An evolutionary progressive multiple sequence alignment

Farhana Naznin, Morikazu Nakamura, Takeo Okazaki, Yumiko Nakajima

研究成果: 著書/レポートタイプへの貢献会議での発言

抄録

This paper proposes an evolutionary tree-base (progressive multiple sequence alignment) method using a genetic algorithm (GA) for solving multiple sequence alignment problems. In our evolutionary tree-base method, chromosomes are represented as guide trees. Two kinds of crossover are proposed for chromosomes of tree structure; subtree selection crossover and tree uniform order crossover. They can generate new chromosomes with inheriting tree structure of parents. The indirect representation of multiple alignments, namely, the guide tree representation of chromosomes, and the proper genetic operations make searching drastically efficient. Experimental results for benchmark problems from BAIiBASE and the NCBI database show that the proposed method is superior to SAGA (a well-known GA-base approach, 1996), T-Coffee (sensitive progressive method, 2000), MUSCLE (progressive/iterative method, 2004), MAFFT (progressive/iterative method, 2005), and ProbCons (probabilistic/consistency method, 2005) with regard to quality of solutions.

元の言語英語
ホスト出版物のタイトル2007 IEEE Congress on Evolutionary Computation, CEC 2007
ページ3886-3893
ページ数8
DOI
出版物ステータス出版済み - 12 1 2007
外部発表Yes
イベント2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , シンガポール
継続期間: 9 25 20079 28 2007

その他

その他2007 IEEE Congress on Evolutionary Computation, CEC 2007
シンガポール
期間9/25/079/28/07

Fingerprint

Multiple Sequence Alignment
Chromosomes
Chromosome
Evolutionary Tree
Crossover
Iterative methods
Tree Structure
Genetic algorithms
Coffee
Genetic Algorithm
Iteration
Alignment
Benchmark
Experimental Results

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Theoretical Computer Science

これを引用

Naznin, F., Nakamura, M., Okazaki, T., & Nakajima, Y. (2007). An evolutionary progressive multiple sequence alignment. : 2007 IEEE Congress on Evolutionary Computation, CEC 2007 (pp. 3886-3893). [4424977] https://doi.org/10.1109/CEC.2007.4424977

An evolutionary progressive multiple sequence alignment. / Naznin, Farhana; Nakamura, Morikazu; Okazaki, Takeo; Nakajima, Yumiko.

2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007. p. 3886-3893 4424977.

研究成果: 著書/レポートタイプへの貢献会議での発言

Naznin, F, Nakamura, M, Okazaki, T & Nakajima, Y 2007, An evolutionary progressive multiple sequence alignment. : 2007 IEEE Congress on Evolutionary Computation, CEC 2007., 4424977, pp. 3886-3893, 2007 IEEE Congress on Evolutionary Computation, CEC 2007, シンガポール, 9/25/07. https://doi.org/10.1109/CEC.2007.4424977
Naznin F, Nakamura M, Okazaki T, Nakajima Y. An evolutionary progressive multiple sequence alignment. : 2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007. p. 3886-3893. 4424977 https://doi.org/10.1109/CEC.2007.4424977
Naznin, Farhana ; Nakamura, Morikazu ; Okazaki, Takeo ; Nakajima, Yumiko. / An evolutionary progressive multiple sequence alignment. 2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007. pp. 3886-3893
@inproceedings{c52f0e608a3e4f0ea30df2333736834e,
title = "An evolutionary progressive multiple sequence alignment",
abstract = "This paper proposes an evolutionary tree-base (progressive multiple sequence alignment) method using a genetic algorithm (GA) for solving multiple sequence alignment problems. In our evolutionary tree-base method, chromosomes are represented as guide trees. Two kinds of crossover are proposed for chromosomes of tree structure; subtree selection crossover and tree uniform order crossover. They can generate new chromosomes with inheriting tree structure of parents. The indirect representation of multiple alignments, namely, the guide tree representation of chromosomes, and the proper genetic operations make searching drastically efficient. Experimental results for benchmark problems from BAIiBASE and the NCBI database show that the proposed method is superior to SAGA (a well-known GA-base approach, 1996), T-Coffee (sensitive progressive method, 2000), MUSCLE (progressive/iterative method, 2004), MAFFT (progressive/iterative method, 2005), and ProbCons (probabilistic/consistency method, 2005) with regard to quality of solutions.",
author = "Farhana Naznin and Morikazu Nakamura and Takeo Okazaki and Yumiko Nakajima",
year = "2007",
month = "12",
day = "1",
doi = "10.1109/CEC.2007.4424977",
language = "English",
isbn = "1424413400",
pages = "3886--3893",
booktitle = "2007 IEEE Congress on Evolutionary Computation, CEC 2007",

}

TY - GEN

T1 - An evolutionary progressive multiple sequence alignment

AU - Naznin, Farhana

AU - Nakamura, Morikazu

AU - Okazaki, Takeo

AU - Nakajima, Yumiko

PY - 2007/12/1

Y1 - 2007/12/1

N2 - This paper proposes an evolutionary tree-base (progressive multiple sequence alignment) method using a genetic algorithm (GA) for solving multiple sequence alignment problems. In our evolutionary tree-base method, chromosomes are represented as guide trees. Two kinds of crossover are proposed for chromosomes of tree structure; subtree selection crossover and tree uniform order crossover. They can generate new chromosomes with inheriting tree structure of parents. The indirect representation of multiple alignments, namely, the guide tree representation of chromosomes, and the proper genetic operations make searching drastically efficient. Experimental results for benchmark problems from BAIiBASE and the NCBI database show that the proposed method is superior to SAGA (a well-known GA-base approach, 1996), T-Coffee (sensitive progressive method, 2000), MUSCLE (progressive/iterative method, 2004), MAFFT (progressive/iterative method, 2005), and ProbCons (probabilistic/consistency method, 2005) with regard to quality of solutions.

AB - This paper proposes an evolutionary tree-base (progressive multiple sequence alignment) method using a genetic algorithm (GA) for solving multiple sequence alignment problems. In our evolutionary tree-base method, chromosomes are represented as guide trees. Two kinds of crossover are proposed for chromosomes of tree structure; subtree selection crossover and tree uniform order crossover. They can generate new chromosomes with inheriting tree structure of parents. The indirect representation of multiple alignments, namely, the guide tree representation of chromosomes, and the proper genetic operations make searching drastically efficient. Experimental results for benchmark problems from BAIiBASE and the NCBI database show that the proposed method is superior to SAGA (a well-known GA-base approach, 1996), T-Coffee (sensitive progressive method, 2000), MUSCLE (progressive/iterative method, 2004), MAFFT (progressive/iterative method, 2005), and ProbCons (probabilistic/consistency method, 2005) with regard to quality of solutions.

UR - http://www.scopus.com/inward/record.url?scp=79955335830&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79955335830&partnerID=8YFLogxK

U2 - 10.1109/CEC.2007.4424977

DO - 10.1109/CEC.2007.4424977

M3 - Conference contribution

AN - SCOPUS:79955335830

SN - 1424413400

SN - 9781424413409

SP - 3886

EP - 3893

BT - 2007 IEEE Congress on Evolutionary Computation, CEC 2007

ER -