Analyze metabolomics data end-to-end — metabolite identification, quantification (TIC normalization, batch correction), differential analysis, and pathway interpretation. Use for processing mass-spec metabolomics output, normalization choice, untargeted metabolomics workflows, and integrating with o

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. 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" 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 Workflow Overview 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](report template.md) for full example output. 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 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 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 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 Reference Files [code examples.md](code examples.md) Python code for all phases

tooluniverse-metabolomics-analysis