.png){kind=icon}
.png){kind=icon}
Return
AlineaGA-a genetic algorithm with local search optimization for multiple sequence alignment
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-009-0189-4.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. Local search optimization can be used to refine the solutions explored by Genetic Algorithms. We have designed a Genetic Algorithm which incorporates local search for this purpose: AlineaGA. We have tested AlineaGA with representative sequence sets of the globin family. We also compare the achieved results with the results provided by T-COFFEE.
Keywords:
Multiple sequence alignments
Genetic algorithms
Local search
Optimization
Hybridization
Bioinformatics