title: Model selection in Bayesian segmentation of multiple DNA alignments.
abstract: BACKGROUND
  The analysis of multiple sequence alignments is allowing researchers to glean valuable insights into evolution, as well as identify genomic regions that may be functional, or discover novel classes of functional elements. Understanding the distribution of conservation levels that constitutes the evolutionary landscape is crucial to distinguishing functional regions from non-functional. Recent evidence suggests that a binary classification of evolutionary rates is inappropriate for this purpose and finds only highly conserved functional elements. Given that the distribution of evolutionary rates is multi-modal, determining the number of modes is of paramount concern. Through simulation, we evaluate the performance of a number of information criterion approaches derived from MCMC simulations in determining the dimension of a model.
  
  
  RESULTS
  We utilize a deviance information criterion (DIC) approximation that is more robust than the approximations from other information criteria, and show our information criteria approximations do not produce superfluous modes when estimating conservation distributions under a variety of circumstances. We analyse the distribution of conservation for a multiple alignment comprising four primate species and mouse, and repeat this on two additional multiple alignments of similar species. We find evidence of six distinct classes of evolutionary rates that appear to be robust to the species used.
  
  
  BACKGROUND
  Source code and data are available at http://dl.dropbox.com/u/477240/changept.zip.
authors.affiliation: Centre for Clinical Epidemiology and Biostatistics, University of Newcastle, NSW, Victoria, Australia. christopher.oldmeadow@newcastle.edu.au
authors.forename: Christopher
authors.initials: C
authors.lastname: Oldmeadow
authors.forename: Jonathan M
authors.initials: JM
authors.lastname: Keith
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D004247:DNA / Q000032:analysis; D019143:Evolution, Molecular; D023281:Genomics / Q000379:methods; D051379:Mice; D015233:Models, Statistical; D011323:Primates; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*
chemical_list: D004247:DNA