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AntiClustAl: Multiple Sequence Alignment by Antipole Clustering

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Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Summary

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.

Keywords

  • Multiple Sequence Alignment
  • Base Number
  • Pairwise Alignment
  • Match Number
  • Match Ratio

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© 2005 Springer-Verlag London Limited

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Di Pietro, C. et al. (2005). AntiClustAl: Multiple Sequence Alignment by Antipole Clustering. In: Wu, X., Jain, L., Wang, J.T., Zaki, M.J., Toivonen, H.T., Shasha, D. (eds) Data Mining in Bioinformatics. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/1-84628-059-1_3

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  • DOI: https://doi.org/10.1007/1-84628-059-1_3

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-671-4

  • Online ISBN: 978-1-84628-059-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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