OmicsBridge: A Shiny-based Interface for Bridging Omics Datasets and Exploring Clinical Relevance

Motivation Comprehensive omics datasets are now routinely generated across diverse biological conditions, including in laboratories without dedicated bioinformatics support. When integrated and analyzed collectively, these datasets can uncover biologically meaningful relationships and molecular targets with potential clinical relevance. However, efficient integration and exploration of these datasets often require advanced computational workflows. Many existing tools are optimized for single-omics analysis or require users to re-upload datasets for each session, which becomes increasingly time-consuming and inefficient as dataset accumulate. Results We developed OmicsBridge, an intuitive and user-friendly platform for integrating and visualizing diverse omics datasets. OmicsBridge supports over 50 visualization and analysis modules, that help researchers identify genes with functional and clinical relevance and generate hypotheses. It also serves as a centralized database for efficient data storage and retrieval, reducing repetitive data uploads and improving data accessibility and workflow efficiency. As a case study, we used OmicsBridge to identify pathophysiologically relevant molecular determinants of T-cell chemoattractant CXCL10 expression, explore the underlying mechanisms, and assess their association with cancer patient outcomes through multi-omics integration. Availability and implementation OmicsBridge is freely available and platform-independent. A demo web interface is accessible at https://omicsbridge.dkfz.de, and the R/Shiny source code for local deployment is available at https://github.com/htsmto/OmicsBridge.