HolobiomicsLab NMR Metabolomics Profiling
Summary
End-to-end NMR metabolomics: from raw spectra to identified, quantified metabolites and group-wise statistical comparison.
When to use
Use when you have NMR metabolomics data (1D/2D spectra or FIDs) and want a quantified, identified metabolite profile — spectral preprocessing (phase/baseline/referencing, binning), metabolite identification by chemical shift, quantification, and group statistics.
When NOT to use
- The data is not NMR.
- You need a single atomic step, not the full pipeline (use the leaf skill directly via the router).
Stages
Stage 1 — preprocess_nmr
Goal: NMR spectral preprocessing (phase, baseline, referencing, binning)
EDAM operation: operation_3215
Inputs: nmr-spectrum · Outputs: feature-table, nmr-spectrum
Candidate leaf skills: nmr-spectral-preprocessing-and-phasing (primary), nmr-workflow-pipeline-execution, nmr-spectra-preprocessing, metabolite-dataset-preprocessing
Tools (primary): R, Bioconductor, MWASTools, TopSpin 3.2, Bruker Avance III 600 MHz
Other candidate tools: SAND, NMRPipe, NMRBox, PRIMA-Panel
Grounding: 3 KB(s); DOIs: 10.1021/acs.analchem.3c03078, 10.1021/acs.analchem.4c04938, 10.1093/bioinformatics/btx477
Stage 2 — identification
Goal: identify metabolites by chemical shift matching
EDAM operation: operation_3803
Inputs: feature-table · Outputs: tsv
Candidate leaf skills: metabolite-peak-assignment-from-nmr (primary), nmr-metabolite-identity-confirmation, nmr-chemical-shift-interval-matching, hmdb-metabolite-query-and-retrieval
Tools (primary): PyTorch, NumPy, Pandas, SciPy, NMRformer
Other candidate tools: R, Bioconductor, MWASTools, TopSpin 3.2, openpyxl, XlsxWriter, Python, PyQt5, Human Metabolome Database (HMDB), ROIAL-NMR
Grounding: 3 KB(s); DOIs: 10.1002/nbm.70131, 10.1021/acs.analchem.4c05632, 10.1093/bioinformatics/btx477
Stage 3 — quantification
Goal: quantify metabolites from NMR signals
EDAM operation: operation_3799
Inputs: nmr-spectrum, tsv · Outputs: tsv
Candidate leaf skills: nmr-peak-deconvolution (primary), compound-abundance-quantification-from-flow, nmr-peak-table-generation
Tools (primary): SAND, NMRPipe, NMRBox
Other candidate tools: mcfNMR, spec2csv
Grounding: 2 KB(s); DOIs: 10.1021/acs.analchem.3c03078, 10.1021/acs.analchem.4c01652
Stage 4 — statistics
Goal: differential analysis of NMR profiles (univariate; multivariate where a leaf exists)
EDAM operation: operation_3659
Inputs: tsv · Outputs: tsv
Candidate leaf skills: multiple-testing-correction-metabolomics (primary), confounder-adjustment-epidemiological-analysis
Tools (primary): MWASTools, R, Bioconductor
Grounding: 1 KB(s); DOIs: 10.1093/bioinformatics/btx477
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.