Measuring Selection in RNA
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Measuring Selection in RNA . Naila Mimouni, Rune Lyngsoe and Jotun Hein Department of Statistics, Oxford University. Aim • Extract selection information from conservation of secondary structure of alignments of homologous RNA sequences from different species, for different RNA families.

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Measuring Selection in RNA . Naila Mimouni, Rune Lyngsoe and Jotun Hein

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Measuring selection in rna naila mimouni rune lyngsoe and jotun hein

Measuring Selection in RNA.

Naila Mimouni, Rune Lyngsoe and Jotun Hein

Department of Statistics, Oxford University

  • Aim

  • •Extract selection information from conservation of secondary structure of alignments of homologous RNA sequences from different species, for different RNA families.

  • Motivation

  • RNA Evolution:

    + Mutation on the DNA (before it is translated to RNA).

    + Selection on the RNA to preserve structure stability and function.

  • Protein Selection:

    + Periodicity of the 3 bases forming the amino acid.

    + The genetic code.

    + Ka/Ks.

  • RNA Selection: No equivalent of Ka/Ks.

Preliminary Results:

Overall:

Stem Classes:

I: II:

III:

Loop Classes I: II:

III:

Method

Basic idea:

Exploit conservation of RNA secondary structure.

Two Approaches:

A) Counting Approach:

+ Reconstruct phylogeny: Non-directionality.

+ Count Substitution according to their location: stem lengthening/shortening in loops/stems respectively.

+ Class Definitions:

B) Evolutionary Modelling:

+ Reconstructing phylogeny: directionality.

+ Novel Rates: Singlet, doublet and junk rates.

+ Homologous RNAs partitioned according to similar homology.

+ Likelihood test: Given a gene: pseudogene Vs functional.

Data:

+ Rfam: RNA sequence alignments with conserved structure.

+ 503 different RNA families.

+ Largest dataset for investigating RNA selection.

+ Initial focus: 47 miRNA families, each containing aligned sequences with conserved structure.

Future Work

+ Maximum likelihood for phylogeny recontruction.

+ Devise the singlet, doublet rates.

+ construct selection model

+ Expand to other RNA families.

1: Knudsen, B. & Hein, J. 2003. Pfold: RNA secondary structure prediction using stochastic context-free grammars. Nucleic Acids Research, 31(13), 3423-8.

2: Schroeder, S. J., Burkard, M. E. & Turner, D. H. 2001. The Energetics of Small Internal Loops in RNA. Biopolymers Nucleic Acid Sci., 52, 157-167.


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