DeepC: predicting 3D genome folding using megabase-scale transfer learning

Schwessinger, R.; Gosden, M.; Downes, D.; Brown, R. C.; Oudelaar, A. M.; Telenius, J.; Teh, Y. W.; Lunter, G.; Hughes, J. R.

doi:10.1038/s41592-020-0960-3

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DeepC: predicting 3D genome folding using megabase-scale transfer learning

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Oudelaar, A. M.
Lise Meitner Group Genome Organization and Regulation, MPI for Biophysical Chemistry, Max Planck Society;

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Citation

Schwessinger, R., Gosden, M., Downes, D., Brown, R. C., Oudelaar, A. M., Telenius, J., et al. (2020). DeepC: predicting 3D genome folding using megabase-scale transfer learning. Nature Methods, 17(11), 1118-1124. doi:10.1038/s41592-020-0960-3.

Cite as: https://hdl.handle.net/21.11116/0000-0007-7BD3-1

Abstract

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.