LDRGDb

LDRGDb catalogs and integrates disease resistance genes, high-throughput genomic data, quantitative trait loci (QTLs), proteomics, pathway interactions, and associated loci for legumes including pigeon pea (Cajanus cajan), chickpea (Cicer arietinum), soybean (Glycine max), lentil (Lens culinaris), alfalfa (Medicago sativa), barrelclover (Medicago truncatula), common bean (Phaseolus vulgaris), pea (Pisum sativum), faba bean (Vicia faba), and cowpea (Vigna unguiculata) to support analysis of host–pathogen interactions and breeding for disease resistance.

Key Features:

• Species coverage: Catalogs disease resistance–related data for ten legume species including Cajanus cajan, Cicer arietinum, Glycine max, Lens culinaris, Medicago sativa, Medicago truncatula, Phaseolus vulgaris, Pisum sativum, Vicia faba, and Vigna unguiculata.
• Resistance gene catalog: Aggregates annotated disease resistance genes and associated loci across the covered legume species.
• High-throughput genomic integration: Integrates high-throughput genomic data with curated resistance gene information.
• QTL and loci annotations: Incorporates quantitative trait loci (QTLs) and their associated loci relevant to disease resistance.
• Proteomics data: Includes proteomics information linked to resistance genes and pathways.
• Pathway interaction data: Provides pathway interaction data relevant to disease resistance mechanisms.
• Data consolidation and curation: Consolidates fragmented information from multiple sources into a unified curated dataset.

Scientific Applications:

• Host–pathogen interaction analysis: Enables study of complex interactions between legumes and pathogens using integrated genomic, proteomic, and pathway data.
• Breeding strategy development: Informs development of effective breeding strategies to mitigate yield losses from diseases.
• Resistance gene deployment: Supports identification and incorporation of resistance genes into breeding programs.
• QTL and candidate gene research: Facilitates QTL mapping and candidate gene identification for disease resistance traits.
• Comparative genomics and proteomics: Supports comparative analyses across legume species using genomic and proteomic datasets.

Methodology:

Integrates high-throughput genomic data with curated resistance gene and QTL annotations, consolidates data from multiple sources, and provides tools/software to explore proteomics, pathway interactions, and genomics related to disease resistance.