BYU MS Core Facility - Analysis Tools

Mass Spectrometry QC and analysis tools for Brigham Young University's Fritz B. Burns Cancer Research Center MS Core Facility.

🔬 Overview

This repository contains workflows and tools for bottom-up proteomics analysis, focusing on:

• Quality control optimization for MS Core Facility operations
• Spike-in validation and fold change analysis
• Protein identification and quantification
• Data visualization for DIA-NN output

🚀 Quick Start

Prerequisites

• Python 3.10+ (Download)
• Node.js 18+ (Download) - only for web app

Installation

Automated Setup (Recommended):

.\scripts\setup_dev.ps1

Manual Setup:

# 1. Create virtual environment
python -m venv .venv
.\.venv\Scripts\Activate.ps1

# 2. Install dependencies (choose one):
pip install -r requirements.txt           # Production only
pip install -e ".[dev]"                   # With dev tools (Ruff, pytest)
pip install -e ".[dev,jupyter]"           # With Jupyter support

# 3. (Optional) Install web app frontend
cd programs/mspp_web/frontend
npm install

📊 Tools

MSPP Data Plotter (Web App)

Modern web-based interface for proteomics data visualization.

Features:

• Drag-and-drop TSV file upload
• Protein ID bar charts
• E.coli vs Yeast fold change analysis
• Organisms vs HeLa spike-in validation
• Grouped analysis with regex pattern matching
• Dark mode UI

Run:

python programs/mspp_web/launch_app.py

MSPP Data Plotter (Desktop)

Tkinter-based desktop GUI with the same analysis capabilities.

Run:

python programs/pyscripts/MSPP_data_plotter.py

FASTA Filter GUI

Tool for filtering FASTA files by organism patterns.

Run:

python programs/pyscripts/filter_fasta_gui.py

📁 Repository Structure

BYU-Core-MS-Lab/
├── programs/              # Analysis tools
│   ├── mspp_web/         # Web application (React + Flask)
│   └── pyscripts/        # Desktop GUI tools
├── tutorials/            # Workflow tutorials
├── literature/           # Reference literature
├── documentations/       # Technical documentation
├── scripts/              # Setup and utility scripts
├── requirements.txt      # Python dependencies
└── pyproject.toml        # Project metadata

🧪 Typical Workflow

1. Prepare Data: Export protein groups from DIA-NN as TSV
2. Upload Files: Use web app or desktop GUI
3. Analyze:
   • Check protein ID counts by organism
   • Validate spike-in ratios (E.coli vs Yeast)
   • Compare organisms against HeLa median
4. Export: Save plots for reporting

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Development Setup

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/

📖 Documentation

• FA Workflow Tutorial
• Web App README
• Contributing Guidelines
• Changelog

📄 License

This project is licensed under the MIT License - see LICENSE file.

🔗 Links

• GitHub: MSCoreLab/BYU-Core-MS-Lab
• BYU MS Core Facility: Fritz B. Burns Cancer Research Center

✨ Recent Updates

• ✅ Web application with React + TypeScript frontend
• ✅ Performance optimizations (5-10x faster on cached data)
• ✅ Grouped fold change analysis with pattern matching
• ✅ Dark mode UI for all visualizations

See CHANGELOG.md for full history.

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Maintained by: BYU Fritz B. Burns Cancer Research Center MS Core Facility