Abstract
BACKGROUND: Existing studies have demonstrated that the integration analysis of transcriptomic and epigenomic data can be used to better understand the onset and progression of many diseases, as well as identify new diagnostic and prognostic biomarkers. However, such investigations on large-scale sequencing data remain challenging for researchers or clinicians with limited bioinformatics knowledge. RESULTS: To facilitate the interpretation of the gene regulatory landscape, we developed an R Shiny application [Linking of atac-seq to gene expression data (Linkage)] for exploring and visualizing potential cis-regulatory elements (CREs) of genes based on ATAC-seq and RNA-seq data. Linkage offers six modules to identify, annotate, and interpret potential gene regulatory elements from the whole genome step by step. Linkage currently supports the analysis of human and mouse datasets. Linkage can provide interactive visualization for the correlation between chromatin accessibility and gene expression. More than that, Linkage identifies transcription factors (TFs) that potentially drive the chromatin changes through identifying TF binding motifs within the CREs and constructing trans-regulatory networks of the target gene set. CONCLUSIONS: This powerful tool enables researchers to conduct extensive multi-omics integration analysis and generate visually appealing visualizations that effectively highlight the relationship between genes and corresponding regulatory elements. With Linkage, users can obtain publishable results and gain deeper insights into the gene regulatory landscape. AVAILABILITY AND IMPLEMENTATION: ‘Linkage’ is freely available as a Shiny web application https://xulabgdpu.org.cn/linkage. The source code and instructions for Linkage can be accessed via Github https://github.com/luoyyyy/Linkage. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-025-12115-6.