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A deep learning method for replicate-based analysis of chromosome conformation contacts using Siamese neural networks
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A deep learning method for replicate-based analysis of chromosome conformation contacts using Siamese neural networks.pdf | Published version | 5.09 MB | Adobe PDF | View/Open |
Title: | A deep learning method for replicate-based analysis of chromosome conformation contacts using Siamese neural networks |
Authors: | Al-Jibury, E King, JWD Guo, Y Lenhard, B Fisher, AG Merkenschlager, M Rueckert, D |
Item Type: | Journal Article |
Abstract: | The organisation of the genome in nuclear space is an important frontier of biology. Chromosome conformation capture methods such as Hi-C and Micro-C produce genome-wide chromatin contact maps that provide rich data containing quantitative and qualitative information about genome architecture. Most conventional approaches to genome-wide chromosome conformation capture data are limited to the analysis of pre-defined features, and may therefore miss important biological information. One constraint is that biologically important features can be masked by high levels of technical noise in the data. Here we introduce a replicate-based method for deep learning from chromatin conformation contact maps. Using a Siamese network configuration our approach learns to distinguish technical noise from biological variation and outperforms image similarity metrics across a range of biological systems. The features extracted from Hi-C maps after perturbation of cohesin and CTCF reflect the distinct biological functions of cohesin and CTCF in the formation of domains and boundaries, respectively. The learnt distance metrics are biologically meaningful, as they mirror the density of cohesin and CTCF binding. These properties make our method a powerful tool for the exploration of chromosome conformation capture data, such as Hi-C capture Hi-C, and Micro-C. |
Issue Date: | 17-Aug-2023 |
Date of Acceptance: | 31-Jul-2023 |
URI: | http://hdl.handle.net/10044/1/107173 |
DOI: | 10.1038/s41467-023-40547-9 |
ISSN: | 2041-1723 |
Publisher: | Nature Portfolio |
Journal / Book Title: | Nature Communications |
Volume: | 14 |
Issue: | 1 |
Copyright Statement: | © The Author(s) 2023. Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/ licenses/by/4.0/. |
Publication Status: | Published |
Conference Place: | England |
Article Number: | 5007 |
Online Publication Date: | 2023-08-17 |
Appears in Collections: | Computing Institute of Clinical Sciences Faculty of Medicine |
This item is licensed under a Creative Commons License