Sex-based differences in different health scenarios have been thoroughly acknowledged in the literature [1, 2]; however, this variable remains incompletely analyzed in many cases. Studies often neglect sex as a variable when considering the experimental design of studies, leading to experiments with samples of just one sex in extreme cases. As a result, the underlying mechanisms behind sex-based differences in many diseases and disorders remain incompletely established.

Fortunately, the scientific community has worked to significantly improve this situation in recent times, and researchers have begun to include the sex perspective in their research; however, a vast amount of generated data currently stored in public databases [such as Gene Expression Omnibus (GEO) [3] or NCI’s Genomic Data Commons (GDC) [4]] remains unanalyzed from this perspective. The information in these databases represents a powerful resource that must be considered.

When exploiting these resources with a particular objective, multiple studies dealing with similar scientific questions can provide different and often contradictory results. No one study is likely to provide a definitive answer; therefore, integrating all datasets into a single analysis may provide the means to understand the results. Designed for this purpose, meta-analysis is a statistical methodology that considers the relative importance of multiple studies upon combining them into a single integrated analysis and extracts results based on the entirety of the evidence/samples [5,6,7]. Unfortunately, applying advanced statistical techniques such as meta-analysis often remains out of reach for researchers aiming to analyze their data in a straightforward manner.

We designed the “MetaFun” tool to simplify the analytical process and facilitate the application of functional meta-analysis to researchers working with multiple transcriptomic datasets. Meta-analysis approaches can analyze datasets from perspectives such as sex and combine datasets to gain significant statistical power and soundness. MetaFun is a complete suite that allows the analysis of transcriptomics data and the exploration of the results at all levels, performing single-dataset exploratory analysis, differential gene expression, gene set functional enrichment, and finally, combining results in a functional meta-analysis.

There are currently other suitable tools that allow meta-analysis techniques to be applied to omics data, such as MetaGenyo (https://metagenyo.genyo.es/) for the Meta-Analysis of Genetic Association Studies, or ImaGEO (https://imageo.genyo.es/) for the Integrative Meta-Analysis of GEO Data. Compared to these tools, we present Metafun as an powerful alternative due to a double potential: on the one hand, and to our knowledge, it is the only web tool capable of integrating biological functions (while tools usually focus on the meta-analysis of genes or variants). Another important aspect is that Metafun is currently the only tool that can evaluate the different functional profiles, considering sex information. Both features provide a high-performance profiling tool for integrative user analyses.