The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
Nucleic Acids Res. 2023 Jul 5;51(W1):W520-W527. doi: 10.1093/nar/gkad408.
Super-enhancers (SEs) play an essential regulatory role in various biological processes and diseases through their specific interaction with transcription factors (TFs). Here, we present the release of SEanalysis 2.0 (http://licpathway.net/SEanalysis), an updated version of the SEanalysis web server for the comprehensive analyses of transcriptional regulatory networks formed by SEs, pathways, TFs, and genes. The current version added mouse SEs and further expanded the scale of human SEs, documenting 1 167 518 human SEs from 1739 samples and 550 226 mouse SEs from 931 samples. The SE-related samples in SEanalysis 2.0 were more than five times that in version 1.0, which significantly improved the ability of original SE-related network analyses ('pathway downstream analysis', 'upstream regulatory analysis' and 'genomic region annotation') for understanding context-specific gene regulation. Furthermore, we designed two novel analysis models, 'TF regulatory analysis' and 'Sample comparative analysis' for supporting more comprehensive analyses of SE regulatory networks driven by TFs. Further, the risk SNPs were annotated to the SE regions to provide potential SE-related disease/trait information. Hence, we believe that SEanalysis 2.0 has significantly expanded the data and analytical capabilities of SEs, which helps researchers in an in-depth understanding of the regulatory mechanisms of SEs.
超级增强子(SEs)通过与转录因子(TFs)的特定相互作用,在各种生物过程和疾病中发挥着重要的调节作用。在这里,我们发布了 SEanalysis 2.0(http://licpathway.net/SEanalysis),这是 SEanalysis 网络服务器的更新版本,用于全面分析由 SEs、途径、TFs 和基因形成的转录调控网络。当前版本增加了小鼠 SEs,并进一步扩大了人类 SEs 的规模,记录了来自 1739 个样本的 1167518 个人类 SEs 和来自 931 个样本的 550226 个小鼠 SEs。SEanalysis 2.0 中的 SE 相关样本是版本 1.0 的五倍多,这显著提高了原始 SE 相关网络分析(“下游途径分析”、“上游调控分析”和“基因组区域注释”)的能力,有助于理解特定于上下文的基因调控。此外,我们设计了两个新的分析模型,“TF 调控分析”和“样本比较分析”,用于支持更全面的 TF 驱动的 SE 调控网络分析。此外,将风险 SNPs 注释到 SE 区域,以提供潜在的 SE 相关疾病/特征信息。因此,我们相信 SEanalysis 2.0 极大地扩展了 SE 的数据和分析能力,有助于研究人员深入了解 SE 的调控机制。
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