metabolism-skills

Look up any metabolite in the Human Metabolome Database (HMDB) via REST API across 220,000+ entries. Use when: user asks 'what is this metabolite', needs an HMDB ID, wants metabolite pathways or disease associations, queries a metabolite database, or needs cross-references to KEGG/PubChem/ChEBI. Triggers: metabolite lookup, HMDB search, metabolite properties, metabolite spectra, metabolite biomarker, compound information, metabolite concentration, biofluid metabolites, serum metabolites, urine metabolites.

Search and download public metabolomics study data from EMBL-EBI MetaboLights via REST API (2,800+ studies). Use when: user wants to find metabolomics studies, download study data, retrieve ISA-Tab files, access public metabolomics datasets, or get curated metabolite annotations. Triggers: MetaboLights, MTBLS, download study data, public metabolomics data, ISA-Tab, find metabolomics studies, metabolite assignment file, MAF file, deposited metabolomics, open-access metabolomics repository.

Query the NIH Metabolomics Workbench REST API across 4,200+ studies for metabolite data, RefMet nomenclature, and spectral searches. Use when: user asks about Metabolomics Workbench, needs RefMet standardized names, performs m/z search against a database, wants NIH metabolomics data, or retrieves gene-metabolite associations. Triggers: metabolomics workbench, NIH metabolomics, RefMet, m/z search, exact mass search, metabolite structure search, PubChem CID lookup, study metadata, GC-MS/LC-MS/NMR public data.

Constraint-based metabolic modeling with COBRApy. Flux balance analysis (FBA), flux variability (FVA), gene knockouts, flux sampling, production envelopes, and gap filling on genome-scale SBML models. Use when: predicting growth rates, optimizing metabolic fluxes, screening gene deletions, or building metabolic models. Triggers: FBA, FVA, COBRA, metabolic model, SBML, flux analysis, gene knockout simulation, metabolic engineering, growth prediction.

Interpret clinical metabolomics results for inborn errors of metabolism (IEM) screening, newborn screening, and diagnostic reporting. Use when: user has clinical metabolite panels, needs IEM differential diagnosis, wants to analyze acylcarnitine profiles or amino acid panels, or calculate z-scores against reference ranges. Triggers: newborn screening, IEM, inborn error of metabolism, acylcarnitine, amino acid panel, organic acid analysis, clinical diagnosis, tandem MS screening, PKU, MCADD, maple syrup urine disease, clinical metabolomics, reference range, z-score.

Analyze lipidomics data for lipid species identification, quantification, and pathway interpretation using LipidSearch, MS-DIAL, and LIPID MAPS. Use when: user has lipidomics data, needs lipid class annotation, wants to analyze sphingolipids/phospholipids/fatty acids, or interpret lipid-specific pathways. Triggers: lipid species, lipidomics, sphingolipid, phospholipid, fatty acid, ceramide, triglyceride, lipid class, LIPID MAPS, LipidSearch, lipidr, lipid annotation, chain composition, lipid profiling, phosphatidylcholine, PE, PC, SM.

Annotate and identify metabolomics features by matching m/z, retention time, and MS/MS spectra against databases. Use when: user has a feature table and wants compound IDs, needs to annotate m/z values, assign metabolite identities with confidence levels, or match features against HMDB/METLIN/MassBank. Triggers: identify features, m/z annotation, compound identification, metabolite ID, putative annotation, MSI confidence levels, adduct matching, ppm tolerance, neutral mass search, what compound is this m/z.

Process metabolomics data with MS-DIAL for peak detection, alignment, annotation, and export to feature tables. Use when: user has MS-DIAL output files, wants to import MS-DIAL results into R/Python, needs peak alignment from MS-DIAL, or prefers GUI-based LC-MS preprocessing. Triggers: MS-DIAL, MS-DIAL output, MSDIAL, peak alignment, MS-DIAL export, GUI preprocessing, alternative to XCMS, MS-DIAL console mode, metabolomics feature table from MS-DIAL.

