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--- |
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license: mit |
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tags: |
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- cancer-genomics |
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- bioinformatics |
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- graph-database |
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- neo4j |
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- distributed-computing |
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- boinc |
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- healthcare |
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- genomics |
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- fastq |
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- blast |
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- variant-calling |
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- gdc-portal |
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- tcga |
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library_name: cancer-at-home-v2 |
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pipeline_tag: other |
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metrics: |
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- accuracy |
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- bleu |
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- bleurt |
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--- |
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# Cancer@Home v2 |
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A distributed computing platform for cancer genomics research, combining BOINC distributed computing, GDC cancer data analysis, sequence processing (FASTQ/BLAST), and Neo4j graph visualization. |
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## 🚀 Quick Start (5 minutes) |
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### Prerequisites |
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- Python 3.8+ |
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- Docker Desktop |
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- 8GB RAM minimum |
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### Installation |
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1. **Clone and setup** |
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```bash |
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cd CancerAtHome2 |
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python -m venv venv |
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venv\Scripts\activate # Windows |
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pip install -r requirements.txt |
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``` |
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2. **Start Neo4j Database** |
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```bash |
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docker-compose up -d |
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``` |
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3. **Run the application** |
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```bash |
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python run.py |
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``` |
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4. **Open your browser** |
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- Application: http://localhost:5000 |
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- Neo4j Browser: http://localhost:7474 (username: neo4j, password: cancer123) |
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## 🎯 Features |
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### 1. **Distributed Computing (BOINC Integration)** |
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- Submit cancer research computational tasks |
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- Monitor distributed workload processing |
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- Real-time task status tracking |
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### 2. **GDC Data Integration** |
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- Download cancer genomics data from GDC Portal |
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- Support for various cancer types (TCGA, TARGET projects) |
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- Automatic data parsing and normalization |
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### 3. **Sequence Analysis Pipeline** |
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- FASTQ file processing |
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- BLAST sequence alignment |
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- Variant calling and annotation |
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### 4. **Neo4j Graph Database** |
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- Graph-based cancer data modeling |
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- Relationships: Gene → Mutation → Patient → Cancer Type |
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- Interactive graph visualization |
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### 5. **GraphQL API** |
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- Query cancer data flexibly |
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- Filter by gene, mutation, patient cohort |
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- Aggregate statistics |
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### 6. **Interactive Dashboard** |
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- Real-time data visualization |
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- Network graphs for gene interactions |
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- Mutation frequency charts |
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- Patient cohort analysis |
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## 📊 Architecture |
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``` |
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Cancer@Home v2 |
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│ |
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├── Frontend (React + D3.js) |
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│ ├── Dashboard |
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│ ├── Neo4j Visualization |
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│ └── Task Monitor |
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│ |
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├── Backend (FastAPI) |
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│ ├── REST API |
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│ ├── GraphQL Endpoint |
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│ └── WebSocket (real-time updates) |
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│ |
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├── Data Layer |
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│ ├── Neo4j (Graph Database) |
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│ ├── BOINC Client |
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│ └── GDC API Client |
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│ |
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└── Analysis Pipeline |
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├── FASTQ Parser |
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├── BLAST Wrapper |
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└── Variant Annotator |
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``` |
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## 🗂️ Project Structure |
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``` |
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CancerAtHome2/ |
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├── backend/ |
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│ ├── api/ # FastAPI routes |
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│ ├── boinc/ # BOINC integration |
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│ ├── gdc/ # GDC data fetcher |
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│ ├── neo4j/ # Neo4j database layer |
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│ ├── pipeline/ # Bioinformatics pipeline |
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│ └── graphql/ # GraphQL schema |
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├── frontend/ |
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│ ├── public/ |
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│ └── src/ |
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│ ├── components/ # React components |
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│ ├── views/ # Page views |
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│ └── api/ # API client |
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├── data/ # Downloaded datasets |
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├── docker-compose.yml # Neo4j container |
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├── requirements.txt # Python dependencies |
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└── run.py # Main entry point |
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``` |
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## 🧬 Data Flow |
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1. **Data Ingestion**: Download cancer genomics data from GDC Portal |
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2. **Processing**: Run FASTQ/BLAST analysis on distributed BOINC network |
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3. **Storage**: Store results in Neo4j graph database |
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4. **Visualization**: Query and visualize via web dashboard |
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## 🔧 Configuration |
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Edit `config.yml` to customize: |
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- Neo4j connection settings |
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- GDC API parameters |
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- BOINC project URL |
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- Analysis pipeline options |
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## 📖 Usage Examples |
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### Query Mutations by Gene |
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```graphql |
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query { |
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mutations(gene: "TP53") { |
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id |
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position |
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consequence |
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patients { |
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cancerType |
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stage |
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} |
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} |
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} |
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``` |
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### Submit Analysis Task |
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```python |
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from backend.boinc import BOINCClient |
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client = BOINCClient() |
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task_id = client.submit_task( |
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workunit_type="variant_calling", |
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input_file="sample.fastq" |
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) |
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``` |
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## 🤝 Inspired By |
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- [Cancer@Home v1](https://www.herox.com/DCx/round/516/entry/23285) - Distributed cancer research |
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- [Neo4j Cancer Visualization](https://medium.com/neo4j/visualize-cancer-1c80a95f5bb4) - Graph-based cancer data modeling |
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## 📄 License |
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MIT License |
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## 🛟 Support |
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For issues or questions, please open a Huggingface or GitHub issue. |
