Center for Genomics and Systems Biology, Department of Biology, New York University, NY, USA.
USDA ARS Global Change and Photosynthesis Research Unit, Urbana, IL, USA.
Plant Physiol. 2021 Feb 25;185(1):49-66. doi: 10.1093/plphys/kiaa012.
Deciphering gene regulatory networks (GRNs) is both a promise and challenge of systems biology. The promise lies in identifying key transcription factors (TFs) that enable an organism to react to changes in its environment. The challenge lies in validating GRNs that involve hundreds of TFs with hundreds of thousands of interactions with their genome-wide targets experimentally determined by high-throughput sequencing. To address this challenge, we developed ConnecTF, a species-independent, web-based platform that integrates genome-wide studies of TF-target binding, TF-target regulation, and other TF-centric omic datasets and uses these to build and refine validated or inferred GRNs. We demonstrate the functionality of ConnecTF by showing how integration within and across TF-target datasets uncovers biological insights. Case study 1 uses integration of TF-target gene regulation and binding datasets to uncover TF mode-of-action and identify potential TF partners for 14 TFs in abscisic acid signaling. Case study 2 demonstrates how genome-wide TF-target data and automated functions in ConnecTF are used in precision/recall analysis and pruning of an inferred GRN for nitrogen signaling. Case study 3 uses ConnecTF to chart a network path from NLP7, a master TF in nitrogen signaling, to direct secondary TF2s and to its indirect targets in a Network Walking approach. The public version of ConnecTF (https://ConnecTF.org) contains 3,738,278 TF-target interactions for 423 TFs in Arabidopsis, 839,210 TF-target interactions for 139 TFs in maize (Zea mays), and 293,094 TF-target interactions for 26 TFs in rice (Oryza sativa). The database and tools in ConnecTF will advance the exploration of GRNs in plant systems biology applications for model and crop species.
解析基因调控网络(GRNs)是系统生物学的一个承诺和挑战。其承诺在于确定关键转录因子(TFs),使生物体能够对环境变化做出反应。挑战在于通过高通量测序实验确定涉及数百个 TF 和数十万与其基因组范围靶标的相互作用的 GRNs。为了解决这个挑战,我们开发了 ConnecTF,这是一个独立于物种的、基于网络的平台,它集成了 TF-靶标结合、TF-靶标调控以及其他以 TF 为中心的组学数据集的全基因组研究,并利用这些数据来构建和完善验证或推断的 GRNs。我们通过展示整合 TF-靶标数据集内部和跨数据集如何揭示生物学见解来展示 ConnecTF 的功能。案例研究 1 使用 TF-靶标基因调控和结合数据集的整合来揭示 TF 的作用模式,并为脱落酸信号中的 14 个 TF 识别潜在的 TF 伙伴。案例研究 2 演示了如何在 ConnecTF 中使用全基因组 TF-靶标数据和自动化功能进行精度/召回分析,并对氮信号的推断 GRN 进行修剪。案例研究 3 使用 ConnecTF 来绘制氮信号中的主 TF NLP7 到直接二级 TF2 及其在网络漫步方法中的间接靶标的网络路径。ConnecTF 的公共版本(https://ConnecTF.org)包含了拟南芥中 423 个 TF 的 3738278 个 TF-靶标相互作用,玉米中 139 个 TF 的 839210 个 TF-靶标相互作用,以及水稻中 26 个 TF 的 293094 个 TF-靶标相互作用。ConnecTF 中的数据库和工具将推进植物系统生物学应用中对模型和作物物种的 GRN 探索。