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

Schwessinger, Ron; Gosden, Matthew; Downes, Damien; Brown, Richard C; Oudelaar, A Marieke; Telenius, Jelena; Teh, Yee Whye; Lunter, Gerton; Hughes, Jim R

Schwessinger, Ron; Gosden, Matthew; Downes, Damien; Brown, Richard C; Oudelaar, A Marieke; Telenius, Jelena; Teh, Yee Whye; Lunter, Gerton; Hughes, Jim R.

Afiliação

Schwessinger R; MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
Gosden M; MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
Downes D; Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
Brown RC; MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
Oudelaar AM; MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
Telenius J; Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
Teh YW; MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
Lunter G; MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
Hughes JR; MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.

Nat Methods ; 17(11): 1118-1124, 2020 11.

Article em En | MEDLINE | ID: mdl-33046896

ABSTRACT

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.

Assuntos

Genoma Humano/genética; Genômica/métodos; Modelos Genéticos; Redes Neurais de Computação; Sequência de Bases; Fator de Ligação a CCCTC/genética; Cromatina/genética; Simulação por Computador; Variação Estrutural do Genoma; Humanos

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Genoma Humano / Redes Neurais de Computação / Genômica / Modelos Genéticos Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Nat Methods Assunto da revista: TECNICAS E PROCEDIMENTOS DE LABORATORIO Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Reino Unido

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