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A large-scale analysis of bioinformatics code on GitHub.

Pamela H Russell1, Rachel L Johnson1, Shreyas Ananthan2

  • 1Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, United States of America.

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This study analyzed bioinformatics software repositories, finding that code activity and community engagement correlate with scientific topics and team size. Software impact is linked to post-publication code updates, highlighting the importance of ongoing development in bioinformatics.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Software Engineering

Background:

  • The rapid growth of genomic data and bioinformatics tools necessitates an understanding of software reproducibility and usability.
  • The current state of bioinformatics software source code quality and its impact remains largely uncharacterized.
  • Assessing software development practices is crucial for advancing scientific discovery.

Purpose of the Study:

  • To investigate the state of source code in the bioinformatics community.
  • To analyze relationships between code properties, development activity, developer communities, and software impact.
  • To provide a large-scale analysis of bioinformatics code quality and community engagement.

Main Methods:

  • Curated a dataset of 1,720 bioinformatics repositories from GitHub mentions in peer-reviewed articles.
  • Included 23 high-profile repositories identified from an online bioinformatics forum.
  • Analyzed repository metadata, source code, development activity, and team dynamics using GitHub API and article metadata.

Main Results:

  • Specific scientific topics correlate with higher code development activity and community interest.
  • Dynamically typed languages dominate the general dataset, while statically typed languages are prevalent in high-profile repositories.
  • Larger developer teams are associated with greater community engagement and high-profile projects.
  • The proportion of female contributors decreases in high-profile repositories and with author list seniority.
  • Any code modification post-publication is linked to increased project impact.

Conclusions:

  • Code modifications after publication are a strong indicator of software impact in bioinformatics.
  • Developer team size and engagement are key factors in the success of bioinformatics software.
  • Understanding language type preferences in different repository types offers insights into development trends.
  • Addressing gender disparity in contributions is essential for inclusive software development in bioinformatics.