Sustainable data analysis with Snakemake

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.