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MBE Advance Access originally published online on October 22, 2008
Molecular Biology and Evolution 2009 26(1):209-216; doi:10.1093/molbev/msn240
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© The Author 2008. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org

Research Articles

An Analysis of Structural Influences on Selection in RNA Genes

Naila K. Mimouni*, Rune B. Lyngsø*, Sam Griffiths-Jones{dagger} and Jotun Hein*

* Department of Statistics, University of Oxford, Oxford, United Kingdom
{dagger} Faculty of Life Sciences, University of Manchester, Manchester, United Kingdom

E-mail: lyngsoe{at}stats.ox.ac.uk.

Accepted for publication October 14, 2008.

Noncoding RNAs (ncRNAs) are transcripts that do not code for protein but rather function as RNA in catalytic, regulatory, or structural roles in the cell. ncRNAs are involved in universally conserved biological processes, including protein synthesis and gene regulation, and have more specific roles, such as in X-chromosome inactivation in eutherian mammals. In this paper, we propose and investigate a hypothesis for patterns of sequence selection in structurally conserved ncRNAs. Previous attempts at defining RNA selection compared rates of evolution between paired and unpaired bases with largely inconclusive results. Our approach focuses only on paired bases in ncRNAs with conserved structure. By analogy to the different properties of codon positions based on the genetic code, we use a well-developed energy model for RNA structure to classify stem positions into structural classes and argue that they are under different selective constraints. We validate the hypothesis on several RNA families and use simulated data to verify the evolutionary origin of signals. Our class labeling is shown to be a better model of ncRNA evolution than the tradition of treating stem positions equally. As well as providing a better understanding of RNA evolution, the evolutionary footprint we identify can easily be incorporated into gene finders to improve their specificity.

Key Words: RNA genes • sequence evolution • structural selection


Koichiro Tamura, Associate Editor


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