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maxquant-proteomics

Automate MaxQuant proteomics analysis from raw files to statistical visualization.

298|27|Updated Feb 18, 2026
Search Tags:#data analysis#bioinformatics#mass spectrometry#proteomics#maxquant#lfq#silac
One-click install
npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill maxquant-proteomics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: maxquant-proteomics
Source: https://github.com/jaechang-hits/SciAgent-Skills/tree/main/skills/proteomics-protein-engineering/maxquant-proteomics
Command: npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill maxquant-proteomics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy, matplotlib, seaborn, statsmodels, gseapy, pymaxquant, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the complex process of analyzing mass spectrometry-based proteomics data, from raw file processing to statistical analysis and visualization, enabling researchers to identify and quantify proteins and their differential abundance.

Core Features & Use Cases

  • MaxQuant Configuration: Programmatically set up MaxQuant parameters for LFQ and SILAC experiments.
  • Data Processing Pipeline: Execute MaxQuant, parse proteinGroups.txt, filter contaminants/decoys, log2-transform, normalize, and impute missing values.
  • Statistical Analysis: Perform t-tests with FDR correction to identify differentially abundant proteins.
  • Visualization: Generate publication-quality volcano plots and heatmaps.
  • Enrichment Analysis: Perform GO and pathway enrichment using gseapy or STRING-db.
  • Use Case: A researcher has raw LC-MS/MS files from a control and treated group. They can use this Skill to run MaxQuant, perform downstream statistical analysis, and generate a list of significantly changed proteins with a volcano plot and associated pathway enrichment.

Quick Start

Configure MaxQuant parameters using the provided Python script and run the analysis pipeline.

Frequently Asked Questions about maxquant-proteomics

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

FAQPage Schema
How do I automate MaxQuant proteomics data analysis in Python?▼

You can automate MaxQuant proteomics data analysis by configuring parameters via Python scripts to process raw LC-MS/MS files, parse proteinGroups.txt, filter contaminants, log2-transform, and normalize LFQ or SILAC data.

How do I perform statistical analysis on MaxQuant LFQ data?▼

Perform statistical analysis on MaxQuant LFQ data by applying t-tests with FDR correction to identify differentially abundant proteins, followed by generating publication-quality volcano plots and heatmaps for visualization.

Do I need MaxQuant installed on Windows to run this proteomics pipeline?▼

Yes, you need MaxQuant software installed on a Windows environment to process raw mass spectrometry files, alongside Python packages like pandas, scipy, and statsmodels for downstream statistical analysis.

Can I perform GO and pathway enrichment analysis on differentially abundant proteins?▼

Yes, you can perform GO and pathway enrichment analysis on differentially abundant proteins using the integrated gseapy or STRING-db to identify significantly changed biological pathways from your MaxQuant output.

What is the best way to handle missing values in label-free quantification data?▼

The best way to handle missing values in label-free quantification data is through automated imputation after log2-transformation and normalization, ensuring accurate downstream statistical testing for differential protein abundance.

Does this proteomics pipeline support SILAC experiments as well as LFQ?▼

Yes, this proteomics pipeline supports both SILAC and LFQ experiments by programmatically setting up specific MaxQuant parameters to quantify protein abundance across different mass spectrometry labeling strategies.

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