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

Predicting the impact of non-coding genetic variation requires interpreting it in the context of 3D 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 a...

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Detalles Bibliográficos
Autores principales: Schwessinger, Ron, Gosden, Matthew, Downes, Damien, Brown, Richard C, Oudelaar, A. Marieke, Telenius, Jelena, Teh, Yee Whye, Lunter, Gerton, Hughes, Jim R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610627/
https://www.ncbi.nlm.nih.gov/pubmed/33046896
http://dx.doi.org/10.1038/s41592-020-0960-3
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author Schwessinger, Ron
Gosden, Matthew
Downes, Damien
Brown, Richard C
Oudelaar, A. Marieke
Telenius, Jelena
Teh, Yee Whye
Lunter, Gerton
Hughes, Jim R.
author_facet Schwessinger, Ron
Gosden, Matthew
Downes, Damien
Brown, Richard C
Oudelaar, A. Marieke
Telenius, Jelena
Teh, Yee Whye
Lunter, Gerton
Hughes, Jim R.
author_sort Schwessinger, Ron
collection PubMed
description Predicting the impact of non-coding genetic variation requires interpreting it in the context of 3D 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.
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spelling pubmed-76106272021-04-16 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. Nat Methods Article Predicting the impact of non-coding genetic variation requires interpreting it in the context of 3D 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. 2020-11-01 2020-10-12 /pmc/articles/PMC7610627/ /pubmed/33046896 http://dx.doi.org/10.1038/s41592-020-0960-3 Text en http://www.nature.com/authors/editorial_policies/license.html#termsUsers may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Schwessinger, Ron
Gosden, Matthew
Downes, Damien
Brown, Richard C
Oudelaar, A. Marieke
Telenius, Jelena
Teh, Yee Whye
Lunter, Gerton
Hughes, Jim R.
DeepC: predicting 3D genome folding using megabase-scale transfer learning
title DeepC: predicting 3D genome folding using megabase-scale transfer learning
title_full DeepC: predicting 3D genome folding using megabase-scale transfer learning
title_fullStr DeepC: predicting 3D genome folding using megabase-scale transfer learning
title_full_unstemmed DeepC: predicting 3D genome folding using megabase-scale transfer learning
title_short DeepC: predicting 3D genome folding using megabase-scale transfer learning
title_sort deepc: predicting 3d genome folding using megabase-scale transfer learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610627/
https://www.ncbi.nlm.nih.gov/pubmed/33046896
http://dx.doi.org/10.1038/s41592-020-0960-3
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