FishNET: An automated relational database for zebrafish colony management

Area of Science:

• Genetics
• Developmental Biology
• Animal Models

Background:

• Zebrafish (Danio rerio) are vital models for studying genetics, development, and disease.
• Current zebrafish husbandry record-keeping is labor-intensive and lacks standardization.
• Efficient management of numerous unique zebrafish lines is a significant challenge in research facilities.

Purpose of the Study:

• To introduce FishNET, an open-source relational database for zebrafish husbandry and maintenance.
• To provide a standardized, scalable platform for managing zebrafish data across individual labs to entire institutions.
• To enhance the efficiency and accuracy of zebrafish colony management.

Main Methods:

• Development of an intuitive, open-source relational database (FishNET).
• Implementation of a "virtual facility" for remote inspection of resources.
• Integration of automated data entry, flexible query forms, and sample-linking capabilities.
• Inclusion of a shared calendar with automated reminders and reporting functions.

Main Results:

• FishNET enables remote monitoring and detailed tracking of zebrafish rooms, racks, tanks, and lines.
• Automated data entry reduces nomenclature errors and simplifies line maintenance.
• Flexible queries allow efficient retrieval of specific database records.
• Linking of biological samples with data (e.g., genotyping, images) enhances colony management.

Conclusions:

• FishNET offers an efficient solution for real-time zebrafish colony management.
• The database improves vivaria efficiency, experimental data quality control, and information accessibility.
• Its open-source and scalable architecture supports diverse research settings, from single labs to multi-institutional hubs.