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Dependence among sites in RNA evolution.

Jiaye Yu1, Jeffrey L Thorne

  • 1Bioinformatics Research Center, North Carolina State University, USA.

Molecular Biology and Evolution
|May 25, 2006
PubMed
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This study introduces a novel evolutionary model where RNA secondary structure influences genotype evolution by linking sequence stability to fitness. This approach quanties the significant impact of RNA structure on nucleotide substitution rates.

Area of Science:

  • Evolutionary biology
  • Bioinformatics
  • Molecular evolution

Background:

  • Probabilistic models of genotype evolution are well-developed.
  • The influence of phenotype on genotype evolution has received less attention.
  • RNA secondary structure can serve as a link between genotype and phenotype.

Purpose of the Study:

  • To propose an evolutionary model and Bayesian inference procedure to study the effect of phenotype on genotype evolution.
  • To incorporate RNA secondary structure as a surrogate for fitness in evolutionary models.
  • To investigate the impact of RNA secondary structure on nucleotide substitution rates.

Main Methods:

  • Developed a model where RNA secondary structure's free energy approximates fitness.
  • Implemented a Bayesian inference procedure for statistical analysis.

Related Experiment Videos

  • Utilized a software program for analyzing homologous sequences within a known phylogenetic tree topology.

    • Main Results:

    • Demonstrated the significant impact of RNA secondary structure on substitution rates using 5S ribosomal RNA sequences.
    • Showcased reasonable statistical properties of the new inference procedure through analyses on simulated sequences.
    • Highlighted the incorporation of evolutionary dependencies beyond paired positions in RNA helices.

    Conclusions:

    • RNA secondary structure plays a crucial role in shaping genotype evolution.
    • The proposed model and inference procedure offer a novel approach to studying genotype-phenotype relationships.
    • Potential applications include enhanced ancestral sequence inference and identification of functionally important sites.