Normalize metabolomics data and remove batch effects using QC sample-based correction, LOESS, ComBat, and transformation methods. Use when: user needs to correct batch effects, normalize a metabolomics feature table, apply QC-based drift correction, or prepare data for statistical analysis. Triggers: batch correction, QC samples, normalize metabolomics, batch effect, LOESS correction, ComBat, signal drift, QC-RSC, pooled QC, data transformation, log transformation, PQN normalization, MetaboAnalystR normalization.

Map metabolites to KEGG, Reactome, and SMPDB pathways and run enrichment/topology analysis with MetaboAnalyst. Use when: user asks which pathways are affected, wants pathway enrichment from a metabolite list, needs to visualize metabolites on KEGG maps, or interpret metabolomics results biologically. Triggers: pathway enrichment, pathway analysis, KEGG pathway, MetaboAnalyst, which pathways, pathway topology, over-representation analysis, metabolite set enrichment, MSEA, pathway impact, map metabolites to pathways.

Study drug metabolism and pharmacokinetic responses using pharmacometabolomics workflows with RDKit, DrugBank, and MetaboAnalystR. Use when: user asks about drug metabolites, CYP450 metabolism, ADME predictions, phase I/II biotransformation, or drug-response metabolite profiling. Triggers: drug metabolism, CYP450, ADME, drug metabolite, phase I metabolism, phase II conjugation, pharmacokinetics, DrugBank, metabolic soft spots, glucuronidation, sulfation, drug-metabolite interaction, pharmacometabolomics, xenobiotic metabolism.

Run statistical analysis on metabolomics data including univariate testing, multivariate modeling, and visualization. Use when: user wants to find differential metabolites between groups, run PCA on metabolomics data, build PLS-DA or OPLS-DA models, make a volcano plot, or perform t-tests with multiple testing correction. Triggers: differential metabolites, PCA, PLS-DA, OPLS-DA, volcano plot, fold change, limma, t-test metabolomics, heatmap, metabolomics statistics, significant metabolites, multivariate analysis, classification model, biomarker discovery.

Perform targeted metabolomics quantification using MRM/SRM transitions, calibration curves, and internal standards. Use when: user needs absolute quantification of metabolites, has MRM/SRM data, wants to build calibration curves, validate a targeted method, or process Skyline output. Triggers: MRM, SRM, calibration curve, absolute quantification, targeted quantification, internal standard, Skyline, targeted metabolomics, LOD, LOQ, method validation, standard curve, concentration calculation, quantitative metabolomics.

Preprocess raw LC-MS metabolomics data into a feature table using XCMS3 in R. Use when: user needs peak picking from mzML files, retention time alignment, feature grouping, gap filling, or building an untargeted metabolomics feature table. Triggers: peak picking, feature table, raw data processing, CentWave, XCMS, xcmsSet, untargeted metabolomics preprocessing, chromatographic peak detection, RT alignment, correspondence, obiwarp, feature extraction from LC-MS.

Convert mass spectrometry vendor files to open formats using msConvert and pyopenms. Use when: user needs to convert RAW files to mzML, convert WIFF/Agilent .d/Waters .raw to open format, batch convert instrument files, or troubleshoot format issues. Triggers: convert RAW file, mzML conversion, mzXML, vendor format, msConvert, ProteoWizard, Thermo RAW, AB SCIEX WIFF, Agilent .d, Waters .raw, Bruker .d, centroid mode, profile mode, batch conversion, format conversion.

Process GC-MS data for metabolomics and volatile compound analysis including peak detection, deconvolution, and NIST library matching. Use when: user has GC-MS data, needs retention index calculation, wants to identify volatiles, match EI spectra against NIST, or process derivatized metabolites. Triggers: GC-MS, gas chromatography, volatile analysis, NIST library, retention index, Kovats index, EI spectrum, electron ionization, deconvolution, AMDIS, TMS derivatives, volatile profiling, headspace analysis, SPME.

Compare mass spectra and identify unknown compounds by spectral library searching with matchms. Use when: user wants to match MS/MS spectra against a library, compute spectral similarity scores, identify an unknown compound from its spectrum, or process MGF/MSP spectral files. Triggers: spectral library search, MS/MS matching, identify unknown compound, cosine similarity score, modified cosine, spectral matching, MGF file, MSP file, GNPS library, MassBank, compound identification from spectra, tandem MS matching.

