MAIT

MAIT performs end-to-end statistical analysis and peak annotation of liquid chromatography–mass spectrometry (LC/MS) metabolomic data to enable metabolite identification and validation.

Key Features:

• End-to-end statistical analysis: Performs statistical analysis of LC/MS metabolomic datasets.
• Peak annotation: Enhances annotation and characterization of chromatographic peaks to support metabolite identification.
• Modular workflows: Provides modular functions that allow customization of analysis workflows.
• Statistical validation: Implements validation of statistical analysis results to assess reliability.
• Output generation: Produces detailed files containing statistical results, peak annotations, and identified metabolites.

Scientific Applications:

• Biomarker discovery: Supports identification of metabolite biomarkers from LC/MS data.
• Metabolic pathway elucidation: Facilitates elucidation of metabolic pathways through annotated metabolite profiles.
• Systems biology: Enables integration of metabolomic results into systems biology studies.

Methodology:

Processes LC/MS data using peak annotation and statistical analysis implemented as modular functions, validates statistical results, and writes files with statistical results, peak annotations, and identified metabolites.