Comput Methods Programs Biomed - Multiple sequence alignment with affine gap by using multi-objective genetic algorithm.

Tópicos

{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ data(3008) multipl(1320) sourc(1022) }
{ error(1145) method(1030) estim(1020) }
{ howev(809) still(633) remain(590) }
{ problem(2511) optim(1539) algorithm(950) }
{ method(2212) result(1239) propos(1039) }
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{ data(3963) clinic(1234) research(1004) }
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{ gene(2352) biolog(1181) express(1162) }
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{ method(1969) cluster(1462) data(1082) }
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Resumo

Multiple sequence alignment is of central importance to bioinformatics and computational biology. Although a large number of algorithms for computing a multiple sequence alignment have been designed, the efficient computation of highly accurate and statistically significant multiple alignments is still a challenge. In this paper, we propose an efficient method by using multi-objective genetic algorithm (MSAGMOGA) to discover optimal alignments with affine gap in multiple sequence data. The main advantage of our approach is that a large number of tradeoff (i.e., non-dominated) alignments can be obtained by a single run with respect to conflicting objectives: affine gap penalty minimization and similarity and support maximization. To the best of our knowledge, this is the first effort with three objectives in this direction. The proposed method can be applied to any data set with a sequential character. Furthermore, it allows any choice of similarity measures for finding alignments. By analyzing the obtained optimal alignments, the decision maker can understand the tradeoff between the objectives. We compared our method with the three well-known multiple sequence alignment methods, MUSCLE, SAGA and MSA-GA. As the first of them is a progressive method, and the other two are based on evolutionary algorithms. Experiments on the BAliBASE 2.0 database were conducted and the results confirm that MSAGMOGA obtains the results with better accuracy statistical significance compared with the three well-known methods in aligning multiple sequence alignment with affine gap. The proposed method also finds solutions faster than the other evolutionary approaches mentioned above.

Resumo Limpo

multipl sequenc align central import bioinformat comput biolog although larg number algorithm comput multipl sequenc align design effici comput high accur statist signific multipl align still challeng paper propos effici method use multiobject genet algorithm msagmoga discov optim align affin gap multipl sequenc data main advantag approach larg number tradeoff ie nondomin align can obtain singl run respect conflict object affin gap penalti minim similar support maxim best knowledg first effort three object direct propos method can appli data set sequenti charact furthermor allow choic similar measur find align analyz obtain optim align decis maker can understand tradeoff object compar method three wellknown multipl sequenc align method muscl saga msaga first progress method two base evolutionari algorithm experi balibas databas conduct result confirm msagmoga obtain result better accuraci statist signific compar three wellknown method align multipl sequenc align affin gap propos method also find solut faster evolutionari approach mention

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