Process NMR metabolomics data from raw FID through metabolite quantification using nmrglue and speaq. Use when: user has NMR spectra to process, needs phase or baseline correction, wants to identify metabolites from chemical shifts, or quantify metabolites from 1H-NMR. Triggers: NMR spectrum, proton NMR, 1H NMR, 13C NMR, Bruker data, FID processing, chemical shift, ppm, spectral binning, NMR peak picking, BMRB, Chenomx, nmrglue, TOCSY, HSQC, 2D NMR, TSP reference, NMR metabolomics.

Full-featured mass spectrometry data processing with pyopenms (OpenMS Python bindings) for LC-MS, LC-MS/MS, and proteomics pipelines. Use when: user needs to read/write mzML files, run feature detection on LC-MS data, process chromatograms, extract ion chromatograms (XIC/EIC), or build complex MS workflows. Triggers: LC-MS data processing, mzML processing, feature detection, peak picking from mzML, chromatogram extraction, OpenMS, pyopenms, MS1/MS2 data, centroiding, mass spectrometry pipeline. For spectral library matching use matchms instead.

Analyze spatial metabolomics data from MALDI and DESI mass spectrometry imaging (MSI) experiments. Use when: user has imzML files, needs ion images for specific m/z values, wants to segment tissue regions by metabolite profiles, or map metabolite distributions across tissue. Triggers: MALDI imaging, MALDI-MSI, DESI-MSI, MSI data, imzML, mass spectrometry imaging, tissue metabolite distribution, ion image, spatial metabolomics, tissue section analysis, metabolite mapping, imaging mass spec, pyimzML.

Harmonize and preprocess multi-omics datasets for integration using MultiAssayExperiment, batch correction, and feature alignment. Use when: user needs to combine datasets from different platforms, correct batch effects across omics, merge omics data, or handle missing values before integration. Triggers: combine datasets, batch effect, merge omics, data harmonization, MultiAssayExperiment, cross-platform normalization, feature alignment, missing value imputation, multi-omics preprocessing, prepare data for integration.

Link genetic variants to metabolite levels via mGWAS, mQTL analysis, colocalization, and Mendelian randomization. Use when: user asks about genetic associations with metabolites, needs mQTL mapping, wants Mendelian randomization for metabolite-disease causality, or runs GWAS with metabolomics phenotypes. Triggers: genetic association metabolite, mQTL, Mendelian randomization, GWAS metabolomics, mGWAS, PLINK metabolite, colocalization, TwoSampleMR, causal metabolite, SNP-metabolite association, metabolite heritability.

Integrate microbiome (16S/shotgun) with metabolomics to analyze host-microbe metabolic interactions. Use when: user has paired microbiome and metabolomics data, asks about gut metabolites, needs SCFA quantification, bile acid profiling, tryptophan metabolism, or microbe-metabolite co-occurrence. Triggers: gut metabolites, SCFA, short-chain fatty acids, bile acids, microbiome, 16S, microbiome-metabolomics, mmvec, microbe-metabolite, tryptophan pathway, indole derivatives, gut-brain axis metabolites, fecal metabolomics.

Perform supervised multi-omics integration and classification with mixOmics (sPLS, sPLS-DA, DIABLO). Use when: user needs multi-omics classification, wants to find discriminant features across omics layers, or integrate metabolomics with other omics in a supervised framework. Triggers: sPLS, DIABLO, multi-omics classification, mixOmics, sparse PLS, multi-block discriminant analysis, feature selection across omics, supervised integration, sPLS-DA, metabolomics + proteomics classification.

Integrate multiple omics datasets with MOFA2 to discover shared and modality-specific latent factors driving biological variation. Use when: user wants to combine metabolomics with transcriptomics/proteomics, find multi-omics factors, identify shared variation across data types, or run unsupervised multi-omics integration. Triggers: multi-omics factors, shared variation, MOFA, MOFA2, multi-omics integration, latent factors, data modality integration, metabolomics + transcriptomics, factor analysis across omics.

