🏷️ Metabolite Annotation

Metabolite annotation and structural identification against spectral libraries. Supports SIRIUS/CSI:FingerID, GNPS, and MetFrag.

CLI Reference

python omicsclaw.py run met-annotate --demo
python omicsclaw.py run met-annotate --input <features.csv> --output <dir>

Why This Exists

• Without it: LC-MS peaks remain anonymous "features" defined only by m/z and retention time
• With it: Converts features into candidate chemical structures via spectral networking and in-silico fragmentation
• Why OmicsClaw: Centralizes access to fragmented knowledgebases (SIRIUS, GNPS, MetFrag)

Workflow

1. Calculate: Extract pure MS2 spectra representations.
2. Execute: Query spectral libraries or generate fragmentation trees.
3. Assess: Score candidate chemical formulas and structures.
4. Generate: Output structural mappings of features to molecules.
5. Report: Tabulate top compound identifications with confidence tiers.

Example Queries

• "Annotate these metabolomics features using SIRIUS"
• "Match MS2 spectra against GNPS libraries"

Output Structure

output_directory/
├── report.md
├── result.json
├── annotated.csv
├── figures/
│   └── chemical_class_distribution.png
├── tables/
│   └── compound_identifications.csv
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    └── checksums.sha256

Safety

• Local-first: Local database matching where possible; transparent interactions for external APIs (like GNPS).
• Disclaimer: Requires OmicsClaw reporting structures and disclaimers.
• Audit trail: Hyperparameters and operational flow states are logged fully.

Integration with Orchestrator

Trigger conditions:

• Automatically invoked dynamically based on tool metadata and user intent matching.

Chaining partners:

• peak-detection

  — Upstream feature extraction

• met-diff

  — Downstream structural interpretation of significant hits

Citations

• SIRIUS
• GNPS
• MetFrag