Deciphering the Sequence Basis and Application of Transcriptional Initiation Regulation in Plant Genomes Through Deep Learning

Transcription initiation is a critical regulatory step in plant gene expression, yet its sequence<br>determinants remain largely elusive. Here we introduce GenoRetriever, an interpretable deep<br>learning model that deciphers the sequence basis of transcriptional initiation regulation across<br>plant genomes. Trained on STRIPE-seq data from 16 soybean tissues and six other crop species,<br>GenoRetriever identifies 27 core sequence motifs that govern transcription start site (TSS)<br>selection and usage. The model predicts TSS locations and usage levels with high accuracy, as<br>validated by in silico motif insertions, saturation mutagenesis, and CRISPR-Cas9 promoter editing.<br>It further reveals that 31.85% of natural variation between wild and domesticated soybean drives<br>shifts in promoter motif usage during domestication, and uncovers lineage-specific motif effects<br>between monocots and dicots. This interpretable model and its user-friendly web server for<br>promoter analysis and design make GenoRetriever both a methodological innovation and practical<br>tool for plant functional genomics and crop improvement.<br>