MS2Compound

MS2Compound generates context-specific predicted MS/MS spectral libraries using CFM-ID and matches experimental LC-MS/MS fragment spectra with the mS-score to support metabolite identification in global metabolomics.

Key Features:

• Custom Database Generation: Uses the Competitive Fragmentation Modeling-ID (CFM-ID) algorithm to predict fragmentation spectra and generate context-specific spectral libraries tailored to experimental contexts, models, or species.
• mS-score Scoring Function: Matches raw fragment spectra against predicted spectra and provides a quantitative score for metabolite identification.
• Benchmarking: The mS-score has been benchmarked and reported to perform comparably to established methods such as dot product and hypergeometric score for identifying metabolites.

Scientific Applications:

• LC-MS/MS-based global metabolomics: Identification of metabolites in complex biological samples analyzed by LC-MS/MS.
• Re-analysis of public datasets: Applied to publicly available metabolomic datasets, exemplified by the Triphala dataset (MassIVE id: MSV000086784), to resolve compounds in complex formulations.
• Systems and biomedical research: Support for metabolite identification in omics systems science and biomedical studies requiring tailored spectral libraries.

Methodology:

CFM-ID predicts fragmentation patterns from chemical structures to generate predicted spectral libraries, and the mS-score matches experimental fragment spectra to those predicted spectra and is benchmarked against dot product and hypergeometric scores.