mulea

mulea performs gene set enrichment analysis utilizing multiple ontologies and a progressive empirical false discovery rate method to accurately identify significant terms within interconnected biological data.

Key Features:

• Multiple Ontology Support: Incorporates Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains for flexible enrichment analysis.
• Progressive Empirical False Discovery Rate: Employs a progressive eFDR method specifically designed for interconnected biological data to address overcorrected p-values from gene set interdependence.
• Pre-defined Gene Sets: Provides gene sets in standardised GMT format for 27 model organisms, covering 22 ontology types from 16 databases with various identifiers.
• User-defined Ontology Integration: Supports integration of user-defined ontologies or GMT files from external sources such as MSigDB or Enrichr.
• Comprehensive Analysis Modes: Offers both overrepresentation and functional enrichment analysis.

Scientific Applications:

• Transcriptional Regulator Identification: Identifies enriched transcriptional regulators in gene expression data.
• Protein Domain Analysis: Detects overrepresented protein domains in protein sets.
• Cross-species Functional Enrichment: Enables functional enrichment analysis across diverse model organisms using standardised ontologies.

Methodology:

mulea employs a progressive empirical false discovery rate method designed for interconnected biological data, and provides pre-defined gene sets in GMT format for multiple model organisms and ontology types.