Abstract
This article presents a new approach using clustering technique for creating multiple sequence alignments. Currently, the most widely used strategy is the progressive alignment. However, each step of this strategy might generate an error which will be low for closely related sequences but will increase as sequences diverge. For that reason, determining the order in which the sequences will be aligned is very important. Following this idea, we propose the application of a clustering technique as an alternative way to determine this order. To assess the reliability of this new strategy, two methods were modified in order to apply a clustering technique. The accuracy of their new versions was tested using a reference alignment collection. Besides, the modified methods were also compared with their original versions, obtaining better alignments.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bahr, A., Thompson, J.D., Thierry, J.C., Poch, O.: Balibase (benchmark alignment database): enhancements for repeats, transmembrane sequences and circular permutations. Nucleic Acids Res. 29(1), 323–326 (2001)
Edgar, R.C.: Muscle: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32, 1792–1797 (2004)
Feng, D.F., Doolittle, R.F.: Progressive sequence alignment as a prerequisite to correct phylogenetic trees. J. Mol. Evol. 25, 351–360 (1987)
Gusfield, D.: Algorithms on strings, tress, and sequences: computer science and computational biology. Cambridge University Press, Cambridge (1997)
Korostensky, C., Gonnet, G.: Near optimal multiple sequence alignments using a traveling salesman problem approach. In: Proc. of the 6th International Symposium on String Processing and Information Retrieval, Cancun, Mexico, pp. 105–114 (1999)
Meidanis, J., Setúbal, J.C.: Multiple alignment of biological sequences with gap flexibility. In: Proc. of the 2nd South American Workshop on String Processing, Valparaiso, Chile, pp. 138–153 (1995)
Peres, P.S., Moura, E.S.: Application of clustering technique in multiple sequence alignment (Unpublished manuscript)
Setúbal, J.C., Meidanis, J.: Introduction to coputational molecular biology. PSW Publishing Company (1997)
Thompson, J.D., Higgins, D.G., Gibson, T.J.: Clustal w: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680 (1994)
Thompson, J.D., Plewniak, F., Poch, O.: Balibase: a benchmark alignment database for the evaluation of multiple sequence alignment programs. Bioinformatics 15, 87–88 (1999)
Thompson, J.D., Plewniak, F., Poch, O.: A comprehensive comparison of multiple sequence alignment programs. Nucleic Acids Res. 27, 2682–2690 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Peres, P.S., de Moura, E.S. (2005). Application of Clustering Technique in Multiple Sequence Alignment. In: Consens, M., Navarro, G. (eds) String Processing and Information Retrieval. SPIRE 2005. Lecture Notes in Computer Science, vol 3772. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11575832_22
Download citation
DOI: https://doi.org/10.1007/11575832_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29740-6
Online ISBN: 978-3-540-32241-2
eBook Packages: Computer ScienceComputer Science (R0)