HolobiomicsLab 
Stable-Isotope Tracing (isotopologue extraction -> labelling analysis)
Summary
Labelled LC-MS in, a labelling table out: isotopologue extraction, natural-abundance correction, and mass-isotopomer-distribution / enrichment analysis.
When to use
Use when you have LC-MS data from a stable-isotope (e.g. 13C / 15N) tracing experiment and want labelling / flux information — detect features, extract per-feature isotopologue distributions, correct for natural isotope abundance, and compute mass-isotopomer distributions and fractional labelling enrichment across conditions or timepoints.
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 — preprocess
Goal: raw labelled LC-MS -> aligned feature table (all isotopologues)
EDAM operation: operation_3215
Inputs: mzML · Outputs: feature-table
Candidate leaf skills: peak-detection-and-mass-alignment (primary), mass-spectral-feature-alignment, lcms-peak-detection-and-alignment, isotope-labeling-data-integration, mass-isotopologue-adduct-grouping
Tools (primary): MZmine2, Optimus, OpenMS
Other candidate tools: R, devtools, BiocManager, dplyr, tidyr, readr, stringr, tibble, purrr, ggplot2, IsoPairFinder, ISFrag, XCMS, CAMERA, MS-DIAL, Centwave, FeatureFinderMetabo, ADAP, SLAW
Grounding: 4 KB(s); DOIs: 10.1021/acs.analchem.1c01644, 10.1021/acs.analchem.1c02687, 10.1021/acs.jnatprod.7b00737, 10.1101/2021.12.05.471237v2
Stage 2 — isotopologue_extract
Goal: feature table -> per-metabolite isotopologue intensity distributions
EDAM operation: operation_3799
Inputs: feature-table · Outputs: tsv
Candidate leaf skills: isotope-labelling-feature-interpretation (primary), metabolite-feature-grouping-by-adduct-isotope, isotopologue-signature-detection
Tools (primary): geoRge, R, XCMS
Other candidate tools: khipu, Python, Asari, pandas, numpy, scipy, scikit-learn, matplotlib, MamsiStructSearch, MAMSI (MamsiStructSearch)
Grounding: 4 KB(s); DOIs: 10.1021/acs.analchem.5b03628, 10.1021/acs.analchem.5c01327, 10.1371/journal.pcbi.1011814, 10.1371/journal.pcbi.1011912
Stage 3 — natural_abundance_correction
Goal: correct isotopologue distributions for natural isotope abundance
EDAM operation: operation_3435
Inputs: tsv · Outputs: tsv
Candidate leaf skills: isotopic-impurity-accounting (primary), tracer-impurity-correction-modeling, natural-isotope-abundance-propagation, naturally-occurring-isotope-contribution-accounting, isotopologue-distribution-matrix-construction
Tools (primary): ElemCor
Other candidate tools: IsoCor, FluxFix
Grounding: 1 KB(s); DOIs: 10.1186/s12859-019-2669-9
Stage 4 — labelling_analysis
Goal: corrected distributions -> mass-isotopomer distribution / fractional enrichment
EDAM operation: operation_3799
Inputs: tsv · Outputs: tsv
Candidate leaf skills: stable-isotope-labeling-quantification (primary), fractional-abundance-transformation, metabolite-fold-change-statistical-testing
Tools (primary): R, isoSCAN, mzR, enviPat, Proteowizard MSconvert
Other candidate tools: ElemCor, geoRge, XCMS
Grounding: 3 KB(s); DOIs: 10.1021/acs.analchem.0c02998, 10.1021/acs.analchem.5b03628, 10.1186/s12859-019-2669-9
Stage 5 — report
Goal: consolidate labelling / enrichment results into a tracing report table
EDAM operation: operation_3434
Inputs: tsv · Outputs: tsv
Candidate leaf skills: metabolite-abundance-normalization-across-conditions (primary), tab-delimited-export-formatting-for-metabolomics, stable-isotope-labelling-feature-detection
Tools (primary): INTEGRATE, Agilent 1290 Infinity UHPLC system + Agilent 6550 iFunnel Q-TOF mass spectrometer, constraint-based stoichiometric metabolic models (e.g., ENGRO2)
Other candidate tools: R, rmarkdown, knitr, ggplot2, metaboprep, geoRge, XCMS
Grounding: 3 KB(s); DOIs: 10.1021/acs.analchem.5b03628, 10.1093/bioinformatics/btac059/6522114, 10.1371/journal.pcbi.1009337
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.