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Advantages of using graph databases to explore chromatin conformation capture experiments
BACKGROUND: High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of DNA interactions and 3D chromosome folding at the genome-wide scale. Usually, these data are represented as matrices describing the binary contacts among the different chromosome regions. On the other h...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8073886/ https://www.ncbi.nlm.nih.gov/pubmed/33902433 http://dx.doi.org/10.1186/s12859-020-03937-0 |
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author | D’Agostino, Daniele Liò, Pietro Aldinucci, Marco Merelli, Ivan |
author_facet | D’Agostino, Daniele Liò, Pietro Aldinucci, Marco Merelli, Ivan |
author_sort | D’Agostino, Daniele |
collection | PubMed |
description | BACKGROUND: High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of DNA interactions and 3D chromosome folding at the genome-wide scale. Usually, these data are represented as matrices describing the binary contacts among the different chromosome regions. On the other hand, a graph-based representation can be advantageous to describe the complex topology achieved by the DNA in the nucleus of eukaryotic cells. METHODS: Here we discuss the use of a graph database for storing and analysing data achieved by performing Hi-C experiments. The main issue is the size of the produced data and, working with a graph-based representation, the consequent necessity of adequately managing a large number of edges (contacts) connecting nodes (genes), which represents the sources of information. For this, currently available graph visualisation tools and libraries fall short with Hi-C data. The use of graph databases, instead, supports both the analysis and the visualisation of the spatial pattern present in Hi-C data, in particular for comparing different experiments or for re-mapping omics data in a space-aware context efficiently. In particular, the possibility of describing graphs through statistical indicators and, even more, the capability of correlating them through statistical distributions allows highlighting similarities and differences among different Hi-C experiments, in different cell conditions or different cell types. RESULTS: These concepts have been implemented in NeoHiC, an open-source and user-friendly web application for the progressive visualisation and analysis of Hi-C networks based on the use of the Neo4j graph database (version 3.5). CONCLUSION: With the accumulation of more experiments, the tool will provide invaluable support to compare neighbours of genes across experiments and conditions, helping in highlighting changes in functional domains and identifying new co-organised genomic compartments. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-020-03937-0. |
format | Online Article Text |
id | pubmed-8073886 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-80738862021-04-26 Advantages of using graph databases to explore chromatin conformation capture experiments D’Agostino, Daniele Liò, Pietro Aldinucci, Marco Merelli, Ivan BMC Bioinformatics Research BACKGROUND: High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of DNA interactions and 3D chromosome folding at the genome-wide scale. Usually, these data are represented as matrices describing the binary contacts among the different chromosome regions. On the other hand, a graph-based representation can be advantageous to describe the complex topology achieved by the DNA in the nucleus of eukaryotic cells. METHODS: Here we discuss the use of a graph database for storing and analysing data achieved by performing Hi-C experiments. The main issue is the size of the produced data and, working with a graph-based representation, the consequent necessity of adequately managing a large number of edges (contacts) connecting nodes (genes), which represents the sources of information. For this, currently available graph visualisation tools and libraries fall short with Hi-C data. The use of graph databases, instead, supports both the analysis and the visualisation of the spatial pattern present in Hi-C data, in particular for comparing different experiments or for re-mapping omics data in a space-aware context efficiently. In particular, the possibility of describing graphs through statistical indicators and, even more, the capability of correlating them through statistical distributions allows highlighting similarities and differences among different Hi-C experiments, in different cell conditions or different cell types. RESULTS: These concepts have been implemented in NeoHiC, an open-source and user-friendly web application for the progressive visualisation and analysis of Hi-C networks based on the use of the Neo4j graph database (version 3.5). CONCLUSION: With the accumulation of more experiments, the tool will provide invaluable support to compare neighbours of genes across experiments and conditions, helping in highlighting changes in functional domains and identifying new co-organised genomic compartments. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-020-03937-0. BioMed Central 2021-04-26 /pmc/articles/PMC8073886/ /pubmed/33902433 http://dx.doi.org/10.1186/s12859-020-03937-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 | Research D’Agostino, Daniele Liò, Pietro Aldinucci, Marco Merelli, Ivan Advantages of using graph databases to explore chromatin conformation capture experiments |
title | Advantages of using graph databases to explore chromatin conformation capture experiments |
title_full | Advantages of using graph databases to explore chromatin conformation capture experiments |
title_fullStr | Advantages of using graph databases to explore chromatin conformation capture experiments |
title_full_unstemmed | Advantages of using graph databases to explore chromatin conformation capture experiments |
title_short | Advantages of using graph databases to explore chromatin conformation capture experiments |
title_sort | advantages of using graph databases to explore chromatin conformation capture experiments |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8073886/ https://www.ncbi.nlm.nih.gov/pubmed/33902433 http://dx.doi.org/10.1186/s12859-020-03937-0 |
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