MEMO

MEMO performs retention time-agnostic comparison and clustering of metabolomics samples by converting LC-MS/MS fragmentation (MS2) data into MS2 fingerprints for analysis of chemodiverse natural product extracts.

Key Features:

• Retention Time-Agnostic Alignment: Enables clustering and comparison of samples without using retention time information.
• MS2 Fingerprinting: Generates MS2 fingerprints by counting occurrences of MS2 peaks and neutral losses relative to each sample's precursor.
• Efficient Clustering Performance: Clusters samples based on fragmentation spectra with performance comparable to state-of-the-art metrics.
• Reduced Computational Time: Reduces computational time relative to existing approaches for analyzing large sample sets.

Scientific Applications:

• Natural Products Research: Compares chemodiverse natural product extracts analyzed by LC-MS/MS across varied chromatographic conditions.
• Cross-Batch Comparative Analysis and Metabolite Annotation: Establishes relationships among analytes and supports metabolite annotation across batches with varying experimental conditions by avoiding retention time alignment.
• Large-Scale Heterogeneous Sample Comparison: Facilitates large-scale comparative metabolomics of heterogeneous sample sets collected over extended periods.

Methodology:

Processes LC-MS/MS fragmentation (MS2) data to create MS2 fingerprints by counting MS2 peaks and neutral losses relative to each precursor, then clusters and compares samples based on these fingerprints in a retention time-agnostic manner without prior feature alignment.