Gronning, A. G. B.; Scheele, C.

The analysis of bulk omics data, such as RNA-seq and proteomics, has enabled numerous biological discoveries. Standard analytical workflows typically comprise dimensionality reduction, group-wise statistical comparisons, functional enrichment analysis, and mapping of molecules to biological networks. Although informative, these steps are often applied independently, limiting integrative interpretation and the efficient identification of functional drivers and candidate targets. To address these limitations, we developed BioTrendFinder, an interactive web tool for exploring functional drivers in gene- and protein-level bulk omics data. BioTrendFinder employs a sample-ranking strategy to identify significant molecular trendlines that capture expression patterns across ranked sample compositions in dimensionally reduced data. These trends are integrated with statistical results, sample-group metadata and functional information from STRING and eleven bio-ontologies, enabling interactive network-based exploration and the generation of entity-ranked functional modules. BioTrendFinder's unique approach and functionalities add additional analytical dimensions to bulk omics data by facilitating the extraction of high-level information from alternative analytical perspectives. Using previously published proteomics and transcriptomics datasets, we demonstrate that BioTrendFinder supports both hypothesis-driven and exploratory investigations, enabling the prioritization of candidate molecular targets and effectively narrowing the search space for downstream validation steps.