Simple and Efficient Data Analysis Dissemination for Individual Laboratories.

Area of Science:

• Computational Biology
• Bioinformatics
• Scientific Reproducibility

Background:

• Scientific advancement relies on building upon previous work.
• Reproducibility in science requires transparent sharing of experimental and analytical methods.
• Current methods for sharing raw data exist, but sharing analysis methods lacks standardization.

Purpose of the Study:

• To present a simple, efficient, and widely adoptable pattern for sharing scientific analysis methods.
• To enhance the reproducibility and transparency of biological experiments, particularly those using molecular omics data.
• To provide a practical solution for individual laboratories without requiring new infrastructure or advanced computing skills.

Main Methods:

• A semistructured pattern for sharing analysis methods is proposed.
• This pattern involves using publicly accessible repositories (e.g., GitHub).
• Required components include data files, a universal input/output (I/O) script, and analysis scripts for figures and metrics.

Main Results:

• The proposed pattern facilitates the sharing of exact analysis methods as software.
• Implementation is achievable using existing software and requires no new infrastructure.
• The pattern enhances the clarity and reproducibility of connecting raw data to scientific conclusions.

Conclusions:

• Sharing exact analysis methods alongside raw data is crucial for scientific reproducibility and transparency.
• The presented pattern offers a practical and accessible solution for researchers, especially in molecular omics.
• Adoption of this pattern will strengthen the foundation of scientific progress by ensuring methods are thoroughly examinable.