mineMS2: annotation of spectral libraries with exact fragmentation patterns

Area of Science:

• Metabolomics
• Computational Chemistry
• Bioinformatics

Background:

• Metabolite identification is challenging due to structural diversity.
• Tandem mass spectrometry (MS/MS) is crucial for molecular structure characterization.
• High-throughput MS/MS instruments generate large datasets requiring efficient analysis.

Purpose of the Study:

• To develop an innovative de novo strategy for identifying metabolites by searching for exact fragmentation patterns in MS/MS spectra.
• To improve the structural interpretation of molecular networks and the elucidation of unknown spectra.
• To present a novel approach for mining MS/MS data and facilitating metabolite discovery.

Main Methods:

• Representing MS/MS spectra as graphs of m/z differences.
• Employing an efficient frequent-subgraph mining algorithm to identify patterns.
• Implementing the strategy in the publicly available mineMS2 software package (R).

Main Results:

• The new fragmentation patterns capture similarities missed by existing methods.
• Demonstrated effectiveness on spectral databases and biological matrices.
• Facilitated structural interpretation of molecular network components and elucidation of unknown spectra.

Conclusions:

• The proposed strategy offers a powerful de novo approach for metabolite identification.
• Fragmentation graphs of m/z differences provide valuable insights complementary to existing methods.
• The mineMS2 software enables efficient MS/MS data mining for structural elucidation.