| ---
|
| license: mit
|
| tags:
|
| - cancer-genomics
|
| - bioinformatics
|
| - graph-database
|
| - neo4j
|
| - distributed-computing
|
| - boinc
|
| - healthcare
|
| - genomics
|
| - fastq
|
| - blast
|
| - variant-calling
|
| - gdc-portal
|
| - tcga
|
| library_name: fastapi
|
| pipeline_tag: other
|
| ---
|
|
|
| # Cancer@Home v2
|
|
|
| <div align="center">
|
| <img src="https://img.shields.io/badge/version-2.0.0-blue.svg" alt="Version">
|
| <img src="https://img.shields.io/badge/license-MIT-green.svg" alt="License">
|
| <img src="https://img.shields.io/badge/python-3.8+-blue.svg" alt="Python">
|
| <img src="https://img.shields.io/badge/neo4j-5.13-brightgreen.svg" alt="Neo4j">
|
| </div>
|
|
|
| ## 🧬 Overview
|
|
|
| Cancer@Home v2 is a comprehensive distributed computing platform for cancer genomics research that combines **BOINC distributed computing**, **GDC cancer data analysis**, **sequence processing (FASTQ/BLAST)**, and **Neo4j graph visualization** into a unified, easy-to-use system.
|
|
|
| Inspired by [Cancer@Home v1](https://www.herox.com/DCx/round/516/entry/23285) and [Andrew Kamal's Neo4j Dashboard](https://medium.com/neo4j/visualize-cancer-1c80a95f5bb4), this platform makes cancer genomics research accessible, distributed, and visual.
|
|
|
| ## 🎯 Key Features
|
|
|
| - 🌐 **Interactive Web Dashboard** - Modern UI with real-time visualizations
|
| - 🔍 **Neo4j Graph Database** - Model complex gene-mutation-patient relationships
|
| - ⚡ **BOINC Integration** - Distributed computing for intensive analyses
|
| - 📊 **GraphQL API** - Flexible data querying
|
| - 🧪 **Bioinformatics Pipeline** - FASTQ processing, BLAST alignment, variant calling
|
| - 📚 **GDC Portal Integration** - Access TCGA/TARGET cancer datasets
|
| - 🚀 **Quick Setup** - Running in under 5 minutes
|
|
|
| ## 🏗️ Architecture
|
|
|
| ```
|
| ┌─────────────────────────────────────────────┐
|
| │ Web Dashboard (D3.js + Chart.js) │
|
| ├─────────────────────────────────────────────┤
|
| │ FastAPI Backend (REST + GraphQL) │
|
| ├──────┬──────┬──────┬──────┬────────────────┤
|
| │Neo4j │BOINC │ GDC │FASTQ │ BLAST/Variant │
|
| │Graph │Client│ API │ QC │ Calling │
|
| └──────┴──────┴──────┴──────┴────────────────┘
|
| ```
|
|
|
| ## 📦 Installation
|
|
|
| ### Prerequisites
|
| - Python 3.8+
|
| - Docker Desktop
|
| - 8GB RAM (16GB recommended)
|
|
|
| ### Quick Start
|
|
|
| **Windows:**
|
| ```powershell
|
| git clone https://huggingface.co/OpenPeerAI/CancerAtHomeV2
|
| cd CancerAtHomeV2
|
| .\setup.ps1
|
| python run.py
|
| ```
|
|
|
| **Linux/Mac:**
|
| ```bash
|
| git clone https://huggingface.co/OpenPeerAI/CancerAtHomeV2
|
| cd CancerAtHomeV2
|
| chmod +x setup.sh
|
| ./setup.sh
|
| python run.py
|
| ```
|
|
|
| Then open: **http://localhost:5000**
|
|
|
| ## 🚀 Usage
|
|
|
| ### Web Dashboard
|
| Access the interactive dashboard at http://localhost:5000 with:
|
| - **Dashboard Tab**: Overview statistics and mutation charts
|
| - **Neo4j Visualization**: Interactive graph of cancer relationships
|
| - **BOINC Tasks**: Submit and monitor distributed computing tasks
|
| - **GDC Data**: Browse and download cancer datasets
|
| - **Pipeline Tools**: Run FASTQ QC, BLAST, and variant calling
|
|
|
| ### GraphQL API
|
|
|
| Query cancer data at http://localhost:5000/graphql
|
|
|
| **Example: Get mutations in TP53 gene**
|
| ```graphql
|
| query {
|
| mutations(gene: "TP53") {
|
| mutation_id
|
| chromosome
|
| position
|
| consequence
|
| }
|
| }
|
| ```
|
|
|
| **Example: Get patient statistics**
|
| ```graphql
|
| query {
|
| cancerStatistics(cancer_type_id: "BRCA") {
|
| total_patients
|
| total_mutations
|
| avg_mutations_per_patient
|
| }
|
| }
|
| ```
|
|
|
| ### REST API
|
|
|
| **Database Summary:**
|
| ```bash
|
| curl http://localhost:5000/api/neo4j/summary
|
| ```
|
|
|
| **Submit BOINC Task:**
|
| ```bash
|
| curl -X POST http://localhost:5000/api/boinc/submit \
|
| -H "Content-Type: application/json" \
|
| -d '{"workunit_type": "variant_calling", "input_file": "sample.fastq"}'
|
| ```
|
|
|
| ### Python API
|
|
|
| **FASTQ Processing:**
|
| ```python
|
| from backend.pipeline import FASTQProcessor
|
|
|
| processor = FASTQProcessor()
|
| stats = processor.calculate_statistics("input.fastq")
|
