WideEffHunter: An Algorithm to Predict Canonical and Non-Canonical Effectors in Fungi and Oomycetes

Area of Science:

• Plant Pathology
• Mycology
• Bioinformatics

Background:

• Traditional effector prediction focuses on canonical small, secreted, cysteine-rich proteins.
• Novel effector identification strategies are needed to capture non-canonical effectors (NCEs).
• Existing methods are often species- or kingdom-specific, limiting broad application.

Purpose of the Study:

• To develop a unified algorithm for predicting effectoromes in both oomycetes and fungi.
• To identify novel, non-canonical effectors (NCEs) that constitute a significant portion of effectoromes.
• To advance effectoromics for improved agricultural disease management.

Main Methods:

• Developed WideEffHunter, a Bash-based algorithm integrating effector motifs, domains, and homology.
• Applied the algorithm to predict effectoromes in oomycetes and fungi.
• Compared effector motif and domain presence across taxonomic kingdoms.

Main Results:

• NCEs represent approximately 90% of identified effectoromes.
• WideEffHunter successfully identified both canonical and non-canonical effectors.
• Similar numbers of effectors with motifs and domains were found in fungi and oomycetes, suggesting conserved mechanisms.

Conclusions:

• WideEffHunter unifies effectorome prediction across oomycetes and fungi, regardless of pathogenicity or lifestyle.
• The algorithm facilitates the identification of NCEs, crucial for understanding host-pathogen interactions.
• Elucidating complete effectoromes is vital for advancing effectoromics and developing effective disease management strategies in agriculture.