PyMS

PyMS aligns GC-MS peak lists and corrects retention time drift to enable accurate non-targeted metabolomics profiling.

Key Features:

• Retention Time Correction: Automates retention time correction to enable accurate peak alignment across GC-MS experiments.
• Dynamic Programming Approach: Uses a progressive dynamic programming method to match signal peaks by aligning all possible pairs of peak lists and estimating their similarity.
• Guide Tree Construction: Builds a guide tree from pairwise similarity estimates to order progressive alignments starting with the most similar peak lists.
• Within-State and Between-State Alignment: Performs within-state alignment of replicate experiments and then aligns results across different sample states.
• Balanced Peak Similarity Function: Employs a peak similarity function that balances retention time and mass spectra similarities for sensitive peak matching.
• Scalability: Handles an arbitrary number of peak lists for large-scale metabolomics studies.

Scientific Applications:

• Metabolic Profiling: Applied to non-targeted metabolomics GC-MS data, including studies of wild-type and mutant Leishmania mexicana parasites.
• High-Throughput Data Processing: Processes large sets of GC-MS peak lists for high-throughput metabolomics analyses.

Methodology:

Align all possible pairs of peak lists and estimate pairwise similarity; construct a guide tree from these similarities; progressively align peak lists according to the guide tree, starting with the most similar pairs and extending through the dataset.