Harnessing DNA Foundation Models for Cross-Species Transcription Factor Binding Site Prediction in Plant Genomes

Accurate prediction of transcription factor binding sites (TFBSs) is crucial for understanding gene regulation. While experimental methods such as ChIP-seq and DAP-seq are informative, they are labor-intensive and species-specific. Recent advancements in large-scale pretrained DNA foundation models have shown promise in overcoming these limitations. This study evaluates the performance of three such models--DNABERT-2, AgroNT, and HyenaDNA--in predicting TFBSs in plants. Using DAP-seq data from Arabidopsis thaliana and Sisymbrium irio, we benchmark their accuracy against specialized approaches, including a motif-based method and two deep learning models, DeepBind and BERT-TFBS. Our results demonstrate that foundation models, particularly HyenaDNA, offer superior predictive accuracy and computational efficiency, highlighting their potential for scalable, genome-wide TFBS prediction in plants.