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Semi-nonparametric modeling of topological domain formation from epigenetic data

BACKGROUND: Hi-C experiments capturing the 3D genome architecture have led to the discovery of topologically-associated domains (TADs) that form an important part of the 3D genome organization and appear to play a role in gene regulation and other functions. Several histone modifications have been i...

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Autores principales: Sefer, Emre, Kingsford, Carl
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6399866/
https://www.ncbi.nlm.nih.gov/pubmed/30867673
http://dx.doi.org/10.1186/s13015-019-0142-y
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author Sefer, Emre
Kingsford, Carl
author_facet Sefer, Emre
Kingsford, Carl
author_sort Sefer, Emre
collection PubMed
description BACKGROUND: Hi-C experiments capturing the 3D genome architecture have led to the discovery of topologically-associated domains (TADs) that form an important part of the 3D genome organization and appear to play a role in gene regulation and other functions. Several histone modifications have been independently associated with TAD formation, but their combinatorial effects on domain formation remain poorly understood at a global scale. RESULTS: We propose a convex semi-nonparametric approach called nTDP based on Bernstein polynomials to explore the joint effects of histone markers on TAD formation as well as predict TADs solely from the histone data. We find a small subset of modifications to be predictive of TADs across species. By inferring TADs using our trained model, we are able to predict TADs across different species and cell types, without the use of Hi-C data, suggesting their effect is conserved. This work provides the first comprehensive joint model of the effect of histone markers on domain formation. CONCLUSIONS: Our approach, nTDP, can form the basis of a unified, explanatory model of the relationship between epigenetic marks and topological domain structures. It can be used to predict domain boundaries for cell types, species, and conditions for which no Hi-C data is available. The model may also be of use for improving Hi-C-based domain finders.
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spelling pubmed-63998662019-03-13 Semi-nonparametric modeling of topological domain formation from epigenetic data Sefer, Emre Kingsford, Carl Algorithms Mol Biol Research BACKGROUND: Hi-C experiments capturing the 3D genome architecture have led to the discovery of topologically-associated domains (TADs) that form an important part of the 3D genome organization and appear to play a role in gene regulation and other functions. Several histone modifications have been independently associated with TAD formation, but their combinatorial effects on domain formation remain poorly understood at a global scale. RESULTS: We propose a convex semi-nonparametric approach called nTDP based on Bernstein polynomials to explore the joint effects of histone markers on TAD formation as well as predict TADs solely from the histone data. We find a small subset of modifications to be predictive of TADs across species. By inferring TADs using our trained model, we are able to predict TADs across different species and cell types, without the use of Hi-C data, suggesting their effect is conserved. This work provides the first comprehensive joint model of the effect of histone markers on domain formation. CONCLUSIONS: Our approach, nTDP, can form the basis of a unified, explanatory model of the relationship between epigenetic marks and topological domain structures. It can be used to predict domain boundaries for cell types, species, and conditions for which no Hi-C data is available. The model may also be of use for improving Hi-C-based domain finders. BioMed Central 2019-03-05 /pmc/articles/PMC6399866/ /pubmed/30867673 http://dx.doi.org/10.1186/s13015-019-0142-y Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 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.
spellingShingle Research
Sefer, Emre
Kingsford, Carl
Semi-nonparametric modeling of topological domain formation from epigenetic data
title Semi-nonparametric modeling of topological domain formation from epigenetic data
title_full Semi-nonparametric modeling of topological domain formation from epigenetic data
title_fullStr Semi-nonparametric modeling of topological domain formation from epigenetic data
title_full_unstemmed Semi-nonparametric modeling of topological domain formation from epigenetic data
title_short Semi-nonparametric modeling of topological domain formation from epigenetic data
title_sort semi-nonparametric modeling of topological domain formation from epigenetic data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6399866/
https://www.ncbi.nlm.nih.gov/pubmed/30867673
http://dx.doi.org/10.1186/s13015-019-0142-y
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