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