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

Journal: Nature methods
PMID: 33046896

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.

Authors

  • Ron Schwessinger
    MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
  • Matthew Gosden
    MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
  • Damien Downes
    MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
  • Richard C Brown
  • A Marieke Oudelaar
    MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
  • Jelena Telenius
    MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
  • Yee Whye Teh
    Department of Statistics, University of Oxford, Oxford, United Kingdom.
  • Gerton Lunter
  • Jim R Hughes
    MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK. jim.hughes@imm.ox.ac.uk.

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