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.
Measuring Selection in RNA.
Naila Mimouni, Rune Lyngsoe and Jotun Hein
Department of Statistics, Oxford University
+ Mutation on the DNA (before it is translated to RNA).
+ Selection on the RNA to preserve structure stability and function.
+ Periodicity of the 3 bases forming the amino acid.
+ The genetic code.
Loop Classes I: II:
Exploit conservation of RNA secondary structure.
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.
+ 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.
+ 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.