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Mining Promoter In Biosequences Using Non-dominated Sorting Genetic Algorithm

Posted on:2016-03-03Degree:MasterType:Thesis
Country:ChinaCandidate:B MengFull Text:PDF
GTID:2180330464970824Subject:Computer technology
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The promoter discovery problem is one of the significant research problems in bioinformatics. Promoter regions have a vital role on the survival of the organism. If the promoter can be found out from DNA, then gene can be found. It can also help researchers to find out the position of the transcription factor binding site. This paper introduces two common organization promoter specificity experiment methods in molecular biology research—GUS reporter gene and 5 ’RACE technique. The two methods can be used in validation promoter discovery and localization algorithm with high cost and much time. The traditional experiment method for discovery promoter has some limitations. In order to design experiments more purposefully and reduce the experimental trial work. it is necessary to explore effective promoter recognition algorithm and computer aided tools. Xanthomonas campestris pv. Campestris(Referred to as Xcc) is a kind of bacteria of crucifer black rot which can cause all important pathogenic. It is the important patterns of microbial and host interaction mechanism. In this paper we choose Xcc as the experimental object and studying the characteristics of Xcc promoter.The promoter is a conservative fragment biological sequence. The essence of promoter discovery problem in this paper is to find out the similar Motif form biosequences. The promoter discovery is a multi-objective optimization problem, which need to find a Solution set for decision makers. In view of the existing promoter recognition and localization algorithm. We establish three target optimization calculation function and use the dominant genetic algorithm based on the strategy of zone elite to design of promoter recognition algorithm for Xcc conservative segment of the data set (Motif).The experimental results show that the algorithm does not need to specify the length of the Motif and obtained supports multiple sets of different degree candidate promoters, compared with existing algorithms. We have a lot of obtained data sets for decision makers. The algorithm has a good expansibility and a high efficiency. The scope of target biosequences can be modified according to the demand. It also allows the use of any similarity measure. It can be applied to a variety of different length promoter.
Keywords/Search Tags:biological sequences, promoter, Motif, multi-objective optimization and genetic algorithm, Xanthomonas campestris pv. campestris
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