In our lab, we examine mechanistic relationships between genetic information and crop quality traits. Utilizing deep learning on extensive sequence and omics datasets, we identify and functionally characterize genetic sequence features that affect gene expression and translate to macro-phenotypes. Our strategy integrates multi-modal data, including genomic, transcriptomic, metabolomic, and phenomic information.