Predicting the impact of noncoding genetic variation requires interpreting it in the context of three-dimensional genome architecture. We have developed deepC, a transfer-learning-based deep neural network that accurately predicts genome folding from megabase-scale DNA sequence. DeepC predicts domain boundaries at high resolution, learns the sequence determinants of genome folding and predicts the impact of both large-scale structural and single base-pair variations.
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Base Sequence, CCCTC-Binding Factor, Chromatin, Computer Simulation, Genome, Human, Genomic Structural Variation, Genomics, Humans, Models, Genetic, Neural Networks, Computer