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A Multigraph-Based Representation of Hi-C Data
Chromatin–chromatin interactions and three-dimensional (3D) spatial structures are involved in transcriptional regulation and have a decisive role in DNA replication and repair. To understand how individual genes and their regulatory elements function within the larger genomic context, and how the g...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778156/ https://www.ncbi.nlm.nih.gov/pubmed/36553456 http://dx.doi.org/10.3390/genes13122189 |
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author | Makai, Diána Cseh, András Sepsi, Adél Makai, Szabolcs |
author_facet | Makai, Diána Cseh, András Sepsi, Adél Makai, Szabolcs |
author_sort | Makai, Diána |
collection | PubMed |
description | Chromatin–chromatin interactions and three-dimensional (3D) spatial structures are involved in transcriptional regulation and have a decisive role in DNA replication and repair. To understand how individual genes and their regulatory elements function within the larger genomic context, and how the genome reacts to environmental stimuli, the linear sequence information needs to be interpreted in three-dimensional space, which is still a challenging task. Here, we propose a novel, heuristic approach to represent Hi-C datasets by a whole-genomic pseudo-structure in 3D space. The baseline of our approach is the construction of a multigraph from genomic-sequence data and Hi-C interaction data, then applying a modified force-directed layout algorithm. The resulting layout is a pseudo-structure. While pseudo-structures are not based on direct observation and their details are inherent to settings, surprisingly, they demonstrate interesting, overall similarities of known genome structures of both barley and rice, namely, the Rabl and Rosette-like conformation. It has an exciting potential to be extended by additional omics data (RNA-seq, Chip-seq, etc.), allowing to visualize the dynamics of the pseudo-structures across various tissues or developmental stages. Furthermore, this novel method would make it possible to revisit most Hi-C data accumulated in the public domain in the last decade. |
format | Online Article Text |
id | pubmed-9778156 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97781562022-12-23 A Multigraph-Based Representation of Hi-C Data Makai, Diána Cseh, András Sepsi, Adél Makai, Szabolcs Genes (Basel) Technical Note Chromatin–chromatin interactions and three-dimensional (3D) spatial structures are involved in transcriptional regulation and have a decisive role in DNA replication and repair. To understand how individual genes and their regulatory elements function within the larger genomic context, and how the genome reacts to environmental stimuli, the linear sequence information needs to be interpreted in three-dimensional space, which is still a challenging task. Here, we propose a novel, heuristic approach to represent Hi-C datasets by a whole-genomic pseudo-structure in 3D space. The baseline of our approach is the construction of a multigraph from genomic-sequence data and Hi-C interaction data, then applying a modified force-directed layout algorithm. The resulting layout is a pseudo-structure. While pseudo-structures are not based on direct observation and their details are inherent to settings, surprisingly, they demonstrate interesting, overall similarities of known genome structures of both barley and rice, namely, the Rabl and Rosette-like conformation. It has an exciting potential to be extended by additional omics data (RNA-seq, Chip-seq, etc.), allowing to visualize the dynamics of the pseudo-structures across various tissues or developmental stages. Furthermore, this novel method would make it possible to revisit most Hi-C data accumulated in the public domain in the last decade. MDPI 2022-11-23 /pmc/articles/PMC9778156/ /pubmed/36553456 http://dx.doi.org/10.3390/genes13122189 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Technical Note Makai, Diána Cseh, András Sepsi, Adél Makai, Szabolcs A Multigraph-Based Representation of Hi-C Data |
title | A Multigraph-Based Representation of Hi-C Data |
title_full | A Multigraph-Based Representation of Hi-C Data |
title_fullStr | A Multigraph-Based Representation of Hi-C Data |
title_full_unstemmed | A Multigraph-Based Representation of Hi-C Data |
title_short | A Multigraph-Based Representation of Hi-C Data |
title_sort | multigraph-based representation of hi-c data |
topic | Technical Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778156/ https://www.ncbi.nlm.nih.gov/pubmed/36553456 http://dx.doi.org/10.3390/genes13122189 |
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