DeepC: predicting 3D genome folding using megabase-scale transfer learning
| Autor: | Ron Schwessinger, A M Oudelaar, Yee Whye Teh, R C Brown, Jelena Telenius, Jim R. Hughes, Damien J. Downes, Gerton Lunter, M Gosden |
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| Rok vydání: | 2020 |
| Předmět: |
CCCTC-Binding Factor
Computer science Genomic Structural Variation Context (language use) Genomics Computational biology Biochemistry Genome DNA sequencing Article 03 medical and health sciences Humans Computer Simulation Molecular Biology 030304 developmental biology Sequence (medicine) 0303 health sciences Base Sequence Models Genetic Genome Human Cell Biology Folding (DSP implementation) Human genetics Chromatin Neural Networks Computer Biotechnology |
| Zdroj: | Nature methods |
| ISSN: | 1548-7105 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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