Segmenting eukaryotic genomes with the Generalized Gibbs Sampler.
Eukaryotic genomes display segmental patterns of variation in various properties, including GC content and degree of evolutionary conservation. DNA segmentation algorithms are aimed at identifying statistically significant boundaries between such segments. Such algorithms may provide a means of discovering new classes of functional elements in eukaryotic genomes. This paper presents a model and an algorithm for Bayesian DNA segmentation and considers the feasibility of using it to segment whole eukaryotic genomes. The algorithm is tested on a range of simulated and real DNA sequences, and the following conclusions are drawn. Firstly, the algorithm correctly identifies non-segmented sequence, and can thus be used to reject the null hypothesis of uniformity in the property of interest. Secondly, estimates of the number and locations of change-points produced by the algorithm are robust to variations in algorithm parameters and initial starting conditions and correspond to real features in the data. Thirdly, the algorithm is successfully used to segment human chromosome 1 according to GC content, thus demonstrating the feasibility of Bayesian segmentation of eukaryotic genomes. The software described in this paper is available from the author's website (www.uq.edu.au/ approximately uqjkeith/) or upon request to the author.
Keith
Jonathan M
JM
Department of Mathematics, University of Queensland, Brisbane, Australia. j.keith1@uq.edu.au
D000465:Algorithms; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D005057:Eukaryotic Cells / Q000378:metabolism*; D016678:Genome / Q000235:genetics*; D008285:Major Histocompatibility Complex / Q000235:genetics; D008957:Models, Genetic