# Gcms Deconvolution Identification Workflow

> Use when you have GC-MS data (mzML / CDF, typically EI) and want deconvolved, retention-index-validated compound identifications — spectral deconvolution of co-eluting peaks, EI library matching, RI calibration, and differential analysis.

- Skill: `holobiomicslab/gcms-deconvolution-identification-workflow` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add holobiomicslab/gcms-deconvolution-identification-workflow`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/gcms-deconvolution-identification-workflow/raw
- Safety review: pending
- 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/gcms-deconvolution-identification-workflow

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# GC-MS Deconvolution and Identification

## Summary

End-to-end GC-MS annotation: deconvolve co-eluting EI spectra, match to GC-MS libraries with retention-index support, and compare groups.


## When to use

Use when you have GC-MS data (mzML / CDF, typically EI) and want deconvolved, retention-index-validated compound identifications — spectral deconvolution of co-eluting peaks, EI library matching, RI calibration, and differential analysis.


## When NOT to use

- The data is not GC-MS.
- You need a single atomic step, not the full pipeline (use the leaf skill directly via the router).

## Stages

### Stage 1 — deconvolution

**Goal:** GC-MS EI spectral deconvolution + peak detection

**EDAM operation:** operation_3215

**Inputs:** mzML · **Outputs:** feature-table, mgf

**Candidate leaf skills:** `gcms-spectrum-deconvolution` (primary), `gc-ms-spectral-deconvolution`, `pure-component-spectrum-extraction`, `mass-spectral-component-extraction`, `deconvolved-spectrum-comparison`

**Tools (primary):** GNPS_GC

**Other candidate tools:** MSHub, GNPS, PyTorch, Python 3, conda, GCMSFormer

**Grounding:** 2 KB(s); DOIs: 10.1021/acs.analchem.3c05772, 10.1038/s41587-020-0700-3

### Stage 2 — ei_library_match

**Goal:** identify compounds by EI spectral library matching

**EDAM operation:** operation_3631

**Inputs:** mgf · **Outputs:** tsv

**Candidate leaf skills:** `gc-ms-spectral-library-matching` (primary), `low-resolution-compound-identification`, `electron-ionization-spectral-comparison`, `spectral-similarity-scoring-ei-simple`, `spectral-library-molecular-networking`

**Tools (primary):** CoreMS, LowResMassSpectralMatch, GC_RI_Calibration, MetaMS

**Other candidate tools:** PNNLMetV20191015.MSL, mssearchr, R, NIST API, MSHub, GNPS

**Grounding:** 3 KB(s); DOIs: 10.1021/jasms.5c00322, 10.1038/s41587-020-0700-3, 10.5281/zenodo.14009575

### Stage 3 — retention_index

**Goal:** retention index calibration + RI-filtered identifications

**EDAM operation:** operation_3695

**Inputs:** tsv · **Outputs:** tsv

**Candidate leaf skills:** `mass-spectrometry-column-polarity-filtering` (primary), `retention-index-calibration-application`, `retention-index-assignment-and-filtering`, `gc-column-polarity-specific-ri-filtering`, `kovats-retention-index-extraction-and-assignment`

**Tools (primary):** mspcompiler, R, NIST

**Other candidate tools:** CoreMS, GC_RI_Calibration, LowResMassSpectralMatch, PNNLMetV20191015.MSL, future, future.apply, Lib2NIST, MS-DIAL, MoNA, RIKEN, NIST MS Search, MS Search, R statistical environment, NIST Library Installation

**Grounding:** 2 KB(s); DOIs: 10.1021/acs.analchem.2c05389, 10.5281/zenodo.14009575

### Stage 4 — statistics

**Goal:** differential GC-MS feature analysis between groups

**EDAM operation:** operation_3659

**Inputs:** feature-table, tsv · **Outputs:** tsv

**Candidate leaf skills:** `group-comparison-statistics` (primary), `gc-ms-data-preprocessing-and-normalization`, `univariate-statistical-testing-for-metabolomics`, `permanova-statistical-testing-multivariate-groups`

**Tools (primary):** LargeMetabo, Marker_Identify, e1071, FSelector, mixOmics, siggenes

**Other candidate tools:** NPFimg, XCMS, R, omu (omu_summary function), assign_hierarchy, omu_summary, omu_anova, count_fold_changes, transform_samples, MetaboDirect, vegan (R package), Python 3.8, R 4.0.2, vegan, Python

**Grounding:** 5 KB(s); DOIs: 10.1021/acs.analchem.1c03163, 10.1021/acs.analchem.1c03163?ref=, 10.1093/bib/bbac455, 10.1128/mra.00129-19 …

### Stage 5 — fusion

**Goal:** consolidate GC-MS identifications + stats into a master table

**EDAM operation:** operation_3434

**Inputs:** feature-table, tsv · **Outputs:** tsv

**Candidate leaf skills:** `compound-area-aggregation-across-samples` (primary), `mass-spectrometry-feature-grouping`, `feature-alignment-metabolomics`

**Tools (primary):** R, spreadOut(), mzExacto(), Agilent Unknowns Analysis

**Other candidate tools:** patRoon, XCMS, OpenMS, enviPick, KPIC2, Python, PFΔScreen, pyOpenMS, pandas, MsFeatures, faahKO, openNAU, MetaQC

**Grounding:** 5 KB(s); DOIs: 10.1007/s00216-023-05070-2, 10.1021/ac051437y, 10.1186/s13321-020-00477-w, 10.1371/journal.pone.0306202 …

## 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`.