Integrations

Supercharge Rat Genome Database with Relevance AI

The Rat Genome Database (RGD) is a comprehensive repository of genomic, phenotypic, and functional data for laboratory rat research.

Enhance your research capabilities with AI Agents that can automatically query, analyze, and derive insights from complex genomic datasets.

Give your AI Agents Rat Genome Database Superpowers

RGD provides extensive genomic and phenotypic data for biomedical research. Relevance AI transforms this data into actionable insights with intelligent AI Agents that can analyze, interpret, and automate research workflows.

Rapid Data Access

The agent enables instant retrieval of genomic and phenotypic data from RGD.

Automated Insights Generation

The agent analyzes data to provide actionable insights without manual intervention.

Informed Decision-Making

It empowers users to make data-driven decisions in drug development and research.

Tools

Equip AI Agents with the Rat Genome Database Tools they need

Relevance AI gives you access to RGD's comprehensive genomic and phenotypic data tools within your AI-powered workflows.

Security & Reliability

The integration leverages secure OAuth authentication to access the Rat Genome Database, with Relevance AI managing API operations for genomic data retrieval and manipulation. The system automatically handles request formatting, rate limits, and data validation for RGD endpoints.

Built-in data type conversion ensures consistent handling of genetic sequences, annotations, and phenotype data across workflows.

No training on your data

Your data remains private and is never utilized for model training purposes.

Security first

We never store anything we don’t need to. The inputs or outputs of your tools are never stored.

To get the most out of the Rat Genome Database + Relevance AI integration without writing code:
  • Use proper authentication: Ensure your OAuth credentials are correctly configured and permissions are set.
  • Structure API requests: Follow RGD's REST API patterns for consistent data retrieval and manipulation.
  • Optimize queries: Use specific endpoints and parameters to fetch only the genomic data you need.
  • Handle responses carefully: Account for different response formats when processing genetic data.
  • Monitor API usage: Stay within rate limits and implement proper error handling for reliable operations.