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The Impact of Among-Site Rate Variation on Recombination Detection and Its Solution

Author: DaiJiaQing
Tutor: TaoShiZuo
School: Northwest University of Science and Technology
Course: Biochemistry and Molecular Biology
Keywords: Reorganization Evolutionary rate Hidden Markov Gamma distribution Monte Carlo
CLC: Q78
Type: Master's thesis
Year: 2007
Downloads: 34
Quote: 0
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Molecular evolutionary tree has the advantage of making it is expected to clarify many intractable problems for the classical pathway of the phylogenetic tree of life, and the reconstruction of the evolutionary history of the species. Molecular phylogenetic tree can be constructed in accordance with the different species of nucleic acid sequence differences. The phylogenetic tree is given a graphical topology (branch level) and the length of the branches. The graphical topology is a reflection of the relationship between the various species, the length of the branches reflect evolutionary distance. But now, for the construction of the phylogenetic tree are based on a single evolutionary background - only take into account the impact of the mutation of evolution. Eukaryotic species recombination events are relatively rare, so this is a single background-based analysis of the evolutionary tree of eukaryotes, can get to the correct conclusion. However, the the prokaryotic microorganisms recombinant (including transformation, transduction, conjugation, protoplast fusion) is an important source for the evolution of prokaryotic microorganisms. If analysis of the DNA sequences of prokaryotic microorganisms occurred through restructuring, while the analysis is based on only a single mutation background, lead to the construction of the phylogenetic tree error occurred. Therefore, the before conducting phylogenetic tree analysis, we must first analyze the sequence analysis reorganization happened. Within the last decade, the proposed many detected restructuring. Many of which are based on an idea: set up a fixed-size window, move the window along the sequence direction and calculates the probability of the sequence of the various window where each move, react further recombination sites to statistical probability. There is another method based on hidden Markov and Bayesian thinking. The reorganization of the existing detection methods prerequisite: sequence evolution rate for all sites. Contrary to the true evolution conditions. According to previous studies, we can use a discrete or continuous mathematical model loci evolutionary rate law for modeling. Currently use a wide range of continuous mathematical model of the gamma distribution. Gamma distribution with two parameters, the shape parameter determines the distribution pattern of the loci of evolutionary rate. Previous studies have shown that the mutation rate of evolution of the loci in the real world distribution shape parameter in the range of 0.2 to 3.5. As a precondition of the reorganization of the existing detection methods: the sequence evolution rate for all sites, contrary to the true evolutionary conditions. The purpose of this study is the rate of evolution is inconsistent the recombinant fragment detection solution. We use the gamma distribution to build a rate inconsistent model, the use of computer simulation to generate evolutionary sequence of different shape parameters. The mathematical model of hidden Markov chain to construct the recombinant detection sample values, using the Monte Carlo method to extract parameters from the posterior distribution of the parameters of simulated sequence analysis. This study Table Mingjia Ma distribution shape parameter associated with the accuracy of the detection method of the existing recombinant: the smaller the value of shape parameter, the smaller the accuracy of the existing recombinant detection method, and its performance is more unstable. Comparison of the existing restructuring detection method can be seen from the proposed method with the case of this study to better detect recombinant fragment, especially gamma distribution shape parameter smaller.

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CLC: > Biological Sciences > Molecular Biology > Genetic engineering (genetic engineering)
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