TaxaGO: a novel, phylogenetically informed gene ontology enrichment analysis tool

The functional interpretation of genes and their protein products across diverse species remains a central challenge in genomics, particularly as datasets grow in scale and complexity. The Gene Ontology (GO) knowledgebase offers a detailed resource of accessing a gene's function. While GO enrichment analysis tools are widely used to uncover biological insights, they are designed for single-species analyses and are not able to integrate phylogenetic relationships into the enrichment analyses. To address this, we created TaxaGO, a high-performance, multi-taxonomic GO enrichment analysis tool that incorporates evolutionary distances with species-level enrichment results to unravel GO enrichment profiles at a taxonomic level. Implemented in Rust for speed and scalability, TaxaGO enables robust cross-species GO enrichment analyses by combining species-specific results through phylogeny-aware statistical frameworks. It supports FASTA and CSV inputs, provides curated background populations for 12 131 species across Archaea, Bacteria, and Eukaryota, and offers advanced features such as count propagation, common ancestor analysis, semantic similarity calculation, and interactive visualizations. When benchmarking against established tools, TaxaGO demonstrates a maximum of 70.33× faster performance and 3.79× reduced memory usage. With an intuitive command-line interface and a user-friendly graphical interface, TaxaGO provides a powerful and accessible platform for functional genomics, evolutionary biology, and systems-level studies across the tree of life.

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Publication Details

Journal
Briefings in Bioinformatics
Published
2025-10-09
DOI
https://doi.org/10.1093/bib/bbaf572
Citations
1
Primary Topic
Bioinformatics and Genomic Networks
Type
article
Field-Weighted Citation Impact
0.81

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article

TaxaGO: a novel, phylogenetically informed gene ontology enrichment analysis tool

Anastassios C. Papageorgiou, Dionysios V. Chartoumpekis, Ilias Georgakopoulos‐Soares, George Lagoumintzis et al.
1 citations
Briefings in Bioinformatics
Bioinformatics and Genomic Networks
0.81
article

TaxaGO: a novel, phylogenetically informed gene ontology enrichment analysis tool

Anastassios C. Papageorgiou, Dionysios V. Chartoumpekis, Ilias Georgakopoulos‐Soares, George Lagoumintzis, Eleftherios Bochalis
article en
1 citations

Abstract

The functional interpretation of genes and their protein products across diverse species remains a central challenge in genomics, particularly as datasets grow in scale and complexity. The Gene Ontology (GO) knowledgebase offers a detailed resource of accessing a gene's function. While GO enrichment analysis tools are widely used to uncover biological insights, they are designed for single-species analyses and are not able to integrate phylogenetic relationships into the enrichment analyses. To address this, we created TaxaGO, a high-performance, multi-taxonomic GO enrichment analysis tool that incorporates evolutionary distances with species-level enrichment results to unravel GO enrichment profiles at a taxonomic level. Implemented in Rust for speed and scalability, TaxaGO enables robust cross-species GO enrichment analyses by combining species-specific results through phylogeny-aware statistical frameworks. It supports FASTA and CSV inputs, provides curated background populations for 12 131 species across Archaea, Bacteria, and Eukaryota, and offers advanced features such as count propagation, common ancestor analysis, semantic similarity calculation, and interactive visualizations. When benchmarking against established tools, TaxaGO demonstrates a maximum of 70.33× faster performance and 3.79× reduced memory usage. With an intuitive command-line interface and a user-friendly graphical interface, TaxaGO provides a powerful and accessible platform for functional genomics, evolutionary biology, and systems-level studies across the tree of life.

Briefings in BioinformaticsVol. 26(6)
Austin College (US), University of Patras (GR)
National Institutes of Health, National Institute of General Medical Sciences
Openalex Percentile: Top 22%
Bioinformatics and Genomic Networks
0.81
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