PROJECT_SUMMARY.md

Cancer@Home v2 - Project Summary

🎯 Project Overview

Cancer@Home v2 is a comprehensive distributed computing platform for cancer genomics research that successfully integrates:

1. Distributed Computing (BOINC) - Submit and manage computationally intensive cancer research tasks
2. Cancer Data Portal (GDC) - Access and download cancer genomics datasets from TCGA and TARGET
3. Graph Database (Neo4j) - Model complex relationships between genes, mutations, patients, and cancer types
4. Bioinformatics Pipeline - Process FASTQ files, run BLAST searches, and call genetic variants
5. Interactive Dashboard - Web-based GUI with real-time visualizations and data exploration

📁 Project Structure

CancerAtHome2/
├── backend/
│   ├── api/
│   │   └── main.py                 # FastAPI application with REST & GraphQL
│   ├── boinc/
│   │   └── client.py               # BOINC distributed computing client
│   ├── gdc/
│   │   └── client.py               # GDC Portal API integration
│   ├── neo4j/
│   │   ├── db_manager.py          # Neo4j database operations
│   │   ├── graphql_schema.py      # GraphQL schema definitions
│   │   └── data_importer.py       # Sample data initialization
│   └── pipeline/
│       ├── fastq_processor.py     # FASTQ quality control
│       ├── blast_runner.py        # BLAST sequence alignment
│       └── variant_caller.py      # Genetic variant identification
├── frontend/
│   └── index.html                 # Interactive web dashboard
├── config.yml                     # Configuration file
├── docker-compose.yml             # Neo4j container setup
├── requirements.txt               # Python dependencies
├── run.py                         # Main application launcher
├── setup.ps1                      # Windows setup script
├── setup.sh                       # Linux/Mac setup script
├── README.md                      # Comprehensive documentation
├── QUICKSTART.md                  # Quick start guide
├── USER_GUIDE.md                  # Detailed user guide
├── GRAPHQL_EXAMPLES.md            # GraphQL query examples
└── LICENSE                        # MIT License

🚀 Key Features Implemented

1. Web Dashboard

• Modern UI: Clean, gradient-based design with responsive layout
• 5 Main Tabs: Dashboard, Neo4j Visualization, BOINC Tasks, GDC Data, Pipeline
• Real-time Statistics: Live data from Neo4j showing genes, mutations, patients
• Interactive Charts: Chart.js visualizations for mutation distributions
• D3.js Graph: Interactive network visualization of cancer genomics relationships

2. Neo4j Graph Database

• Node Types: Gene, Mutation, Patient, CancerType
• Relationships:
  • Gene ← AFFECTS ← Mutation
  • Patient → HAS_MUTATION → Mutation
  • Patient → DIAGNOSED_WITH → CancerType
• Sample Data: Pre-loaded with 7 genes, 5 mutations, 5 patients, 4 cancer types
• Optimized: Constraints and indexes for fast queries

3. GraphQL API

• Flexible Queries: Get genes, mutations, patients, cancer types
• Filtering: Query by gene symbol, chromosome, project ID, cancer type
• Aggregations: Mutation frequency, cancer statistics
• Playground: Interactive GraphQL explorer at /graphql

4. REST API Endpoints

• /api/health - System health check
• /api/neo4j/summary - Database statistics
• /api/neo4j/genes/{symbol} - Gene information
• /api/boinc/tasks - List BOINC tasks
• /api/boinc/submit - Submit new task
• /api/boinc/statistics - Task statistics
• /api/gdc/projects - Available cancer projects
• /api/gdc/files/{project_id} - Search GDC files
• /api/gdc/download - Download GDC data
• /api/pipeline/* - Bioinformatics pipeline endpoints

5. BOINC Integration

• Task Submission: Support for variant calling, BLAST, alignment tasks
• Status Tracking: Monitor pending, running, completed, failed tasks
• Statistics: Total tasks, completion rates, average times
• Task Manager: High-level interface for common workflows

6. GDC Data Integration

• Search API: Query files by project, data type, experimental strategy
• Download: Retrieve cancer genomics datasets
• Projects Supported: TCGA-BRCA, TCGA-LUAD, TCGA-COAD, TCGA-GBM, TARGET-AML
• Parsers: MAF, VCF, and clinical data parsing utilities

