Milletdb

MilletDB provides an integrated multi-omics resource for millet research, enabling analysis of genomes, a graph-based pan-genome, GWAS results, and stress-related multi-omics data to support functional genomics and molecular breeding.

Key Features:

• Extensive Genomic Data: Includes genomes from six millet species and one related species to support comparative genomics.
• Pan-Genomics Analysis: Implements a graph-based pan-genome of pearl millet for exploration of genetic diversity and evolutionary insights across accessions.
• Stress-Related Multi-Omics Data: Contains comprehensive datasets related to abiotic stress responses for identification of genes involved in stress tolerance and yield stability.
• GWAS Integration: Stores genome-wide association study results for 20 yield-related traits across three environments—field (no stress), early drought, and late drought—collected over two years.
• Functional Genomics Tools: Provides 20 analytical tools, including gene mapping, co-expression analysis, and KEGG/GO enrichment analysis.
• Gene Identification and Analysis: Reports genes such as PMA1G03779.1 linked to yield and stress response and documents expansion of the PLATZs transcription factor family in 87.5% of millet accessions associated with vegetative growth and abiotic stress responses.

Scientific Applications:

• Stress-related Gene Mining: Enables identification of candidate genes underlying abiotic stress tolerance in millets.
• Molecular Breeding and Genome Editing: Supports selection of candidate genes for genome editing and molecular breeding to improve resilience and yield.
• Comparative Genomics and Evolution: Facilitates comparative and evolutionary analyses across six millet species, one related species, and the pearl millet pan-genome.
• Functional Genomics Integration: Combines GWAS, co-expression, and enrichment analyses to infer gene function and trait associations.

Methodology:

Methods explicitly include construction and use of a graph-based pan-genome for pearl millet, genome-wide association studies (GWAS) across 20 yield traits and three environments over two years, and analytical methods such as gene mapping, co-expression analysis, and KEGG/GO enrichment analysis.