tooluniverse-metabolomics-analysis-b23674 — independently scanned and version-tracked by SaferSkills.
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The primary manifest — the file an agent reads to learn what this artifact does.
Comprehensive analysis of metabolomics data from metabolite identification through quantification, statistical analysis, pathway interpretation, and integration with other omics layers.
Metabolomics quantification depends critically on normalization. Total ion current (TIC) normalization corrects for sample-loading variation and works well for global abundance changes; internal standard normalization is more accurate for targeted analysis where specific metabolite concentrations matter. Missing values in a peak table may reflect signal below the detection limit — not true absence — and should be imputed or handled explicitly rather than treated as zero. Failing to account for batch effects across instrument runs is a frequent source of spurious differential metabolites.
Metabolite_search and Metabolite_get_info to confirm names, CIDs, and HMDB IDs; never assume identity from m/z alone.Metabolite_get_diseases; do not infer clinical relevance without database evidence.Triggers:
Example Questions:
| Capability | Description |
|---|---|
| Data Import | LC-MS, GC-MS, NMR, targeted/untargeted platforms |
| Metabolite Identification | Match to HMDB, KEGG, PubChem, spectral libraries |
| Quality Control | Peak quality, blank subtraction, internal standard normalization |
| Normalization | Probabilistic quotient, total ion current, internal standards |
| Statistical Analysis | Univariate and multivariate (PCA, PLS-DA, OPLS-DA) |
| Differential Analysis | Identify significant metabolite changes |
| Pathway Enrichment | KEGG, Reactome, BioCyc metabolic pathway analysis |
| Metabolite-Enzyme Integration | Correlate with expression data |
| Flux Analysis | Metabolic flux balance analysis (FBA) |
| Biomarker Discovery | Multi-metabolite signatures |
Input: Metabolomics Data (Peak Table or Spectra)
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Phase 1: Data Import & Metabolite Identification
|-- Load peak table or process raw spectra
|-- Match features to HMDB, KEGG (accurate mass +/- 5 ppm)
|-- Confidence scoring (Level 1-4)
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Phase 2: Quality Control & Filtering
|-- CV in QC samples (<30%)
|-- Blank subtraction (sample/blank > 3)
|-- Remove features with >50% missing
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Phase 3: Normalization
|-- Sample-wise: TIC, PQN, or internal standards
|-- Transformation: log2, Pareto, or auto-scaling
|-- Batch effect correction (if multi-batch)
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Phase 4: Exploratory Analysis
|-- PCA for sample clustering
|-- PLS-DA for supervised separation
|-- Outlier detection
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Phase 5: Differential Analysis
|-- t-test / ANOVA / Wilcoxon
|-- Fold change + FDR correction
|-- Volcano plots, heatmaps
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Phase 6: Pathway Analysis
|-- Metabolite set enrichment (MSEA)
|-- KEGG/Reactome pathway mapping
|-- Pathway topology (hub/bottleneck metabolites)
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Phase 7: Multi-Omics Integration
|-- Metabolite-enzyme Spearman correlation
|-- Pathway-level concordance scoring
|-- Metabolic flux inference
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Phase 8: Generate Report
|-- Summary statistics, differential metabolites
|-- Pathway diagrams, biomarker panelLoad peak tables (CSV/TSV) or process raw spectra (mzML). Match features to HMDB by accurate mass (+/- 5 ppm). Assign confidence levels: L1 (standard match), L2 (MS/MS), L3 (mass only), L4 (unknown).
MS/MS spectral-library matching for L2 confidence (MassBank Europe): when a feature has an unknown identity and only a fragmentation spectrum, use src/tooluniverse/data/massbank_tools.json's 5 tools to search MassBank Europe's open-access reference spectral library (~139,000 records) rather than guessing an identity from mass alone (mass-only matches are L3 at best; only a matching MS/MS fragmentation pattern justifies L2):
| Tool | Use for |
|---|---|
MassBank_spectral_similarity_search | Core unknown-ID workflow. Submit the query MS/MS peak list as comma-separated mz;intensity pairs (e.g. "163.0601;100,145.0495;60,127.0390;30") and get back cosine-scored, ranked candidate accessions (score in [0,1], 1.0 = identical spectrum). Default threshold 0.5; raise toward 0.8+ for high-confidence-only matches, lower toward 0.3 to widen candidates. |
MassBank_get_record_by_accession | Resolve a candidate accession (from any of the other 4 tools) into the full record: compound names, formula, exact mass, SMILES/InChI, instrument/acquisition parameters, and cross-references to CAS/CHEBI/KEGG/PubChem. |
MassBank_search_by_compound | Look up reference spectra for an already-suspected identity by name (e.g. "glucose") — useful to confirm a hypothesis or pull a reference spectrum for comparison, not for de novo identification. |
MassBank_search_by_formula | Same idea, filtered by molecular formula (e.g. "C6H12O6") when a formula has been inferred from accurate mass but the specific compound/isomer is still unknown. |
MassBank_search_records_advanced | Narrow a large candidate set with AND-combined filters: exact_mass+mass_tolerance, a required fragment peaks value, a neutral_loss, ion_mode, ms_type, instrument_type, or a SMILES/SMARTS substructure match. Returns accessions only — resolve each with MassBank_get_record_by_accession. |
Verified live (MassBank Europe API, tu test/tu run, all 5 tools, 11/11 examples pass): a real search for "glucose" returns spectra for glucose and its biological derivatives (e.g. glucose-6-phosphate, MSBNK-Antwerp_Univ-METOX_P100333_9EE2, formula C6H13O9P, mass 260.0297, with CAS/CHEBI/KEGG/PUBCHEM/INCHIKEY cross-references) — search results are not limited to the exact queried compound, so check each candidate's formula/mass before treating a hit as a confirmed match. Note this is genuinely different from tooluniverse-metabolomics's HMDB-based lookup: use MassBank when identity is unknown and you only have a spectrum; use tooluniverse-metabolomics/HMDB once a name or ID is already suspected and you want pathway/biological context instead.
