J Integr Bioinform - Parallel Niche Pareto AlineaGA--an evolutionary multiobjective approach on multiple sequence alignment.

Tópicos

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Resumo

Multiple sequence alignment is one of the most recurrent assignments in Bioinformatics. This method allows organizing a set of molecular sequences in order to expose their similarities and their differences. Although exact methods exist for solving this problem, their use is limited by the computing demands which are necessary for exploring such a large and complex search space. Genetic Algorithms are adaptive search methods which perform well in large and complex spaces. Parallel Genetic Algorithms, not only increase the speed up of the search, but also improve its efficiency, presenting results that are better than those provided by the sum of several sequential Genetic Algorithms. Although these methods are often used to optimize a single objective, they can also be used in multidimensional domains, finding all possible tradeoffs among multiple conflicting objectives. Parallel AlineaGA is an Evolutionary Algorithm which uses a Parallel Genetic Algorithm for performing multiple sequence alignment. We now present the Parallel Niche Pareto AlineaGA, a multiobjective version of Parallel AlineaGA. We compare the performance of both versions using eight BAliBASE datasets. We also measure up the quality of the obtained solutions with the ones achieved by T-Coffee and ClustalW2, allowing us to observe that our algorithm reaches for better solutions in the majority of the datasets.

Resumo Limpo

multipl sequenc align one recurr assign bioinformat method allow organ set molecular sequenc order expos similar differ although exact method exist solv problem use limit comput demand necessari explor larg complex search space genet algorithm adapt search method perform well larg complex space parallel genet algorithm increas speed search also improv effici present result better provid sum sever sequenti genet algorithm although method often use optim singl object can also use multidimension domain find possibl tradeoff among multipl conflict object parallel alineaga evolutionari algorithm use parallel genet algorithm perform multipl sequenc align now present parallel nich pareto alineaga multiobject version parallel alineaga compar perform version use eight balibas dataset also measur qualiti obtain solut one achiev tcoffe clustalw allow us observ algorithm reach better solut major dataset

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