The increasing adoption of sequencing and omics technologies has driven a massive expansion of bioinformatics software within the biomedical field. The release of the first human reference genome in 2000 marked the beginning of a surge in bioinformatics, computational biology, and precision medicine research. Today, large-scale sequencing data can be quickly transformed into new scientific insights and data-driven decisions, impacting areas such as drug discovery, personalized medicine, clinical diagnostics, and infectious disease surveillance.
However, while high-quality genomic research relies on robust bioinformatics software, this is not always the case. Many bioinformatics tools lack the rigorous testing and maintenance standards typical of non-scientific software. Bugs that affect the accuracy of results often go unnoticed during development, and even small errors or unvalidated changes in the code can lead to serious consequences, including paper retractions, delays in drug development, or clinical misdiagnoses.
There is currently an unmet need for more reliable and robust software in the scientific community. Experimental design and data interpretation are often prioritized over validation testing of the software, even though the latter is equally critical for ensuring the integrity of research findings.
A paradigm shift is needed to integrate proper software development and validation processes into scientific research.
Fortunately, we don’t need to reinvent the wheel. Software engineering standards and best practices can be seamlessly integrated into bioinformatics software development. One of the major challenges in computational biology is data reproducibility—different software versions, operating systems, or computing environments can generate inconsistent results from the same input data. When software is no longer maintained or becomes obsolete, comparing results between past and present datasets becomes even more difficult.
By adopting automated, continuous integration and testing processes, it’s possible to evaluate result accuracy and software performance with every code change. This approach streamlines software development and routine updates, reduces the time spent on bug detection and fixes, and maximizes the time available for scientific innovation.
