Bionitio: demonstrating and facilitating best practices for bioinformatics command-line software

Peter Georgeson; Anna Syme; Clare Sloggett; Jessica Chung; Harriet Dashnow; Michael Milton; Andrew Lonsdale; David Powell; Torsten Seemann; Bernard Pope

doi:10.1093/gigascience/giz109

Bionitio: demonstrating and facilitating best practices for bioinformatics command-line software

Gigascience. 2019 Sep 1;8(9):giz109. doi: 10.1093/gigascience/giz109.

Authors

Peter Georgeson^{1

2}, Anna Syme^{1

3}, Clare Sloggett¹, Jessica Chung¹, Harriet Dashnow^{4

5}, Michael Milton^{1

6}, Andrew Lonsdale^{4

7}, David Powell⁸, Torsten Seemann^{1

9}, Bernard Pope^{1

2

10}

Affiliations

¹ Melbourne Bioinformatics, The University of Melbourne, 187 Grattan Street, Carlton, Victoria, Australia 3053.
² Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Victorian Comprehensive Cancer Centre, 305 Grattan Street, Melbourne, Victoria, Australia 3000.
³ Royal Botanic Gardens Victoria, Birdwood Avenue, Melbourne, Victoria, Australia 3004.
⁴ Bioinformatics, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, Victoria, Australia 3052.
⁵ School of BioSciences, The University of Melbourne, Royal Parade, Parkville, Victoria, Australia 3052.
⁶ Melbourne Genomics Health Alliance, Walter and Eliza Hall Institute, 1G Royal Parade, Parkville, Victoria, Australia 3052.
⁷ ARC Centre of Excellence in Plant Cell Walls, School of BioSciences, The University of Melbourne, Royal Parade, Parkville, Victoria, Australia 3052.
⁸ Monash Bioinformatics Platform, Biomedicine Discovery Institute, Faculty of Medicine, Nursing and Health Sciences, 15 Innovation Walk, Monash University, Clayton, Victoria, Australia 3800.
⁹ Department of Microbiology and Immunology, Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street Melbourne, Victoria, Australia 3000.
¹⁰ Department of Medicine, Central Clinical School, Monash University, Clayton, Victoria, Australia 3800.

Abstract

Background: Bioinformatics software tools are often created ad hoc, frequently by people without extensive training in software development. In particular, for beginners, the barrier to entry in bioinformatics software development is high, especially if they want to adopt good programming practices. Even experienced developers do not always follow best practices. This results in the proliferation of poorer-quality bioinformatics software, leading to limited scalability and inefficient use of resources; lack of reproducibility, usability, adaptability, and interoperability; and erroneous or inaccurate results.

Findings: We have developed Bionitio, a tool that automates the process of starting new bioinformatics software projects following recommended best practices. With a single command, the user can create a new well-structured project in 1 of 12 programming languages. The resulting software is functional, carrying out a prototypical bioinformatics task, and thus serves as both a working example and a template for building new tools. Key features include command-line argument parsing, error handling, progress logging, defined exit status values, a test suite, a version number, standardized building and packaging, user documentation, code documentation, a standard open source software license, software revision control, and containerization.

Conclusions: Bionitio serves as a learning aid for beginner-to-intermediate bioinformatics programmers and provides an excellent starting point for new projects. This helps developers adopt good programming practices from the beginning of a project and encourages high-quality tools to be developed more rapidly. This also benefits users because tools are more easily installed and consistent in their usage. Bionitio is released as open source software under the MIT License and is available at https://github.com/bionitio-team/bionitio.

Keywords: best practices; bioinformatics; software development; training.

Publication types

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

MeSH terms

Computational Biology*
Software*