README.md

---
license: mit
tags:
- cancer-genomics
- bioinformatics
- graph-database
- neo4j
- distributed-computing
- boinc
- healthcare
- genomics
- fastq
- blast
- variant-calling
- gdc-portal
- tcga
library_name: cancer-at-home-v2
pipeline_tag: other
metrics:
- accuracy
- bleu
- bleurt
---

# Cancer@Home v2

A distributed computing platform for cancer genomics research, combining BOINC distributed computing, GDC cancer data analysis, sequence processing (FASTQ/BLAST), and Neo4j graph visualization.

## 🚀 Quick Start (5 minutes)

### Prerequisites
- Python 3.8+
- Docker Desktop
- 8GB RAM minimum

### Installation

1. **Clone and setup**
```bash
cd CancerAtHome2
python -m venv venv
venv\Scripts\activate  # Windows
pip install -r requirements.txt
```

2. **Start Neo4j Database**
```bash
docker-compose up -d
```

3. **Run the application**
```bash
python run.py
```

4. **Open your browser**
- Application: http://localhost:5000
- Neo4j Browser: http://localhost:7474 (username: neo4j, password: cancer123)

## 🎯 Features

### 1. **Distributed Computing (BOINC Integration)**
- Submit cancer research computational tasks
- Monitor distributed workload processing
- Real-time task status tracking

### 2. **GDC Data Integration**
- Download cancer genomics data from GDC Portal
- Support for various cancer types (TCGA, TARGET projects)
- Automatic data parsing and normalization

### 3. **Sequence Analysis Pipeline**
- FASTQ file processing
- BLAST sequence alignment
- Variant calling and annotation

### 4. **Neo4j Graph Database**
- Graph-based cancer data modeling
- Relationships: Gene → Mutation → Patient → Cancer Type
- Interactive graph visualization

### 5. **GraphQL API**
- Query cancer data flexibly
- Filter by gene, mutation, patient cohort
- Aggregate statistics

### 6. **Interactive Dashboard**
- Real-time data visualization
- Network graphs for gene interactions
- Mutation frequency charts
- Patient cohort analysis

## 📊 Architecture

```
Cancer@Home v2
│
├── Frontend (React + D3.js)
│   ├── Dashboard
│   ├── Neo4j Visualization
│   └── Task Monitor
│
├── Backend (FastAPI)
│   ├── REST API
│   ├── GraphQL Endpoint
│   └── WebSocket (real-time updates)
│
├── Data Layer
│   ├── Neo4j (Graph Database)
│   ├── BOINC Client
│   └── GDC API Client
│
└── Analysis Pipeline
    ├── FASTQ Parser
    ├── BLAST Wrapper
    └── Variant Annotator
```

## 🗂️ Project Structure

```
CancerAtHome2/
├── backend/
│   ├── api/              # FastAPI routes
│   ├── boinc/            # BOINC integration
│   ├── gdc/              # GDC data fetcher
│   ├── neo4j/            # Neo4j database layer
│   ├── pipeline/         # Bioinformatics pipeline
│   └── graphql/          # GraphQL schema
├── frontend/
│   ├── public/
│   └── src/
│       ├── components/   # React components
│       ├── views/        # Page views
│       └── api/          # API client
├── data/                 # Downloaded datasets
├── docker-compose.yml    # Neo4j container
├── requirements.txt      # Python dependencies
└── run.py               # Main entry point
```

## 🧬 Data Flow

1. **Data Ingestion**: Download cancer genomics data from GDC Portal
2. **Processing**: Run FASTQ/BLAST analysis on distributed BOINC network
3. **Storage**: Store results in Neo4j graph database
4. **Visualization**: Query and visualize via web dashboard

## 🔧 Configuration

Edit `config.yml` to customize:
- Neo4j connection settings
- GDC API parameters
- BOINC project URL
- Analysis pipeline options

## 📖 Usage Examples

### Query Mutations by Gene
```graphql
query {
  mutations(gene: "TP53") {
    id
    position
    consequence
    patients {
      cancerType
      stage
    }
  }
}
```

### Submit Analysis Task
```python
from backend.boinc import BOINCClient

client = BOINCClient()
task_id = client.submit_task(
    workunit_type="variant_calling",
    input_file="sample.fastq"
)
```

## 🤝 Inspired By

- [Cancer@Home v1](https://www.herox.com/DCx/round/516/entry/23285) - Distributed cancer research
- [Neo4j Cancer Visualization](https://medium.com/neo4j/visualize-cancer-1c80a95f5bb4) - Graph-based cancer data modeling

## 📄 License

MIT License

## 🛟 Support

For issues or questions, please open a Huggingface or GitHub issue.