AlineaGA: A genetic algorithm for multiple sequence alignment

Fernando José Mateus da Silva; Juan Manuel Pérez; Juan Antonio Pulido; Miguel A. Rodríguez

doi:10.1007/978-3-540-79355-7_30

AlineaGA: A genetic algorithm for multiple sequence alignment

Fernando José Mateus da Silva, Juan Manuel Pérez, Juan Antonio Pulido, Miguel A. Rodríguez

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

8 Scopus citations

Abstract

The alignment and comparison of DNA, RNA and Protein sequences is one of the most common and important tasks in Bioinformatics. However, due to the size and complexity of the search space involved, the search for the best possible alignment for a set of sequences is not trivial. Genetic Algorithms have a predisposition for optimizing general combinatorial problems and therefore are serious candidates for solving multiple sequence alignment tasks. We have designed a Genetic Algorithm for this purpose: AlineaGA. We have tested AlineaGA with representative sequence sets of the hemoglobin family. We also present the achieved results so as the comparisons performed with results provided by T-COFFEE.

Original language	English
Title of host publication	New Challenges in Applied Intelligence Technologies
Editors	Radoslaw Katarzyniak
Pages	309-318
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-540-79355-7_30
State	Published - 2008
Externally published	Yes

Publication series

Name	Studies in Computational Intelligence
Volume	134
ISSN (Print)	1860-949X

Keywords

Bioinformatics
Genetic algorithms
Multiple sequence alignments

Access to Document

10.1007/978-3-540-79355-7_30

Cite this

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abstract = "The alignment and comparison of DNA, RNA and Protein sequences is one of the most common and important tasks in Bioinformatics. However, due to the size and complexity of the search space involved, the search for the best possible alignment for a set of sequences is not trivial. Genetic Algorithms have a predisposition for optimizing general combinatorial problems and therefore are serious candidates for solving multiple sequence alignment tasks. We have designed a Genetic Algorithm for this purpose: AlineaGA. We have tested AlineaGA with representative sequence sets of the hemoglobin family. We also present the achieved results so as the comparisons performed with results provided by T-COFFEE.",

keywords = "Bioinformatics, Genetic algorithms, Multiple sequence alignments",

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AlineaGA: A genetic algorithm for multiple sequence alignment. / da Silva, Fernando José Mateus; Pérez, Juan Manuel; Pulido, Juan Antonio et al.
New Challenges in Applied Intelligence Technologies. ed. / Radoslaw Katarzyniak. 2008. p. 309-318 (Studies in Computational Intelligence; Vol. 134).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - AlineaGA

T2 - A genetic algorithm for multiple sequence alignment

AU - da Silva, Fernando José Mateus

AU - Pérez, Juan Manuel

AU - Pulido, Juan Antonio

AU - Rodríguez, Miguel A.

PY - 2008

Y1 - 2008

N2 - The alignment and comparison of DNA, RNA and Protein sequences is one of the most common and important tasks in Bioinformatics. However, due to the size and complexity of the search space involved, the search for the best possible alignment for a set of sequences is not trivial. Genetic Algorithms have a predisposition for optimizing general combinatorial problems and therefore are serious candidates for solving multiple sequence alignment tasks. We have designed a Genetic Algorithm for this purpose: AlineaGA. We have tested AlineaGA with representative sequence sets of the hemoglobin family. We also present the achieved results so as the comparisons performed with results provided by T-COFFEE.

AB - The alignment and comparison of DNA, RNA and Protein sequences is one of the most common and important tasks in Bioinformatics. However, due to the size and complexity of the search space involved, the search for the best possible alignment for a set of sequences is not trivial. Genetic Algorithms have a predisposition for optimizing general combinatorial problems and therefore are serious candidates for solving multiple sequence alignment tasks. We have designed a Genetic Algorithm for this purpose: AlineaGA. We have tested AlineaGA with representative sequence sets of the hemoglobin family. We also present the achieved results so as the comparisons performed with results provided by T-COFFEE.

KW - Bioinformatics

KW - Genetic algorithms

KW - Multiple sequence alignments

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BT - New Challenges in Applied Intelligence Technologies

A2 - Katarzyniak, Radoslaw

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AlineaGA: A genetic algorithm for multiple sequence alignment

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