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<p>Common bean (<i>Phaseolus vulgaris</i> L.) is crucial for global food security but is highly vulnerable to fungal pathogens that impact yield and quality. Molecular tools have transformed pathogen detection, enabling accuracy, sensitivity, and rapid diagnostics. This review, following PRISMA 2020 guidelines, synthesizes recent advancements in molecular methods, including PCR, qPCR, isothermal amplification, next-generation sequencing (NGS), and bioinformatics pipelines for the detection and identification of fungal pathogens in common bean. While PCR-based methods dominate, NGS and metabarcoding offer deeper insights into fungal communities. Challenges remain, such as primer specificity, high costs, and the need for robust databases. Additionally, the growing use of NGS highlights the demand for trained bioinformaticians to manage large datasets. Addressing these challenges through training programs and interdisciplinary collaboration is crucial for improving pathogen detection. This review underscores the potential of emerging molecular technologies to enhance fungal disease management and calls for scalable, affordable solutions suited to diverse agricultural systems.</p>

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Advances in Molecular Tools for Fungal Pathogen Detection and Identification in Common Bean: A Systematic Review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月31日

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  • Khulasande Siyabonga Ngcobo,
  • Sandiswa Figlan,
  • Molemi Rauwane

摘要

Common bean (Phaseolus vulgaris L.) is crucial for global food security but is highly vulnerable to fungal pathogens that impact yield and quality. Molecular tools have transformed pathogen detection, enabling accuracy, sensitivity, and rapid diagnostics. This review, following PRISMA 2020 guidelines, synthesizes recent advancements in molecular methods, including PCR, qPCR, isothermal amplification, next-generation sequencing (NGS), and bioinformatics pipelines for the detection and identification of fungal pathogens in common bean. While PCR-based methods dominate, NGS and metabarcoding offer deeper insights into fungal communities. Challenges remain, such as primer specificity, high costs, and the need for robust databases. Additionally, the growing use of NGS highlights the demand for trained bioinformaticians to manage large datasets. Addressing these challenges through training programs and interdisciplinary collaboration is crucial for improving pathogen detection. This review underscores the potential of emerging molecular technologies to enhance fungal disease management and calls for scalable, affordable solutions suited to diverse agricultural systems.