Etalon Lab

Bio-tech platform that offers advanced DNA testing with insights into horse genetics, including breed, health risks, coat colors, and ancestry.

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Technologies Used

React
Next.js
TypeScript
TailwindCSS
C#
.NET
Entity Framework
MySQL
Docker
Kubernetes
AWS

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Introduction

Etalon Lab is a bio-tech DNA testing platform focused on equine genetics. It enables breeders, veterinarians, and researchers to gain insights into breed lineage, health predispositions, coat colors, and ancestry mapping through advanced genomic analysis.

By combining genetic science with a modern cloud platform, Etalon Lab delivers actionable insights in a secure, intuitive, and scalable system.

Key Features

  • Genetic Testing & Insights: Identify breed, detect health risks, and analyze hereditary traits through advanced DNA sequencing.
  • Coat Color & Trait Prediction: Predict coat colors and other genetic traits using validated markers.
  • Ancestry & Lineage Mapping: Visualize bloodlines and ancestry for better breeding and diversity preservation.
  • Veterinary & Breeder Portal: Secure dashboards for managing tests, viewing reports, and tracking horses over time.
  • Health Risk Reporting: Automated reports highlighting potential predispositions to guide preventive care.

Technical Insights

  • Frontend: Built with React, Next.js, and TypeScript, providing a responsive, interactive user experience. TailwindCSS ensures a clean, accessible interface for genomic reporting dashboards.
  • Backend & APIs: Powered by C# and .NET Core, exposing secure REST and GraphQL APIs. Entity Framework manages database interactions for genetic data, test results, and user records.
  • Database & Persistence: MySQL serves as the primary relational database, optimized with Entity Framework for schema management and query abstraction.
  • Bioinformatics & Data Processing: DNA analysis pipelines integrate with the backend, processing genomic datasets and applying algorithms for trait prediction and risk scoring.
  • Infrastructure & Deployment: Hosted on AWS, deployed as Dockerized microservices orchestrated with Kubernetes, enabling high availability and auto-scaling for heavy analysis workloads.

Challenges and Solutions

  • Handling Large DNA Data Files: Genomic datasets are computationally intensive. We implemented containerized bioinformatics workloads running in Kubernetes clusters for scalable parallel processing.
  • Accuracy of Genetic Predictions: Coat color and health risk assessments required high reliability. We validated results against reference datasets and applied ML models to refine predictive accuracy.
  • Data Security & Compliance: Genetic data is highly sensitive. We enforced encryption, HIPAA/GDPR compliance, and role-based access controls to protect user and animal data.
  • Integration with Veterinary Workflows: Traditional workflows made adoption challenging. We built custom APIs and exports to integrate seamlessly into veterinary and breeder management systems.
  • Scalability for Growing Demand: Increased adoption demanded elasticity. With Kubernetes auto-scaling on AWS, the platform scaled efficiently to support large spikes in DNA test requests.