数字农科院2.0

EasyOmics: A graphical interface for population-scale omics data association, integration, and visualization

文献类型： 外文期刊

作者： Han, Yu;Du, Qiao;Dai, Yifei;Gu, Shaobo;Lei, Mengyu;Liu, Wei;Zhang, Wenjia;Zhu, Mingjia;Feng, Landi;Si, Huan;Liu, Jianquan;Zan, Yanjun

作者机构：

关键词： omics data;association analysis;bioinformatics;data visualization

期刊名称： PLANT COMMUNICATIONS

ISSN： 2590-3462

年卷期： 2025 年 6 卷 5 期

页码：

收录情况： SCIE（2025版） ; ; CSCD（2025-2026年度）

摘要： The rapid growth of population-scale whole-genome resequencing, RNA sequencing, bisulfite sequencing, and metabolomic and proteomic profiling has led quantitative genetics into the era of big omics data. Association analyses of omics data, such as genome-, transcriptome-, proteome-, and methylome-wide association studies, along with integrative analyses of multiple omics datasets, require various bioinformatics tools, which rely on advanced programming skills and command-line interfaces and thus pose challenges for wet-lab biologists. Here, we present EasyOmics, a stand-alone R Shiny application with a user-friendly interface that enables wet-lab biologists to perform population-scale omics data association, integration, and visualization. The toolkit incorporates multiple functions designed to meet the increasing demand for population-scale omics data analyses, including data quality control, heritability estimation, genome-wide association analysis, conditional association analysis, omics quantitative trait locus mapping, omics-wide association analysis, omics data integration, and visualization. A wide range of publication-quality graphs can be prepared in EasyOmics by pointing and clicking. EasyOmics is a platform-independent software that can be run under all operating systems, with a docker container for quick installation. It is freely available to non-commercial users at Docker Hub https://hub.docker.com/r/yuhan2000/easyomics.

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