Plant adaptive responses to their environment are orchestrated by transcriptional reprogramming, regulated by sequence-specific transcription factors (TFs) recognizing specific TF-binding sites (TFBSs), and understanding their regulatory grammar is crucial for unravelling plant adaptation mechanisms. Empirical data have contributed to the elucidation of TFBS sequence motifs involved in biological processes, but comprehensive experimental approaches may not be feasible, especially in non-model species. Different computational algorithms have facilitated the elucidation of TFBS sequence motifs and the construction of predictive models, shedding light on plant gene regulatory networks. In this context, the development of straightforward computational pipelines and easy-to-use bioinformatics tools is of particular relevance to make gene expression analysis accessible to the research community. This chapter presents a methodology to infer TF regulators applicable to 60 plant species from RNA sequencing (RNA-seq) data as the starting point. It includes RNA-seq analysis and quantification, gene clustering using WGCNA to identify co-regulated gene modules, and searching for enriched TFBSs associated with these modules. The methodology is illustrated using Arabidopsis RNA-seq data related to abiotic stress. By providing a user-friendly pipeline, researchers are empowered to unravel the molecular basis of gene expression dynamics in plants.