MODEL_CARD.md

metadata

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

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

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:

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

Copyright (c) 2025 OpenPeer AI, Riemann Computing Inc., Bleunomics, Andrew Magdy Kamal

🙏 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

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Made with ❤️ by OpenPeer AI, Riemann Computing Inc., Bleunomics, and Andrew Magdy Kamal

For cancer research, by researchers, accessible to all.