bronze
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Effectors as Tools in Disease Resistance Breeding Against Biotrophic, Hemibiotrophic, and Necrotrophic Plant Pathogens. https://app.cochat.ai/papers/10.1094/mpmi-10-13-0313-ia
- The paper
- Vleeshouwers, V. G. A. A., & Oliver, R. P. (2014). Effectors as Tools in Disease Resistance Breeding Against Biotrophic, Hemibiotrophic, and Necrotrophic Plant Pathogens. Molecular Plant-Microbe Interactions®. https://doi.org/10.1094/mpmi-10-13-0313-ia
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.