7. Bioinformatics Pipeline

• FASTQ Processing:

  • Quality filtering
  • Adapter trimming
  • Statistics calculation
  • Quality control reports

• BLAST Integration:

  • BLASTN and BLASTP support
  • XML output parsing
  • Hit filtering by identity/e-value

• Variant Calling:

  • VCF generation
  • Quality filtering
  • Variant annotation
  • Cancer variant identification
  • Tumor mutation burden calculation

🛠️ Technology Stack

• Backend: FastAPI (Python 3.8+)
• Database: Neo4j 5.13 (Graph Database)
• API: GraphQL (Strawberry), REST
• Frontend: HTML5, CSS3, JavaScript
• Visualization: D3.js, Chart.js
• Bioinformatics: Biopython
• Data Source: GDC Portal API
• Containerization: Docker, Docker Compose
• Distributed Computing: BOINC framework

📊 Sample Data Included

Genes (7)

• TP53 (Tumor protein p53)
• BRAF (B-Raf proto-oncogene)
• BRCA1, BRCA2 (Breast cancer genes)
• PIK3CA, KRAS, EGFR (Oncogenes)

Mutations (5)

• Various missense mutations in cancer-associated genes
• Includes position, reference/alternate alleles, quality scores

Patients (5)

• Representative cases from TCGA-BRCA, TCGA-LUAD, TCGA-COAD
• Demographic data, vital status

Cancer Types (4)

• Breast Cancer (BRCA)
• Lung Adenocarcinoma (LUAD)
• Colon Adenocarcinoma (COAD)
• Glioblastoma (GBM)

🎨 Design Principles

1. Simplicity: One-command setup, intuitive interface
2. Speed: Fast to install and get started (< 5 minutes)
3. Modularity: Clean separation of concerns
4. Extensibility: Easy to add new data sources and analyses
5. Visual: Rich visualizations for data exploration
6. Professional: Production-quality code with error handling

🔧 Configuration Options

All configurable via config.yml:

• Neo4j connection settings
• GDC API parameters
• BOINC server configuration
• Pipeline quality thresholds
• Output directories
• Logging levels

📖 Documentation Provided

1. README.md - Complete project overview and installation
2. QUICKSTART.md - Fast setup and first steps
3. USER_GUIDE.md - Comprehensive usage documentation
4. GRAPHQL_EXAMPLES.md - GraphQL query examples
5. Inline Code Comments - Well-documented Python modules
6. API Documentation - Auto-generated Swagger UI at /docs

🌟 Unique Features

1. All-in-One Solution: Complete stack from data acquisition to visualization
2. Graph-Based: Leverages Neo4j's power for complex relationship queries
3. Real-Time: Live dashboard updates and task monitoring
4. Research-Ready: Built for actual cancer genomics research workflows
5. Extensible: Easy to integrate additional data sources and tools
6. Educational: Great for learning cancer genomics and graph databases

🚦 Getting Started (Quick)

# Windows
.\setup.ps1
python run.py

# Linux/Mac
./setup.sh
python run.py

# Open browser
http://localhost:5000

🎯 Use Cases

1. Research: Analyze cancer genomics data with distributed computing
2. Education: Learn about cancer genetics and bioinformatics
3. Visualization: Explore gene-mutation-patient relationships
4. Data Integration: Combine multiple cancer data sources
5. Pipeline Development: Test bioinformatics workflows

🔮 Future Enhancements (Optional)

• Machine learning for mutation prediction
• Multi-omics data integration (RNA-seq, proteomics)
• Survival analysis and clinical outcomes
• Drug response prediction
• Advanced graph algorithms (PageRank, community detection)
• Real-time collaboration features
• Mobile responsive design
• Export/report generation

📝 License

MIT License - Free for academic and commercial use

🙏 Acknowledgments

Inspired by:

• Cancer@Home v1 (HeroX DCx Challenge)
• Andrew Kamal's Neo4j Cancer Visualization
• GDC Portal and TCGA Project
• BOINC Distributed Computing Framework

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Cancer@Home v2 successfully combines modern web technologies, graph databases, distributed computing, and bioinformatics tools into a cohesive platform that is both powerful and easy to use. The system is production-ready, well-documented, and designed for real-world cancer genomics research.