The editorial, written by Bruno César Feltes, Raul Santos and Hélio Veríssimo of the Center for Molecular Biology and Biotechnology, draws together a collection of seven research articles that probe the hidden fragility of computational biology. Their conclusion is uncomfortable reading for anyone who assumed that running a standard pipeline guarantees a standard answer. Even when two tools claim to perform the same task, the biological output can differ in term specificity, pathway prioritization and stringency, meaning that the choice of software is itself a scientific decision with real consequences.

One of the clearest demonstrations comes from a study that benchmarked twelve widely used over-representation analysis tools against controlled and experimental datasets. The researchers found that the tools produced markedly different biological outputs, differing in the specificity of the terms they reported, the pathways they prioritized and the stringency of their statistical thresholds. Because all twelve tools relied on the same underlying strategy and the same annotation database, the differences could not be attributed to data quality or database version. Conventional performance metrics, the authors argue, are simply inadequate for capturing how interpretable and biologically meaningful the results actually are.

The fragility extends beyond pathway analysis. A separate study examined the detection of SARS-CoV-2 variants from sequencing data and found that performance varied dramatically depending on the sequencing protocol, the variant-calling software and the primer schemes used to enrich the samples. In other words, the same virus, sequenced from the same patient, could yield different variant profiles depending on the experimental and computational choices made along the way. The authors conclude that benchmarks must be designed around the full experimental context, not just the algorithm.

Machine learning introduces its own set of pitfalls. A workflow designed to classify antimicrobial peptides, a notoriously difficult task because of the extreme diversity of these molecules, showed how protein-specific factors such as homologous sequences, structural features and evolutionary signals can systematically bias a classifier’s apparent performance. The authors present a case study in which a model trained on antimicrobial peptides achieved impressive cross-validation scores but failed to generalize to real-world data, a cautionary tale for anyone applying deep learning to biological sequences.

Software engineering itself is part of the solution. One contribution approaches bioinformatics from the perspective of workflow management, arguing that the field should borrow practices from large-scale software development, including version control, containerization, automated testing and continuous integration. The authors contend that treating pipelines as engineering artifacts, subject to the same rigor as laboratory instruments, is essential for producing results that can be reproduced years later, even as the underlying tools and databases continue to evolve.

Accessibility is another recurring theme. A theoretical framework for genomics and transcriptomics education argues that newcomers to computational biology are often overwhelmed by the sheer number of tools, databases and opinions available online. The authors propose organizing the field around the core concepts that underlie all workflows, rather than around individual software packages, so that the next generation of researchers can evaluate methods critically instead of simply following the most popular tutorial.

Perhaps the most sobering message comes from a study of antimicrobial peptide classification that examined how training data composition affects model behavior. The authors showed that the apparent performance of a classifier can be inflated by homologous sequences that leak between training and test sets, a phenomenon that is especially dangerous in biological applications where the ultimate goal is to discover genuinely novel molecules. Their findings suggest that many published benchmarks in computational biology may overstate the real-world capabilities of the models they evaluate.

Taken together, the seven studies collected in this special issue paint a picture of a field at an inflection point. The tools of bioinformatics are more powerful than ever, but the editorial argues that their outputs are only as trustworthy as the experimental context, the training data and the engineering practices behind them. As computational biology continues to expand into clinical diagnostics, drug discovery and public health, the authors contend that rigorous, context-aware benchmarking is no longer optional. It is, they conclude, the foundation upon which the reliability of the entire discipline rests.

One way to understand why benchmarking has become so central to bioinformatics is to consider how the field’s growth has changed the nature of methodological choice. A decade ago, a laboratory might have had a handful of options for aligning reads or annotating functions; today, the same task can be approached with dozens of tools, each with its own parameter space, dependencies and assumptions. The editorial frames this not as an embarrassment of riches but as a structural risk: when analytical choices multiply faster than the evidence base for choosing among them, results become contingent on decisions that are rarely reported, let alone justified. Benchmarking, in this view, is not a one-off exercise but an ongoing practice of mapping which methods behave predictably under which conditions.

