tooluniverse-metabolomics-analysis

Metabolomics Analysis

Comprehensive analysis of metabolomics data from metabolite identification through quantification, statistical analysis, pathway interpretation, and integration with other omics layers.

Domain Reasoning

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.

LOOK UP DON'T GUESS

• Metabolite identities: use Metabolite_search and Metabolite_get_info to confirm names, CIDs, and HMDB IDs; never assume identity from m/z alone.
• Pathway memberships: query KEGG, MetaCyc, or Reactome tools; do not list pathways from memory.
• Disease associations: retrieve from CTD via Metabolite_get_diseases; do not infer clinical relevance without database evidence.
• CV thresholds and QC criteria: apply the values defined in this workflow (CV < 30%, blank ratio > 3x); do not override with guesses.

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When to Use This Skill

Triggers:

• User has metabolomics data (LC-MS, GC-MS, NMR)
• Questions about metabolite abundance or concentrations
• Differential metabolite analysis requests
• Metabolic pathway analysis
• Multi-omics integration with metabolomics
• Metabolic biomarker discovery
• Flux balance analysis or metabolic modeling
• Metabolite-enzyme correlation

Example Questions:

1. "Analyze this LC-MS metabolomics data for differential metabolites"
2. "Which metabolic pathways are dysregulated between conditions?"
3. "Identify metabolite biomarkers for disease classification"
4. "Correlate metabolite levels with enzyme expression"
5. "Perform pathway enrichment for differential metabolites"
6. "Integrate metabolomics with transcriptomics data"

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Core Capabilities

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

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Workflow Overview

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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    v
Phase 2: Quality Control & Filtering
    |-- CV in QC samples (<30%)
    |-- Blank subtraction (sample/blank > 3)
    |-- Remove features with >50% missing
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    v
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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    v
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 panel

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Phase Summaries

Phase 1: Data Import & Identification

Load 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.

Phase 2: Quality Control

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).

Phase 3: Normalization

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.

Phase 4: Exploratory Analysis

PCA reveals sample grouping and batch effects. PLS-DA provides supervised separation (report R2 and Q2 for model quality). Flag and investigate outliers.

Phase 5: Differential Analysis

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.

Phase 6: Pathway Analysis

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.

Phase 7: Multi-Omics Integration

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.

Phase 8: Report

See report_template.md for full example output.

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Integration with ToolUniverse

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

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Quantified Minimums

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

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Limitations

• Identification: Many features remain unidentified (Level 4)
• Coverage: Cannot detect all metabolites (depends on method)
• Quantification: Relative abundance (not absolute without standards)
• Isomers: Difficult to distinguish structural isomers
• Ion suppression: Matrix effects can affect quantification
• Dynamic range: Limited compared to targeted methods

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References

Methods:

• MetaboAnalyst: https://doi.org/10.1093/nar/gkab382
• XCMS: https://doi.org/10.1021/ac051437y
• MSEA: https://doi.org/10.1186/1471-2105-11-395

Databases:

• HMDB: https://hmdb.ca
• KEGG Compound: https://www.genome.jp/kegg/compound/
• Reactome: https://reactome.org

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Reference Files

• code_examples.md - Python code for all phases (data loading, QC, normalization, statistics, pathway analysis)
• report_template.md - Full example report (LC-MS disease vs control)