Metabolomics on the platform

Metabolomics takes mass-spectrometry peak tables through a four-stage pipeline — preprocessing, metabolite annotation, differential analysis, and pathway enrichment — to tell you which small molecules change between conditions and which metabolic pathways they implicate. Reach for it when your readout is metabolites rather than genes or proteins, such as comparing treated versus control samples or finding dysregulated metabolic processes. It maps onto the same KEGG and Reactome pathways that the genomics toolkit queries at the gene level, giving you a metabolite-side view of the same biology. See what you can achieve for the full set.

Metabolomics overview

The Metabolomics toolkit provides a four-stage pipeline for mass spectrometry metabolomics data: preprocessing, annotation, differential analysis, and pathway enrichment. Each stage can be run independently or chained together for end-to-end analysis.

Architecture

User Data (raw mzML/mzXML, or a peak table CSV/TSV)
  --> Intent Detection
       |- Data preprocessing --> metabolomics_preprocess
       |    (peak detection and alignment via XCMS, MZmine, or OpenMS)
       |- Metabolite annotation --> metabolomics_annotate
       |    (m/z mass matching against HMDB, LipidMaps, or KEGG)
       |- Differential analysis --> metabolomics_differential
       |    (two-group contrast, fold change, statistical testing)
       +- Pathway enrichment --> metabolomics_pathway
            (KEGG enrichment or topology analysis)
  --> Async Modal Compute (2-30 min depending on stage)
  --> Structured Results with Visualizations

Tool inventory

Preprocessing

Preprocessing software is selectable via method (xcms, mzmine, or openms; default XCMS), with a configurable mass accuracy tolerance in ppm and an ionization polarity (positive/negative).

Annotation

Annotation matches feature m/z values against the selected database(s) (databases: hmdb, lipidmaps, kegg, or all) within a configurable mass_tolerance_ppm, with an optional list of adducts to consider. Matches are putative mass matches, not MS/MS-confirmed identifications — there is no MSI confidence tiering.

Differential analysis

Supports Welch's t-test, limma, or Wilcoxon (method, default limma) for a two-group contrast (e.g., "treated_vs_control") resolved against your sample metadata. Multiple testing correction is Benjamini-Hochberg (FDR). Results include fold changes, p-values, and adjusted p-values.

Pathway enrichment

Supports enrichment or topology analysis (analysis_type, default enrichment) against KEGG metabolic pathways. Input metabolites can be HMDB, KEGG, or PubChem identifiers (id_type, default hmdb). Organism is limited to human (hsa) or mouse (mmu).

Capabilities

Data preprocessing

Preprocessing turns a raw LC-MS run into an analysis-ready feature table: peak detection, chromatographic alignment, and gap-filling, run through XCMS, MZmine, or OpenMS. Choose the software with method and set a mass accuracy tolerance in ppm.

Metabolite identification

Annotation matches a feature table's m/z values against HMDB, LipidMaps, or KEGG within a configurable ppm tolerance, optionally restricted to a given set of adducts. Matches are putative — ranked by mass accuracy, not by MS/MS spectral confirmation.

Statistical analysis

Differential analysis compares metabolite abundances between two groups (e.g., treated vs. control) defined by a contrast string. Choose the test with method (Welch's t-test, limma, or Wilcoxon). Results include fold changes, raw p-values, and Benjamini-Hochberg-adjusted p-values.

Pathway mapping

Pathway enrichment maps a list of metabolite identifiers onto KEGG metabolic pathways to identify dysregulated biological processes. Choose enrichment (over-representation) or topology-based analysis via analysis_type.

When to use each tool

When to use other tools instead

Licensing

Citations

Wishart DS, et al. HMDB 5.0: the Human Metabolome Database for 2022. Nucleic Acids Res. 2022;50(D1):D622-D631. doi: 10.1093/nar/gkab1062

Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000;28(1):27-30. doi: 10.1093/nar/28.1.27

Chat Usage

Example Prompts

• "Preprocess my raw LC-MS mzML file with XCMS at 10 ppm mass accuracy"
• "Annotate my LC-MS features against HMDB with 5 ppm m/z tolerance"
• "Find metabolites that are significantly different between treated and control samples"
• "What metabolic pathways are enriched in my upregulated metabolites?"
• "Run differential analysis on the treated_vs_control contrast with limma"
• "Show me the pathway enrichment results for my significant HMDB metabolites"

Workflow Chaining

• Feeds into: Compound Passport (drug-likeness assessment), ADMET prediction (pharmacokinetics), KEGG/Reactome pathway visualization, Literature search (metabolite-disease associations)
• Accepts from: Data upload (CSV/TSV peak tables), Sample metadata (group labels, batch info), Genomics tools (gene-metabolite mapping), Compound search (target metabolite identification)

FAQ

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