• Journal of Intelligence and Information Systems
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• v.13 no.2
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• pp.55-68
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• 2007

DNA sequence alignment algorithms in computational molecular biology have been improved by diverse methods. In this paper, we proposed a DNA sequence alignment algorithm utilizing quality information and a fuzzy inference method utilizing characteristics of DNA sequence fragments and a fuzzy logic system in order to improve conventional DNA sequence alignment methods using DNA sequence quality information. In conventional algorithms, DNA sequence alignment scores were calculated by the global sequence alignment algorithm proposed by Needleman-Wunsch applying quality information of each DNA fragment. However, there may be errors in the process for calculating DNA sequence alignment scores in case of low quality of DNA fragment tips, because overall DNA sequence quality information are used. In the proposed method, exact DNA sequence alignment can be achieved in spite of low quality of DNA fragment tips by improvement of conventional algorithms using quality information. And also, mapping score parameters used to calculate DNA sequence alignment scores, are dynamically adjusted by the fuzzy logic system utilizing lengths of DNA fragments and frequencies of low quality DNA bases in the fragments. From the experiments by applying real genome data of NCBI (National Center for Biotechnology Information), we could see that the proposed method was more efficient than conventional algorithms using quality information in DNA sequence alignment.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.11_12
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• pp.578-586
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• 2005

In this Paper we consider the problem of sequence alignment with quality scores. DNA sequences produced by a base-calling program (as part of sequencing) have quality scores which represent the confidence level for individual bases. However, previous sequence alignment algorithms do not consider such quality scores. To solve sequence alignment with quality scores, we propose a measure of an alignment of two sequences with orality scores. We show that an optimal alignment in this measure can be found by dynamic programming.

• Genomics & Informatics
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• v.11 no.4
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• pp.191-199
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• 2013

High-throughput next-generation sequencing (NGS) technology produces a tremendous amount of raw sequence data. The challenges for researchers are to process the raw data, to map the sequences to genome, to discover variants that are different from the reference genome, and to prioritize/rank the variants for the question of interest. The recent development of many computational algorithms and programs has vastly improved the ability to translate sequence data into valuable information for disease gene identification. However, the NGS data analysis is complex and could be overwhelming for researchers who are not familiar with the process. Here, we outline the analysis pipeline and describe some of the most commonly used principles and tools for analyzing NGS data for disease gene identification.

• Journal of KIISE
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• v.41 no.12
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• pp.1007-1012
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• 2014

Software birthmarks are used to detect software plagiarism. For binaries, however, only a few birthmarks have been developed. In this paper, we propose a static approach to generate API sequences along major paths, which are analyzed from control flow graphs of the binaries. Since our API sequences are extracted along the most plausible paths of the binary codes, they can represent actual API sequences produced from binary executions, but in a more concise form. Our similarity measures use the Smith-Waterman algorithm that is one of the popular sequence alignment algorithms for DNA sequence analysis. We evaluate our static path-based API sequence with multiple versions of five applications. Our experiment indicates that our proposed method provides a quite reliable similarity birthmark for binaries.