PREDICT: Advancing Accurate Gene Expression Prediction and Motif Identification in Plant Stress Responses

Cells respond to environmental stimuli through transcriptional responses, orchestrated by transcription factors (TFs) that interpret the gene cis -regulatory DNA sequences, determining gene expression dynamics timing and locations. Diversification in TFs and cis -regulatory element (CRE) interactions result in unique gene regulatory networks (GRNs) that underpin plant adaptation. A primary challenge is identifying Transcription Factor Binding Motifs (TFBMs) for temporal and condition-specific gene expressions in plants. While the Multiple EM for Motif Elicitation (MEME) suite identifies stress-responsive CREs in Arabidopsis, its predictive power for gene expression remains uncertain. Alternatively, the k -mer approach identifies CRE sites and consensus TF motifs, thereby improving gene expression prediction models. In this study, we harnessed the power of a k -mer pipeline to address sequence-to-expression prediction problems across diverse abiotic stresses, in both bryophytic and vascular plants, including monocots and dicots. Moreover, we characterized both un-gapped and gapped CREs and, coupled with GRN analyses, pinpointed key TFs within transcriptional cascades. Lastly, we developed the P redictive R egulatory E lement D atabase for Identifying C is -regulatory elements and T ranscription factors (PREDICT), a web tool for efficient k -mer identification. This advancement will enrich our understanding of the cis -regulatory code landscape that shapes gene regulation in plant adaptation. PREDICT web tool is available at [ http://predict.southerngenomics.org/kmers/kmers.php ].