ToppCluster

ToppCluster performs comparative enrichment analysis and network visualization of multiple gene lists across human and mammalian genomes to identify shared and unique functional features.

Key Features:

• Enrichment Analysis: Uses hypergeometric tests to calculate P-values for enrichment of annotations.
• Annotation Categories: Assesses enrichment across 17 distinct categories of human-ortholog gene annotations, including Gene Ontology terms, protein interactions, disease associations, mammalian phenotypes, promoter cis-elements, and miRNA binding sites.
• Comparative Matrix Analysis: Accepts multiple gene lists arranged as columns in a matrix with rows representing overrepresented features for cross-list comparison.
• Multiple Testing Correction: Applies user-selectable cutoffs and multiple testing correction methods to control false discovery rates.
• Visualization: Produces tabular outputs, hierarchical clustering, and heatmap generation to represent enrichment patterns.
• Network Export: Transforms selected features from the functional enrichment matrix into network formats compatible with Cytoscape (XGMML) and Gephi (GEXF).
• Systems Biology Support: Enables identification of specialized biological functions and regulatory networks and supports analysis of tissue-specific gene lists to identify phenotypic and regulatory element features.

Scientific Applications:

• Functional Genomics: Exploration of functional implications of gene sets derived from different experimental conditions or disease states.
• Regulatory Network Analysis: Mapping regulatory networks by identifying promoter cis-elements and miRNA binding sites associated with specific gene lists.
• Phenotypic Studies: Linking genetic data to mammalian phenotypes to investigate genotype–phenotype relationships.
• Disease Research: Comparative analysis of gene lists from different diseases or conditions to identify disease-specific biomarkers and therapeutic targets.

Methodology:

Performs hypergeometric tests to calculate enrichment P-values across 17 annotation categories; constructs a functional enrichment matrix with gene lists as columns and overrepresented features as rows; applies user-selectable cutoffs and multiple testing correction to control false discovery rate; generates hierarchical clustering, heatmaps, tabular outputs, and exports selected features to XGMML or GEXF.