Deciphering the sequence basis and application of transcriptional initiation regulation in plant genomes through deep learning.

ABSTRACT:

Background

Transcription initiation is a key checkpoint in plant gene regulation, yet the DNA features that determine where and the frequency of the genes start transcription remain unclear.

Results

We develop GenoRetriever, an interpretable deep learning model trained on base pair resolution STRIPE-seq data from multiple crop genomes, to systematically reveal and quantify the sequence code that governs transcription start sites (TSSs). Using TSS profiles from 16 soybean tissues and six additional crops, GenoRetriever identifies 27 core promoter motifs, including canonical TATA box and initiator elements, that together dictate TSS choice and activity. Model interpretation shows how each motif modulates both initiation frequency and precise start site position; these effects

SUBMITTER: Gao P 

PROVIDER: S-EPMC12455780 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature