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DeepWheat: predicting the effects of genomic variants on gene expression and regulatory activities across tissues and
Zhigang Ma1, Jiazi Zhang1, Hongcui Pei1
1State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Abstract:
Spatiotemporal gene expression shapes key agronomic traits, yet tissue-specific prediction remains challenging in complex crops. We present DeepWheat, a broadly applicable deep learning framework comprising DeepEXP and DeepEPI, for accurate, tissue-specific gene expression prediction. DeepEXP integrates sequence and epigenomic features to predict gene expression (PCC 0.82-0.88), while DeepEPI predicts epigenomic maps from DNA sequence to support model transfer across varieties. Validations in five wheat cultivars confirm robustness and accuracy. DeepWheat also identifies regulatory variants with strong expression effects, enabling targeted cis-regulatory elements editing and offering a powerful tool for crop functional genomics and breeding.
