Deep Learning for Studying Plant Transcriptional Factors

Transcription factors (TFs) are proteins that identify and attach to specific DNA sequences, which influence the structure of chromatin and regulate gene transcription. These factors play a critical role in controlling gene expression and ensuring that the genes are either repressed or activated in reaction to different environmental conditions and cellular signals. By facilitating gene expression under a complex regulatory network, TFs contribute to essential biological processes such as development, differentiation, and stress response. How TFs affect gene expression is a major concern for contemporary genomic researchers. However, identifying the fastening (binding) sites of TF (TFBS) and determining the precise role of TFs in the regulation of transcription remain significant challenges due to the complexity and variability of DNA–protein interactions. This chapter provides an in-depth overview of research on TFBS prediction. First, it reviews experimental methods for identifying TFBS, highlighting various laboratory techniques and available databases that store TF–DNA interaction data. These methods help in mapping TF binding patterns across the genome, providing valuable insights into gene regulation. Second, it explores computational approaches for TFBS prediction, with a particular emphasis on deep learning models. These advanced techniques leverage large-scale genomic data to improve the efficiency and accuracy of TFBS identification. Finally, this chapter discusses the major issues in the prediction of TFBS, including data limitations, sequence variability, and the influence of chromatin accessibility.