DeepC: Predicting 3D genome folding using megabase-scale transfer learning
- 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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- Peer review status:
- Peer reviewed
(Accepted manuscript, 32.8MB)
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- Schwessinger et al.
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- © The Author(s), under exclusive licence to Springer Nature America, Inc. 2020.
- This is the accepted manuscript version of the article. The final version is available online from Springer Nature at: https://doi.org/10.1038/s41592-020-0960-3
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