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Median and small parsimony problems on RNA trees.

Bertrand Marchand1, Yoann Anselmetti1, Manuel Lafond1

  • 1Department of Computer Science, University of Sherbrooke, Sherbrooke, QC J1K 2R1, Canada.

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We developed new tree distance metrics to analyze noncoding RNA (ncRNA) structures, aiding the study of RNA evolution and function. These methods offer improved insights into RNA structural conservation and divergence within families and clans.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Molecular Evolution

Background:

  • Noncoding RNAs (ncRNAs) perform functions through specific molecular structures.
  • RNA secondary structures are conserved within functional families, while tertiary structures can differ within RNA clans.
  • Understanding RNA structural evolution is key to deciphering functional adaptations and the Ancient RNA World Hypothesis.

Purpose of the Study:

  • To introduce and analyze tree distance metrics for inferring the evolution of noncoding RNA structures.
  • To address the median and small parsimony problems for ncRNA families using novel and existing tree distances.
  • To develop efficient algorithms for reconstructing RNA evolutionary histories.

Main Methods:

  • Representation of ncRNA secondary structures as leaf-labeled trees.
  • Utilizing the Robinson-Foulds (RF) tree distance and introducing the Internal-Leafset (IL) distance.
  • Considering a generalized tree edit distance for non-perfectly aligned base pairs.
  • Analyzing the theoretical complexity and developing polynomial-time maximum parsimony algorithms.

Main Results:

  • The IL distance better captures differences in RNA structural elements compared to the RF distance.
  • Theoretical complexity of median and small parsimony problems was studied under different distance metrics.
  • Polynomial-time maximum parsimony algorithms were developed and applied to ncRNA families.
  • Practical utility demonstrated through application to RFAM database ncRNA families.

Conclusions:

  • Novel tree distance metrics provide valuable tools for studying ncRNA structural evolution.
  • Efficient algorithms facilitate the reconstruction of evolutionary histories for RNA families and clans.
  • The study enhances our understanding of RNA functional adaptation and evolutionary processes.