SMART 2.0 Statistical Metabolomics Analysis: An R Tool 2.0

Area of Science:

• Computational Biology
• Bioinformatics
• Metabolomics

Background:

• Metabolomics analysis is rapidly advancing due to technological progress.
• Integrated tools are needed to streamline complex metabolomics workflows.

Purpose of the Study:

• To introduce SMART 2.0, an enhanced integrated tool for metabolomics analysis.
• To expand capabilities for targeted metabolomics, data normalization, quality control, and advanced analytics.
• To explore the use of large language models for peak annotation.

Main Methods:

• Development of SMART 2.0 with new modules for targeted metabolomics, normalization, and quality control.
• Implementation of advanced dimensionality reduction and classification algorithms.
• Integration of omics pathway analysis and post-analysis tasks like peak identification and calibration.
• Exploration of large language models for automated peak annotation.

Main Results:

• SMART 2.0 provides a comprehensive platform for both untargeted and targeted metabolomics.
• New modules enhance data quality assessment and analytical power.
• Integrative omics analysis and AI-driven peak annotation show promising results.
• Demonstrated utility with narcotics and breast cancer datasets.

Conclusions:

• SMART 2.0 offers a robust and versatile solution for advanced metabolomics research.
• The integration of AI for peak annotation represents a significant step forward.
• The tool facilitates streamlined, comprehensive analysis from raw data to biological interpretation.