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

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Improving code quality in computational biology is essential for research reproducibility. This study uses biological analogies to make best practices accessible, promoting reusable and repeatable scientific code.

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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.