A deep learning method for replicate-based analysis of chromosome conformation contacts using Siamese neural networks
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 architectu...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-40547-9 |
| _version_ | 1797557902993522688 |
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| author | Ediem Al-jibury James W. D. King Ya Guo Boris Lenhard Amanda G. Fisher Matthias Merkenschlager Daniel Rueckert |
| author_facet | Ediem Al-jibury James W. D. King Ya Guo Boris Lenhard Amanda G. Fisher Matthias Merkenschlager Daniel Rueckert |
| author_sort | Ediem Al-jibury |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T17:23:49Z |
| format | Article |
| id | doaj.art-d7dda5f518cd4ccaa907111a9072e516 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-10T17:23:49Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-d7dda5f518cd4ccaa907111a9072e5162023-11-20T10:16:34ZengNature PortfolioNature Communications2041-17232023-08-0114111310.1038/s41467-023-40547-9A deep learning method for replicate-based analysis of chromosome conformation contacts using Siamese neural networksEdiem Al-jibury0James W. D. King1Ya Guo2Boris Lenhard3Amanda G. Fisher4Matthias Merkenschlager5Daniel Rueckert6MRC LMS, Imperial College LondonMRC LMS, Imperial College LondonMRC LMS, Imperial College LondonMRC LMS, Imperial College LondonMRC LMS, Imperial College LondonMRC LMS, Imperial College LondonDepartment of Computing, Imperial College LondonAbstract 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.https://doi.org/10.1038/s41467-023-40547-9 |
| spellingShingle | Ediem Al-jibury James W. D. King Ya Guo Boris Lenhard Amanda G. Fisher Matthias Merkenschlager Daniel Rueckert A deep learning method for replicate-based analysis of chromosome conformation contacts using Siamese neural networks Nature Communications |
| title | A deep learning method for replicate-based analysis of chromosome conformation contacts using Siamese neural networks |
| title_full | A deep learning method for replicate-based analysis of chromosome conformation contacts using Siamese neural networks |
| title_fullStr | A deep learning method for replicate-based analysis of chromosome conformation contacts using Siamese neural networks |
| title_full_unstemmed | A deep learning method for replicate-based analysis of chromosome conformation contacts using Siamese neural networks |
| title_short | A deep learning method for replicate-based analysis of chromosome conformation contacts using Siamese neural networks |
| title_sort | deep learning method for replicate based analysis of chromosome conformation contacts using siamese neural networks |
| url | https://doi.org/10.1038/s41467-023-40547-9 |
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