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Sustainable data analysis with Snakemake.

Felix Mölder1,2, Kim Philipp Jablonski3,4, Brice Letcher5

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Summary
This summary is machine-generated.

Ensuring reproducible, adaptable, and transparent data analysis is crucial. The Snakemake workflow management system facilitates these properties, improving data analysis from processing to visualization.

Keywords:
adaptabilitydata analysisreproducibilityscalabilitysustainabilitytransparencyworkflow management

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Data Science

Background:

  • Data analysis involves diverse steps, from command-line tools to scripting languages (R, Python).
  • Reproducibility is essential for validating and regenerating analysis results.
  • Reproducibility alone is insufficient for lasting impact; adaptability and transparency are also vital.

Purpose of the Study:

  • To analyze the properties required for reproducible, adaptable, and transparent data analysis.
  • To demonstrate how Snakemake can ensure these essential analysis characteristics.
  • To highlight Snakemake's role in creating a unified representation of data analysis workflows.

Main Methods:

  • Analysis of data analysis properties for reproducibility, adaptability, and transparency.
  • Utilizing the Snakemake workflow management system.
  • Demonstrating Snakemake's capability to manage heterogeneous analysis steps.

Main Results:

  • Snakemake enables reproducible, adaptable, and transparent data analysis.
  • It provides an ergonomic and unified representation of all analysis steps.
  • Facilitates data processing, quality control, and interactive result exploration.

Conclusions:

  • Snakemake is a valuable tool for enhancing the quality and impact of data analysis.
  • Achieving reproducibility, adaptability, and transparency leads to more sustainable and understandable research.
  • Integrated workflow management systems like Snakemake are key for modern scientific data analysis.