mulea: An R package for enrichment analysis using multiple ontologies and empirical false discovery rate.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Traditional gene set enrichment analyses often use limited ontologies and fail to account for interdependencies, leading to inaccurate p-values.
• Existing tools lack flexibility in ontology support and handling interconnected biological data.

Purpose of the Study:

• To introduce mulea, an R package for comprehensive overrepresentation and functional enrichment analysis.
• To address limitations of traditional methods by implementing a progressive empirical false discovery rate (eFDR) for interconnected data.
• To provide a flexible and versatile tool supporting a wide array of ontologies and user-defined datasets.

Main Methods:

• Developed mulea as an R package utilizing a progressive empirical false discovery rate (eFDR) method.
• Integrated support for diverse ontologies including Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains.
• Provided pre-defined gene sets for 27 model organisms and facilitated integration of user-defined ontologies (e.g., MSigDB, Enrichr).

Main Results:

• mulea accurately identifies significant terms within diverse and interconnected biological datasets.
• The package supports a broad spectrum of ontologies (22 types from 16 databases) and gene sets for 27 model organisms.
• mulea offers flexibility for tailored enrichment analyses, such as identifying enriched transcriptional regulators or protein domains.

Conclusions:

• mulea provides a powerful and flexible toolkit for functional enrichment analysis, overcoming limitations of traditional approaches.
• The progressive eFDR method and broad ontology support enhance the accuracy and applicability of enrichment studies.
• mulea facilitates the exploration of diverse biological questions across multiple model organisms.