MStractor

MStractor performs non-targeted processing of liquid chromatography–mass spectrometry (LC-MS) data in R to support untargeted metabolomics preprocessing, molecular feature extraction, quality control, and visualization.

Key Features:

• R workflow package: Implements an R-based workflow for end-to-end non-targeted LC-MS data processing.
• Streamlined pre-processing: Provides computational pre-processing steps tailored to metabolomics LC-MS datasets.
• Molecular feature extraction: Integrates molecular feature extraction to detect and quantify chromatographic and mass spectrometric features.
• Quality control and statistics: Produces graphical quality-control outputs and descriptive statistics for data integrity assessment.
• Sample-type versatility: Applicable to diverse biological sample types including human, animal, plant, fungal, and microbial samples.

Scientific Applications:

• Untargeted metabolomics profiling: Processing and feature discovery in untargeted metabolomics experiments using LC-MS data.
• Large-scale LC-MS dataset analysis: Preprocessing and QC of extensive, complex LC-MS datasets for downstream statistical analysis.
• Cross-sample-type metabolomic comparison: Comparative metabolomic analyses across human, animal, plant, fungal, and microbial samples.

Methodology:

Computational steps explicitly include LC-MS pre-processing, molecular feature extraction, generation of graphical quality-control outputs and descriptive statistics, and performance evaluation via detailed comparison with XCMS Online.