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hicGAN infers super resolution Hi-C data with generative adversarial networks

MOTIVATION: Hi-C is a genome-wide technology for investigating 3D chromatin conformation by measuring physical contacts between pairs of genomic regions. The resolution of Hi-C data directly impacts the effectiveness and accuracy of downstream analysis such as identifying topologically associating d...

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Autores principales: Liu, Qiao, Lv, Hairong, Jiang, Rui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6612845/
https://www.ncbi.nlm.nih.gov/pubmed/31510693
http://dx.doi.org/10.1093/bioinformatics/btz317
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author Liu, Qiao
Lv, Hairong
Jiang, Rui
author_facet Liu, Qiao
Lv, Hairong
Jiang, Rui
author_sort Liu, Qiao
collection PubMed
description MOTIVATION: Hi-C is a genome-wide technology for investigating 3D chromatin conformation by measuring physical contacts between pairs of genomic regions. The resolution of Hi-C data directly impacts the effectiveness and accuracy of downstream analysis such as identifying topologically associating domains (TADs) and meaningful chromatin loops. High resolution Hi-C data are valuable resources which implicate the relationship between 3D genome conformation and function, especially linking distal regulatory elements to their target genes. However, high resolution Hi-C data across various tissues and cell types are not always available due to the high sequencing cost. It is therefore indispensable to develop computational approaches for enhancing the resolution of Hi-C data. RESULTS: We proposed hicGAN, an open-sourced framework, for inferring high resolution Hi-C data from low resolution Hi-C data with generative adversarial networks (GANs). To the best of our knowledge, this is the first study to apply GANs to 3D genome analysis. We demonstrate that hicGAN effectively enhances the resolution of low resolution Hi-C data by generating matrices that are highly consistent with the original high resolution Hi-C matrices. A typical scenario of usage for our approach is to enhance low resolution Hi-C data in new cell types, especially where the high resolution Hi-C data are not available. Our study not only presents a novel approach for enhancing Hi-C data resolution, but also provides fascinating insights into disclosing complex mechanism underlying the formation of chromatin contacts. AVAILABILITY AND IMPLEMENTATION: We release hicGAN as an open-sourced software at https://github.com/kimmo1019/hicGAN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-66128452019-07-12 hicGAN infers super resolution Hi-C data with generative adversarial networks Liu, Qiao Lv, Hairong Jiang, Rui Bioinformatics Ismb/Eccb 2019 Conference Proceedings MOTIVATION: Hi-C is a genome-wide technology for investigating 3D chromatin conformation by measuring physical contacts between pairs of genomic regions. The resolution of Hi-C data directly impacts the effectiveness and accuracy of downstream analysis such as identifying topologically associating domains (TADs) and meaningful chromatin loops. High resolution Hi-C data are valuable resources which implicate the relationship between 3D genome conformation and function, especially linking distal regulatory elements to their target genes. However, high resolution Hi-C data across various tissues and cell types are not always available due to the high sequencing cost. It is therefore indispensable to develop computational approaches for enhancing the resolution of Hi-C data. RESULTS: We proposed hicGAN, an open-sourced framework, for inferring high resolution Hi-C data from low resolution Hi-C data with generative adversarial networks (GANs). To the best of our knowledge, this is the first study to apply GANs to 3D genome analysis. We demonstrate that hicGAN effectively enhances the resolution of low resolution Hi-C data by generating matrices that are highly consistent with the original high resolution Hi-C matrices. A typical scenario of usage for our approach is to enhance low resolution Hi-C data in new cell types, especially where the high resolution Hi-C data are not available. Our study not only presents a novel approach for enhancing Hi-C data resolution, but also provides fascinating insights into disclosing complex mechanism underlying the formation of chromatin contacts. AVAILABILITY AND IMPLEMENTATION: We release hicGAN as an open-sourced software at https://github.com/kimmo1019/hicGAN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2019-07 2019-07-05 /pmc/articles/PMC6612845/ /pubmed/31510693 http://dx.doi.org/10.1093/bioinformatics/btz317 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Ismb/Eccb 2019 Conference Proceedings
Liu, Qiao
Lv, Hairong
Jiang, Rui
hicGAN infers super resolution Hi-C data with generative adversarial networks
title hicGAN infers super resolution Hi-C data with generative adversarial networks
title_full hicGAN infers super resolution Hi-C data with generative adversarial networks
title_fullStr hicGAN infers super resolution Hi-C data with generative adversarial networks
title_full_unstemmed hicGAN infers super resolution Hi-C data with generative adversarial networks
title_short hicGAN infers super resolution Hi-C data with generative adversarial networks
title_sort hicgan infers super resolution hi-c data with generative adversarial networks
topic Ismb/Eccb 2019 Conference Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6612845/
https://www.ncbi.nlm.nih.gov/pubmed/31510693
http://dx.doi.org/10.1093/bioinformatics/btz317
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