Building High-Confidence Gene Regulatory Networks by Integrating Validated TF-Target Gene Interactions Using ConnecTF.
Building High-Confidence Gene Regulatory Networks by Integrating Validated TF-Target Gene Interactions Using ConnecTF.
复制标题
使用 ConnecTF 集成经过验证的 TF-目标基因相互作用，构建高可信度的基因调控网络。
DOI：
10.1007/978-1-0716-3354-0_13
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Brooks,MatthewD
中科院分区：
文献类型：
--
作者：
Huang,Ji;Katari,ManpreetS;Juang,Che-Lun;Coruzzi,GloriaM;Brooks,MatthewD
目前已有多种方法用于鉴定或预测植物转录因子的靶基因。这些包括实验方法，如体内或体外TF-靶基因结合测定和用于鉴定突变体、转基因或分离的植物细胞中的调节靶的各种方法。此外，计算方法用于从调节元件或基因表达变化推断TF-靶基因相互作用。虽然这些方法中的每一种现在都已应用于来自许多物种的大量TF，但每种方法都有其自身的局限性，这就需要整合多种数据类型以构建植物中基因调控网络的最准确表示。为了使TF-靶标相互作用数据集的分析可用于更广泛的研究社区，我们开发了ConnectTF网络平台（https：//connectf.org/）。在这一章中，我们描述了如何连接TF可以被用来整合验证和预测TF-靶基因的相互作用，以剖析TF在发育和应激反应途径中的调节作用。使用我们的例子KN 1和RA 1，两种参与花组织发育的良好表征的玉米TF，我们展示了如何使用ConnectTF来（1）比较TF之间的靶基因，（2）通过组合TF结合和TF调节数据集来识别直接与间接靶标，（3）绘制和可视化TF及其下游靶标之间的网络路径，以及（4）使用经验证的TF-靶基因数据修剪推断的用户网络以获得高置信度的预测的相互作用。最后，我们提供了建立私人版本的ConnectTF的说明，使研究小组能够存储和分析自己的TF-靶基因相互作用数据集。
Many methods are now available to identify or predict the target genes of transcription factors (TFs) in plants. These include experimental approaches such as in vivo or in vitro TF-target gene-binding assays and various methods for identifying regulated targets in mutants, transgenics, or isolated plant cells. In addition, computational approaches are used to infer TF-target gene interactions from the regulatory elements or gene expression changes across treatments. While each of these approaches has now been applied to a large number of TFs from many species, each method has its own limitations which necessitates that multiple data types are integrated to build the most accurate representation of the gene regulatory networks operating in plants. To make the analyses of TF-target interaction datasets available to the broader research community, we have developed the ConnecTF web platform (https://connectf.org/). In this chapter, we describe how ConnecTF can be used to integrate validated and predicted TF-target gene interactions in order to dissect the regulatory role of TFs in developmental and stress response pathways. Using as our examples KN1 and RA1, two well-characterized maize TFs involved in developing floral tissue, we demonstrate how ConnecTF can be used to (1) compare the target genes between TFs, (2) identify direct vs. indirect targets by combining TF-binding and TF-regulation datasets, (3) chart and visualize network paths between TFs and their downstream targets, and (4) prune inferred user networks for high-confidence predicted interactions using validated TF-target gene data. Finally, we provide instructions for setting up a private version of ConnecTF that enables research groups to store and analyze their own TF-target gene interaction datasets.