Community-curated Galaxy interfaces with the Galaxy Labs Engine

Area of Science:

• Bioinformatics
• Computational Biology
• Data Science

Background:

• Galaxy is a global platform for data-intensive research with many tools.
• Decentralized nature causes usability issues in finding relevant tools and workflows.
• Existing Galaxy Flavours have limitations in deployment, replication, and UI consistency.

Purpose of the Study:

• To introduce the Galaxy Labs Engine (GLE) for creating improved, domain-specific Galaxy entry points.
• To address limitations of Galaxy Flavours by enabling synchronized, reusable web content.
• To enhance the usability and discoverability of Galaxy resources for diverse research communities.

Main Methods:

• Developed the Galaxy Labs Engine (GLE) as a service to create Galaxy Labs.
• Separated content management from deployment for flexible customization.
• Structured content for curated tools, workflows, and training resources.

Main Results:

• GLE enables globally synchronized, domain-specific Galaxy entry points (Labs).
• Labs provide consistent, customizable interfaces for researchers new to specific domains.
• The system supports FAIR principles and scalable improvements over previous Flavours.

Conclusions:

• The Galaxy Labs Engine offers a scalable solution to enhance Galaxy's usability and support for diverse research communities.
• GLE strengthens Galaxy's role in collaborative, reproducible, and user-centered scientific research.
• GLE is open-source and actively supporting user groups through deployed Labs.