| filtered = processor.quality_filter("input.fastq")
|
| ```
|
|
|
| **Variant Calling:**
|
| ```python
|
| from backend.pipeline import VariantCaller, VariantAnalyzer
|
|
|
| caller = VariantCaller()
|
| vcf_file = caller.call_variants("alignment.bam", "reference.fa")
|
| variants = caller.filter_variants(vcf_file)
|
|
|
| analyzer = VariantAnalyzer()
|
| cancer_variants = analyzer.identify_cancer_variants(variants)
|
| tmb = analyzer.calculate_mutation_burden(variants)
|
| ```
|
|
|
| **Neo4j Queries:**
|
| ```python
|
| from backend.neo4j import DatabaseManager
|
|
|
| db = DatabaseManager()
|
| query = """
|
| MATCH (g:Gene {symbol: 'TP53'})<-[:AFFECTS]-(m:Mutation)
|
| RETURN m.position, m.consequence
|
| """
|
| results = db.execute_query(query)
|
| db.close()
|
| ```
|
|
|
| ## 📊 Data Model
|
|
|
| ### Neo4j Graph Schema
|
|
|
| **Nodes:**
|
| - **Gene**: Genes with mutations (TP53, BRCA1, KRAS, etc.)
|
| - **Mutation**: Genetic variants with position and consequence
|
| - **Patient**: Individual cases with demographics
|
| - **CancerType**: Cancer classifications (BRCA, LUAD, COAD, GBM)
|
|
|
| **Relationships:**
|
| - `Gene ← AFFECTS ← Mutation`
|
| - `Patient → HAS_MUTATION → Mutation`
|
| - `Patient → DIAGNOSED_WITH → CancerType`
|
|
|
| ### Sample Data Included
|
|
|
| - **7 Genes**: TP53, BRAF, BRCA1, BRCA2, PIK3CA, KRAS, EGFR
|
| - **5 Mutations**: Cancer-associated variants
|
| - **5 Patients**: Representative TCGA cases
|
| - **4 Cancer Types**: BRCA, LUAD, COAD, GBM
|
|
|
| ## 🔧 Technology Stack
|
|
|
| - **Backend**: FastAPI, Python 3.8+
|
| - **Database**: Neo4j 5.13 (Graph Database)
|
| - **API**: GraphQL (Strawberry), REST
|
| - **Frontend**: HTML5, CSS3, JavaScript, D3.js, Chart.js
|
| - **Bioinformatics**: Biopython, BLAST+
|
| - **Data Source**: GDC Portal API (TCGA/TARGET)
|
| - **Infrastructure**: Docker, Docker Compose
|
| - **Distributed Computing**: BOINC Framework
|
|
|
| ## 📚 Documentation
|
|
|
| - [README.md](README.md) - Complete project overview
|
| - [QUICKSTART.md](QUICKSTART.md) - 5-minute setup guide
|
| - [USER_GUIDE.md](USER_GUIDE.md) - Detailed usage documentation
|
| - [GRAPHQL_EXAMPLES.md](GRAPHQL_EXAMPLES.md) - Query examples
|
| - [ARCHITECTURE.md](ARCHITECTURE.md) - System architecture
|
| - [PROJECT_SUMMARY.md](PROJECT_SUMMARY.md) - Feature overview
|
|
|
| ## 🎓 Use Cases
|
|
|
| 1. **Cancer Research**: Analyze genomics data with distributed computing
|
| 2. **Education**: Learn cancer genetics and bioinformatics
|
| 3. **Data Visualization**: Explore gene-mutation-patient relationships
|
| 4. **Pipeline Development**: Test bioinformatics workflows
|
| 5. **Graph Analytics**: Query complex biological networks
|
|
|
| ## 🔬 Supported Cancer Projects
|
|
|
| - **TCGA-BRCA**: Breast Cancer (1,098 cases)
|
| - **TCGA-LUAD**: Lung Adenocarcinoma (585 cases)
|
| - **TCGA-COAD**: Colon Adenocarcinoma (461 cases)
|
| - **TCGA-GBM**: Glioblastoma (617 cases)
|
| - **TARGET-AML**: Acute Myeloid Leukemia (238 cases)
|
|
|
| ## 📈 Bioinformatics Pipeline
|
|
|
| ### FASTQ Processing
|
| - Quality control and filtering
|
| - Adapter trimming
|
| - Statistics calculation
|
| - QC report generation
|
|
|
| ### BLAST Alignment
|
| - BLASTN for nucleotide sequences
|
| - BLASTP for protein sequences
|
| - Hit filtering by identity/e-value
|
| - Homology detection
|
|
|
| ### Variant Calling
|
| - VCF generation from alignments
|
| - Quality filtering
|
| - Cancer variant identification
|
| - Tumor mutation burden (TMB) calculation
|
|
|
| ## 🌐 Access Points
|
|
|
| - **Application**: http://localhost:5000
|
| - **API Docs**: http://localhost:5000/docs (Swagger UI)
|
| - **GraphQL**: http://localhost:5000/graphql
|
| - **Neo4j Browser**: http://localhost:7474 (neo4j/cancer123)
|
|
|
| ## 🛠️ Configuration
|
|
|
| Edit `config.yml` to customize:
|
|
|
| ```yaml
|
| neo4j:
|
| uri: "bolt://localhost:7687"
|
| password: "cancer123"
|
|
|
| gdc:
|
| download_dir: "./data/gdc"
|
| projects: ["TCGA-BRCA", "TCGA-LUAD", "TCGA-COAD"]
|
|
|
| pipeline:
|
| fastq:
|
| quality_threshold: 20
|
| min_length: 50
|
| blast:
|
| evalue: 0.001
|
| num_threads: 4
|
| ```
|
|
|
| ## 🤝 Contributing
|
|
|
| Contributions are welcome! This project is open source under the MIT License.
