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Reproducible, scalable, and shareable analysis pipelines with bioinformatics workflow managers.

Laura Wratten1, Andreas Wilm2, Jonathan Göke3

  • 1Genome Institute of Singapore, Singapore, Singapore.

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Summary

Workflow managers are crucial for handling complex biomedical data. They enhance the scalability and reproducibility of computational analysis, making research shareable and efficient.

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Area of Science:

  • Biomedical Research
  • Computational Biology
  • Bioinformatics

Background:

  • High-throughput technologies generate vast and complex biomedical data.
  • Scalability and reproducibility are critical for computational analysis in research.
  • Data analysis requires numerous tools, parameter optimization, and dynamic reference data integration.

Purpose of the Study:

  • To highlight key features of workflow managers.
  • To compare common bioinformatics workflow approaches.
  • To guide computational and noncomputational users in adopting workflow managers.

Main Methods:

  • Review and comparison of existing workflow management systems.
  • Discussion of features supporting pipeline development, resource optimization, and software management.
  • Exploration of community-curated pipeline initiatives.

Main Results:

  • Workflow managers simplify pipeline development and execution.
  • They ensure software installation, version control, and cross-platform compatibility.
  • Community initiatives facilitate complex analyses for users of all levels.

Conclusions:

  • Workflow managers are essential for scalable and reproducible biomedical data analysis.
  • They promote sharing of computational methods and results.
  • Adoption of workflow managers enhances the efficiency and reliability of biomedical research.