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An Evolutionary Model for Maximum Likelihood Alignment of DNA Sequences

Author: HeHua
Tutor: HuoHongWei
School: Xi'an University of Electronic Science and Technology
Course: Computer Software and Theory
Keywords: Bioinformatics Sequence alignment Evolutionary model Maximum likelihood Evolutionary parameters Parameter estimates
CLC: TP301.6
Type: Master's thesis
Year: 2008
Downloads: 104
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Abstract


Sequence alignment is an important research topic in the bioinformatics , function , structure and evolution of the sequence information is found . Many of the existing alignment algorithms are based on the objective function , the objective function substitution matrix and gap penalty contrast to the process and the results of scoring Hutchison score to determine the sequence alignment results , good or bad . The drawback of the algorithm based on the ratio of the objective function is little change in the scoring system may lead to dramatic changes in local or global alignment . Thus, the DNA sequence of the likelihood of the DNA sequence alignment algorithms for sequence alignment . This paper first describes the sequence than on the basic concept , detailed substitution matrix , gap penalty points and the objective function as well as their impact on sequence alignment , and in-depth study of the double sequence a variety of commonly used than the algorithm : dot matrix analysis , dynamic planning algorithm and a word or k- string method , and gives the algorithm thinking or pseudo- code . Then , according to the disadvantage of objective function - based algorithm , the proposed maximum likelihood matching algorithm, which is divided into two parts : parameter estimation algorithm and alignment algorithms . The likelihood ratio of the first to use the algorithm evolution parameter estimation algorithm parameters on one pair DNA sequence evolution valuation than estimated parameter values ??of the algorithm uses the DNA sequence . It is an independent , completely avoid the problem of DNA sequence similarity of different substitution matrix with suitable substitution matrices and gap penalty sequence alignment algorithm . Finally, the evolutionary model is verified through evolutionary model for maximum likelihood DNA sequence matching algorithm than than the results of the program results and FASTA comparison , the maximum likelihood DNA alignment algorithm correctness and accuracy.

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