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Joint inference and alignment of genome structures enables characterization of compartment-independent reorganization across cell types
BACKGROUND: Comparisons of Hi–C data sets between cell types and conditions have revealed differences in topologically associated domains (TADs) and A/B compartmentalization, which are correlated with differences in gene regulation. However, previous comparisons have focused on known forms of 3D org...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6784335/ https://www.ncbi.nlm.nih.gov/pubmed/31594535 http://dx.doi.org/10.1186/s13072-019-0308-3 |
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author | Rieber, Lila Mahony, Shaun |
author_facet | Rieber, Lila Mahony, Shaun |
author_sort | Rieber, Lila |
collection | PubMed |
description | BACKGROUND: Comparisons of Hi–C data sets between cell types and conditions have revealed differences in topologically associated domains (TADs) and A/B compartmentalization, which are correlated with differences in gene regulation. However, previous comparisons have focused on known forms of 3D organization while potentially neglecting other functionally relevant differences. We aimed to create a method to quantify all locus-specific differences between two Hi–C data sets. RESULTS: We developed MultiMDS to jointly infer and align 3D chromosomal structures from two Hi–C data sets, thereby enabling a new way to comprehensively quantify relocalization of genomic loci between cell types. We demonstrate this approach by comparing Hi–C data across a variety of cell types. We consistently find relocalization of loci with minimal difference in A/B compartment score. For example, we identify compartment-independent relocalizations between GM12878 and K562 cells that involve loci displaying enhancer-associated histone marks in one cell type and polycomb-associated histone marks in the other. CONCLUSIONS: MultiMDS is the first tool to identify all loci that relocalize between two Hi–C data sets. Our method can identify 3D localization differences that are correlated with cell-type-specific regulatory activities and which cannot be identified using other methods. |
format | Online Article Text |
id | pubmed-6784335 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-67843352019-10-17 Joint inference and alignment of genome structures enables characterization of compartment-independent reorganization across cell types Rieber, Lila Mahony, Shaun Epigenetics Chromatin Methodology BACKGROUND: Comparisons of Hi–C data sets between cell types and conditions have revealed differences in topologically associated domains (TADs) and A/B compartmentalization, which are correlated with differences in gene regulation. However, previous comparisons have focused on known forms of 3D organization while potentially neglecting other functionally relevant differences. We aimed to create a method to quantify all locus-specific differences between two Hi–C data sets. RESULTS: We developed MultiMDS to jointly infer and align 3D chromosomal structures from two Hi–C data sets, thereby enabling a new way to comprehensively quantify relocalization of genomic loci between cell types. We demonstrate this approach by comparing Hi–C data across a variety of cell types. We consistently find relocalization of loci with minimal difference in A/B compartment score. For example, we identify compartment-independent relocalizations between GM12878 and K562 cells that involve loci displaying enhancer-associated histone marks in one cell type and polycomb-associated histone marks in the other. CONCLUSIONS: MultiMDS is the first tool to identify all loci that relocalize between two Hi–C data sets. Our method can identify 3D localization differences that are correlated with cell-type-specific regulatory activities and which cannot be identified using other methods. BioMed Central 2019-10-08 /pmc/articles/PMC6784335/ /pubmed/31594535 http://dx.doi.org/10.1186/s13072-019-0308-3 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 | Methodology Rieber, Lila Mahony, Shaun Joint inference and alignment of genome structures enables characterization of compartment-independent reorganization across cell types |
title | Joint inference and alignment of genome structures enables characterization of compartment-independent reorganization across cell types |
title_full | Joint inference and alignment of genome structures enables characterization of compartment-independent reorganization across cell types |
title_fullStr | Joint inference and alignment of genome structures enables characterization of compartment-independent reorganization across cell types |
title_full_unstemmed | Joint inference and alignment of genome structures enables characterization of compartment-independent reorganization across cell types |
title_short | Joint inference and alignment of genome structures enables characterization of compartment-independent reorganization across cell types |
title_sort | joint inference and alignment of genome structures enables characterization of compartment-independent reorganization across cell types |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6784335/ https://www.ncbi.nlm.nih.gov/pubmed/31594535 http://dx.doi.org/10.1186/s13072-019-0308-3 |
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