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

Jason Y Cain; Jessica S Yu; Neda Bagheri

doi:10.1016/j.cels.2022.11.006

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

Cell Syst. 2023 Jan 18;14(1):1-6. doi: 10.1016/j.cels.2022.11.006.

Authors

Jason Y Cain¹, Jessica S Yu², Neda Bagheri³

Affiliations

¹ Department of Chemical Engineering, University of Washington, Seattle, WA 98195, USA.
² Department of Biology, University of Washington, Seattle, WA 98195, USA.
³ Department of Chemical Engineering, University of Washington, Seattle, WA 98195, USA; Department of Biology, University of Washington, Seattle, WA 98195, USA. Electronic address: nbagheri@uw.edu.

PMID: 36657389
DOI: 10.1016/j.cels.2022.11.006

Abstract

"Good code" is often regarded as a nebulous, impractical ideal. Common best practices toward improving code quality can be inaccessible to those without a rigorous computer science or software engineering background, contributing to a gap between advancing scientific research and FAIR practices. We seek to equip researchers with the necessary background and context to tackle the challenge of improving code quality in computational biology research using analogies from biology to synthesize why certain best practices are critical for advancing computational research. Improving code quality requires active stewardship; we encourage researchers to deliberately adopt and share practices that ensure reusability, repeatability, and reproducibility.

Keywords: code quality; computational biology; reproducibility; reusability.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Computational Biology*
Humans
Reproducibility of Results
Research Personnel
Software*