EasyOmics: A graphical interface for population-scale omics data association, integration, and visualization. Han, Y., Du, Q., Dai, Y., Gu, S., Lei, M., Liu, W., Zhang, W., Zhu, M., Feng, L., Si, H., Liu, J., & Zan, Y. Plant Communications, 6(5):101293, May, 2025. ![EasyOmics: A graphical interface for population-scale omics data association, integration, and visualization [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{han_easyomics_2025,
title = {{EasyOmics}: {A} graphical interface for population-scale omics data association, integration, and visualization},
volume = {6},
issn = {2590-3462},
shorttitle = {{EasyOmics}},
url = {https://www.sciencedirect.com/science/article/pii/S2590346225000550},
doi = {10.1016/j.xplc.2025.101293},
abstract = {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.},
number = {5},
urldate = {2026-05-19},
journal = {Plant Communications},
author = {Han, Yu and Du, Qiao and Dai, Yifei and Gu, Shaobo and Lei, Mengyu and Liu, Wei and Zhang, Wenjia and Zhu, Mingjia and Feng, Landi and Si, Huan and Liu, Jianquan and Zan, Yanjun},
month = may,
year = {2025},
keywords = {association analysis, bioinformatics, data visualization, omics data},
pages = {101293},
}
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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. 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