# 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) ```bash # 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 --- **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.