effectR

effectR predicts candidate RxLR and CRN effector proteins from oomycete open reading frames using regular expressions and hidden Markov models to support studies of host–pathogen interactions.

Key Features:

• Motif-based prediction: Identifies ORFs containing RxLR and CRN amino acid motifs using explicit motif criteria.
• Regular expressions: Applies regular expression pattern matching to detect specific amino acid sequences indicative of effectors.
• Hidden Markov models (HMM): Uses HMMs to capture sequence variability and probabilistically model effector motif regions.
• ORF-centric search: Performs systematic searches within predicted open reading frames to locate candidate effector sequences.
• Customizability: Allows integration of custom scripts and novel motif definitions and can be adapted for non-oomycete genomes.
• Reproducibility: Enables consistent application of the same prediction criteria across datasets for reproducible analyses.
• Validation: Has been validated against published oomycete genomes to assess prediction performance.

Scientific Applications:

• Effector discovery in plant pathology: Identification of candidate RxLR and CRN effectors from oomycete genomes to study pathogenicity.
• Host–pathogen interaction studies: Provides candidate effectors for experimental investigation of molecular interactions between oomycetes and plant hosts.
• Breeding and resistance research: Supplies effector candidates that can inform breeding programs and the development of disease-resistant plant varieties.
• Evolutionary and comparative genomics: Supports detection of novel effector motifs and comparative analyses across genomes to study effector evolution.

Methodology:

Performs a systematic search of ORFs for specific amino acid motifs using regular expressions for exact pattern matching and hidden Markov models for probabilistic sequence modeling.