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Inferring time series chromatin states for promoter-enhancer pairs based on Hi-C data

BACKGROUND: Co-localized combinations of histone modifications (“chromatin states”) have been shown to correlate with promoter and enhancer activity. Changes in chromatin states over multiple time points (“chromatin state trajectories”) have previously been analyzed at promoter and enhancers separat...

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Autores principales: Miko, Henriette, Qiu, Yunjiang, Gaertner, Bjoern, Sander, Maike, Ohler, Uwe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7841892/
https://www.ncbi.nlm.nih.gov/pubmed/33509077
http://dx.doi.org/10.1186/s12864-021-07373-z
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author Miko, Henriette
Qiu, Yunjiang
Gaertner, Bjoern
Sander, Maike
Ohler, Uwe
author_facet Miko, Henriette
Qiu, Yunjiang
Gaertner, Bjoern
Sander, Maike
Ohler, Uwe
author_sort Miko, Henriette
collection PubMed
description BACKGROUND: Co-localized combinations of histone modifications (“chromatin states”) have been shown to correlate with promoter and enhancer activity. Changes in chromatin states over multiple time points (“chromatin state trajectories”) have previously been analyzed at promoter and enhancers separately. With the advent of time series Hi-C data it is now possible to connect promoters and enhancers and to analyze chromatin state trajectories at promoter-enhancer pairs. RESULTS: We present TimelessFlex, a framework for investigating chromatin state trajectories at promoters and enhancers and at promoter-enhancer pairs based on Hi-C information. TimelessFlex extends our previous approach Timeless, a Bayesian network for clustering multiple histone modification data sets at promoter and enhancer feature regions. We utilize time series ATAC-seq data measuring open chromatin to define promoters and enhancer candidates. We developed an expectation-maximization algorithm to assign promoters and enhancers to each other based on Hi-C interactions and jointly cluster their feature regions into paired chromatin state trajectories. We find jointly clustered promoter-enhancer pairs showing the same activation patterns on both sides but with a stronger trend at the enhancer side. While the promoter side remains accessible across the time series, the enhancer side becomes dynamically more open towards the gene activation time point. Promoter cluster patterns show strong correlations with gene expression signals, whereas Hi-C signals get only slightly stronger towards activation. The code of the framework is available at https://github.com/henriettemiko/TimelessFlex. CONCLUSIONS: TimelessFlex clusters time series histone modifications at promoter-enhancer pairs based on Hi-C and it can identify distinct chromatin states at promoter and enhancer feature regions and their changes over time. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-021-07373-z.
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spelling pubmed-78418922021-01-28 Inferring time series chromatin states for promoter-enhancer pairs based on Hi-C data Miko, Henriette Qiu, Yunjiang Gaertner, Bjoern Sander, Maike Ohler, Uwe BMC Genomics Methodology Article BACKGROUND: Co-localized combinations of histone modifications (“chromatin states”) have been shown to correlate with promoter and enhancer activity. Changes in chromatin states over multiple time points (“chromatin state trajectories”) have previously been analyzed at promoter and enhancers separately. With the advent of time series Hi-C data it is now possible to connect promoters and enhancers and to analyze chromatin state trajectories at promoter-enhancer pairs. RESULTS: We present TimelessFlex, a framework for investigating chromatin state trajectories at promoters and enhancers and at promoter-enhancer pairs based on Hi-C information. TimelessFlex extends our previous approach Timeless, a Bayesian network for clustering multiple histone modification data sets at promoter and enhancer feature regions. We utilize time series ATAC-seq data measuring open chromatin to define promoters and enhancer candidates. We developed an expectation-maximization algorithm to assign promoters and enhancers to each other based on Hi-C interactions and jointly cluster their feature regions into paired chromatin state trajectories. We find jointly clustered promoter-enhancer pairs showing the same activation patterns on both sides but with a stronger trend at the enhancer side. While the promoter side remains accessible across the time series, the enhancer side becomes dynamically more open towards the gene activation time point. Promoter cluster patterns show strong correlations with gene expression signals, whereas Hi-C signals get only slightly stronger towards activation. The code of the framework is available at https://github.com/henriettemiko/TimelessFlex. CONCLUSIONS: TimelessFlex clusters time series histone modifications at promoter-enhancer pairs based on Hi-C and it can identify distinct chromatin states at promoter and enhancer feature regions and their changes over time. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-021-07373-z. BioMed Central 2021-01-28 /pmc/articles/PMC7841892/ /pubmed/33509077 http://dx.doi.org/10.1186/s12864-021-07373-z Text en © The Author(s) 2021 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 Methodology Article
Miko, Henriette
Qiu, Yunjiang
Gaertner, Bjoern
Sander, Maike
Ohler, Uwe
Inferring time series chromatin states for promoter-enhancer pairs based on Hi-C data
title Inferring time series chromatin states for promoter-enhancer pairs based on Hi-C data
title_full Inferring time series chromatin states for promoter-enhancer pairs based on Hi-C data
title_fullStr Inferring time series chromatin states for promoter-enhancer pairs based on Hi-C data
title_full_unstemmed Inferring time series chromatin states for promoter-enhancer pairs based on Hi-C data
title_short Inferring time series chromatin states for promoter-enhancer pairs based on Hi-C data
title_sort inferring time series chromatin states for promoter-enhancer pairs based on hi-c data
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7841892/
https://www.ncbi.nlm.nih.gov/pubmed/33509077
http://dx.doi.org/10.1186/s12864-021-07373-z
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