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Built with Google's Agent Development Kit (ADK) and deployed on Google Kubernetes Engine (GKE), this system processes millions of variants through a sophisticated multi-agent pipeline.\n\n### Key Capabilities\n\n- **🔬 Comprehensive Analysis**: Process 7.8M+ variants from whole-genome VCF files\n- **🤖 AI-Powered Insights**: Natural language interface for complex genomic queries\n- **⚡ Optimized Performance**: VEP annotation in ~60 minutes (vs 6+ hours standard)\n- **🌍 Population Context**: Integrated gnomAD frequencies across multiple ancestries\n- **📊 Clinical Assessment**: Automated pathogenicity evaluation and gene-disease associations\n- **💬 Conversational Interface**: Ask follow-up questions about specific genes instantly\n\n## ✨ Features\n\n### For Clinicians \u0026 Researchers\n- **Natural Language Processing**: Chat with your genomic data like you would with a colleague\n- **Background Processing**: Submit jobs and return later - analysis continues automatically\n- **Instant Queries**: Once processed, get answers about specific genes in seconds\n- **Population Insights**: Compare variants against global population frequencies\n- **Clinical Prioritization**: Automatic identification of pathogenic variants\n\n### For Developers \u0026 IT Teams\n- **Scalable Architecture**: Kubernetes-native design with auto-scaling\n- **Multi-Agent System**: Modular pipeline with specialized agents for each task\n- **Production Ready**: HTTPS support, authentication, and monitoring built-in\n- **Cost Optimized**: Efficient resource usage with on-demand scaling\n- **Open Source**: Fully customizable and extensible\n\n## 🏗️ Architecture\n\n![architecture](/frontend/public/architecture.png)\n\n### Technology Stack\n\n#### Frontend (`/frontend`)\n- **Framework**: Next.js 14 with App Router\n- **UI**: React + TypeScript + Tailwind CSS\n- **Components**: Shadcn/ui component library\n- **Auth**: Firebase Authentication\n- **Real-time**: Server-Sent Events (SSE)\n\n#### Backend (`/backend`)\n- **Framework**: FastAPI + Python 3.10\n- **AI/ML**: Google ADK + Gemini API\n- **Genomics**: VEP 113 + ClinVar + gnomAD\n- **Infrastructure**: GKE + Cloud Tasks + Firestore\n- **Storage**: Google Cloud Storage + BigQuery\n\n## 🚦 Quick Start\n\n### Prerequisites\n- Google Cloud Project with billing enabled\n- `gcloud` CLI installed and configured\n- Docker installed\n- Node.js 18+ and Python 3.10+\n\n### Local Development\n\n1. **Clone the repository**\n   ```bash\n   git clone https://github.com/ayoisio/variant-agents.git\n   cd variant-agents\n   ```\n\n2. **Set up the frontend**\n   ```bash\n   cd frontend\n   npm install\n   cp .env.example .env.local\n   # Configure your Firebase and API settings\n   npm run dev\n   ```\n\n3. **Set up the backend**\n   ```bash\n   cd backend\n   python -m venv venv\n   source venv/bin/activate  # On Windows: venv\\Scripts\\activate\n   pip install -r requirements.txt\n   ```\n\n4. **Access the application**\n   - Frontend: http://localhost:3000\n   - Backend API: http://localhost:8080\n\n### Production Deployment\n\nSee [backend/README.md](backend/README.md) for detailed GKE deployment instructions.\n\n## 🎯 Usage Workflow\n\n### 1. Start Analysis\n```javascript\n// Simply provide a VCF file path in natural language\n\"Please analyze gs://genomics-data/patient123.vcf\"\n\"Check gs://bucket/sample.vcf for cardiac variants\"\n```\n\n### 2. Background Processing (~60-70 min)\n- VCF parsing and validation\n- VEP annotation with consequence prediction\n- gnomAD population frequency queries\n- ClinVar pathogenicity assessment\n\n### 3. Get Results\n```javascript\n// Ask for your report when ready\n\"Is my analysis complete? Please provide the report.\"\n```\n\n### 4. Interactive Queries\n```javascript\n// Ask specific questions instantly\n\"Were any pathogenic variants found in the BRCA1 gene?\"\n\"Show me all variants with AF \u003c 0.01\"\n\"List cardiac-related findings\"\n```\n\n## 📊 Performance Metrics\n\n| Operation | Time | Throughput |\n|-----------|------|------------|\n| VCF Parsing | ~30 sec | 7.8M variants |\n| VEP Annotation | ~60 min | 130K variants/min |\n| gnomAD Query | ~30 sec | 10K variants |\n| Clinical Assessment | ~2 min | 2K pathogenic variants |\n| Gene Query | \u003c5 sec | Instant |\n\n## 🔒 Security \u0026 Compliance\n\n- **Authentication**: Firebase Authentication with JWT tokens\n- **Authorization**: Role-based access control (RBAC)\n- **Data Encryption**: TLS 1.3 in transit, AES-256 at rest\n- **Audit Logging**: Comprehensive activity tracking\n- **HIPAA Ready**: Architecture supports HIPAA compliance requirements\n\n## 📄 License\n\nThis project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.\n\n## 🙏 Acknowledgments\n\n- [Google Agent Development Kit](https://github.com/google/adk-python) for the multi-agent framework\n- [Ensembl VEP](https://www.ensembl.org/vep) for variant annotation\n- [gnomAD](https://gnomad.broadinstitute.org/) for population frequencies\n- [ClinVar](https://www.ncbi.nlm.nih.gov/clinvar/) for clinical significance\n\n## 📧 Contact\n\nFor questions, issues, or collaboration opportunities:\n- Open an [Issue](https://github.com/ayoisio/variant-agents/issues)\n- Email: ayoad@google.com\n\n---\n\n\u003cdiv align=\"center\"\u003e\n  \u003cp\u003eBuilt with ❤️ for the genomics community\u003c/p\u003e\n\u003c/div\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fayoisio%2Fvariant-agents","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fayoisio%2Fvariant-agents","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fayoisio%2Fvariant-agents/lists"}