Arabidopsis Hormone Database 2.0 (AHD2.0)

Arabidopsis Hormone Database 2.0 (AHD2.0) provides an integrated resource of Arabidopsis thaliana hormone-related genes, biosynthesis and signaling pathway diagrams, orthologs, predicted miRNA splicing sites, genetic interactions, expression profiles, and links to OrthoMCL-DB, protein databases, and the WebLab analysis platform to support phytohormonal research.

Key Features:

• Comprehensive Gene Collection: AHD2.0 includes genes related to eight major phytohormones: abscisic acid, auxin, brassinosteroid, cytokinin, ethylene, gibberellin, jasmonic acid, and salicylic acid.
• Schematic Diagrams: Detailed schematic diagrams depict each hormone's biosynthesis and signaling pathways in Arabidopsis thaliana.
• Ortholog Integration: Orthologs for sequenced plants are included as listed in OrthoMCL-DB and linked to each AHRG.
• miRNA Predictions: Predicted miRNA splicing sites are provided for each AHRG to indicate potential post-transcriptional regulation.
• Genetic Interactions: Gene–gene interactions associated with each AHRG were compiled from literature mining.
• Bioinformatics Analysis Platform: Links to the WebLab online analysis platform enable computational analyses from the database.
• Protein Database Links: Integrated links connect AHRGs to external protein databases for protein-level data and functional annotation.
• Expression Profiling Information: Extensive expression profiling data are incorporated to analyze gene expression patterns related to phytohormones.

Scientific Applications:

• Functional Genomics: Supports functional characterization of hormone-related genes using genetic interaction and ortholog data.
• Systems Biology: Enables systems-level analysis of biosynthesis and signaling networks using pathway diagrams.
• Comparative Genomics: Facilitates comparative genomic studies across sequenced plants via OrthoMCL-DB ortholog data.
• Transcriptomics and Proteomics: Supports transcriptomic and proteomic analyses through incorporated expression profiles and protein database links.
• Regulatory Network Analysis: Enables analysis of regulatory networks by combining predicted miRNA sites with genetic interaction data.

Methodology:

Genetic interaction data were derived from literature mining; orthologs were integrated from OrthoMCL-DB; miRNA splicing sites were predicted.