DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure

Non-coding variants have been shown to be related to disease by alteration of 3D genome structures. We propose a deep learning method, DeepMILO, to predict the effects of variants on CTCF/cohesin-mediated insulator loops. Application of DeepMILO on variants from whole-genome sequences of 1834 patien...

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Autores principales: Trieu, Tuan, Martinez-Fundichely, Alexander, Khurana, Ekta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7098089/
https://www.ncbi.nlm.nih.gov/pubmed/32216817
http://dx.doi.org/10.1186/s13059-020-01987-4
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author Trieu, Tuan
Martinez-Fundichely, Alexander
Khurana, Ekta
author_facet Trieu, Tuan
Martinez-Fundichely, Alexander
Khurana, Ekta
author_sort Trieu, Tuan
collection PubMed
description Non-coding variants have been shown to be related to disease by alteration of 3D genome structures. We propose a deep learning method, DeepMILO, to predict the effects of variants on CTCF/cohesin-mediated insulator loops. Application of DeepMILO on variants from whole-genome sequences of 1834 patients of twelve cancer types revealed 672 insulator loops disrupted in at least 10% of patients. Our results show mutations at loop anchors are associated with upregulation of the cancer driver genes BCL2 and MYC in malignant lymphoma thus pointing to a possible new mechanism for their dysregulation via alteration of insulator loops.
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spelling pubmed-70980892020-03-27 DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure Trieu, Tuan Martinez-Fundichely, Alexander Khurana, Ekta Genome Biol Method Non-coding variants have been shown to be related to disease by alteration of 3D genome structures. We propose a deep learning method, DeepMILO, to predict the effects of variants on CTCF/cohesin-mediated insulator loops. Application of DeepMILO on variants from whole-genome sequences of 1834 patients of twelve cancer types revealed 672 insulator loops disrupted in at least 10% of patients. Our results show mutations at loop anchors are associated with upregulation of the cancer driver genes BCL2 and MYC in malignant lymphoma thus pointing to a possible new mechanism for their dysregulation via alteration of insulator loops. BioMed Central 2020-03-26 /pmc/articles/PMC7098089/ /pubmed/32216817 http://dx.doi.org/10.1186/s13059-020-01987-4 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Trieu, Tuan
Martinez-Fundichely, Alexander
Khurana, Ekta
DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure
title DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure
title_full DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure
title_fullStr DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure
title_full_unstemmed DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure
title_short DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure
title_sort deepmilo: a deep learning approach to predict the impact of non-coding sequence variants on 3d chromatin structure
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7098089/
https://www.ncbi.nlm.nih.gov/pubmed/32216817
http://dx.doi.org/10.1186/s13059-020-01987-4
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