NLRexpress

NLRexpress detects and analyzes conserved motifs in plant NLR proteins, focusing on canonical domains (CC/TIR/RPW8, NBS/Nucleotide-Binding Site including the NB-ARC switch domain, and LRR/Leucine-Rich Repeat) to characterize motif relationships using machine learning.

Key Features:

• Machine Learning-Based Predictors: A bundle of 17 machine learning-based predictors detects and analyzes conserved motifs within CC/TIR/RPW8, NBS (Nucleotide-Binding Site), and LRR (Leucine-Rich Repeat) domains and is optimized for speed and accuracy.
• Scalability: Designed to screen large datasets such as proteomes, transcriptomes, or genomes to identify integral NLRs and differentiate incomplete sequences that lack essential motifs.
• Motif Analysis: Applies unsupervised machine learning techniques to uncover structural correlations beneath apparent pattern variability, with emphasis on the conserved NB-ARC switch domain and the diverse, length-variable LRR domain with repeat irregularities.
• Structural Invariance Insights: Highlights the role of structural invariance in shaping NLR sequence diversity and reveals relationships between motif subclasses within both NB-ARC and LRR domains, informing mechanisms of NLR stability and diversity.

Scientific Applications:

• Plant immunity research and NLR discovery: Enables detailed motif detection and analysis to study how sequence conservation and variability influence pathogen recognition and immune response in plants, and supports large-scale genomic identification and characterization of NLRs across diverse plant species.

Methodology:

NLRexpress integrates machine learning algorithms, using 17 trained predictors for motif recognition and unsupervised learning techniques to detect patterns and correlations in NLR sequences.