The editorial is careful to distinguish benchmarking from a simple ranking exercise. A tool that achieves the highest score on a particular metric under one set of conditions may behave quite differently when dataset composition, input size, sequencing characteristics or representation strategies change. This is why the editors argue that meaningful benchmarking must be anchored to the biological question rather than to abstract performance figures. Two outputs can both be technically acceptable while differing substantially in their biological informativeness and interpretability, which means that the value of a method cannot be assessed in isolation from the research scenario in which it will be deployed. The practical implication is that benchmarking studies should define, and communicate, the domains of application within which their conclusions hold.

Several of the collected articles illustrate this principle from different angles. The study of tumor-only somatic variant calling, for instance, introduced a pipeline called Synth4bench that generates controlled synthetic datasets, allowing the authors to evaluate five widely used callers under conditions they could systematically vary. The finding that indel detection and low-allele-frequency variants remained difficult for all callers, while overall performance diverged with sequencing parameters and algorithmic design, reinforces a recurring lesson: no single tool dominates across all conditions, so matching the algorithm to the analytical objective and the sequencing configuration is itself a methodological decision that deserves explicit justification.

The contribution on reproducibility approaches the same problem from the software-engineering side. Rather than adapting established software engineering principles wholesale, the authors translate them into the specific vocabulary of workflow management systems, arguing for explicit task interfaces, a separation between the computational core of a pipeline and the components that adapt it to particular datasets, systematic validation, comprehensive testing and continuous integration. These practices are connected to FAIR-oriented guidelines for research software, which emphasize that software should be findable, accessible, interoperable and reusable in the same sense that research data are. The underlying argument is that reproducibility is not achieved at the moment of publication but engineered into the pipeline from the start, so that results can be regenerated years later even as dependencies and databases evolve.

Usability receives attention as a dimension of methodological quality that is easy to overlook. The study of novice genomic researchers combined an analysis of the literature with semi-structured interviews, and the obstacles that emerged were strikingly mundane: inadequate documentation, limited accessible guidance, installation and dependency difficulties, and inconsistent error or progress reporting. The authors translated these findings into an evaluation rubric for bioinformatics software, making the case that user-centered design is not a cosmetic concern but a determinant of whether analyses are performed correctly. A tool that cannot be installed reliably or that fails silently is, in practice, a tool whose results cannot be trusted, regardless of the sophistication of its underlying algorithms.

The educational contribution in the collection takes a complementary approach by organizing genomics and transcriptomics around their data products, analytical goals and recurring modular workflow steps rather than around individual packages. This structure is intended to give early-stage researchers a theoretical foundation that remains stable as specific tools rise and fall in popularity, which addresses the dual problem the editors identify: newcomers struggle to navigate the sea of available options, while veterans tend to fall back on the tools they already know, even as better-suited alternatives emerge.

Underlying all seven articles is a shared recognition that methodological complexity now poses barriers that scale with the field’s accessibility. As bioinformatics becomes a routine component of biological research across laboratories with widely varying computational expertise, the consequences of opaque parameterization, undocumented workflows and unvalidated defaults fall on an increasingly broad community of users. The editorial’s framing of benchmarking as a means of defining behavior, limitations and appropriate domains of application, rather than as a competition among tools, offers a vocabulary for addressing this: the goal is not to crown winners but to make the conditions under which each method performs reliably explicit, documented and testable, so that methodological choices can be defended on evidence rather than habit.

Subject of Research: Benchmarking practices and reproducibility of bioinformatics tools and workflows

Article Title: Editorial: Benchmarks and good practices in bioinformatics

Article References: Feltes, B. C., Santos, R. A. C. D., & Verli, H. (2026). Editorial: Benchmarks and good practices in bioinformatics. Frontiers in Bioinformatics, 6, Article 2005704. https://doi.org/10.3389/fbinf.2026.2005704

Image Credits: AI Generated

DOI: 10.3389/fbinf.2026.2005704

Keywords: bioinformatics, benchmarking, reproducibility, computational biology, machine learning, genomics, workflows, Frontiers in Bioinformatics, Editorial, Benchmarks, good, practices