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Documents authored by Barth, Dominique

Document
DOI: 10.4230/LIPIcs.SEA.2025.1
Polymorphic Cycle Basis in a Sequence of Graphs to Analyze the Structural Evolution of a Molecular Dynamic Trajectory

Authors: Ylène Aboulfath, Dominique Barth, Thierry Mautor, Dimitri Watel, and Marc-Antoine Weisser

Published in: LIPIcs, Volume 338, 23rd International Symposium on Experimental Algorithms (SEA 2025)

Abstract
Molecular dynamics analysis is a fundamental topic in chemistry, in particular the study of the formation and dissolution of hydrogen bonds over time. The dynamics of these bonds create and break cycles which are crucial to the structure of the molecules. The challenge in cycle analysis is twofold: there is an exponential number of cycles, and some cycles are very close. We introduce a graph-based approach using minimum cycle bases to assist in molecular dynamics analysis. Given a set of graphs representing a molecule trajectory, we determine, for each graph, a minimum cycle basis and construct a graph of cycles which represents the cycles of minimum bases and their interactions. Then, we aggregate all information from these graphs of cycles into a polygraph. Each vertex of the polygraph represents a class of cycles appearing in different minimum bases and playing equivalent roles in the trajectory. This paper introduces our approach, establishes the complexity of associated problems, and suggests an implementation. Simulations are conducted on both real and generated data to evaluate the performance of our approach.
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Ylène Aboulfath, Dominique Barth, Thierry Mautor, Dimitri Watel, and Marc-Antoine Weisser. Polymorphic Cycle Basis in a Sequence of Graphs to Analyze the Structural Evolution of a Molecular Dynamic Trajectory. In 23rd International Symposium on Experimental Algorithms (SEA 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 338, pp. 1:1-1:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{aboulfath_et_al:LIPIcs.SEA.2025.1,
  author =	{Aboulfath, Yl\`{e}ne and Barth, Dominique and Mautor, Thierry and Watel, Dimitri and Weisser, Marc-Antoine},
  title =	{{Polymorphic Cycle Basis in a Sequence of Graphs to Analyze the Structural Evolution of a Molecular Dynamic Trajectory}},
  booktitle =	{23rd International Symposium on Experimental Algorithms (SEA 2025)},
  pages =	{1:1--1:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-375-1},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{338},
  editor =	{Mutzel, Petra and Prezza, Nicola},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2025.1},
  URN =		{urn:nbn:de:0030-drops-232399},
  doi =		{10.4230/LIPIcs.SEA.2025.1},
  annote =	{Keywords: Graph theory, Cycle basis, Molecular analysis}
}
Document
DOI: 10.4230/LIPIcs.SEA.2021.19
A Graph-Based Similarity Approach to Classify Recurrent Complex Motifs from Their Context in RNA Structures

Authors: Coline Gianfrotta, Vladimir Reinharz, Dominique Barth, and Alain Denise

Published in: LIPIcs, Volume 190, 19th International Symposium on Experimental Algorithms (SEA 2021)

Abstract
This article proposes to use an RNA graph similarity metric, based on the MCES resolution problem, to compare the occurrences of specific complex motifs in RNA graphs, according to their context represented as subgraph. We rely on a new modeling by graphs of these contexts, at two different levels of granularity, and obtain a classification of these graphs, which is consistent with the RNA 3D structure. RNA many non-translational functions, as a ribozyme, riboswitch, or ribosome, require complex structures. Those are composed of a rigid skeleton, a set of canonical interactions called the secondary structure. Decades of experimental and theoretical work have produced precise thermodynamic parameters and efficient algorithms to predict, from sequence, the secondary structure of RNA molecules. On top of the skeleton, the nucleotides form an intricate network of interactions that are not captured by present thermodynamic models. This network has been shown to be composed of modular motifs, that are linked to function, and have been leveraged for better prediction and design. A peculiar subclass of complex structural motifs are those connecting RNA regions far away in the secondary structure. They are crucial to predict since they determine the global shape of the molecule, therefore important for the function. In this paper, we show by using our graph approach that the context is important for the formation of conserved complex structural motifs. We furthermore show that a natural classification of structural variants of the motifs emerges from their context. We explore the cases of three known motif families and we exhibit their experimentally emerging classification.
Cite as

Coline Gianfrotta, Vladimir Reinharz, Dominique Barth, and Alain Denise. A Graph-Based Similarity Approach to Classify Recurrent Complex Motifs from Their Context in RNA Structures. In 19th International Symposium on Experimental Algorithms (SEA 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 190, pp. 19:1-19:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{gianfrotta_et_al:LIPIcs.SEA.2021.19,
  author =	{Gianfrotta, Coline and Reinharz, Vladimir and Barth, Dominique and Denise, Alain},
  title =	{{A Graph-Based Similarity Approach to Classify Recurrent Complex Motifs from Their Context in RNA Structures}},
  booktitle =	{19th International Symposium on Experimental Algorithms (SEA 2021)},
  pages =	{19:1--19:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-185-6},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{190},
  editor =	{Coudert, David and Natale, Emanuele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2021.19},
  URN =		{urn:nbn:de:0030-drops-137912},
  doi =		{10.4230/LIPIcs.SEA.2021.19},
  annote =	{Keywords: Graph similarity, clustering, RNA 3D folding, RNA motif}
}
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