EnrichDO

EnrichDO implements disease ontology enrichment analysis through a double weighted model that integrates Disease Ontology graph topology on a global scale to discover associations between genes and diseases.

Key Features:

• Double weighted model: Leverages the latest annotations of the human genome with DO terms while integrating DO graph topology globally to alleviate over-enrichment problems.
• Specific term identification: Accurately identifies more specific DO terms without ignoring truly associated parent terms, as demonstrated in Alzheimer's disease cases where AD ranked first.
• Statistical models: Includes various statistical models for analyzing biological big data.
• Visualization schemes: Provides visualization schemes for discovering gene-disease associations.
• Cross-ontology performance: Performs in both Gene Ontology and Disease Ontology enrichment analysis cases.

Scientific Applications:

• Gene-disease association discovery: Identifies associations between genes and diseases from biological big data, including gene expression profile datasets.
• Microorganism host gene analysis: Applied to host gene sets of microorganisms to expand analytical scope beyond human datasets.
• Hallmark gene set analysis: Used to analyze hallmark gene sets for disease relevance.
• Alzheimer's disease research: Demonstrated accurate identification of Alzheimer's disease as the top-ranked term in case studies.

Methodology:

EnrichDO employs a double weighted model based on the latest annotations of the human genome with DO terms, integrating DO graph topology on a global scale rather than using term-for-term approaches. The method was validated through simulated tests and data perturbation tests for accuracy and robustness.