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Comparing RNA Structures with Biologically Relevant Operations Cannot Be Done without Strong Combinatorial Restrictions

  • Guillaume Blin
  • Sylvie Hamel
  • Stéphane Vialette
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5942)

Abstract

Arc-annotated sequences are useful for representing structural information of RNAs and have been extensively used for comparing RNA structures in both terms of sequence and structural similarities. Among the many paradigms referring to arc-annotated sequences and RNA structures comparison (see [2] for more details), the most important one is the general edit distance. The problem of computing an edit distance between two non-crossing arc-annotated sequences was introduced in [5]. The introduced model uses edit operations that involve either single letters or pairs of letters (never considered separately) and is solvable in polynomial-time [12].

To account for other possible RNA structural evolutionary events, new edit operations, allowing to consider either silmutaneously or separately letters of a pair were introduced in [9]; unfortunately at the cost of computational tractability. It has been proved that comparing two RNA secondary structures using a full set of biologically relevant edit operations is NP-complete. Nevertheless, in [8], the authors have used a strong combinatorial restriction in order to compare two RNA stem-loops with a full set of biologically relevant edit operations; which have allowed them to design a polynomial-time and space algorithm for comparing general secondary RNA structures.

In this paper we will prove theoretically that comparing two RNA structures using a full set of biologically relevant edit operations cannot be done without strong combinatorial restrictions.

Keywords

Truth Assignment Edit Operation Computational Tractability Tree Edit Distance Satisfying Truth Assignment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Guillaume Blin
    • 1
  • Sylvie Hamel
    • 2
  • Stéphane Vialette
    • 1
  1. 1.Université Paris-Est, LIGM - UMR CNRS 8049France
  2. 2.DIROUniversité de MontréalCanada

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