Please use this identifier to cite or link to this item: https://une.intersearch.com.au/unejspui/handle/1959.11/2915
Title: A simple genetic algorithm for multiple sequence alignment
Contributor(s): Gondro, Cedric (author)orcid ; Kinghorn, Brian (author)
Publication Date: 2007
Handle Link: https://hdl.handle.net/1959.11/2915
Abstract: Multiple sequence alignment plays an important role in molecular sequence analysis. An alignment is the arrangement of two (pairwise alignment) or more (multiple alignment) sequences of 'residues' (nucleotides or amino acids) that maximizes the similarities between them. Algorithmically, the problem consists of opening and extending gaps in the sequences to maximize an objective function (measurement of similarity). A simple genetic algorithm was developed and implemented in the software MSA-GA. Genetic algorithms, a class of evolutionary algorithms, are well suited for problems of this nature since residues and gaps are discrete units. An evolutionary algorithm cannot compete in terms of speed with progressive alignment methods but it has the advantage of being able to correct for initially misaligned sequences; which is not possible with the progressive method. This was shown using the BaliBase benchmark, where Clustal-W alignments were used to seed the initial population in MSA-GA, improving outcome. Alignment scoring functions still constitute an open field of research, and it is important to develop methods that simplify the testing of new functions. A general evolutionary framework for testing and implementing different scoring functions was developed. The results show that a simple genetic algorithm is capable of optimizing an alignment without the need of the excessively complex operators used in prior study. The clear distinction between objective function and genetic algorithms used in MSA-GA makes extending and/or replacing objective functions a trivial task.
Publication Type: Journal Article
Source of Publication: Genetics and Molecular Research, 6(4), p. 964-982
Publisher: Fundacao de Pesquisas Cientificas de Ribeirao Preto
Place of Publication: Brazil
ISSN: 1676-5680
Field of Research (FOR): 080108 Neural, Evolutionary and Fuzzy Computation
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Other Links: http://www.funpecrp.com.br/GMR/year2007/vol4-6/xm0016_abstract.html
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