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Study on Sequential Motif Discovery Algorithms Based on Subgraph Density

Author: HouQingMin
Tutor: GaoLin
School: Xi'an University of Electronic Science and Technology
Course: Applied Computer Technology
Keywords: Motif discovery Sequence Chart Maximum density subgraph
CLC: TP301.6
Type: Master's thesis
Year: 2009
Downloads: 22
Quote: 0
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Abstract


With the human genome project carried out with modern biological technology, the rapid development of bioinformatics data growth has explosive potential, which to uncover the mysteries of life provides the data base ; computing capabilities and Internet development makes for large scale data storage, processing, retrieval and interpretation provides a theoretical basis . And how to apply information science and computing technology means, through data analysis and processing , revealing huge amounts of data between the intrinsic link and biological meaning , interpretation of their implication structure and function of the information , and then extract useful biological knowledge , is the biological informatics research purposes. Motif is a code of life as a form of expression, motif discovery is to reveal the biological sequence data, the implication of the biological significance of the basic method is one of the bioinformatics field an important research questions . Motif discovery problem is an NP-complete problem, although people have explored a number of efficient algorithms , but these algorithms have certain limitations. As data continues to expand and new problems are emerging, many algorithms have been unable to adapt to the problem needs. Therefore , to explore more effective motif discovery algorithms has become biological sequence motif discovery research in the field of major issues , and are subject to more and more attention article first motif discovery algorithms commonly used models are analyzed , and the right based on different models of motif discovery algorithms research and analysis ; on this basis , we propose an approach based on the maximum density subgraph exhaustive search algorithm, the input sequence into a graph, vertices in the graph corresponds to a sequence specific length sequence fragment , the edge of the weight of said two vertices of the similarity between , so can the die body found is transformed in the diagram to find the maximum density subgraph problem, and by the position weight matrix PWM (Position Weight Matrix) model get phantom . Through the algorithm theoretical analysis as well as in synthetic data and real data on the simulation results show that the algorithm can find the optimal motif , but can be a good solution ( 15,4 ) challenges .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > General issues > Theories, methods > Algorithm Theory
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