Stratify patients into subtypes by fusing multi-omics similarity networks with SNF (Similarity Network Fusion). Use when: user wants patient clustering from multi-omics data, needs subtype discovery, or wants to build a unified patient similarity network from metabolomics/transcriptomics/proteomics. Triggers: patient clustering, SNF, subtype discovery, patient stratification, similarity network fusion, multi-omics clustering, disease subtyping, SNFtool, patient similarity, precision medicine stratification.

Query 40+ bioinformatics databases (KEGG, UniProt, ChEMBL, Reactome, ChEBI) from Python with a unified API via bioservices. Use when: user needs to map metabolite IDs across databases, query KEGG pathways/reactions programmatically, retrieve enzyme data from UniProt, search ChEMBL for bioactive compounds, or cross-reference identifiers. Triggers: bioservices, KEGG API, Reactome API, ChEBI lookup, UniProt query, cross-database ID mapping, metabolite ID conversion, enzyme lookup, compound-target interaction, pathway query from Python.

Build tissue-specific and condition-specific metabolic models by integrating gene expression data with GEMs using GIMME, iMAT, and INIT. Use when: user wants tissue-specific metabolism modeling, needs to constrain a metabolic model with RNA-seq data, or create context-specific FBA models. Triggers: tissue-specific metabolism, GIMME, iMAT, INIT, context-specific model, expression-constrained FBA, cell-type metabolism, condition-specific metabolic model, transcriptomics + FBA, liver/brain/cancer metabolism model.

Predict metabolic fluxes and growth rates using flux balance analysis (FBA/FVA) with COBRApy on genome-scale models. Use when: user needs FBA, wants to predict growth rate, simulate metabolic flux distributions, run flux variability analysis, or optimize metabolic objective functions. Triggers: FBA, flux balance analysis, FVA, growth rate prediction, metabolic flux, COBRApy, optimize biomass, SBML model simulation, knockout simulation, metabolic phenotype prediction, constraint-based modeling.

Predict essential genes and synthetic lethal pairs via in silico gene knockout screens using COBRApy single/double deletions. Use when: user wants to find essential genes in a metabolic model, simulate gene knockouts, identify synthetic lethality for drug targets, or screen for lethal gene pairs. Triggers: essential genes, gene knockout, synthetic lethality, gene deletion, drug target prediction, lethal gene pairs, single gene knockout, double deletion, growth phenotype prediction, essential reaction screen.

Quantify intracellular metabolic fluxes from 13C tracer experiments using isotopomer analysis and MFA solvers. Use when: user has 13C labeling data, needs natural abundance correction, measures mass isotopomer distributions (MIDs), or wants to estimate fluxes from tracer experiments. Triggers: 13C labeling, isotope tracer, MID, mass isotopomer distribution, flux measurement, 13C-MFA, IsoCor, natural abundance correction, U-13C glucose, isotope enrichment, INCA, OpenFLUX, isotopomer, metabolic flux quantification.

Build genome-scale metabolic models (GEMs) from genome sequences using CarveMe and gapseq for automated reconstruction. Use when: user wants to build a metabolic model from a genome, create a GEM for a new organism, reconstruct metabolism from FASTA/GenBank, or generate a draft SBML model. Triggers: build metabolic model, genome-scale model, CarveMe, gapseq, metabolic reconstruction, GEM, draft model, SBML generation, organism-specific model, de novo metabolic model, reconstruct metabolism.

Validate and curate genome-scale metabolic models using memote quality scores, gap filling, and SBML compliance checks. Use when: user wants to check model quality, run memote scoring, fill gaps in a metabolic model, fix dead-end metabolites, or prepare a GEM for publication. Triggers: model quality, memote score, gap filling, model curation, SBML validation, dead-end metabolites, blocked reactions, mass/charge balance, model debugging, metabolic model QC, improve draft model, annotation completeness.

Visualize metabolic networks and overlay flux data onto pathway maps using Escher, Cytoscape, KEGG API, and NetworkX. Use when: user wants to draw a metabolic pathway, create a flux map from FBA results, visualize network topology, or generate publication-ready pathway figures. Triggers: visualize pathway, flux map, Escher, metabolic network diagram, Cytoscape metabolic, KEGG pathway coloring, network visualization, pathway drawing, metabolic map, overlay fluxes, reaction graph, publication figure pathway.