Gango + BioFunctional: A computational tool for efficient functional gene analysis

Alejandro Rodriguez-Mena; Xavier Tarragó-Claramunt; Giulia Castellani; Javier Méndez-Viera; Antonio Monleón-Getino

doi:10.62063/ecb-63

Authors

Alejandro Rodriguez-Mena Universitat de Barcelona https://orcid.org/0009-0008-5839-0511
Xavier Tarragó-Claramunt Universitat de Barcelona https://orcid.org/0009-0007-8364-3632
Giulia Castellani Università di Camerino https://orcid.org/0009-0005-5022-6918
Javier Méndez-Viera Universitat de Barcelona https://orcid.org/0000-0003-3723-8787
Antonio Monleón-Getino Universitat de Barcelona https://orcid.org/0000-0001-8214-3205

DOI:

https://doi.org/10.62063/ecb-63

Keywords:

AI Integration, Computational Tool, Functional Gene Analysis, Gene Ontology, KEGG Pathways, Shiny Application

Abstract

Functional gene analysis is crucial for understanding gene roles in biological processes. However, analyzing data with multiple experimental groups presents significant challenges due to the complexity of data processing and the limitations of existing tools. GANGO + BioFuncional, an R-based Shiny application designed for end-users, addresses these challenges by providing a streamlined and comprehensive workflow for functional gene analysis. This interactive and freely available tool requires no installation, thus significantly enhancing its accessibility. The application is composed of two primary modules: GANGO, which efficiently processes input data and performs functional annotation to Gene Ontology (GO) terms and KEGG pathways; and BioFuncional, dedicated to in-depth analysis and interpretation. Key advantages include a highly user-friendly interface that eliminates the need for programming expertise, robust multi-group analytical capabilities, comprehensive visualization tools (interactive networks and significance-driven bar plots), and seamless compatibility with AI-driven interpretation tools like CURIE. Hosted on a server, GANGO + BioFuncional enhances the efficiency and accessibility of functional gene analysis, making it a valuable asset for both specialists and AI applications, ultimately facilitating deeper biological insights.

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