HolobiomicsLab 
Pathway & Functional Analysis (m/z to Biology)
Summary
End-to-end functional analysis: turn a ranked m/z feature list into predicted pathway activity and enriched metabolite sets, even without confident structure annotations.
When to use
Use when you have an LC-MS metabolomics feature list (m/z, optionally p-values/fold changes) and want biological interpretation without prior identification — feature preparation, mummichog functional analysis from m/z, pathway/enrichment analysis, and pathway-level interpretation.
When NOT to use
- The data is not LC-MS.
- You need a single atomic step, not the full pipeline (use the leaf skill directly via the router).
Stages
Stage 1 — feature_prep
Goal: prepare a ranked m/z feature list for functional analysis
EDAM operation: operation_3435
Inputs: feature-table · Outputs: tsv
Candidate leaf skills: untargeted-metabolomics-feature-analysis (primary), metabolomics-data-quality-assessment, metabolite-feature-column-mapping, metabolomic-feature-table-assembly
Tools (primary): Mummichog 3, metDataModel, JMS, mass2chem
Other candidate tools: MetaboAnalystR, R, metabCombiner, JPA, XCMS, MS-Convert
Grounding: 4 KB(s); DOIs: 10.1021/acs.analchem.0c03693, 10.1038/s41467-024-48009-6, 10.1371/journal.pcbi.1003123, 10.3390/metabo12030212
Stage 2 — mummichog
Goal: functional analysis directly from m/z (mummichog)
EDAM operation: operation_3928
Inputs: tsv · Outputs: tsv
Candidate leaf skills: pathway-activity-propagation-inference (primary), metabolic-network-mapping, functional-module-inference-from-networks, network-based-functional-prediction, mass-feature-to-node-mapping
Tools (primary): Python, mummichog (v3), JMS, metDataModel, mass2chem
Other candidate tools: Mummichog 3, mummichog
Grounding: 1 KB(s); DOIs: 10.1371/journal.pcbi.1003123
Stage 3 — pathway_enrichment
Goal: pathway + metabolite-set enrichment
EDAM operation: operation_3928
Inputs: tsv, tsv · Outputs: tsv
Candidate leaf skills: metabolite-set-analysis (primary), metabolite-set-enrichment-analysis, comparative-enrichment-method-evaluation, untargeted-metabolomics-feature-interpretation
Tools (primary): PALS (Pathway Activity Level Scoring), PALS Viewer, ORA (Over-Representation Analysis), GSEA (Gene Set Enrichment Analysis), GNPS (Global Natural Products Social Molecular Networking), MS2LDA
Other candidate tools: R, fgsea, readr, readxl, KEGG, enrichmet, KEGGREST, igraph, Python, mummichog, metDataModel, JMS, mass2chem
Grounding: 4 KB(s); DOIs: 10.1101/2025.08.28.672951v2, 10.1186/1471-2105-6-225, 10.1371/journal.pcbi.1003123, 10.3390/metabo11020103
Stage 4 — interpretation
Goal: interpret + visualize enriched pathways
EDAM operation: operation_3659
Inputs: tsv, tsv · Outputs: tsv, html
Candidate leaf skills: pathway-metabolite-mapping-integration (primary), pathway-enrichment-visualization, metabolite-kegg-pathway-enrichment, enrichment-score-computation, metabolomic-biomarker-pathway-association
Tools (primary): R, fgsea, readr, readxl, enrichmet, KEGGREST, igraph
Other candidate tools: clusterProfiler, margheRita, ComplexHeatmap, ggplot2, KEGG_Enrich_PlotPanel, Enrichment, KEGG_Enrich_Plot, Python (pandas, NumPy, SciPy), Statistical analysis libraries (scipy.stats for enrichment tests), MetENP, pathview, SciPy (scipy.stats)
Grounding: 5 KB(s); DOIs: 10.1093/bib/bbac455, 10.1101/2020.11.20.391912, 10.1101/2024.06.20.599545, 10.1101/2024.06.20.599545v1 …
Grounding
Each stage carries the kb_slugs/dois of the leaves it draws on. Ground any stage against its source paper with the collection's /ground command or bin/perspicacite_kb_bind.py (Perspicacité KB; serverless local-clone fallback).
Verification contract
workflow.yaml is gradable by asb solve-workflow (checkpoint mode). Each stage declares typed outputs; the final stage emits the master deliverable.
Provenance
Generated by compose_workflows.py (semantic binding + EDAM-aware primary selection). derived_from_workflows lists ASB per-paper workflows whose structure corroborated this pipeline — the eval-ablation set (SPEC §8). Staging only; promote via release_gate.py.