Innovative omics technologies, advanced bioinformatics, and machine learning methods are rapidly becoming integral tools for plant functional genomics, with tremendous recent advances made in this field. In transcriptional regulation, an initial lag in the accumulation of plant omics data relative to that of animals stimulated the development of computational methods capable of extracting maximum information from the available data sets. Recent comprehensive studies of transcription factor–binding profiles in Arabidopsis and maize and the accumulation of uniformly processed omics data in public databases have brought plant biologists into the big leagues, with many cutting-edge methods available. Here, we summarize the state-of-the-art bioinformatics approaches used to predict or infer the cis-regulatory code behind transcriptional gene regulation, focusing on their plant research applications.

创新的组学技术、先进的生物信息学和机器学习方法正迅速成为植物功能基因组学不可或缺的工具，该领域最近取得了巨大进展。在转录调控中，植物组学数据积累相对于动物的初始滞后刺激了能够从可用数据集中提取最大信息的计算方法的发展。最近对拟南芥和玉米中转录因子结合谱的综合研究以及公共数据库中统一处理的组学数据的积累使植物生物学家进入了大联盟，并提供了许多可用的尖端方法。在这里，我们总结了用于预测或推断顺式的最先进的生物信息学方法- 转录基因调控背后的调控代码，专注于它们的植物研究应用。