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Lord1Egypt
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proteomics-analysis

Process MaxQuant and DIA-NN outputs to identify and quantify protein expression changes.

2|Updated Jun 4, 2026
Search Tags:#data analysis#bioinformatics#mass spectrometry#differential expression#proteomics#maxquant#dia-nn
One-click install
npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill proteomics-analysis-lord1egypt
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: proteomics-analysis
Source: https://github.com/Lord1Egypt/scientific-agent-toolkit/tree/main/scientific-skills/proteomics-analysis
Command: npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill proteomics-analysis-lord1egypt

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy, statsmodels, matplotlib, seaborn, scikit-learn, pyteomics, spectrum_utils, gseapy.

What problem does it solve?

This skill streamlines the complex, multi-step process of analyzing mass spectrometry-based proteomics data, reducing the time required to move from raw output files to biological insights.

Core Features & Use Cases

  • Data Processing: Automates the parsing and filtering of MaxQuant and DIA-NN output files.
  • Statistical Analysis: Performs differential expression analysis, missing value imputation, and FDR correction.
  • Visualization: Generates publication-quality volcano plots and clustered heatmaps for protein abundance.
  • Use Case: Researchers can use this skill to process raw protein intensity matrices, identify significantly differentially expressed proteins between control and treatment groups, and visualize the results for downstream pathway analysis.

Quick Start

Use the proteomics-analysis skill to process the proteinGroups.txt file and generate a volcano plot comparing the control and treatment conditions.

Frequently Asked Questions about proteomics-analysis

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

FAQPage Schema
How do I perform differential expression analysis on MaxQuant output?▼

Differential expression analysis on MaxQuant output is performed by parsing proteinGroups.txt files, applying statistical testing, and executing FDR correction to identify significantly changed proteins between conditions.

What is the best way to handle missing value imputation in mass spectrometry proteomics data?▼

Missing value imputation in mass spectrometry proteomics data is handled using standard scientific Python libraries like scipy and scikit-learn to estimate absent protein intensities before downstream statistical testing.

Can I generate volcano plots and heatmaps from DIA-NN proteomics output?▼

Volcano plots and clustered heatmaps can be generated from DIA-NN proteomics output using matplotlib and seaborn to visualize protein abundance differences and differential expression results.

Does this proteomics analysis workflow support pathway analysis after identifying differentially expressed proteins?▼

Proteomics analysis workflows support pathway analysis after identifying differentially expressed proteins by utilizing the gseapy dependency to map significant protein lists to biological pathways.

How do I parse and filter raw protein intensity matrices for mass spectrometry data?▼

Raw protein intensity matrices for mass spectrometry data are parsed and filtered using pandas and numpy, automating the extraction of relevant protein groups from MaxQuant and DIA-NN output files.

What statistical methods are used for FDR correction in proteomics differential expression?▼

FDR correction in proteomics differential expression is applied using the statsmodels library, adjusting p-values from statistical tests to control the false discovery rate across multiple protein comparisons.

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