proteomics-de

Automate differential expression analysis of proteomics data with Python libraries.

Updated May 10, 2026

Search Tags:#bioinformatics #differential expression #proteomics #MaxQuant #DIA-NN

Ready to run

npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill proteomics-de-mubashermohammed

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: proteomics-de
Source: https://github.com/MubasherMohammed/opencode-BioInfo/tree/main/python/skills/proteomics-de
Command: npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill proteomics-de-mubashermohammed

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib, scikit-learn, scipy, seaborn, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the analysis of proteomics differential expression from MaxQuant and DIA-NN outputs, including preprocessing, statistical testing, and visualization, saving time and improving accuracy.

Core Features & Use Cases

Proteomics Analysis: Perform differential expression analysis on LFQ proteomics data from MaxQuant and DIA-NN outputs.
Preprocessing: Preprocess data with filtering and log2 scaling.
Statistical Testing: Conduct t-tests and s0-based FDR correction.
Visualization: Generate PCA plots and volcano plots.
Use Case: Suppose you have proteomics data from two experimental groups. Use this Skill to analyze the differential expression of proteins between the two groups and identify significantly regulated proteins.

Quick Start

Run the proteomics-de skill with the MaxQuant proteinGroups.txt file and metadata file: proteomics_de.py --input proteinGroups.txt --input-type maxquant --metadata metadata.csv

Frequently Asked Questions about proteomics-de

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

FAQPage Schema

How do I analyze proteomics differential expression from MaxQuant output?▼

To analyze proteomics differential expression from MaxQuant output, you can use a Python-based workflow to preprocess LFQ intensity data, apply log2 scaling, perform statistical t-tests, and generate volcano plots.

Can I use Python for DIA-NN proteomics data analysis and visualization?▼

Yes, you can use Python for DIA-NN proteomics data analysis by loading the output tables into pandas, performing statistical testing with scipy, and visualizing protein quantification results using matplotlib and seaborn.

What statistical methods are used for protein differential expression analysis?▼

Protein differential expression analysis typically uses t-tests for statistical comparison combined with s0-based false discovery rate (FDR) correction to identify significantly regulated proteins between experimental groups.

How do I generate a volcano plot from proteinGroups.txt data?▼

To generate a volcano plot from proteinGroups.txt data, you need to preprocess the MaxQuant LFQ intensities, calculate statistical significance and fold changes, and plot the results using Python visualization libraries like matplotlib.

Does this proteomics analysis approach support PCA plotting for experimental groups?▼

Yes, this proteomics analysis approach supports PCA plotting to visualize variance between experimental groups by utilizing scikit-learn for principal component analysis and seaborn for rendering the plots.

What Python dependencies are required for bioinformatics proteomics workflows?▼

Bioinformatics proteomics workflows require Python dependencies including pandas and numpy for data manipulation, scipy and scikit-learn for statistical analysis, and matplotlib and seaborn for data visualization.