The five pillars of computational reproducibility: bioinformatics and beyond

Area of Science:

• Bioinformatics
• Computational Biology
• Data Science

Background:

• Computational reproducibility is crucial for scientific rigor but difficult to implement in practice.
• Existing efforts to enhance reproducibility in bioinformatics provide a foundation for new frameworks.
• The complexity of computational workflows necessitates standardized approaches.

Purpose of the Study:

• To present a practical framework, the five pillars of reproducible computational research, to address the challenges of computational reproducibility.
• To provide actionable guidelines for bioinformaticians and data analysts to improve the reproducibility of their work.
• To enhance the long-term accessibility and reusability of computational research findings.

Main Methods:

• The framework integrates five key components: literate programming, code version control and sharing, compute environment control, persistent data sharing, and comprehensive documentation.
• These pillars are designed to be implemented systematically within bioinformatics research projects.
• The approach emphasizes practical application for researchers and trainees.

Main Results:

• The proposed five pillars offer a structured methodology for achieving high levels of computational reproducibility.
• Implementation of these practices facilitates the quick and easy reproduction of research outcomes.
• The framework is adaptable and beneficial for bioinformatics data analysts and trainees.

Conclusions:

• Adopting the five pillars of reproducible computational research is essential for maintaining the integrity and reliability of scientific findings.
• This framework empowers researchers to ensure their computational work is reproducible now and in the future.
• The guide serves as a valuable resource for improving reproducibility standards in bioinformatics and related fields.