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Measuring Selection in RNA.
Naila Mimouni, Rune Lyngsoe and Jotun Hein
Department of Statistics, Oxford University
Aim
Preliminary Results:
• Extract selection information from conservation of secondary structure
of alignments of homologous RNA sequences from different species, for
different RNA families.
Overall:
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.
Method
Basic idea:
Exploit conservation of RNA secondary structure.
Stem Classes:
I:
II:
Two Approaches:
A) Counting Approach:
+ Reconstruct phylogeny: Non-directionality.
+ Count Substitution according to their location: stem
lengthening/shortening in loops/stems respectively.
+ Class Definitions:
III:
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.
Loop Classes I:
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.
III:
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.
II: