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

Turek, Cezary and Olbei, Marton and Stirling, Tamás and Fekete, Gergely and Tasnádi, Ervin Áron and Gul, Leila and Bohár, Balázs and Papp, Balázs and Jurkowski, Wiktor and Ari, Eszter (2024) mulea - An R package for enrichment analysis using multiple ontologies and empirical false discovery rate. BMC BIOINFORMATICS, 25 (1). No.-334. ISSN 1471-2105

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Abstract

Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package ofering comprehensive overrepresentation and functional enrichment analysis. mulea employs a progressive empirical false discovery rate (eFDR) method, specifcally designed for interconnected biological data, to accurately identify signifcant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This fexibility enables researchers to tailor enrichment analysis to their specifc questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifers resulting in almost 900 fles. Additionally, the muleaData ExperimentData Bioconductor package simplifes access to these pre-defned ontologies. Finally, mulea’s architecture allows for easy integration of userdefned ontologies, or GMT fles from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. mulea is distributed as a CRAN R package downloadable from https://cran.r-project.org/web/packages/mulea/ and https://github.com/ELTEbioinformatics/mulea. It ofers researchers a powerful and fexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms

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