RGADE

RGADE analyzes Resistance Gene Analogs (RGAs) and their differential expression in sugarcane to elucidate genomic and transcriptomic bases of smut disease resistance.

Key Features:

• Genomic RGA prediction: Predicts RGAs across the polyploid, heterozygous sugarcane genome and identifies orthologs in an elite cultivar genome with comparisons to sorghum and Saccharum spontaneum.
• Transcriptomic differential expression: Analyzes transcriptomes from sugarcane genotypes with contrasting smut resistance to identify RGAs differentially expressed (RGADE) between resistant and susceptible genotypes.
• Cluster organization and synteny: Detects RGA clusters (up to 39% organized into clusters) and reports that 40% of these clusters share synteny across genomes.
• Chromosomal localization: Maps predicted NBS-encoding genes to chromosomes and identifies a strong concentration on chromosome 5 of Saccharum spontaneum, including many orthologs responsive to smut infection in modern sugarcane.
• Gene family analysis: Characterizes RGA families including RLK and RLP and notes a higher number of differentially expressed genes from these classes in resistant genotypes.
• Tandem duplication contribution: Highlights the role of tandem duplications in expanding RGA clusters and predicted RGADE clades.
• Pathogen response profiling: Profiles responses of RGA families to pathogen infection, emphasizing TM-LRR family responsiveness in resistant genotypes during early infection stages.

Scientific Applications:

• Breeding for smut resistance: Provides candidate RGAs and expression patterns to inform selection of smut-resistant sugarcane cultivars.
• Genetic marker identification: Identifies RGAs and orthologs that can serve as genetic markers associated with disease resistance.
• Comparative and evolutionary analysis: Enables analysis of RGA cluster organization, synteny, and tandem duplication to study evolutionary dynamics of resistance genes across Saccharum and sorghum.

Methodology:

Performs genomic predictions, ortholog searches, transcriptomic analyses, and synteny mapping using data from modern sugarcane cultivars, ancestral genomes (Saccharum spontaneum), and de novo transcripts.