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A comprehensive application designed for the interpretation and visualization of the functional analysis related to KEGG pathways and gene ontologies gives researchers and specialists a tool to get detailed functional information about their data, specifically going deep into biological pathways and gene functions information. By using a variety of techniques and libraries, such as Shiny, htrr, dplyr, tibble, and rvest, we have developed an application that provides a well-designed user-oriented interface with all the facilities to assess their data and start analyzing it directly from scratch through a few steps.

The software allows an exhaustive exploration of KEGG pathways and Gene Ontologies, facilitating the analysis of complex biological processes. To achieve this, functions described in the scripts integrate data manipulation methods and web scraping techniques to extract the necessary information from online official databases, Kyoto Encyclopedia of Genes and Genomes (KEGG) and QuickGo. Furthermore, those functions are computed by parallel processing, resulting in efficient petitions to the database servers and allowing the user to get quick results from a large dataset.

A crucial feature of BioFunctional is its ability to obtain ancestral information for KEGG pathways and gene ontologies, using the techniques described above. This makes it easier to understand the hierarchy of these ontologies and how each sample in a dataset is classified within them, offering users a way to study the dataset at different taxonomic levels directly from the raw data. Additionally, the app implements the capability to create interactive networks, representing all experimental data to visualize the relationships between groups and ontologies without neglecting the established classification. This is a primary tool for understanding the meaning of the relationships observed within the displayed system.

Key features of Biofunctional include

• Interactive visualization: Create and explore networks to visualize relationships between groups and ontologies.

• Hierarchical analysis: Trace ancestral information for KEGG pathways and Gene Ontologies to understand hierarchical classifications.

• Efficient processing: Employ parallel processing for rapid data analysis, even with large datasets.

• Intuitive interface: A user-friendly design simplifies data exploration and analysis.

Due to these attributes, the software represents a valuable tool for analysts involved in the study of KEGG pathways and Gene Ontologies. By providing an intuitive interface with advanced data processing techniques, it empowers researchers to unravel the intricacies of biological functions and gain insights into the relationships between genes or molecular components.

Supplementary information

https://github.com/alexrodriguezmena/BIOFunctional