HolobiomicsLab 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.