license

mit

Cancer@Home v2

🧬 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 and Andrew Kamal's Neo4j Dashboard, 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:

git clone https://huggingface.co/OpenPeerAI/CancerAtHomeV2
cd CancerAtHomeV2
.\setup.ps1
python run.py

Linux/Mac:

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

query {
  mutations(gene: "TP53") {
    mutation_id
    chromosome
    position
    consequence
  }
}

Example: Get patient statistics

query {
  cancerStatistics(cancer_type_id: "BRCA") {
    total_patients
    total_mutations
    avg_mutations_per_patient
  }
}

REST API

Database Summary:

curl http://localhost:5000/api/neo4j/summary

Submit BOINC Task:

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:

from backend.pipeline import FASTQProcessor

processor = FASTQProcessor()
stats = processor.calculate_statistics("input.fastq")
filtered = processor.quality_filter("input.fastq")

Variant Calling:

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:

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 - Complete project overview
QUICKSTART.md - 5-minute setup guide
USER_GUIDE.md - Detailed usage documentation
GRAPHQL_EXAMPLES.md - Query examples
ARCHITECTURE.md - System architecture
PROJECT_SUMMARY.md - Feature overview

🎓 Use Cases

Cancer Research: Analyze genomics data with distributed computing
Education: Learn cancer genetics and bioinformatics
Data Visualization: Explore gene-mutation-patient relationships
Pipeline Development: Test bioinformatics workflows
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:

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

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 file

🙏 Acknowledgments

Inspiration

Cancer@Home v1 - HeroX DCx Challenge
Andrew Kamal's Neo4j Cancer Visualization

Data Sources

Genomic Data Commons (GDC) Portal
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:

@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.

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