DeepWheat: Predicting the Effects of Genomic Variants on Gene Expression and Regulatory Activities Across Tissues and Varieties in Wheat Using Deep Learning [ChIP-Seq]
Accurate prediction of genomic variant effects and gene expression is essential for identifying functional variations and enabling precise genome editing of cis-regulatory elements (CREs). Spatiotemporal gene expression patterns are fundamental to the formation of key traits, yet tissue-specific predictions remain inaccurate, particularly in large-genome crops like wheat. In this study, we developed DeepWheat, a suite of two models for predicting epigenomic features and gene expression in wheat. DeepEXP, a deep learning model, integrates epigenomic and transcriptomic data across various wheat tissues, achieving Pearson correlation coefficients (PCC) over 0.8 and outperforming sequence-only models, especially for tissue-specific genes. DeepEPI predicts epigenomic features from DNA sequences, helping identify regulatory sequences and facilitating model transfer across wheat varieties. Using chromatin accessibility and transcriptomic data from 9 additional wheat varieties, we validated the model's accuracy and transfer efficiency. Our analysis further revealed that indels have a greater impact on gene expression than SNPs, and that, compared to promoter regions, the 5'UTR, 3'UTR, and introns exert even stronger regulatory effects on gene expression. These models also identified mutations that alter gene expression, supporting precise CRE editing. They provide valuable tools for tissue-specific predictions, regulatory sequence identification, and saturation mutagenesis to pinpoint high-effect sites. Overall design: RNA-seq, ATAC-seq and Chip-seq in multiple wheat different tissues