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Semantic workflows for benchmark challenges: Enhancing comparability, reusability and reproducibility.

Arunima Srivastava1, Ravali Adusumilli, Hunter Boyce

  • 1Computer Science and Engineering, The Ohio State University, 2015 Neil Ave Columbus, OH 43210, USA, srivatava.1@osu.edu.

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Summary
This summary is machine-generated.

Benchmark challenges in bioinformatics are enhanced by WINGS semantic workflows, enabling detailed comparison of methods and parameters for improved reproducibility and reuse. This approach facilitates deeper insights beyond just end-to-end performance metrics.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Scientific Workflow Systems

Background:

  • Benchmark challenges like CASP and DREAM have driven bioinformatics method development through blinded predictions.
  • Current automated challenges using Docker containers evaluate end-to-end performance but lack detailed methodological comparison.
  • Limitations include difficulty in reusing approaches, ambiguity in tools/parameters, and lack of explicit workflow definitions.

Purpose of the Study:

  • To introduce an enhanced approach for benchmark challenges using the WINGS semantic workflow system.
  • To enable comparison of methodologies and parameters, not just end-to-end performance.
  • To improve the reusability and understanding of bioinformatics workflows submitted to challenges.

Main Methods:

  • Submitting complete semantic workflows as challenge entries using the WINGS system.
  • Utilizing a component-driven workflow design with intelligent parameter and data selection based on data characteristics.
  • Leveraging a cloud-based setup for data, dependency, and workflow storage, with scalability via Pegasus for distributed computing.

Main Results:

  • WINGS enables direct comparison of challenger methodologies and parameters, offering deeper insights into performance variations.
  • Abstract workflows facilitate easy comparison and reuse of different challenge entries.
  • The system was successfully applied to the DREAM proteogenomic challenge, demonstrating its practical utility.

Conclusions:

  • The WINGS semantic workflow system significantly enhances benchmark challenges by providing a framework for detailed methodological comparison and reuse.
  • This approach addresses limitations of current systems, promoting greater transparency, reproducibility, and scientific insight in bioinformatics.
  • The cloud-based, scalable architecture ensures efficient execution and accessibility of complex bioinformatics workflows.