Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Bioinformatics Advances

DOI: 10.1093/bioadv/vbag340

Authors: Hitoshi Matsuo, Mohamed A A Mahmoud, Zhaoqing Hu, Cecilia Berzain Battioni, Chong Sun

Institutions: German Cancer Research Center, Heidelberg University, Deutsches Konsortium für Translationale Krebsforschung

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,726