Detecting selection in noncoding regions of nucleotide sequences.
We present a maximum-likelihood method for examining the selection pressure and detecting positive selection in noncoding regions using multiple aligned DNA sequences. The rate of substitution in noncoding regions relative to the rate of synonymous substitution in coding regions is modeled by a parameter zeta. When a site in a noncoding region is evolving neutrally zeta = 1, while zeta > 1 indicates the action of positive selection, and zeta < 1 suggests negative selection. Using a combined model for the evolution of noncoding and coding regions, we develop two likelihood-ratio tests for the detection of selection in noncoding regions. Data analysis of both simulated and real viral data is presented. Using the new method we show that positive selection in viruses is acting primarily in protein-coding regions and is rare or absent in noncoding regions.
Wong
Wendy S W
WS
Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York 14850, USA. sww8@cornell.edu
Nielsen
Rasmus
R
D001483:Base Sequence; D003062:Codon / Q000235:genetics; D005815:Genetic Code; D008957:Models, Genetic; D008962:Models, Theoretical; D012641:Selection, Genetic
D003062:Codon