Probability Models for DNA Sequence Evolution (Probability and Its Applications)

Models of Molecular Evolution and Phylogeny
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Population ratio refers to its N e after versus before this change. B The effect of an increased C to T substitution rate.

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Categories A, B, and C are defined in Table 1. In the third set of simulations, an acceleration in the C to T substitution rate was incorporated, thereby modeling an increase in their mutation rate due to the deamination of methylated C's in CpG pairs [ 14 ]. The introduction of this bias resulted in significant deviations of f in either direction from 0. Thus, the value of f can vary considerably when the rates for reciprocal mutations are unequal.

Our weak selection model relies on the fixation probabilities of mutant alleles with additive genie selection and equal mutation rates for reciprocal substitutions. Collectively, the three sets of simulations highlight that the f parameter is complex and can be influenced by a number of different factors [ 4 ].

This complexity limits its biological interpretation and the use of its expected value of 0. Nevertheless, as widely acknowledged, simpler models have their place, since they allow one to maximize analytical power for more limited data, while minimizing the risk of over-parameterization [ 13 , 16 ]. J Mol Evol.

Goldman N, Whelan S: A novel use of equilibrium frequencies in models of sequence evolution. Mol Biol Evol. Mammalian Protein Metabolism. Edited by: Munro HN. Kimura M: A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. Ohta T: Slightly deleterious mutant substitutions in evolution. Ohta T: The nearly neutral theory of molecular evolution. Annu Rev Ecol Syst.

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Probability and Its Applications. Free Preview cover. © Probability Models for DNA Sequence Evolution. Authors: Durrett, Richard. Free Preview. Second. if observed DNA sequences are consistent with the assumptions of the “null model” identity by descent and its study by Fourier analysis (), the “ continuous” tween probability and biology (in my case, genetics and ecology): Janet Best.

Buhner M: The selection-mutation-drift theory of synonymous codon usage. Kimura M: On the probability of fixation of mutant genes in populations. Kimura M, Ohta T: Population genetics, molecular biometry, and evolution. Felsenstein J: Inferring phylogenies. Li WH: Maintenance of genetic variability under mutation and selection pressures in a finite population.

Download references. Both authors also thank the Department of Zoology, University of Florida for its support.

Correspondence to Bjarne Knudsen. Both authors contributed to the conception and design of this study and to the writing, reviewing, and final approval of this article. Reprints and Permissions. Knudsen, B. Aydin, G. Watters, R. Olson, and J. F Kitchell. Visualizing the food-web effects of fishing for tunas in the Pacific Ocean.

Huelsenbeck, J.

Stochastic models by Nick Barton

Bioinformatics 17 8 : Hwang, D. Bayesian Markov chain Monte Carlo sequence analysis reveals varying neutral substitution patterns in mammalian evolution. Kettlewell, H. Selection experiments on industrial melanism in the Lepidoptera. Heredity 9: Kimura, M. The Neutral Theory of Molecular Evolution. Kingman, J.

Probability Models for DNA Sequence Evolution (Probability and Its Applications)

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Ecology Pearl, R.

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