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Wrangling Galaxy's reference data.

Daniel Blankenberg1, James E Johnson2,

  • 1Department of Biochemistry and Molecular Biology, Penn State University, University Park, PA 16802, USA, http://www.galaxyproject.org, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA, Department of Biology and Department of Mathematics and Computer Science, Emory University, Atlanta, GA 30322, USADepartment of Biochemistry and Molecular Biology, Penn State University, University Park, PA 16802, USA, http://www.galaxyproject.org, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA, Department of Biology and Department of Mathematics and Computer Science, Emory University, Atlanta, GA 30322, USA.

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Summary
This summary is machine-generated.

The Galaxy Data Manager framework automates reference data installation, enhancing reproducibility and provenance for the Galaxy platform. This GUI-driven process simplifies data management for bioinformatics analyses.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Management

Background:

  • Galaxy is a collaborative workbench for reproducible data analysis.
  • Many Galaxy tools require reference data (e.g., alignment indexes) for efficient operation.
  • Manual installation of this reference data is error-prone and lacks reproducibility.

Purpose of the Study:

  • To introduce the Galaxy Data Manager framework.
  • To automate the management of reference data within the Galaxy platform.
  • To enhance reproducibility and provenance in Galaxy data management.

Main Methods:

  • The Galaxy Data Manager framework is implemented in Python and integrated into the core Galaxy platform.
  • Data Manager tools are GUI-driven, allowing real-time download, creation, and installation of reference datasets.
  • Tools can be defined locally or installed from a ToolShed with versioning and dependency support.

Main Results:

  • The Data Manager framework transforms reference data management from a manual to an automated process.
  • It ensures the same level of openness, reproducibility, and provenance for data management as for analysis tools.
  • Galaxy administrators can efficiently manage diverse reference datasets.

Conclusions:

  • The Galaxy Data Manager framework significantly improves the efficiency and reliability of reference data management.
  • It enhances the reproducibility and provenance of data-driven analyses in the Galaxy platform.
  • This framework empowers the Galaxy community to create and share custom Data Manager tools.