CottonGen

CottonGen provides an integrated database and analysis resource for cotton genomics, genetics, and breeding research.

Key Features:

• Data Integration: Consolidates annotated whole genome sequences, unigenes from expressed sequence tags (ESTs), markers, trait loci, genetic maps, genes, taxonomy, germplasm, and publications.
• Enhanced Data Accessibility: Provides tools for data sharing, data mining, visualization, and retrieval.
• Genomic Visualization Tools: Implements GBrowse to view whole genome sequences of Gossypium raimondii with aligned genetic markers and transcripts and CMap for comparative viewing of published cotton genetic maps.
• Search Functionality: Offers comprehensive search across markers, quantitative trait loci (QTLs), germplasm, publications, and trait evaluation data using materialized views and the Chado Search module for improved performance and flexibility.
• Data Loading and Customization: Supports data loading via the Chado Loader module with Excel templates for metadata and uses the Chado Data Display module for customizable visualization of data types and metadata.
• Online Analysis Tools: Integrates NCBI BLAST and Batch BLAST for sequence analysis.

Scientific Applications:

• Genomic data integration and analysis: Enable access, visualization, and analysis of cotton genomic and genetic data, including whole genome sequences and EST-derived unigenes.
• Breeding and trait discovery: Support breeding programs and trait discovery by comparing genetic maps and trait loci (QTLs) to identify loci associated with desirable traits for crop improvement.
• Marker development and mapping: Facilitate marker discovery, mapping, and alignment of markers to genomes using BLAST and aligned marker data.
• Germplasm and taxonomy curation: Provide curated germplasm and taxonomy data to support germplasm evaluation and genetic diversity studies.

Methodology:

Implements a relational database integrated with Tripal and Chado modules (Chado Loader, Chado Data Display, Chado Search), employs materialized views for performance and flexible data modeling, and uses GBrowse, CMap, and NCBI BLAST/Batch BLAST for genome visualization, comparative mapping, and sequence analysis.