We present IDEA (the Induction Dynamics gene Expression Atlas), a dataset constructed by independently inducing hundreds of transcription factors (TFs) and measuring timecourses of the resulting gene expression responses in budding yeast. Each experiment captures a regulatory cascade connecting a single induced regulator to the genes it causally regulates. We discuss the regulatory cascade of a single TF, Aft1, in detail; however, IDEA contains > 200 TF induction experiments with 20 million individual observations and 100,000 signal-containing dynamic responses. As an application of IDEA, we integrate all timecourses into a whole-cell transcriptional model, which is used to predict and validate multiple new and underappreciated transcriptional regulators. We also find that the magnitudes of coefficients in this model are predictive of genetic interaction profile similarities. In addition to being a resource for exploring regulatory connectivity between TFs and their target genes, our modeling approach shows that combining rapid perturbations of individual genes with genome-scale time-series measurements is an effective strategy for elucidating gene regulatory networks.

中文翻译：

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使用诱导型转录因子和转录组范围的时间序列学习因果网络。

我们提出了IDEA（诱导动力学基因表达图谱），该数据集是通过独立诱导数百个转录因子（TF）并测量发芽酵母中所得基因表达反应的时程而构建的。每个实验捕获一个调节级联，将单个诱导的调节子与其因果调节的基因联系起来。我们将详细讨论单个TF Aft1的监管级联。但是，IDEA包含200多个TF诱导实验，其中包含2000万个独立观察值和100,000个包含信号的动态响应。作为IDEA的应用程序，我们将所有时间过程都集成到全细胞转录模型中，该模型可用于预测和验证多个新的和未被充分认识的转录调节因子。我们还发现，该模型中系数的大小可预测遗传相互作用图谱的相似性。除了作为探索TF及其靶基因之间的调控连接性的资源之外，我们的建模方法还表明，将单个基因的快速扰动与基因组规模的时间序列测量相结合是阐明基因调控网络的有效策略。