From binding to networks: methods for identifying transcription factor targets in plant systems

Transcription factors (TFs) orchestrate gene expression programs by binding regulatory DNA sequences and modulating transcription of target genes. Identifying TF-target gene relationships is fundamental to understanding plant development, stress responses, and metabolic regulation. However, determining which genes a TF regulates remains technically challenging. This review provides a decision-oriented framework, that integrates experimental and computational plant TF-target identification. Placing emphasis on plant-specific constraints and practical method selection to guide researchers from initial TF discovery through comprehensive network characterization. We compare biochemical approaches (EMSA, Y1H), genome-wide mapping methods (ChIP-seq, DAP-seq, CUT&Tag), expression profiling techniques (RNA-seq on mutants and overexpression lines), and computational prediction tools (GENIE3, PTFSpot, ConnecTF). Critical trade-offs are discussed, between binding potential and functional regulation, throughput and resolution, and between different model and non-model plant systems. Finally, we highlight emerging technologies including high-throughput enhancer screening, single-cell approaches, and machine learning-based prediction platforms that promise to accelerate functional characterization of plant TFs and their regulatory networks.