Predector

Predector ranks candidate effector proteins in fungal plant pathogens by aggregating features from multiple prediction tools and applying a pairwise learning-to-rank strategy to prioritize likely effectors.

Key Features:

• Feature aggregation: Aggregates diverse features associated with fungal effector proteins into a combined representation.
• Multi-tool integration: Leverages multiple software tools and methodologies to generate input features for effector prediction.
• Pairwise learning-to-rank: Applies a pairwise learning-to-rank algorithm to prioritize candidate effector proteins.
• Secretome and effector annotations: Provides predictions and supporting information pertinent to effector and secretome prediction.
• Benchmarking: Validated on a curated dataset of confirmed effectors from multiple species and reported to outperform alternative methods on that dataset.

Scientific Applications:

• Effector candidate prioritization: Prioritizes candidate effector proteins for experimental validation in fungal plant pathogens.
• Pathogenesis mechanism studies: Supports investigation of effector roles in virulence and host–pathogen interactions.
• Comparative effector genomics: Enables comparative analyses of effector repertoires across species.
• Host resistance research: Assists identification of effector targets relevant to breeding or engineering disease resistance.

Methodology:

Aggregates features produced by multiple prediction tools and methodologies and employs a pairwise learning-to-rank strategy to prioritize candidate effector proteins, with validation performed on a curated dataset of confirmed effectors from multiple species.