maxquant-proteomics

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.