CluMSID

CluMSID clusters tandem mass spectra (MS2) using spectral similarity and neutral loss patterns to facilitate feature annotation and compound identification in untargeted metabolomics.

Key Features:

• Similarity-Based Clustering: Clusters tandem mass spectra (MS2) by spectral similarity and neutral loss patterns to group known and unknown analytes that share structural features.
• Comprehensive Analysis Workflow: Provides R functions for importing raw MS/MS data and executing downstream analysis steps for feature-level investigation.
• Unsupervised Multivariate Methods: Implements unsupervised multivariate methods for clustering and data mining of MS2 datasets.
• Interactive Visualizations: Produces visualizations for exploring cluster membership and spectral relationships among analytes.

Scientific Applications:

• Metabolite identification: Facilitates annotation and identification of small molecules in complex untargeted metabolomics datasets.
• Structural inference: Groups spectra to highlight potential structural similarities and neutral-loss-derived relationships among analytes.
• Biomarker and pathway analysis: Supports discovery of biomarkers and interpretation of metabolic pathways through clustered feature annotation and comparative analysis.

Methodology:

CluMSID analyzes tandem mass spectra using spectral similarity metrics and neutral loss patterns and applies unsupervised multivariate clustering methods to group spectra and compare clusters with reference spectra.