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Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

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FUNKI: interactive functional footprint-based analysis of omics data.

Rosa Hernansaiz-Ballesteros¹, Christian H Holland^1,2, Aurelien Dugourd^1,2

¹Institute for Computational Biomedicine, Heidelberg University, Heidelberg University Hospital, Faculty of Medicine, Bioquant, Heidelberg 69120, Germany.

Bioinformatics (Oxford, England)

|February 8, 2022

Summary

FUNKI is a new R-based tool for analyzing omics data, enabling researchers to estimate molecular pathway activity and identify key drivers. This functional toolkit offers a user-friendly interface for transcriptomics, phosphoproteomics, and metabolomics data analysis.

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Area of Science:

Molecular biology
Systems biology
Bioinformatics

Background:

Omics data provide snapshots of cellular molecular states.
Changes in omics data can infer activity of pathways, transcription factors, and kinases (footprints).
Causal reasoning on signaling networks can elucidate molecular drivers of observed activities.

Purpose of the Study:

To develop a user-friendly toolkit for functional footprint analysis.
To enable rapid analysis of diverse omics data types.
To facilitate the estimation of molecular pathways driving cellular activities.

Main Methods:

Development of FUNKI (FUNctional toolKIt) based on R and Shiny.
Implementation of footprint analysis for transcriptomics, phosphoproteomics, and metabolomics data.
Support for both bulk and single-cell experimental data.
Inclusion of visualization and post-analysis options.
Provision of a scripted R version for reproducibility.

Main Results:

FUNKI provides a user-friendly interface for efficient omics data analysis.
The toolkit enables the estimation of pathway and transcription factor activity.
FUNKI facilitates causal reasoning on signaling networks to identify molecular drivers.
The tool supports various omics data types and experimental designs.

Conclusions:

FUNKI offers a comprehensive solution for functional omics data analysis.
The toolkit simplifies the estimation of molecular footprints and causal pathways.
FUNKI enhances the interpretation of complex biological data from omics experiments.