{"title":"EasyOmics:用于群体规模 omics 数据关联、整合和可视化的图形界面。","authors":"Yu Han, Qiao Du, Yifei Dai, Shaobo Gu, Mengyu Lei, Wei Liu, Wenjia Zhang, Mingjia Zhu, Landi Feng, Huan Si, Jianquan Liu, Yanjun Zan","doi":"10.1016/j.xplc.2025.101293","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":52373,"journal":{"name":"Plant Communications","volume":" ","pages":"101293"},"PeriodicalIF":9.4000,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"EasyOmics: A graphical interface for population-scale omics data association, integration, and visualization.\",\"authors\":\"Yu Han, Qiao Du, Yifei Dai, Shaobo Gu, Mengyu Lei, Wei Liu, Wenjia Zhang, Mingjia Zhu, Landi Feng, Huan Si, Jianquan Liu, Yanjun Zan\",\"doi\":\"10.1016/j.xplc.2025.101293\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":52373,\"journal\":{\"name\":\"Plant Communications\",\"volume\":\" \",\"pages\":\"101293\"},\"PeriodicalIF\":9.4000,\"publicationDate\":\"2025-02-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Plant Communications\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1016/j.xplc.2025.101293\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Plant Communications","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.xplc.2025.101293","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
EasyOmics: A graphical interface for population-scale omics data association, integration, and visualization.
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.
期刊介绍:
Plant Communications is an open access publishing platform that supports the global plant science community. It publishes original research, review articles, technical advances, and research resources in various areas of plant sciences. The scope of topics includes evolution, ecology, physiology, biochemistry, development, reproduction, metabolism, molecular and cellular biology, genetics, genomics, environmental interactions, biotechnology, breeding of higher and lower plants, and their interactions with other organisms. The goal of Plant Communications is to provide a high-quality platform for the dissemination of plant science research.