|
|
|
| ### Development Setup
|
| ```bash
|
| python -m venv venv
|
| source venv/bin/activate # or venv\Scripts\activate on Windows
|
| pip install -r requirements.txt
|
| pytest test_cancer_at_home.py
|
| ```
|
|
|
| ## 📄 License
|
|
|
| MIT License - See [LICENSE](LICENSE) file
|
|
|
| Copyright (c) 2025 OpenPeer AI, Riemann Computing Inc., Bleunomics, Andrew Magdy Kamal
|
|
|
| ## 🙏 Acknowledgments
|
|
|
| ### Inspiration
|
| - [Cancer@Home v1](https://www.herox.com/DCx/round/516/entry/23285) - HeroX DCx Challenge
|
| - [Andrew Kamal's Neo4j Cancer Visualization](https://medium.com/neo4j/visualize-cancer-1c80a95f5bb4)
|
|
|
| ### Data Sources
|
| - [Genomic Data Commons (GDC) Portal](https://portal.gdc.cancer.gov/)
|
| - The Cancer Genome Atlas (TCGA) Program
|
| - Therapeutically Applicable Research to Generate Effective Treatments (TARGET)
|
|
|
| ### Technologies
|
| - Neo4j Graph Database
|
| - BOINC Distributed Computing Project
|
| - Biopython Community
|
| - FastAPI Framework
|
|
|
| ## 👥 Authors
|
|
|
| - **OpenPeer AI** - Core development and architecture
|
| - **Riemann Computing Inc.** - Distributed computing integration
|
| - **Bleunomics** - Bioinformatics pipeline and genomics expertise
|
| - **Andrew Magdy Kamal** - Graph database design and visualization
|
|
|
| ## 📞 Support
|
|
|
| - **Documentation**: See project documentation files
|
| - **Issues**: Check logs in `logs/cancer_at_home.log`
|
| - **Configuration**: Review `config.yml`
|
| - **Health Check**: http://localhost:5000/api/health
|
|
|
| ## 🔮 Roadmap
|
|
|
| ### Planned Features
|
| - Machine learning for mutation prediction
|
| - Multi-omics data integration (RNA-seq, proteomics)
|
| - Survival analysis and clinical outcomes
|
| - Advanced graph algorithms (PageRank, community detection)
|
| - Cloud deployment support (AWS, Azure, GCP)
|
| - Mobile-responsive design
|
| - User authentication and authorization
|
|
|
| ## 📊 Statistics
|
|
|
| - **Lines of Code**: ~5,000+
|
| - **Modules**: 9 Python modules
|
| - **API Endpoints**: 15+ REST + GraphQL
|
| - **Documentation**: 2,500+ lines
|
| - **Setup Time**: < 5 minutes
|
| - **Sample Data**: 7 genes, 5 mutations, 5 patients
|
|
|
| ## 🎯 Citation
|
|
|
| If you use Cancer@Home v2 in your research, please cite:
|
|
|
| ```bibtex
|
| @software{cancer_at_home_v2,
|
| title = {Cancer@Home v2: Distributed Cancer Genomics Research Platform},
|
| author = {OpenPeer AI and Riemann Computing Inc. and Bleunomics and Andrew Magdy Kamal},
|
| year = {2025},
|
| url = {https://huggingface.co/OpenPeerAI/CancerAtHomeV2},
|
| license = {MIT}
|
| }
|
| ```
|
|
|
| ## 🏷️ Tags
|
|
|
| `cancer-genomics` `bioinformatics` `neo4j` `graph-database` `distributed-computing` `boinc` `fastq` `blast` `variant-calling` `gdc-portal` `tcga` `target` `graphql` `fastapi` `python` `docker` `healthcare` `precision-medicine` `computational-biology`
|
|
|
| ---
|
|
|
| **Made with ❤️ by OpenPeer AI, Riemann Computing Inc., Bleunomics, and Andrew Magdy Kamal**
|
|
|
| **For cancer research, by researchers, accessible to all.**
|
|
|
