In this chapter, we present a new multiple sequence alignment algorithm called AntiClustAl. The method makes use of the commonly used idea of aligning homologous sequences belonging to classes generated by some clustering algorithm and then continuing the alignment process in a bottom-up way along a suitable tree structure. The final result is then read at the root of the tree. Multiple sequence alignment in each cluster makes use of progressive alignment with the 1-median (center) of the cluster. The 1-median of set S of sequences is the element of S that minimizes the average distance from any other sequence in S. Its exact computation requires quadratic time. The basic idea of our proposed algorithm is to make use of a simple and natural algorithmic technique based on randomized tournaments, an idea that has been successfully applied to large-size search problems in general metric spaces. In particular, a clustering data structure called antipole tree and an approximate linear 1-median computation are used. Our algorithm enjoys a better running time with equivalent alignment quality compared with ClustalW, a widely used tool for multiple sequence alignment. A successful biological application showing high amino acid conservation during evolution of Xenopus laevis SOD2 is illustrated.

ANTICLUSTAL: Multiple Sequence Alignment by Antipole Clustering

DI PIETRO, Cinzia Santa;FERRO, Alfredo;PULVIRENTI, ALFREDO;PURRELLO, Michele;RAGUSA, MARCO;
2005-01-01

Abstract

In this chapter, we present a new multiple sequence alignment algorithm called AntiClustAl. The method makes use of the commonly used idea of aligning homologous sequences belonging to classes generated by some clustering algorithm and then continuing the alignment process in a bottom-up way along a suitable tree structure. The final result is then read at the root of the tree. Multiple sequence alignment in each cluster makes use of progressive alignment with the 1-median (center) of the cluster. The 1-median of set S of sequences is the element of S that minimizes the average distance from any other sequence in S. Its exact computation requires quadratic time. The basic idea of our proposed algorithm is to make use of a simple and natural algorithmic technique based on randomized tournaments, an idea that has been successfully applied to large-size search problems in general metric spaces. In particular, a clustering data structure called antipole tree and an approximate linear 1-median computation are used. Our algorithm enjoys a better running time with equivalent alignment quality compared with ClustalW, a widely used tool for multiple sequence alignment. A successful biological application showing high amino acid conservation during evolution of Xenopus laevis SOD2 is illustrated.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/64492
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