Abstract
Motivation: Motif identification for sequences has many important applications in biological studies, e.g., diagnostic probe design, locating binding sites and regulatory signals, and potential drug target identification. There are two versions.
-
1
Single Group: Given a group of n sequences, find a length-l motif that appears in each of the given sequences and those occurrences of the motif are similar.
-
1
Two Groups: Given two groups of sequences B and G, find a length-l (distinguishing) motif that appears in every sequence in B and does not appear in anywhere of the sequences in G.
Here the occurrences of the motif in the given sequences have errors. Currently, most of existing programs can only handle the case of single group. Moreover, it is very difficult to use edit distance (allowing indels and replacements) for motif detection.
Results: (1) We propose a randomized algorithm for the one group problem that can handle indels in the occurrences of the motif. (2) We give an algorithm for the two groups problem. (3) Extensive simulations have been done to evaluate the algorithms.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bailey, T., 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 (ISMB-1994), pp. 28–36. AAAI Press, Menlo PArk (1994)
Bailey, T., Elkan, C.: Unsupervised learning of multiple motifs in biopolymers using expectation maximization. Machine Learning 21, 51–80 (1995)
Buhler, J., Tompa, M.: Finding motifs using random projections. Journal of Computational Biology 9, 225–242 (2002)
Cardon, L.R., Stormo, G.D.: Expectation maximization algorithm for identifying protein-binding sites with variable lengths from unaligned DNA fragments. J. Mol. Biol. 223, 159–170 (1992)
Deng, X., Li, G., Li, Z., Ma, B., Wang, L.: Generic Drug Design without Side Effect. SIAM J on Computing 32(4), 1073–1090 (2003)
Dopazo, J., Rodríguez, A., Sáiz, J.C., Sobrino, F.: Design of primers for PCR amplification of highly variable genomes. CABIOS 9, 123–125 (1993)
Gusfield, D.: Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology. Cambridge University Press, Cambridge (1997)
Hertz, G., Stormo, G.: Identification of consensus patterns in unaligned DNA and protein sequences: a large-deviation statistical basis for penalizing gaps. In: Proc. 3rd Intl Conf. Bioinformatics and Genome Research, pp. 201–216 (1995)
Hu, Y.-J.H: Finding subtle motifs with variable gaps in unaligned DNA sequences. Computer Methods and Programs in Biomedicine 70, 11–20 (2003)
Keich, U., Pevzner, P.: Finding motifs in the twilight zone. Bioinformatics 18, 1374–1381 (2002a)
Keich, U., Pevzner, P.: Subtle motifs: defining the limits of motif finding algorithms. Bioinformatics 18, 1382–1390 (2002b)
Lanctot, K., Li, M., Ma, B., Wang, S., Zhang, L.: Distinguishing string selection problems. In: Proc. 10th ACM-SIAM Symp. on Discrete Algorithms, pp. 633–642 (Also to appear in Information and Computation)
Lawrence, C., Reilly, A.: An expectation maximization (EM) algorithm for the identification and characterization of common sites in unaligned biopolymer sequences. Proteins 7, 41–51 (1990)
Li, M., Ma, B., Wang, L.: Finding Similar Regions in Many Strings. In: Proceedings of the Thirty-first Annual ACM Symposium on Theory of Computing, Atlanta, pp. 473–482 (1999)
Li, M., Ma, B., Wang, L.: Finding Similar Regions in Many Sequences (special issue for Thirty-first Annual ACM Symposium on Theory of Computing). J. Comput. Syst. Sci. 65, 73–96 (2002a)
Li, M., Ma, B., Wang, L.: On the closest string and substring problems. JACM 49(2), 157–171 (2002b)
Lucas, K., Busch, M., Mössinger, S., Thompson, J.A.: An improved microcomputer program for finding gene- or gene family-specific oligonucleotides suitable as primers for polymerase chain reactions or as probes. CABIOS 7, 525–529 (1991)
Pevzner, P., Sze, S.: Combinatorial approaches to finding subtle signals in DNA sequences. In: Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology. pp. 269–278 (2000)
Proutski, V., Holme, E.C.: Primer Master: a new program for the design and analysis of PCR primers. CABIOS 12, 253–255 (1996)
Stormo, G.: Consensus patterns in DNA. In: Doolittle, R.F.(ed.) Molecular evolution: computer analysis of protein and nucleic acid sequences, Methods in Enzymology, vol. 183, pp. 211–221 (1990)
Price, A., Ramabhadran, S., Pevzner, P.: Finding Subtle Motifs by Branching from Sample Strings, Bioinformatics 19, 149–155 (2003)
Keller, G.H., Manak, M.M.: DNA Probes, Stockton Press, p. 12 (1989)
McPearson, M.J., Quirke, M.J., Taylor, G.R: PCR A Practical Approach, p. 8. Oxford University Press, New York (1991)
Wang, L., Dong, L., Fan, H.: Randomized Algorithms for Motif Detection. In: Fleischer, R., Trippen, G. (eds.) ISAAC 2004. LNCS, vol. 3341, pp. 884–895. Springer, Heidelberg (2004)
Waterman, M., Arratia, R., Galas, E.: Pattern recognition in several sequences:consenus and alignment. Bull. Math. Biol. 46, 515–527 (1984)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Feng, W., Wang, Z., Wang, L. (2007). Identification of Distinguishing Motifs. In: Ma, B., Zhang, K. (eds) Combinatorial Pattern Matching. CPM 2007. Lecture Notes in Computer Science, vol 4580. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73437-6_26
Download citation
DOI: https://doi.org/10.1007/978-3-540-73437-6_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73436-9
Online ISBN: 978-3-540-73437-6
eBook Packages: Computer ScienceComputer Science (R0)