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A supervised learning framework for chromatin loop detection in genome-wide contact maps
Accurately predicting chromatin loops from genome-wide interaction matrices such as Hi-C data is critical to deepening our understanding of proper gene regulation. Current approaches are mainly focused on searching for statistically enriched dots on a genome-wide map. However, given the availability...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7347923/ https://www.ncbi.nlm.nih.gov/pubmed/32647330 http://dx.doi.org/10.1038/s41467-020-17239-9 |
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author | Salameh, Tarik J. Wang, Xiaotao Song, Fan Zhang, Bo Wright, Sage M. Khunsriraksakul, Chachrit Ruan, Yijun Yue, Feng |
author_facet | Salameh, Tarik J. Wang, Xiaotao Song, Fan Zhang, Bo Wright, Sage M. Khunsriraksakul, Chachrit Ruan, Yijun Yue, Feng |
author_sort | Salameh, Tarik J. |
collection | PubMed |
description | Accurately predicting chromatin loops from genome-wide interaction matrices such as Hi-C data is critical to deepening our understanding of proper gene regulation. Current approaches are mainly focused on searching for statistically enriched dots on a genome-wide map. However, given the availability of orthogonal data types such as ChIA-PET, HiChIP, Capture Hi-C, and high-throughput imaging, a supervised learning approach could facilitate the discovery of a comprehensive set of chromatin interactions. Here, we present Peakachu, a Random Forest classification framework that predicts chromatin loops from genome-wide contact maps. We compare Peakachu with current enrichment-based approaches, and find that Peakachu identifies a unique set of short-range interactions. We show that our models perform well in different platforms, across different sequencing depths, and across different species. We apply this framework to predict chromatin loops in 56 Hi-C datasets, and release the results at the 3D Genome Browser. |
format | Online Article Text |
id | pubmed-7347923 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-73479232020-07-13 A supervised learning framework for chromatin loop detection in genome-wide contact maps Salameh, Tarik J. Wang, Xiaotao Song, Fan Zhang, Bo Wright, Sage M. Khunsriraksakul, Chachrit Ruan, Yijun Yue, Feng Nat Commun Article Accurately predicting chromatin loops from genome-wide interaction matrices such as Hi-C data is critical to deepening our understanding of proper gene regulation. Current approaches are mainly focused on searching for statistically enriched dots on a genome-wide map. However, given the availability of orthogonal data types such as ChIA-PET, HiChIP, Capture Hi-C, and high-throughput imaging, a supervised learning approach could facilitate the discovery of a comprehensive set of chromatin interactions. Here, we present Peakachu, a Random Forest classification framework that predicts chromatin loops from genome-wide contact maps. We compare Peakachu with current enrichment-based approaches, and find that Peakachu identifies a unique set of short-range interactions. We show that our models perform well in different platforms, across different sequencing depths, and across different species. We apply this framework to predict chromatin loops in 56 Hi-C datasets, and release the results at the 3D Genome Browser. Nature Publishing Group UK 2020-07-09 /pmc/articles/PMC7347923/ /pubmed/32647330 http://dx.doi.org/10.1038/s41467-020-17239-9 Text en © The Author(s) 2020 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/. |
spellingShingle | Article Salameh, Tarik J. Wang, Xiaotao Song, Fan Zhang, Bo Wright, Sage M. Khunsriraksakul, Chachrit Ruan, Yijun Yue, Feng A supervised learning framework for chromatin loop detection in genome-wide contact maps |
title | A supervised learning framework for chromatin loop detection in genome-wide contact maps |
title_full | A supervised learning framework for chromatin loop detection in genome-wide contact maps |
title_fullStr | A supervised learning framework for chromatin loop detection in genome-wide contact maps |
title_full_unstemmed | A supervised learning framework for chromatin loop detection in genome-wide contact maps |
title_short | A supervised learning framework for chromatin loop detection in genome-wide contact maps |
title_sort | supervised learning framework for chromatin loop detection in genome-wide contact maps |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7347923/ https://www.ncbi.nlm.nih.gov/pubmed/32647330 http://dx.doi.org/10.1038/s41467-020-17239-9 |
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