Dataset Information

Natural selection and the frequency distributions of "silent" DNA polymorphism in Drosophila.

ABSTRACT: In Escherichia coli, Saccharomyces cerevisiae, and Drosophila melanogaster, codon bias may be maintained by a balance among mutation pressure, genetic drift, and natural selection favoring translationally superior codons. Under such an evolutionary model, silent mutations fall into two fitness categories: preferred mutations that increase codon bias and unpreferred changes in the opposite direction. This prediction can be tested by comparing the frequency spectra of synonymous changes segregating within populations; natural selection will elevate the frequencies of advantageous mutations relative to that of deleterious changes. The frequency distributions of preferred and unpreferred mutations differ in the predicted direction among 99 alleles of two D. pseudoobscura genes and five alleles of eight D. simulans genes. This result confirms the existence of fitness classes of silent mutations. Maximum likelihood estimates suggest that selection intensity at silent sites is, on average, very weak in both D. pseudoobscura and D. simulans (magnitude of NS approximately 1). Inference of evolutionary processes from within-species sequence variation is often hindered by the assumption of a stationary frequency distribution. This assumption can be avoided when identifying the action of selection and tested when estimating selection intensity.

SUBMITTER: Akashi H

PROVIDER: S-EPMC1207945 | biostudies-other | 1997 May

REPOSITORIES: biostudies-other

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Natural selection and the frequency distributions of "silent" DNA polymorphism in Drosophila.

Akashi H H Schaeffer S W SW

Genetics 19970501 1

In Escherichia coli, Saccharomyces cerevisiae, and Drosophila melanogaster, codon bias may be maintained by a balance among mutation pressure, genetic drift, and natural selection favoring translationally superior codons. Under such an evolutionary model, silent mutations fall into two fitness categories: preferred mutations that increase codon bias and unpreferred changes in the opposite direction. This prediction can be tested by comparing the frequency spectra of synonymous changes segregatin ...[more]

PMID: 9136019

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Project description:BackgroundDNA double-strand breaks can be counted as discrete foci by imaging techniques. In personalized medicine and pharmacology, the analysis of counting data is relevant for numerous applications, e.g., for cancer and aging research and the evaluation of drug efficacy. By default, it is assumed to follow the Poisson distribution. This assumption, however, may lead to biased results and faulty conclusions in datasets with excess zero values (zero-inflation), a variance larger than the mean (overdispersion), or both. In such cases, the assumption of a Poisson distribution would skew the estimation of mean and variance, and other models like the negative binomial (NB), zero-inflated Poisson or zero-inflated NB distributions should be employed. The model chosen has an influence on the parameter estimation (mean value and confidence interval). Yet the choice of the suitable distribution model is not trivial.MethodsTo support, simplify and objectify this process, we have developed the countfitteR software as an R package. We used a Bayesian approach for distribution model selection and the shiny web application framework for interactive data analysis.ResultsWe show the application of our software based on examples of DNA double-strand break count data from phenotypic imaging by multiplex fluorescence microscopy. In analyzing numerous datasets of molecular pharmacological markers (phosphorylated histone H2AX and p53 binding protein), countfitteR demonstrated an equal or superior statistical performance compared to the usually employed two-step procedure, with an overall power of up to 98%. In addition, it still gave information in cases with no result at all from the two-step procedure. In our data sample we found that the NB distribution was the most frequent, with the Poisson distribution taking second place.ConclusionscountfitteR can perform an automated distribution model selection and thus support the data analysis and lead to objective statistically verifiable estimated values. Originally designed for the analysis of foci in biomedical image data, countfitteR can be used in a variety of areas where non-Poisson distributed counting data is prevalent.

Dataset Information

Natural selection and the frequency distributions of "silent" DNA polymorphism in Drosophila.

Publications

Natural selection and the frequency distributions of "silent" DNA polymorphism in Drosophila.

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