MAIN


size=2 width="100%" align=center>

©1996-2014 All Rights Reserved. Online Journal of Bioinformatics . You may not store these pages in any form except for your own personal use. All other usage or distribution is illegal under international copyright treaties. Permission to use any of these pages in any other way besides the before mentioned must be gained in writing from the publisher. This article is exclusively copyrighted in its entirety to OJB publications. This article may be copied once but may not be, reproduced or re-transmitted without the express permission of the editors. This journal satisfies the refereeing requirements (DEST) for the Higher Education Research Data Collection (Australia). Linking:To link to this page or any pages linking to this page you must link directly to this page only here rather than put up your own page.


OJBTM

Online Journal of Bioinformatics ©

Volume 14(2):207-212, 2013


Selection pressure analysis of African swine fever virus attachment protein p12 gene.

 

You-Fang Chen1, Youhua Chen2*

 

1School of Software, Harbin Normal University, Heilongjiang Province, China 2Department of Renewable Resources, University of Alberta, Edmonton, T6G 2H1, Canada *Email: youhuach@gmail.com

 

ABSTRACT

 

Chen YF, Chen Y., Selection pressure analysis of African swine fever virus attachment protein p12 gene, Onl J Bioinform., 14(2):207-212, 2013. A study to determine whether there is positive selection of the attachment protein p12 gene of African swine fever virus (ASFV) is described. The functional divergence among the sequences was very low as shown by nucleotide diversity. It was found that the gene is most likely undergoing purifying selection instead of positive selection. Through the likelihood-ratio test of nested models, one positively selected site 47D (in the template M84183) was identified by Bayes Empirical Bayes analysis but this was not statistically significant.  In conclusion, adaptive evolution is unlikely for this structural gene.

 

KEYWORDS: natural selection, structural protein, adaptive evolution, Bayesian probability


 

MAIN

 

FULL-TEXT(SUBSCRIBERS)