VLAD

VLAD performs ontology-based enrichment analysis to identify overrepresented Gene Ontology (GO) and Mammalian Phenotype Ontology terms in gene sets, revealing functional themes and phenotypic associations in mouse gene data.

Key Features:

• Ontology-Based Enrichment Analysis: Performs statistical enrichment analysis using ontologies including the Gene Ontology (GO) and the Mammalian Phenotype Ontology.
• Statistical Overrepresentation Testing: Evaluates whether specific ontology terms are overrepresented in a gene set compared to expectation and reports associated p-values.
• Custom Annotations and Ontologies: Accepts user-uploaded annotation files and ontologies to enable customized term enrichment analyses.
• Multiple Gene Set Analysis: Supports simultaneous analysis of multiple gene sets for comparative investigations.
• Tabular Results with Statistics: Outputs tables of ontology terms sorted by significance that include the number of annotated genes, p-values, and associated gene symbols.
• Graphical Ontology Visualization: Generates graphical displays of portions of the ontology hierarchy with node sizes correlated to statistical significance (p-values).
• Integration with MGI Database: Links analysis results to curated mouse gene information from the Mouse Genome Informatics (MGI) database.

Scientific Applications:

• Pathway and Process Identification: Identifies biological processes and pathways implicated by input gene sets.
• Phenotype Exploration: Explores phenotypic consequences associated with genetic variation or experimental conditions.
• Comparative Functional Analysis: Conducts comparative analyses across datasets to uncover shared functional themes.

Methodology:

Performs statistical analyses to detect ontology-term enrichment and evaluate overrepresentation relative to expectation; accepts user-supplied annotation files and ontologies; supports simultaneous analysis of multiple gene sets; outputs tables with counts, p-values, and gene symbols and generates ontology-hierarchy visualizations with node sizes proportional to p-values; integrates MGI gene annotations.