scientific-proteomics-mass-spectrometry

Automate LC-MS/MS proteomics data analysis from spectral preprocessing to differential expression.

3|1|Updated Feb 11, 2026

Search Tags:#mass-spectrometry #proteomics #ptm #lfq #silac #tmt #spectral-networking

Ready to run

npx skills add https://github.com/nahisaho/satori --skill scientific-proteomics-mass-spectrometry

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scientific-proteomics-mass-spectrometry
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-proteomics-mass-spectrometry
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-proteomics-mass-spectrometry

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides an end-to-end LC-MS/MS proteomics and metabolomics mass spectrometry data analysis pipeline, enabling spectral preprocessing, peptide/compound identification, quantification, and differential analysis in a unified workflow.

Core Features & Use Cases

End-to-end workflow covering spectral preprocessing, peptide identification, protein quantification (LFQ/TMT/SILAC/iBAQ), PTM mapping, and spectral similarity scoring.
Supports molecular networking and compound annotation via GNPS-like capabilities for expanded interpretation of mass spectrometry data.
Use Case: A proteomics study compares condition A vs B to identify differential protein expression and post-translational modification patterns across samples, with downstream pathway enrichment and visualization.

Quick Start

Run the proteomics pipeline on LC-MS/MS data using the included scripts to generate feature tables, identifications, quantifications, and differential results.

Frequently Asked Questions about scientific-proteomics-mass-spectrometry

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

FAQPage Schema

How do I analyze LC-MS/MS proteomics data for differential protein expression?▼

To analyze LC-MS/MS proteomics data for differential protein expression, you can use an end-to-end pipeline that automates spectral preprocessing, peptide identification, and protein quantification to output differential results and figures.

Can I quantify proteins using LFQ, TMT, and SILAC in a single mass spectrometry workflow?▼

Yes, you can quantify proteins using LFQ, TMT, SILAC, and iBAQ methods within a single mass spectrometry workflow that processes LC-MS/MS data to generate standard feature tables and quantifications.

How do I map post-translational modifications (PTMs) from mass spectrometry data?▼

To map post-translational modifications (PTMs) from mass spectrometry data, the workflow identifies modified peptides during spectral preprocessing and outputs the mapped PTM sites alongside differential protein expression results.

What Python libraries are needed for spectral networking and peptide identification?▼

Spectral networking and peptide identification require PyOpenMS, matchms, pandas, numpy, scipy, and networkx to process mass spectrometry data and output GraphML files for molecular networking.

Does this proteomics pipeline support GNPS-like molecular networking for compound annotation?▼

Yes, this proteomics pipeline supports GNPS-like molecular networking for compound annotation by calculating spectral similarity scores and generating network graphs for expanded interpretation of mass spectrometry data.

What output formats are generated by an automated LC-MS/MS data analysis pipeline?▼

An automated LC-MS/MS data analysis pipeline generates standard output formats including CSVs for feature tables and quantifications, GraphML for spectral networks, and figures for visualizing differential protein expression.