bio-metabolomics-metabolite-annotation

What problem does it solve?

Many untargeted metabolomics experiments produce feature tables with m/z, retention time, and sometimes MS/MS spectra but lack reliable compound identities; this Skill automates matching those features to metabolite databases, handles adduct conversions, and assigns MSI confidence levels to reduce manual curation time.

Core Features & Use Cases

• Mass-based database matching: convert observed m/z to neutral masses for common adducts and search HMDB/KEGG/METLIN within ppm tolerance.
• MS/MS spectral matching: compute cosine-style similarity against spectral libraries (MassBank, GNPS) to prioritize candidates.
• RT prediction and validation: optionally compare observed retention times to predicted values to strengthen annotations.
• Confidence assignment & reporting: apply MSI confidence levels and export annotated feature tables for downstream pathway mapping or statistical analysis.
• Use case: annotate an untargeted LC-MS feature table to produce putative compound IDs, adduct assignments, and a confidence-filtered CSV for pathway analysis.

Quick Start

Annotate my feature_table.csv against HMDB using 10 ppm mass tolerance in positive mode and assign MSI confidence levels.