- THIS ARTICLE
- Full Text
- Full Text (PDF)
- Alert me when this article is cited
- Alert me if a correction is posted
- SERVICES
- Email this article to a friend
- Similar articles in this journal
- Similar articles in PubMed
- Alert me to new issues of the journal
- Download to citation manager
- Reprints & Permissions
- CITING ARTICLES
- Citing Articles via HighWire
- Citing Articles via Google Scholar
- GOOGLE SCHOLAR
- Articles by Wong, W. S. W.
- Articles by Nielsen, R.
- Search for Related Content
- PUBMED
- PubMed Citation
- Articles by Wong, W. S. W.
- Articles by Nielsen, R.
Genetics, Vol. 167, 949-958, June 2004, Copyright © 2004
doi:10.1534/genetics.102.010959
Detecting Selection in Noncoding Regions of Nucleotide Sequences
Wendy S. W. Wong1 and Rasmus Nielsen
Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York 14850
1 Corresponding author: 434 Warren Hall, Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853.
E-mail: sww8{at}cornell.edu
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 
. When a site in a noncoding region is evolving neutrally = 1, while > 1 indicates the action of positive selection, and < 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.
This article has been cited by other articles:
 |
|
 |
|
  W. Otto, P. F. Stadler, F. Lopez-Giraldez, J. P. Townsend, V. J. Lynch, and G. P. Wagner Measuring Transcription Factor-Binding Site Turnover: A Maximum Likelihood Approach Using Phylogenies Gen Biol Evol, June 22, 2009; 2009(0): 85 - 98. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 G. A. Babbitt and Y. Kim Inferring Natural Selection on Fine-Scale Chromatin Organization in Yeast Mol. Biol. Evol., August 1, 2008; 25(8): 1714 - 1727. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 T.-K. Seo and H. Kishino Synonymous Substitutions Substantially Improve Evolutionary Inference from Highly Diverged Proteins Syst Biol, June 1, 2008; 57(3): 367 - 377. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Q. Xu, C.-H. C. Cheng, P. Hu, H. Ye, Z. Chen, L. Cao, L. Chen, Y. Shen, and L. Chen Adaptive Evolution of Hepcidin Genes in Antarctic Notothenioid Fishes Mol. Biol. Evol., June 1, 2008; 25(6): 1099 - 1112. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Lu, Y. Fu, S. Kumar, Y. Shen, K. Zeng, A. Xu, R. Carthew, and C.-I Wu Adaptive Evolution of Newly Emerged Micro-RNA Genes in Drosophila Mol. Biol. Evol., May 1, 2008; 25(5): 929 - 938. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
N. Howell, J. L. Elson, C. Howell, and D. M. Turnbull Relative Rates of Evolution in the Coding and Control Regions of African mtDNAs Mol. Biol. Evol., October 1, 2007; 24(10): 2213 - 2221. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. W. Doniger, J. Huh, and J. C. Fay Identification of functional transcription factor binding sites using closely related Saccharomyces species Genome Res., May 1, 2005; 15(5): 701 - 709. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Sinha and E. D. Siggia Sequence Turnover and Tandem Repeats in cis-Regulatory Modules in Drosophila Mol. Biol. Evol., April 1, 2005; 22(4): 874 - 885. [Abstract] [Full Text] [PDF]
|
|