MorphDB

MorphDB facilitates functional classification of gene sequences and prioritization of candidate genes in plant metabolic pathways by integrating comparative genomics with high-throughput gene expression ('omics') datasets.

Key Features:

• Omics integration: Integrates high-throughput gene expression datasets ("omics") with comparative genomics and functional genomics for functional classification of genes.
• MORPH algorithm: Employs a guilt-by-association strategy validated through the MORPH algorithm to infer gene function.
• Missing gene identification: Identifies and prioritizes missing genes in plant metabolic pathways.
• Regulator discovery: Detects candidate biological regulators including transcription factors and signaling genes.
• Cross-species annotation: Annotates candidate genes across multiple plant species to reveal conserved gene functions.
• Gene-centric queries: Provides a gene-centric query utility to retrieve MORPH predictions for specific genes.
• Comparative network approach: Uses a comparative network approach to browse MORPH predictions across functional gene sets and species.
• MORPH bulk toolkit: Supports bulk-mode MORPH analyses on novel datasets to extend analyses to user-specific species.

Scientific Applications:

• Gene function discovery: Identification of missing genes in metabolic pathways and discovery of biological regulators such as transcription factors and signaling genes.
• Functional annotation: Large-scale functional annotation of candidate genes across multiple plant species.
• Comparative genomics: Comparative analyses of gene functions across species to investigate conserved mechanisms and evolutionary relationships.

Methodology:

Combines comparative genomics with functional genomics and applies a guilt-by-association strategy implemented in the MORPH algorithm using high-throughput gene expression datasets and annotations from closely related model organisms, with an option for bulk-mode MORPH analyses on novel datasets.