A large-scale analysis of bioinformatics code on GitHub

Journal Article · 31 October 2018 · PLoS ONE

DOI: https://doi.org/10.1371/journal.pone.0205898 · OSTI ID:1483062

Russell, Pamela H.  ^[1]; Johnson, Rachel L. ^[1]; Ananthan, Shreyas ^[2]; Harnke, Benjamin ^[3]; Carlson, Nichole E. ^[1]; Qin, Zhaohui ^[4]

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1. Colorado School of Public Health, Aurora, CO (United States)
2. National Renewable Energy Lab. (NREL), Golden, CO (United States)
3. Univ. of Colorado Anschutz Medical Campus, Aurora, CO (United States)
4. Emory Univ., Atlanta, GA (United States)

In recent years, the explosion of genomic data and bioinformatic tools has been accompanied by a growing conversation around reproducibility of results and usability of software. However, the actual state of the body of bioinformatics software remains largely unknown. Here, the purpose of this paper is to investigate the state of source code in the bioinformatics community, specifically looking at relationships between code properties, development activity, developer communities, and software impact. To investigate these issues, we curated a list of 1,720 bioinformatics repositories on GitHub through their mention in peer-reviewed bioinformatics articles. Additionally, we included 23 high-profile repositories identified by their popularity in an online bioinformatics forum. We analyzed repository metadata, source code, development activity, and team dynamics using data made available publicly through the GitHub API, as well as article metadata. We found key relationships within our dataset, including: certain scientific topics are associated with more active code development and higher community interest in the repository; most of the code in the main dataset is written in dynamically typed languages, while most of the code in the high-profile set is statically typed; developer team size is associated with community engagement and high-profile repositories have larger teams; the proportion of female contributors decreases for high-profile repositories and with seniority level in author lists; and, multiple measures of project impact are associated with the simple variable of whether the code was modified at all after paper publication. In addition to providing the first large-scale analysis of bioinformatics code to our knowledge, our work will enable future analysis through publicly available data, code, and methods.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE)
• Grant/Contract Number: AC36-08GO28308
• OSTI ID: 1483062
• Report Number(s): NREL/JA--2C00-72836
• Journal Information: PLoS ONE, Journal Name: PLoS ONE Journal Issue: 10 Vol. 13; ISSN 1932-6203
• Publisher: Public Library of ScienceCopyright Statement
• Country of Publication: United States
• Language: English