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

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

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Publisher copy:
10.1038/s41592-020-0960-3

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Institution:
University of Oxford
Division:
MSD
Department:
RDM
Subgroup:
Weatherall Insti. of Molecular Medicine
Role:
Author
ORCID:
0000-0001-7829-8503
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
RDM
Subgroup:
Weatherall Insti. of Molecular Medicine
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
RDM
Subgroup:
Weatherall Insti. of Molecular Medicine
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
RDM
Subgroup:
Weatherall Insti. of Molecular Medicine
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
RDM
Subgroup:
Weatherall Insti. of Molecular Medicine
Role:
Author
ORCID:
0000-0002-4016-6158
Expand authors...
Wellcome Trust More from this funder
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

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