Comprehensive analysis of metabolomics data from metabolite identification through quantification, statistical analysis, pathway interpretation, and integration with other omics layers.
| 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) |
Analyze metabolomics data including metabolite identification, quantification, pathway analysis, and metabolic flux. Processes LC-MS, GC-MS, NMR data from targeted and untargeted experiments. Performs normalization, statistical analysis, pathway enrichment, metabolite-enzyme integration, and biomarker discovery. Use when analyzing metabolomics datasets, identifying differential metabolites, studying metabolic pathways, integrating with transcriptomics/proteomics, discovering metabolic biomarkers, performing flux balance analysis, or characterizing metabolic phenotypes in disease, drug response, or physiological conditions. Source: mims-harvard/tooluniverse.