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gcms-processing

Process GC-MS raw data into deconvolved spectra and library-matched compound annotations.

7|Updated Apr 5, 2026
Search Tags:#metabolomics#nist#deconvolution#pyopenms#matchms#gc-ms#retention-index
One-click install
npx skills add https://github.com/dailycafi/metabolism-skills --skill gcms-processing
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gcms-processing
Source: https://github.com/dailycafi/metabolism-skills/tree/main/skills/ms-data-processing/gcms-processing
Command: npx skills add https://github.com/dailycafi/metabolism-skills --skill gcms-processing

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

GC-MS metabolomics data require specialized handling: raw EI spectra need smoothing, centroiding, deconvolution, retention index normalization, and robust library matching to produce confident compound annotations across samples and instruments.

Core Features & Use Cases

  • Data import & inspection: load mzML/mzXML/CDF files and visualize TIC/EIC for quality control.
  • Peak detection & deconvolution: optimized CentWave and centroiding workflows and export of deconvolved spectra for library searching.
  • Retention index (RI) calculation: Kovats and linear RI functions to normalize retention times using alkane ladders.
  • Library matching: RI-filtered spectral matching using matchms (cosine scoring) with configurable score and matched-peak thresholds for NIST/MSP and MassBank.
  • Practical use case: process a batch of derivatized biological GC-MS samples to generate an annotated feature table suitable for downstream quantification and pathway analysis.

Quick Start

Process my GC-MS mzML file to detect peaks, calculate retention indices using the alkane ladder, deconvolve spectra, and match results against the NIST library to produce an annotated feature table.

Frequently Asked Questions about gcms-processing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I process GC-MS mzML files for peak deconvolution and NIST library matching?▼

To process GC-MS mzML files, this Skill performs peak detection, spectral deconvolution, and retention index calculation, then matches deconvolved spectra against NIST or MSP libraries to output an annotated feature table.

What is the best way to calculate retention indices for GC-MS metabolomics data?▼

Retention index calculation for GC-MS metabolomics data normalizes retention times using alkane ladders via Kovats and linear RI functions, enabling confident compound annotations across different samples and instruments.

Can I use matchms for RI-filtered spectral matching with MSP libraries?▼

Yes, RI-filtered spectral matching uses matchms with cosine scoring and configurable matched-peak thresholds to compare deconvolved spectra against NIST, MSP, and MassBank libraries.

Does this GC-MS deconvolution workflow support mzXML and CDF inputs?▼

Yes, the GC-MS deconvolution workflow supports mzML, mzXML, and CDF file formats, loading raw EI spectra for smoothing, centroiding, and peak detection before library matching.

How do I generate an annotated feature table from raw GC-MS EI spectra?▼

Annotated feature tables are generated from raw GC-MS EI spectra by performing CentWave peak detection, deconvolving spectra, applying retention index filtering, and matching against NIST libraries for compound annotations.

What are the limitations of spectral similarity scoring for volatile analysis GC-MS data?▼

Spectral similarity scoring for volatile analysis GC-MS data relies on configurable cosine scores and minimum matched-peaks thresholds, meaning low-quality deconvolved spectra or missing alkane ladders may reduce annotation confidence.

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