DOSE

DOSE computes semantic similarities among Disease Ontology (DO) terms and gene products and performs enrichment analyses to annotate human genes with disease associations from high-throughput biological data.

Key Features:

• Semantic Similarity Computations: Implements five semantic similarity methods for DO terms and gene products: Resnik, Schlicker, Jiang, Lin, and Wang.
• Enrichment Analyses: Supports enrichment analyses using hypergeometric models and gene set enrichment analysis (GSEA) on gene sets derived from high-throughput experiments.
• Disease Ontology Annotation: Leverages the Disease Ontology to annotate human genes with disease-related terms.
• Comparison of Gene Clusters: Enables comparison among different gene clusters using DO-based semantic similarity and enrichment results.

Scientific Applications:

• High-throughput data interpretation: Analyze and interpret gene-level high-throughput data in the context of Disease Ontology terms.
• Experimental validation: Validate experimental results for disease relevance through enrichment and semantic similarity assessments.
• Discovery of gene-disease associations: Uncover novel associations between genes and diseases by combining DO annotation, semantic similarity, and enrichment analyses.

Methodology:

Uses the Disease Ontology for gene annotation, applies Resnik, Schlicker, Jiang, Lin, and Wang semantic similarity measures to DO terms and gene products, and performs enrichment analyses using hypergeometric models and GSEA.