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Mining for recurrent long-range interactions in RNA structures reveals embedded hierarchies in network families

The wealth of the combinatorics of nucleotide base pairs enables RNA molecules to assemble into sophisticated interaction networks, which are used to create complex 3D substructures. These interaction networks are essential to shape the 3D architecture of the molecule, and also to provide the key el...

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Autores principales: Reinharz, Vladimir, Soulé, Antoine, Westhof, Eric, Waldispühl, Jérôme, Denise, Alain
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5934684/
https://www.ncbi.nlm.nih.gov/pubmed/29608773
http://dx.doi.org/10.1093/nar/gky197
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author Reinharz, Vladimir
Soulé, Antoine
Westhof, Eric
Waldispühl, Jérôme
Denise, Alain
author_facet Reinharz, Vladimir
Soulé, Antoine
Westhof, Eric
Waldispühl, Jérôme
Denise, Alain
author_sort Reinharz, Vladimir
collection PubMed
description The wealth of the combinatorics of nucleotide base pairs enables RNA molecules to assemble into sophisticated interaction networks, which are used to create complex 3D substructures. These interaction networks are essential to shape the 3D architecture of the molecule, and also to provide the key elements to carry molecular functions such as protein or ligand binding. They are made of organised sets of long-range tertiary interactions which connect distinct secondary structure elements in 3D structures. Here, we present a de novo data-driven approach to extract automatically from large data sets of full RNA 3D structures the recurrent interaction networks (RINs). Our methodology enables us for the first time to detect the interaction networks connecting distinct components of the RNA structure, highlighting their diversity and conservation through non-related functional RNAs. We use a graphical model to perform pairwise comparisons of all RNA structures available and to extract RINs and modules. Our analysis yields a complete catalog of RNA 3D structures available in the Protein Data Bank and reveals the intricate hierarchical organization of the RNA interaction networks and modules. We assembled our results in an online database (http://carnaval.lri.fr) which will be regularly updated. Within the site, a tool allows users with a novel RNA structure to detect automatically whether the novel structure contains previously observed RINs.
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spelling pubmed-59346842018-05-09 Mining for recurrent long-range interactions in RNA structures reveals embedded hierarchies in network families Reinharz, Vladimir Soulé, Antoine Westhof, Eric Waldispühl, Jérôme Denise, Alain Nucleic Acids Res Computational Biology The wealth of the combinatorics of nucleotide base pairs enables RNA molecules to assemble into sophisticated interaction networks, which are used to create complex 3D substructures. These interaction networks are essential to shape the 3D architecture of the molecule, and also to provide the key elements to carry molecular functions such as protein or ligand binding. They are made of organised sets of long-range tertiary interactions which connect distinct secondary structure elements in 3D structures. Here, we present a de novo data-driven approach to extract automatically from large data sets of full RNA 3D structures the recurrent interaction networks (RINs). Our methodology enables us for the first time to detect the interaction networks connecting distinct components of the RNA structure, highlighting their diversity and conservation through non-related functional RNAs. We use a graphical model to perform pairwise comparisons of all RNA structures available and to extract RINs and modules. Our analysis yields a complete catalog of RNA 3D structures available in the Protein Data Bank and reveals the intricate hierarchical organization of the RNA interaction networks and modules. We assembled our results in an online database (http://carnaval.lri.fr) which will be regularly updated. Within the site, a tool allows users with a novel RNA structure to detect automatically whether the novel structure contains previously observed RINs. Oxford University Press 2018-05-04 2018-03-28 /pmc/articles/PMC5934684/ /pubmed/29608773 http://dx.doi.org/10.1093/nar/gky197 Text en © The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Computational Biology
Reinharz, Vladimir
Soulé, Antoine
Westhof, Eric
Waldispühl, Jérôme
Denise, Alain
Mining for recurrent long-range interactions in RNA structures reveals embedded hierarchies in network families
title Mining for recurrent long-range interactions in RNA structures reveals embedded hierarchies in network families
title_full Mining for recurrent long-range interactions in RNA structures reveals embedded hierarchies in network families
title_fullStr Mining for recurrent long-range interactions in RNA structures reveals embedded hierarchies in network families
title_full_unstemmed Mining for recurrent long-range interactions in RNA structures reveals embedded hierarchies in network families
title_short Mining for recurrent long-range interactions in RNA structures reveals embedded hierarchies in network families
title_sort mining for recurrent long-range interactions in rna structures reveals embedded hierarchies in network families
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5934684/
https://www.ncbi.nlm.nih.gov/pubmed/29608773
http://dx.doi.org/10.1093/nar/gky197
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