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)

```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.