A large-scale analysis of bioinformatics code on GitHub

• Published in: PloS one Vol. 13; no. 10; p. e0205898
• Main Authors: Russell, Pamela H, Johnson, Rachel L, Ananthan, Shreyas, Harnke, Benjamin, Carlson, Nichole E
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 31-10-2018; Public Library of Science (PLoS)
• Subjects: Bibliometrics; Bioinformatics; Biology; Biology and Life Sciences; Carpentry; Community involvement; Computational Biology - trends; Computer and Information Sciences; Computer programs; Datasets as Topic; Ecology and Environmental Sciences; Encyclopedias; Engineering and Technology; Genome; genome analysis; Genomes; Humans; Informatics; Internet; MATHEMATICS AND COMPUTING; Metadata - statistics & numerical data; Popularity; programming languages; Public health; Repositories; Reproducibility; Research and Analysis Methods; Shannon index; Software; Software - statistics & numerical data; Software - supply & distribution; software development; software engineering; software tools; Source code; Sustainability; Team size; Usability; United States > US; Colorado
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0205898

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. 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. Code to generate the dataset and reproduce the analysis is provided under the MIT license at https://github.com/pamelarussell/github-bioinformatics. Data are available at https://doi.org/10.17605/OSF.IO/UWHX8.