A second MS/MS spectral library (GNPS), plus natural-product biosynthetic classification: src/tooluniverse/data/gnps_tools.json's 4 tools cover GNPS (Global Natural Products Social Molecular Networking) — a sibling reference library to MassBank, not a replacement. Both are open, community-curated MS/MS libraries; GNPS's own community and contribution pipeline (molecular networking) skews its coverage toward natural products and microbial/environmental metabolites, while MassBank Europe's contributor base skews toward standards-run instrument labs — in practice this means a compound absent from one may well be present in the other, so for a genuinely unidentified feature it is worth checking both rather than treating a MassBank miss as conclusive.
| Tool | Use for |
|---|---|
GNPS_get_spectrum | Retrieve a GNPS reference spectrum by its USI (Universal Spectrum Identifier, format mzspec:GNPS:GNPS-LIBRARY:accession:CCMSLIB{id} — a cross-repository spectrum-addressing standard, not GNPS-specific notation). Returns precursor m/z/charge, peak count, m/z range, and the top 20 peaks by intensity. |
GNPS_compare_spectra | Compare two USIs side by side (precursor m/z + peak count for each) and get a mirror_plot_url for the interactive visual comparison — use to sanity-check a proposed match visually before committing to it. |
GNPS_get_library_record | Resolve a CCMSLIB accession (from a molecular-networking hit or a GNPS_get_spectrum USI) into its full compound annotation: name, adduct, SMILES, InChI/InChIKey, instrument, ion mode, contributing PI, and library membership. This is the GNPS equivalent of MassBank_get_record_by_accession. |
GNPS_npclassifier_from_smiles | Not spectral matching — a separate, later step. Takes a SMILES (e.g. already resolved via GNPS_get_library_record, MassBank, or HMDB) and returns its natural-product biosynthetic classification: pathway (e.g. Alkaloids, Shikimates and Phenylpropanoids), superclass, class (each an array — a structure can carry more than one class, e.g. morphine returns both Isoquinoline alkaloids and Morphinan alkaloids), and an is_glycoside flag. Works on any SMILES with no library lookup needed, so it's useful even for a structure proposed from first-principles fragment interpretation rather than a database hit. Use this once you have (or are proposing) a structure and want its natural-product context — not for identifying an unknown spectrum in the first place. |
Verified live (GNPS API via gnps2, tu test/tu run, all 4 tools, 7/7 examples pass): GNPS_get_library_record("CCMSLIB00005435737") resolves to "Lovastatin M+H; Mevinolin annotated in standard" with full SMILES/InChI and an annotations array carrying the underlying GNPS record fields (Scan, Library_Class, task_id, etc.) alongside the flattened top-level fields; GNPS_npclassifier_from_smiles correctly classified quercetin as Flavonols/Flavonoids/Shikimates and Phenylpropanoids and morphine as the two-class Isoquinoline alkaloids+Morphinan alkaloids result above — confirming class can legitimately hold more than one entry.
Assess CV in QC samples (reject >30%), compute blank ratios (keep >3x blank), filter features with >50% missing values. Check internal standard recovery (95-105% acceptable).
Three methods available: TIC (simple, assumes similar total abundance), PQN (robust to large changes, recommended), Internal Standard (most accurate with spiked standards). Follow with log2 transform or Pareto scaling.
PCA reveals sample grouping and batch effects. PLS-DA provides supervised separation (report R2 and Q2 for model quality). Flag and investigate outliers.
Welch's t-test (two groups) or ANOVA (multiple groups) with Benjamini-Hochberg FDR correction. Significance thresholds: adj. p < 0.05 and |log2FC| > 1.0.
Map differential metabolites to KEGG compound IDs. Perform MSEA for pathway enrichment. Consider topology: metabolites at pathway hubs (high degree/betweenness centrality) have greater impact.
Correlate metabolite levels with enzyme expression (Spearman). Expected: substrate-enzyme negative correlation (consumption), product-enzyme positive correlation (production). Score pathway dysregulation using combined metabolite + gene evidence.
See report_template.md for full example output.
| Skill | Used For | Phase |
|---|---|---|
tooluniverse-gene-enrichment | Pathway enrichment | Phase 6 |
tooluniverse-rnaseq-deseq2 | Enzyme expression for integration | Phase 7 |
tooluniverse-proteomics-analysis | Protein levels for integration | Phase 7 |
tooluniverse-multi-omics-integration | Comprehensive integration | Phase 7 |
| Component | Requirement |
|---|---|
| Metabolites | At least 50 identified metabolites |
| Replicates | At least 3 per condition |
| QC | CV < 30% in QC samples, blank subtraction |
| Statistical test | t-test or Wilcoxon with FDR correction |
| Pathway analysis | MSEA with KEGG or Reactome |
| Report | QC, differential metabolites, pathways, visualizations |
Methods:
Databases:
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.