Bioinformatics is an inherently interdisciplinary field that integrates biology, medicine, computer science, statistics, and related areas. While this diversity greatly enriches the field, it also introduces challenges, particularly in selecting appropriate computational pipelines, tools, and validation strategies. The rapid proliferation of software packages and biological databases makes it increasingly difficult for researchers to stay current. This highlights the need for comprehensive benchmarking studies that compare tool performance and result accuracy across different biological contexts.

Comparative analyses of pipelines, along with clear methodological guidelines, are essential for informed decision-making, reproducibility, and robust peer review. Furthermore, community-driven reviews and frameworks are vital in promoting standardization, transparency, and best practices across research and education in bioinformatics.

In this Research Topic, we welcome contributions in the following areas:

(i) Benchmarking studies that evaluate the performance of widely used tools in genomics, transcriptomics, and proteomics. Such comparisons should go beyond isolated machine learning models and focus on tools actively used by the research community, or, at the very least, be compared and validated against them.

(ii) Studies assessing the accuracy of biological information generated by various databases, computational tools, or algorithms. These studies should focus on the biological interpretability and reliability of the outputs across use cases.

(iii) Benchmarking of machine learning algorithms, with a specific emphasis on the biological validity of their predictions. Submissions should not be limited to performance metrics alone (e.g., accuracy, F1-score), but should contextualize results within relevant biological frameworks. Authors are strongly encouraged to collaborate across disciplines to enrich interpretation.

(iv) Reviews, opinion pieces, and guidelines on best practices in bioinformatics. Submissions may focus on any relevant subfield, such as omics data analysis, systems biology, data mining, artificial intelligence, macromolecular simulations, peer review, or editorial processes. Opinion articles must maintain a respectful tone and be grounded in factual evidence, particularly when addressing malpractice issues.

(v) Articles showcasing community-led efforts to improve bioinformatics teaching and dissemination across the globe. These contributions should propose broadly applicable strategies or tools and avoid solutions that require exclusive infrastructure, such as high-performance computing clusters.

(vi) Methodological articles that introduce innovative approaches to analyze, interpret, or combine existing pipelines. These studies should clearly distinguish from standard tool usage and compare alternative workflows or published protocols. Submissions should not present novel software or algorithms but focus on workflow optimization and integration.

(vii) Curated databases offering high-quality datasets designed to overcome existing limitations in the field. These databases should not merely repackage public data, but add value through new metrics, processing methods, or benchmarking insights.

Please note that the following are outside the scope of this Research Topic:

(i) The development of entirely new algorithms or software tools for biological data analysis;

(ii) Manuscripts reporting original biological data (e.g., transcriptomic, genomic, proteomic, epigenomic, or structural data). Authors may use public datasets, but must not include novel primary data collection.

Are you interested in contributing a manuscript to this Research Topic?

Benchmarks and Good Practices in Bioinformatics