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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.

SUBMITTER: Schwessinger R

PROVIDER: S-EPMC7610627 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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

Schwessinger Ron R Gosden Matthew M Downes Damien D Brown Richard C RC Oudelaar A Marieke AM Telenius Jelena J Teh Yee Whye YW Lunter Gerton G Hughes Jim R JR

Nature methods 20201012 11

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. ...[more]

PMID: 33046896

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