Overview
MS2Lipid predicts lipid subclasses from tandem mass spectra using a trained machine-learning model. It provides an orthogonal class-level annotation signal for lipidomics and can be used through Python or the supplied Colab example.
What can this tool do?
Common uses
- A Python package for machine-learning prediction of lipid subclasses from tandem mass spectra using curated lipid MS/MS reference data.
Scientific fit
Primary function
Metabolite annotation and identification
Other functions
Machine learning and prediction
Analysis types
Lipidomics
Data compatibility
Inputs
Mass spectra
Outputs
Compound list or annotations
Input formats
Spectrum peak arrays and metadata via the documented Python/Colab interface
Output formats
Predicted lipid subclasses and associated classifier results
Scope and considerations
Designed for
- A Python package for machine-learning prediction of lipid subclasses from tandem mass spectra using curated lipid MS/MS reference data.
Not designed for
- Full lipid molecular-species identification, acyl-chain assignment or structural confirmation from a subclass label alone.
Important requirements
- The documented compatible Python environment, trained model and spectra/precursor metadata arranged according to the example notebook's input interface.
Considerations
- Class coverage and domain differences from the curated training spectra affect results. Inspect mixed/chimeric spectra and score interpretation before using labels in downstream networks.
Access and documentation
MIT source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested.
License: MIT
Evidence and review
Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch2.md.
Core publications
Resource papers, direct updates and tool-specific methods. Selection policy.
- MS2Lipid: A Lipid Subclass Prediction Program Using Machine Learning and Curated Tandem Mass Spectral Data. Metabolites (2024). doi:10.3390/metabo14110602
Related tools
Entry history
Changes to this catalogue entry, not the tool's software release history. Acceptance and editorial review are separate.
- 2026-10-27 · First committed entry
Add 24 reviewed Enveda tools in a separate second batch
View change · Entry at this revision