- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
- Publisher copy:
- 10.1038/s41592-020-0960-3
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- Publisher:
- Springer Nature Publisher's website
- Journal:
- Nature Methods Journal website
- Volume:
- 17
- Issue:
- 11
- Pages:
- 1118-1124
- Place of publication:
- United States
- Publication date:
- 2020-10-12
- Acceptance date:
- 2020-08-20
- DOI:
- EISSN:
-
1548-7105
- ISSN:
-
1548-7105
- Pmid:
-
33046896
- Pubs id:
-
1137419
- Local pid:
- pubs:1137419
- Language:
- English
- Keywords:
- Format:
- Print-Electronic
- Copyright holder:
- Schwessinger et al.
- Copyright date:
- 2020
- Rights statement:
- © The Author(s), under exclusive licence to Springer Nature America, Inc. 2020.
Journal article
DeepC: Predicting 3D genome folding using megabase-scale transfer learning
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