Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao zbMATH Openarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
zbMATH Open
Article
Data sources: zbMATH Open
Biometrics
Article . 1995 . Peer-reviewed
Data sources: Crossref
Biometrics
Article . 1995
versions View all 3 versions
addClaim

Mixed Model Analysis of DNA Sequence Evolution

Mixed model analysis of DNA sequence evolution
Authors: Yang, Ziheng; Wang, Tianlin;

Mixed Model Analysis of DNA Sequence Evolution

Abstract

Nucleotides in a DNA sequence may be changing at different rates, because they are located in different structural and functional regions of the gene, and are thus subject to different mutational pressures or selective restrictions. Knowledge of substitution rates at specific sites is important for understanding the forces and mechanisms that have shaped the evolution of the DNA sequences. The gamma distribution has previously been proposed to model such rate variation among nucleotide sites. Based on mixed model methodology we present in this paper a method for predicting substitution rates at nucleotide sites by using homologous DNA sequences. The predictor is unbiased and "best" in the sense that it minimizes the mean squared error and maximizes the correlation between the predictor and the true value. It is also quite robust to errors in estimates of parameters in the model. A numerical example is given, with guidelines for the practical use of the approach. The most influential factor affecting the accuracy of prediction is the number of sequences; to get a correlation of over .7 between the predictor and the true value, about six to seven sequences are needed, depending on the overall similarity of the sequences.

Related Organizations
Keywords

mixed models, Primates, best unbiased predictor, maximum likelihood method, evolution of DNA sequences, empirical Bayes estimation, spatial rate variation, DNA sequences, DNA, Mitochondrial, Applications of statistics to biology and medical sciences; meta analysis, Problems related to evolution, Animals, Humans, mean squared error, gamma distribution, Models, Statistical, Base Sequence, Models, Genetic, DNA, Protein sequences, DNA sequences, Biological Evolution, substitution rates at nucleotide sites, Mathematics

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    60
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 1%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
60
Top 10%
Top 1%
Top 10%
Upload OA version
Are you the author of this publication? Upload your Open Access version to Zenodo!
It’s fast and easy, just two clicks!