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Colony: a framework for reproducible and easy-to-use data analysis pipelines for biomedical research with singularity
Sebastian Eschner1, Mohammad Alabdullah2, Martin Dugas1
1Institute of Medical Informatics, Heidelberg University Hospital, 69120 Heidelberg, Germany.
Bioinformatics Advances
|January 9, 2026
Summary
This study introduces a graphical user interface to make containerized bioinformatics software, like Singularity, accessible to biologists without programming experience, enhancing reproducible research.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Reproducible bioinformatics analysis requires adherence to FAIR data principles (Findability, Accessibility, Interoperability, Reusability).
- Software containers (e.g., Singularity) promote software reuse across diverse computing environments but present a steep learning curve for non-programmers.
- Many biologists lack command-line proficiency, hindering their ability to utilize powerful containerized tools effectively.
Purpose of the Study:
- To develop a user-friendly graphical interface (GUI) for interacting with containerized bioinformatics software.
- To lower the barrier to entry for biologists using command-line based tools.
- To enhance the adoption of FAIR principles in bioinformatics research through improved accessibility.
Main Methods:
- Development of a novel graphical user interface (GUI) designed for intuitive interaction with containerized applications.
- Integration of the GUI with commonly used bioinformatics software, specifically evaluating its feasibility with tools employed at the TRR156 research network.
- User testing and feasibility assessment with biologists lacking extensive programming experience.
Main Results:
- The developed GUI successfully enables biologists without prior programming experience to effectively utilize containerized bioinformatics software.
- Demonstrated feasibility and usability of the GUI with specific software relevant to the TRR156 research context.
- Positive feedback on ease of use and increased productivity compared to traditional command-line interfaces.
Conclusions:
- The GUI significantly improves accessibility and usability of containerized bioinformatics tools for researchers without coding expertise.
- This approach facilitates the implementation of FAIR data principles by making complex software more approachable.
- The tool has the potential to accelerate bioinformatics research by empowering a wider range of scientists to leverage advanced computational methods.
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