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Automated Design of Dynamic Programming Schemes for RNA Folding with Pseudoknots

Authors Bertrand Marchand , Sebastian Will , Sarah J. Berkemer , Laurent Bulteau , Yann Ponty



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Bertrand Marchand
  • LIX (UMR 7161), Ecole Polytechnique, Institut Polytechnique de Paris, France
  • LIGM, CNRS, Univ Gustave Eiffel, F77454 Marne-la-vallée France
Sebastian Will
  • LIX (UMR 7161), Ecole Polytechnique, Institut Polytechnique de Paris, France
Sarah J. Berkemer
  • LIX (UMR 7161), Ecole Polytechnique, Institut Polytechnique de Paris, France
Laurent Bulteau
  • LIGM, CNRS, Univ Gustave Eiffel, F77454 Marne-la-vallée France
Yann Ponty
  • LIX (UMR 7161), Ecole Polytechnique, Institut Polytechnique de Paris, France

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Bertrand Marchand, Sebastian Will, Sarah J. Berkemer, Laurent Bulteau, and Yann Ponty. Automated Design of Dynamic Programming Schemes for RNA Folding with Pseudoknots. In 22nd International Workshop on Algorithms in Bioinformatics (WABI 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 242, pp. 7:1-7:24, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.WABI.2022.7

Abstract

Despite being a textbook application of dynamic programming (DP) and routine task in RNA structure analysis, RNA secondary structure prediction remains challenging whenever pseudoknots come into play. To circumvent the NP-hardness of energy minimization in realistic energy models, specialized algorithms have been proposed for restricted conformation classes that capture the most frequently observed configurations. While these methods rely on hand-crafted DP schemes, we generalize and fully automatize the design of DP pseudoknot prediction algorithms. We formalize the problem of designing DP algorithms for an (infinite) class of conformations, modeled by (a finite number of) fatgraphs, and automatically build DP schemes minimizing their algorithmic complexity. We propose an algorithm for the problem, based on the tree-decomposition of a well-chosen representative structure, which we simplify and reinterpret as a DP scheme. The algorithm is fixed-parameter tractable for the tree-width tw of the fatgraph, and its output represents a 𝒪(n^{tw+1}) algorithm for predicting the MFE folding of an RNA of length n. Our general framework supports general energy models, partition function computations, recursive substructures and partial folding, and could pave the way for algebraic dynamic programming beyond the context-free case.

Subject Classification

ACM Subject Classification
  • Applied computing → Computational biology
  • Theory of computation → Dynamic programming
Keywords
  • RNA folding
  • treewidth
  • dynamic programming

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