The in silico lab: Improving academic code using lessons from biology

Area of Science:

• Computational Biology
• Bioinformatics
• Scientific Computing

Background:

• "Good code" is often perceived as an abstract concept, hindering practical application.
• Lack of accessible best practices creates a divide between scientific advancement and FAIR data principles.
• Computational biology research requires robust code for reliable results.

Purpose of the Study:

• To provide researchers with the context and background needed to improve code quality in computational biology.
• To bridge the gap between complex software engineering principles and biological researchers.
• To advocate for the adoption of best practices for enhanced research integrity.

Main Methods:

• Utilizing analogies from biological systems to explain software engineering best practices.
• Synthesizing critical elements of code quality relevant to computational research.
• Developing accessible explanations for researchers without formal software engineering training.

Main Results:

• Biological analogies effectively demystify code quality principles for a broader research audience.
• Researchers can better understand the importance of reusability, repeatability, and reproducibility.
• A framework is presented to guide the adoption of better coding standards.

Conclusions:

• Active stewardship and deliberate adoption of coding best practices are crucial.
• Improved code quality directly supports the FAIR data principles (Findable, Accessible, Interoperable, Reusable).
• Sharing and implementing these practices will advance the reliability and impact of computational biology research.