• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.1-10
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• 2005

Multiple sequence alignment(MSA) is a fundamental technique of DNA and Protein sequence analysis. Biological sequences are aligned vertically in order to show the similarities and differences among them. In this Paper, we Propose an effcient group alignment method, which is based on clustering divergency, to Perform the alignment between two groups of sequences. The Proposed algorithm is a clustering divergence(CDMS)-based multiple sequence alignment and a top-down approach. The algorithm builds the tree topology for merging. It is so based on the concept that two sequences having the longest distance should be spilt into two clusters. We expect that our sequence alignment algorithm improves its qualify and speeds up better than traditional algorithm Clustal-W.

• The Journal of the Convergence on Culture Technology
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• v.5 no.3
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• pp.339-344
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• 2019

Global sequence alignment in search of similarity or homology favors larger size of the sequence because it keeps looking for more similar section between two sequences in the hope that it adds up scores for matched part in the rest of the sequence. If a substantial size of mismatched section exists in the middle of the sequence, it greatly reduces the total alignment score. In this case a whole sequence would be better to be divided into multiple sections. Overall alignment score over the multiple sections of the sequence would increase as compared to global alignment. This method is called multiple local alignment. In this paper, we implement a multiple local alignment algorithm, an extension of Smith-Waterman algorithm and show the experimental results for the algorithm that is able to search for sub-optimal sequence.

• Korean Journal of Microbiology
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• v.35 no.2
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• pp.115-120
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• 1999

Multiple Sequence Alignment of DNA and protem sequences is a imnport'mt tool in the study of molecular evolution, gene regulation. and prolein suucture-function relationships. Progressive pairwise alignment method generates multiple sequence alignment fast but not necessarily with optimal costs. Dynamic programming generates multiple sequence alig~~menl with optimal costs in most cases but long execution time. In this paper. we suggest genetlc algorithm lo improve the multiple sequence alignment generated from the cnlent methods, describe the design of the genetic algorithm, and compare the multiple sequence alignments from 0111 method and current methods.

• Journal of KIISE:Software and Applications
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• v.30 no.9
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• pp.803-811
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• 2003

Multiple sequence alignment is a useful tool to identify the relationships among protein sequences. Dynamic programming is the most widely used algorithm to obtain multiple sequence alignment with optimal cost. However, dynamic programming cannot be applied to certain cost function due to its drawback and cannot be used to produce optimal multiple sequence alignment. We propose sub-alignment refinement algorithm to overcome the problem of dynamic programming. Also we show proposed algorithm can solve the problem of dynamic programming efficiently.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.264-267
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• 2003

Multiple sequence alignment is a useful tool to identify the relationships among protein sequences. Dynamic programming is the most widely used algorithm to obtain multiple sequence alignment with optimal cost. However dynamic programming cannot be applied to certain cost function due its drawback and to produce optimal multiple sequence alignment. We proposed sub-alignment refinement algorithm to overcome the problem of dynamic programming and impelmented this algorithm as a module of our MS Windows-based sequence alignment program.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.467-472
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• 2024

Multiple sequence alignment (MSA) is a method of collecting and aligning multiple protein sequences or nucleic acid sequences that perform the same function in various organisms at once. clustalW, a representative multiple sequence alignment algorithm using BioPython, compares the degree of alignment by column position. In addition, a web logo and phylogenetic tree are created to visualize conserved sequences in order to improve understanding. An example was given to confirm the differences between humans and other species, and applications of BioPython are presented.

• Interdisciplinary Bio Central
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• v.6 no.4
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• pp.3.1-3.10
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• 2014

Amino acid substitution matrices are essential tools for protein sequence analysis, homology sequence search in protein databases and multiple sequence alignment. The PAM matrix was the first widely used amino acid substitution matrix. The BLOSUM series then succeeded the PAM matrix. Most substitution matrixes were developed by using the statistical frequency of substitution between each amino acid at blocks representing groups of protein families or related proteins. However, substitution of amino acids is based on the similarity of physiochemical properties of each amino acid. In this study, a new approach was used to obtain major physiochemical properties in multiple sequence alignment. Frequency of amino acid substitution in multiple sequence alignment database and selected attributes of amino acids in physiochemical properties database were merged. This merged data showed the major physiochemical properties through principle components analysis. Using factor analysis, these four principle components were interpreted as flexibility of electronic movement, polarity, negative charge and structural flexibility. Applying these four components, BAPS was constructed and validated for accuracy. When comparing receiver operated characteristic ($ROC_{50}$) values, BAPS scored slightly lower than BLOSUM and PAM. However, when evaluating for accuracy by comparing results from multiple sequence alignment with the structural alignment results of two test data sets with known three-dimensional structure in the homologous structure alignment database, the result of the test for BAPS was comparatively equivalent or better than results for prior matrices including PAM, Gonnet, Identity and Genetic code matrix.

• Genomics & Informatics
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• v.6 no.3
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• pp.157-159
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• 2008

Multiple sequence alignment (MSA) is the most important step for many of biological sequence analyses, homology search, and protein structural assignments. However, large amount of data make biologists difficult to perform MSA analyses and it requires much computational time to align many sequences. Here, we have developed a simple and fast web alignment tool for aligning, editing, and visualizing large amount of sequence data. We used a cluster server installed ClustalW-MPI using web services and message passing interface (MPI). It also enables users to edit multiple sequence alignments for manual editing and to download the input data and results such as alignments and phylogenetic tree.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.27-30
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• 2005

3개 이상의 DNA 혹은 단백질의 염기서열을 정렬하는 다중 서열 정렬(multiple sequence alignment, MSA)은 서열들 사이의 진화관계, 단백질의 구조와 기능에 관한 연구에 필수적인 도구이다. 최적화된 다중서열 정렬을 얻기 위해 사용되는 가장 유용한 방법은 동적 프로그래밍이다. 그러나 동적프로그래밍은 정렬하고자 하는 서열의 수가 증가함에 따라 시간도 지수함수($O(n^k)$)로 증가하기 때문에 다중 서열 정렬에는 효율적이지 못하다. 따라서, 본 논문에서는 최적의 MSA 문제를 해결하기 위해 클러스터링 기반의 새로운 다중 서열 정렬 (Clustering-based Multiple Sequence Alignment, CMSA) 알고리즘을 제안한다. 결과적으로 제안한 CMSA 알고리즘의 기여도는 다중 서열 정렬의 질적 향상과 처리 시간 단축($O(n^3L^2)$)이 기대된다.

• Journal of KIISE
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• v.43 no.3
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• pp.289-295
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• 2016

Malware authors spread malware variants in order to evade detection. It's hard to detect malware variants using static analysis. Therefore dynamic analysis based on API call information is necessary. In this paper, we proposed a malware family recommendation method to assist malware analysts in classifying malware variants. Our proposed method extract API call information of malware families by dynamic analysis. Then the multiple sequence alignment technique was applied to the extracted API call information. A signature of each family was extracted from the alignment results. By the similarity of the extracted signatures, our proposed method recommends three family candidates for unknown malware. We also measured the accuracy of our proposed method in an experiment using real malware samples.