MCnebula

MCnebula streamlines analysis of untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) data to identify and classify chemical features for metabolomics and systems biology studies.

Key Features:

• R-based implementation: Implemented in R for computational analysis of LC-MS/MS datasets.
• Abundance-Based Classes (ABC) Selection Algorithm: Selects and prioritizes compounds based on abundance to focus downstream analysis.
• Critical Chemical Class Identification: Classifies detected features into chemical classes to aid structural characterization beyond spectral libraries.
• Multi-Dimensional Visualization (Child-Nebulae): Generates network graphs (Child-Nebulae) integrating annotations, chemical classifications, and structures for multi-dimensional data exploration.
• Feature Selection: Identifies significant features for further analysis.
• Homology Tracing: Traces top or homologous features to assess biological relevance.
• Pathway Enrichment Analysis: Performs pathway enrichment to relate identified compounds to biological pathways.
• Heat Map Clustering Analysis: Produces heat maps for clustering and pattern visualization.
• Spectral Visualization Analysis: Provides visualization of MS/MS spectra for inspected features.
• Chemical Information Query: Queries chemical information databases for compound annotation.
• Output Analysis Reports: Generates summary reports of analysis results.

Scientific Applications:

• Metabolomics: Facilitates annotation and discovery of metabolites from untargeted LC-MS/MS datasets.
• Systems Biology: Supports integration of chemical feature classification into systems-level analyses.
• Human serum biomarker discovery: Applied to human serum datasets where Acyl carnitines were identified as biomarkers.
• Plant compound discovery: Applied to datasets from E. ulmoides for rapid compound identification.

Methodology:

MCnebula performs ABC selection, critical class identification, and multi-dimensional visualization (network graphs/Child-Nebulae).