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BioMEMS: Forging New Collaborations Between Biologists and Engineers

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The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update.

Enis Afgan¹, Dannon Baker¹, Bérénice Batut²

¹Department of Biology, Johns Hopkins University, Baltimore, MD, USA.

Nucleic Acids Research

|May 24, 2018

Summary

Galaxy is a web-based platform for biomedical data analysis, enhancing accessibility, reproducibility, and communication. Recent improvements support larger datasets and over 5500 tools, with growing community engagement.

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Area of Science:

Bioinformatics
Computational Biology
Data Science

Background:

Galaxy is a widely used web-based platform for analyzing large biomedical datasets.
It addresses key challenges in data-driven biomedical science: accessibility, reproducibility, and communication of analyses.
The platform has been in development since 2005.

Purpose of the Study:

To report on substantial improvements made to the Galaxy platform over the last two years.
To highlight the growth and contributions of the Galaxy community.
To showcase the platform's enhanced capabilities for large-scale biomedical data analysis.

Main Methods:

The Galaxy team and its open-source community focused on improving the core framework, user interface, tools, and training materials.
Development efforts enabled the analysis of tens of thousands of datasets.
Community-driven initiatives led to the creation of numerous high-quality tutorials.

Main Results:

Significant enhancements to Galaxy's core framework and user interface allow for the analysis of tens of thousands of datasets.
Over 5500 tools are now available through the Galaxy ToolShed.
A substantial increase in Galaxy public servers, contributing developers, and users of the main Galaxy server was observed.

Conclusions:

Galaxy continues to evolve as a powerful and accessible platform for biomedical data analysis.
Community involvement is integral to Galaxy's ongoing development and success.
The platform is well-positioned to support the growing demands of data-driven biomedical research.