Omics Untargeted Key Script: R-Based Software Toolbox for Untargeted Metabolomics with Bladder Cancer Biomarkers...

Area of Science:

• Metabolomics
• Systems Biology
• Computational Biology

Background:

• Untargeted LC-MS metabolomics generates complex data requiring sophisticated analysis for systems biology and biomarker discovery.
• Existing data processing methods face challenges with signal complexity and unwanted variations in large-scale metabolomic datasets.

Purpose of the Study:

• To introduce OUKS (Omics Untargeted Key Script), an open-source R-based software collection for comprehensive untargeted LC-MS metabolomic data processing.
• To integrate advanced computational features, including nonlinear regression algorithms for quality control sample-based signal processing.
• To demonstrate the utility of OUKS in a bladder cancer biomarker discovery study.

Main Methods:

• Development of OUKS by integrating R packages and metabolomics software for a customizable data processing pipeline.
• Implementation of novel computational features using gradient boosting, tree-based, and nonlinear regression algorithms for signal processing.
• Application of OUKS to untargeted LC-MS profiling of urine samples for bladder cancer biomarker discovery, including rigorous data curation.

Main Results:

• OUKS provides a unified and accessible platform for untargeted LC-MS metabolomic data processing with advanced computational capabilities.
• The software facilitated the identification and statistical validation of potential bladder cancer biomarkers.
• Metabolism disorders associated with identified biomarkers were described, showcasing the tool's analytical depth.

Conclusions:

• OUKS enhances the accessibility and efficiency of untargeted LC-MS metabolomic profiling for the research community.
• The developed software aids in robust biomarker discovery and the understanding of associated metabolic alterations.
• OUKS represents a significant advancement in computational tools for metabolomics research.