What problem does it solve?
Identifying metabolites from mass-spec experiments and finding relevant metabolomics studies requires manually querying multiple databases (HMDB, MetaboLights, Metabolomics Workbench, PubChem) and stitching results together. This Skill automates that multi-database lookup and compiles findings into a structured markdown research report.
Core Features & Use Cases
- Metabolite Identification & Annotation: Search HMDB by metabolite name to retrieve HMDB IDs, chemical formulas, molecular weights, and pathways, with automatic PubChem fallback for CID and SMILES data.
- Study Retrieval & Search: Fetch study details from MetaboLights (MTBLS IDs) or Metabolomics Workbench (ST IDs), and search studies by disease, compound, or method keywords.
- Structured Report Generation: Run a 4-phase pipeline (identification, study details, study search, database overview) that writes a markdown report with confidence-level guidance (L1-L4) for metabolite identification.
- Use Case: Given a list of detected metabolites like glucose, lactate, and pyruvate from an LC-MS experiment, generate an annotated report with HMDB IDs, formulas, pathways, and related diabetes studies from MetaboLights.
Quick Start
Ask the AI to analyze the metabolites glucose, lactate, and pyruvate, retrieve study MTBLS1, and generate a metabolomics research report.