# Stable Isotope Tracing Fluxomics Workflow

> 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.

- Skill: `holobiomicslab/stable-isotope-tracing-fluxomics-workflow` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add holobiomicslab/stable-isotope-tracing-fluxomics-workflow`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/stable-isotope-tracing-fluxomics-workflow/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/stable-isotope-tracing-fluxomics-workflow

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# 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`.