Ran Xiaojuan, Liu Jian, Qi Meifang, Wang Yuejun, Cheng Jingfei, Zhang Yijing
National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
University of Chinese Academy of Sciences, Beijing, China.
Front Plant Sci. 2018 Jan 24;9:23. doi: 10.3389/fpls.2018.00023. eCollection 2018.
Phytohormones regulate diverse aspects of plant growth and environmental responses. Recent high-throughput technologies have promoted a more comprehensive profiling of genes regulated by different hormones. However, these omics data generally result in large gene lists that make it challenging to interpret the data and extract insights into biological significance. With the rapid accumulation of theses large-scale experiments, especially the transcriptomic data available in public databases, a means of using this information to explore the transcriptional networks is needed. Different platforms have different architectures and designs, and even similar studies using the same platform may obtain data with large variances because of the highly dynamic and flexible effects of plant hormones; this makes it difficult to make comparisons across different studies and platforms. Here, we present a web server providing gene set-level analyses of hormone responses. GSHR collected 333 RNA-seq and 1,205 microarray datasets from the Gene Expression Omnibus, characterizing transcriptomic changes in in response to phytohormones including abscisic acid, auxin, brassinosteroids, cytokinins, ethylene, gibberellins, jasmonic acid, salicylic acid, and strigolactones. These data were further processed and organized into 1,368 gene sets regulated by different hormones or hormone-related factors. By comparing input gene lists to these gene sets, GSHR helped to identify gene sets from the input gene list regulated by different phytohormones or related factors. Together, GSHR links prior information regarding transcriptomic changes induced by hormones and related factors to newly generated data and facilities cross-study and cross-platform comparisons; this helps facilitate the mining of biologically significant information from large-scale datasets. The GSHR is freely available at http://bioinfo.sibs.ac.cn/GSHR/.
植物激素调节植物生长和环境响应的多个方面。最近的高通量技术促进了对不同激素调控基因的更全面分析。然而，这些组学数据通常会产生大量的基因列表，这使得解释数据并提取生物学意义的见解具有挑战性。随着这些大规模实验的迅速积累，特别是公共数据库中可用的转录组数据，需要一种利用这些信息来探索转录网络的方法。不同的平台有不同的架构和设计，甚至使用相同平台的类似研究也可能由于植物激素的高度动态和灵活作用而获得差异很大的数据；这使得跨不同研究和平台进行比较变得困难。在这里，我们展示了一个提供激素反应基因集水平分析的网络服务器。GSHR从基因表达综合数据库收集了333个RNA测序和1205个微阵列数据集，表征了对包括脱落酸、生长素、油菜素内酯、细胞分裂素、乙烯、赤霉素、茉莉酸、水杨酸和独脚金内酯在内的植物激素的转录组变化。这些数据被进一步处理并组织成1368个由不同激素或激素相关因子调控的基因集。通过将输入基因列表与这些基因集进行比较，GSHR有助于从输入基因列表中识别受不同植物激素或相关因子调控的基因集。总之，GSHR将关于激素和相关因子诱导的转录组变化的先验信息与新生成的数据联系起来，并促进跨研究和跨平台比较；这有助于从大规模数据集中挖掘生物学上有意义的信息。GSHR可在http://bioinfo.sibs.ac.cn/GSHR/免费获取。
