MetaboAnalyst

MetaboAnalyst provides comprehensive statistical, functional, and integrative analyses of metabolomics data to support metabolite identification, pathway activity prediction, biomarker discovery, and multi-omics integration.

Key Features:

• Real-time R command tracking (MetaboAnalystR): Tracks R commands and exposes an R package (MetaboAnalystR) for reproducible analysis workflows.
• MS Peaks to Pathways (mummichog): Uses the mummichog algorithm to predict pathway activities directly from untargeted mass spectral peak lists.
• Biomarker Meta-analysis Module: Integrates multiple metabolomic datasets to support robust biomarker identification across studies.
• Network Explorer Module: Enables integrative network analysis of metabolomics with metagenomics and/or transcriptomics data.
• HMDB knowledgebase updates: Incorporates updated data from the Human Metabolome Database (HMDB) for metabolite annotation and interpretation.
• LC-HRMS optimization: Provides stepwise guidance for optimizing parameters in LC-HRMS spectra processing.
• Functional insights from peak list data: Derives functional and pathway-level interpretations directly from peak list inputs.
• Integration with transcriptomics and multi-dataset analysis: Supports combination and joint analysis of metabolomics with transcriptomics and multiple datasets.
• Exploratory statistical analysis with complex metadata: Offers tools for exploratory statistics that leverage complex sample metadata.
• Data processing optimized for LC-HRMS: Implements processing routines tailored to LC-HRMS spectral data.
• Statistical analysis (exploratory and confirmatory): Provides a range of statistical methods for both exploratory and confirmatory analyses.
• Functional interpretation modules: Includes modules (e.g., MS Peaks to Pathways) to translate metabolite-level signals into biological functions and pathways.

Scientific Applications:

• Biomedical research: Identification and validation of disease biomarkers from metabolomic profiles.
• Environmental metabolomics: Analysis of environmental samples for metabolite profiling and ecological studies.
• Global untargeted metabolomics (LC-HRMS): Processing and interpretation of LC-HRMS-based untargeted metabolomics datasets.
• Pathway activity prediction: Inferring pathway-level activities from untargeted mass spectral peak data using mummichog-based methods.
• Multi-omics integration and systems biology: Integrative analysis combining metabolomics with transcriptomics and metagenomics for systems-level insights.
• Meta-analysis across studies: Cross-study integration for robust biomarker discovery and reproducible findings.

Methodology:

Methods explicitly include R-based command tracking via MetaboAnalystR, LC-HRMS spectra processing and parameter optimization, mummichog-based MS Peaks to Pathways pathway prediction, biomarker meta-analysis, network-based integration with metagenomics/transcriptomics, and exploratory and confirmatory statistical analyses using complex metadata.