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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.

Original publication

DOI

10.1038/s41592-020-0960-3

Type

Journal article

Journal

Nat Methods

Publication Date

11/2020

Volume

17

Pages

1118 - 1124