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Updated: Dec 6, 2025

Mapping Mammalian 3D Genome Interactions with Micro-C-XL
Published on: November 3, 2023
DeepC: predicting 3D genome folding using megabase-scale transfer learning
Ron Schwessinger1,2,3, Matthew Gosden1, Damien Downes1
1MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
None:
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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