M-IOLITE

M-IOLITE performs automated processing, normalization, peak identification, and metabolic network analysis of Gas Chromatography–Mass Spectrometry (GC-MS) metabolomics data to support high-throughput metabolomic experiments.

Key Features:

• Streamlined Data Analysis: Automates data processing, validation, and annotation of GC-MS metabolomic datasets.
• Specialized Normalization Methods: Implements normalization techniques tailored for GC-MS data to ensure consistency across experiments.
• Safe Data Repository: Provides a secure repository for storing biological samples and associated metadata.
• Comprehensive Peak Library: Includes the MESBL GC-MS metabolite peak database containing over 900 standardized metabolite peaks and integrates in-house standards, the GOLM database, and the Human Metabolome Database.
• Unknown Peak Identification: Provides methods for identification of unknown peaks detected in GC-MS metabolomic analyses.
• Integration with Metabolic Network Analysis: Incorporates metabolic network analysis into data interpretation to link metabolomic profiles with pathways.

Scientific Applications:

• Biomedical Research: Investigating dynamic metabolic responses in diseases and disorders.
• Pharmacology: Studying drug metabolism and interactions.
• Agriculture: Analyzing plant metabolic pathways for crop improvement.
• Environmental Science: Analyzing microbial communities and their metabolic activities.

Methodology:

Automated data processing, validation, and annotation; specialized normalization methods for GC-MS; matching to the MESBL GC-MS peak library (including in-house standards, GOLM, and the Human Metabolome Database); unknown peak identification methods; and metabolic network analysis.