Automatic Exploration of the Natural Variability of RNA Non-Canonical Geometric Patterns with a Parameterized Sampling Technique

Authors Théo Boury , Yann Ponty , Vladimir Reinharz

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Author Details

Théo Boury
  • Computer Science Department, Ecole Normale Supérieure de Lyon, France
Yann Ponty
  • Laboratoire d'Informatique de l'Ecole Polytechnique (CNRS/LIX, UMR 7161), Institut Polytechnique de Paris, France
Vladimir Reinharz
  • Department of Computer Science, Université du Québec à Montréal, Canada

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Théo Boury, Yann Ponty, and Vladimir Reinharz. Automatic Exploration of the Natural Variability of RNA Non-Canonical Geometric Patterns with a Parameterized Sampling Technique. In 23rd International Workshop on Algorithms in Bioinformatics (WABI 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 273, pp. 20:1-20:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


Motivation. Recurrent substructures in RNA, known as 3D motifs, consist of networks of base pair interactions and are critical to understanding the relationship between structure and function. Their structure is naturally expressed as a graph which has led to many graph-based algorithms to automatically catalog identical motifs found in 3D structures. Yet, due to the complexity of the problem, state-of-the-art methods are often optimized to find exact matches, limiting the search to a subset of potential solutions, or do not allow explicit control over the desired variability. Results. We developed FuzzTree, a method able to efficiently sample approximate instances of an RNA motif, abstracted as a subgraph within a target RNA structure. It is the first method that allows explicit control over (1) the admissible geometric variability in the interactions; (2) the number of missing edges; and (3) the introduction of discontinuities in the backbone given close distances in the 3D structure. Our tool relies on a multidimensional Boltzmann sampling, having complexity parameterized by the treewidth of the requested motif. We applied our method to the well-known internal loop Kink-Turn motif, which can be divided into 12 subgroups. Given only the graph representing the main Kink-Turn subgroup, FuzzTree retrieved over 3/4 of all kink-turns. We also highlighted two occurrences of new sampled patterns. Our tool is available as free software and can be customized for different parameters and types of graphs.

Subject Classification

ACM Subject Classification
  • Applied computing → Molecular structural biology
  • Subgraph Isomorphism
  • 3D RNA
  • Parameterized Complexity
  • Tree Decomposition
  • Boltzmann sampling
  • Neighborhood metrics
  • Kink-Turn family


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