MassCube

MassCube improves accuracy for metabolomics data processing from raw mass spectrometry files to phenotype classifiers through systematic benchmarking against other algorithms.

Key Features:

• Peak Detection: Constructs mass traces through signal clustering and Gaussian-filter assisted edge detection, achieving 100% signal coverage with comprehensive reporting of chromatographic metadata for quality assurance.
• Compound Annotation: Annotates compounds by both identity- and fuzzy searches.
• Adduct and In-Source Fragment Detection: Groups detected peaks for adduct and in-source fragment detection.
• Quality Control: Final data tables undergo quality controls with comprehensive reporting.
• Phenotype Prediction: Supports metabolome-informed phenotype prediction.
• Performance: Outperforms MS-DIAL, MZmine3, and XCMS for speed, isomer detection, and accuracy, capable of handling 105 GB of Astral MS data on a laptop within 64 minutes.
• Batch Effect Handling: Automatically detects biological differences despite batch effects.

Scientific Applications:

• LC-MS-Based Metabolomics: Processes nontargeted metabolomics data from raw LC-MS files through complete workflows.
• Metabolome Atlas Analysis: Automatically detected age, sex, and regional differences when applied to the Metabolome Atlas of the Aging Mouse Brain data.
• Large-Scale Dataset Processing: Handles large datasets including high-resolution Astral MS data for biomedical and omics research.

Methodology:

MassCube detects peaks by constructing mass traces through signal clustering and Gaussian-filter assisted edge detection, groups peaks for adduct and in-source fragment detection, annotates compounds by identity- and fuzzy searches, and applies quality controls to final data tables for metabolome-informed phenotype prediction.