| Summary: | 碩士 === 國立臺灣大學 === 電信工程學研究所 === 99 === In recent years, with the development of biological science, there are more and more biological DNA sequences that are added into the database. Therefore, the analysis of DNA sequences and its application in the biological science are becoming increasingly important. In order to solve many biological problems, the analytical tools in bioinformatics and computational biology help biologists to analyze huge databases, and the developments of bioinformatics and computational biology become more urgent.
The Sequence alignment, which can be used to compare the similarity between two or more DNA sequences, is an elementary and important analysis tool in bioinformatics and computational biology. The Edit distance and the similarity score are used as indicators of aligning DNA sequences. The DNA sequences with high similarity mean that they are homology and they have similar functions or structure. When biologists discovered a DNA sequence of unknown function, they will try to search the database, which contains many known DNA sequences, for some similar DNA sequences to speculate the biological function of the new one.
The dynamic programming method is one of the most common and popular algorithms in bioinformatics and computational biology. It can obtain not only the edit distance or the similarity score between DNA sequences but also the corresponding sequence alignment. However, the dynamic programming method is very time-consuming and requires considerable memory to analyze DNA sequences. To solve these problems, many algorithms have emerged. These algorithms accurately achieve the same results, and they are more efficient in time and memory in addition. We propose our fast algorithm and its simulation results in this thesis.
This thesis is divided into two parts. In the first part, we focus on introducing the dynamic programming method, some existing fast algorithms, and their applications. In the second part of this thesis, we develop a fast algorithm, which can be as accurate as the dynamic programming method, but it can reduce the waste of computation time and memory space. The proposed algorithm compare with the dynamic programming method and some of the known fast algorithms, and the results are presented in this part.
|
|---|
