Abstract
Motif discovery is an important problem in protein sequence analysis. Computationally, it can be viewed as an application of the more general multiple local alignment problem, which often encounters the difficulty of computer time when aligning many sequences. We introduce a new algorithm for multiple local alignment for protein sequences, based on the de Bruijn graph approach first proposed by Zhang and Waterman for aligning DNA sequence. We generalize their approach to aligning protein sequences by building an approximate de Bruijn graph to allow gluing similar but not identical amino acids. We implement this algorithm and test it on motif discovery of 100 sets of protein sequences. The results show that our method achieved comparable results as other popular motif discovery programs, while offering advantages in terms of speed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bailey, T.L., Elkan, C.: Fitting a mixture model by expectation maximization to discover motifs in biopolymers. In: Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology, pp. 28–36. AAAI Press, Menlo Park (1994)
Lawrence, C., Altschul, S., Bogouski, M., Liu, J., Neuwald, A., Wooten, J.: Detecting subtle sequence signals: A gibbs sampling strategy for multiple alignment. Science 262, 208–214 (1993)
Henikoff, S., Henikoff, J.G., Alford, W.J., Pietrokovski, S.: Automated construction and graphical presentation of protein blocks from unaligned sequences. Gene 163, GC17–GC26 (1995)
Zhang, Y., Waterman, M.S.: An Eulerian path approach to local multiple alignment for DNA sequences. PNAS 102, 1285–1290 (2005)
Zhang, Y., Waterman, M.S.: An eulerian path approach to global multiple alignment for DNA sequences. Journal of Computational Biology 10, 803–819 (2003)
Dayhoff, M., Schwartz, R., Orcutt, B.: A model of evolutionary change in proteins. In: Atlas of Protein Sequence and Structure. National Biomedical Research Foundation, vol. 5(3), pp. 345–352 (1978)
Henikoff, S., Henikoff, J.: Amino Acid Substitution Matrices from Protein Blocks. PNAS 89, 10915–10919 (1992)
Falquet, L., Pagni, M., Bucher, P., Hulo, N., Sigrist, C., Hofmann, K., Bairoch, A.: The prosite database, its status in 2002. Nucleic Acids Res. 30, 235–238 (2002)
Jonassen, I.: Efficient discovery of conserved patterns using a pattern graph. CABIOS 13, 509–522 (1997)
van Lint, J., Wilson, R.: A Course in Combinatorics, 2nd edn. Cambridge University Press, Cambridge (2001)
Myers, E.W., Miller, W.: Optimal alignments in linear space. CABIOS 4, 11–17 (1988)
Smith, T., Waterman, M.: Identification of common molecular subsequences. Journal of Molecular Biology 147, 195–197 (1981)
Hart, R., Royyuru, A., Stolovitzky, G., Califano, A.: Systematic and fully automated identification of protein sequence patterns. Journal of Computational Biology 7(3-4), 585–600 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Patwardhan, R., Tang, H., Kim, S., Dalkilic, M. (2006). An Approximate de Bruijn Graph Approach to Multiple Local Alignment and Motif Discovery in Protein Sequences. In: Dalkilic, M.M., Kim, S., Yang, J. (eds) Data Mining and Bioinformatics. VDMB 2006. Lecture Notes in Computer Science(), vol 4316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11960669_14
Download citation
DOI: https://doi.org/10.1007/11960669_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68970-6
Online ISBN: 978-3-540-68971-3
eBook Packages: Computer ScienceComputer Science (R0)