Nature Genetics published a review of genomic deep learning models for predicting noncoding variant effects. The review covers supervised sequence-to-activity models and self-supervised genomic language models. It outlines practical considerations for making predictions. It categorizes ground-truth data types used to evaluate model performance. It highlights where current models are most useful. It notes key considerations for practitioners. It identifies opportunities for improvement in model development and evaluation.