Fungal effectors are key pathogenicity factors, but their identification in wheat pathogens is challenging due to lack of conserved motifs and rapid evolution. This review compares two complementary approaches for effector discovery: bioinformatics pipelines for large-scale in silico prediction and wheat protoplast assays for functional validation in a homologous host system. We analyze the strengths, limitations, and complementarity of each method, and propose an integrated, iterative workflow that sequentially leverages computational prediction and experimental screening. This synergistic strategy accelerates the accurate identification of effectors and their cognate resistance genes, providing a critical foundation for breeding durable disease-resistant wheat varieties and enhancing global food security.
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