Abstract
Finding similar patterns (motifs) in a set of sequences is an important problem in Computational Molecular Biology. Pevzner and Sze [18] defined the planted (l,d)-motif problem as trying to find a length-l pattern that occurs in each input sequence with at most d substitutions. When d is large, this problem is difficult to solve because the input sequences do not contain enough information on the motif. In this paper, we propose a generalized planted (l,d)-motif problem which considers as input an additional set of sequences without any substring similar to the motif (negative set) as extra information. We analyze the effects of this negative set on the finding of motifs, and define a set of unsolvable problems and another set of most difficult problems, known as “challenging generalized problems”. We develop an algorithm called VANS based on voting and other novel techniques, which can solve the (9,3), (11,4),(15,6) and (20,8)-motif problems which were unsolvable before as well as challenging problems of the planted (l,d)-motif problem such as (9,2), (11,3), (15,5) and (20,7)-motif problems.
This research is supported in part by an RGC grant HKU 7135/04E.
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References
Bailey, T., Charles Elkan, C.: Unsupervised learning of multiple motifs in biopolymers using expectation maximization. Machine Learning 21, 51–80 (1995)
Barash, Y., Bejerano, G., Friedman, N.: A Simple Hyper-Geometric Approach for Discovering Putative Transcription Factor Binding Sites. Workshop on Algorithms in Bioinformatics WABI 1, 278–293 (2001)
Brazma, A., Jonassen, I., Eidhammer, I., Gilbert, D.: Approaches to the automatic discovery of patterns in biosequences. Jour. Comp. Biol. 5, 279–305 (1998)
Buhler, J., Tompa, M.: Finding motifs using random projections. Research in Computational Molecular Biology RECOMB 1, 69–76 (2001)
Chin, F., Leung, H.: Voting Algorithms for Discovering Long Motifs. Asia-Pacific Bioinformatics Conference APBC 3, 261–271 (2005)
Chin, F., Leung, H., Yiu, S.M., Lam, T.W., Rosenfeld, R., Tsang, W.W., Smith, D., Jiang, Y.: Finding Motifs for Insufficient Number of Sequences with Strong Binding to Transcription Factor. Research in Computational Molecular Biology RECOMB 4, 125–132 (2004)
Chin, F., Leung, H., Yiu, S.M., Rosenfeld, R., Tsang, W.W.: Finding Motifs with Insufficient Number of Strong Binding Sites. Jour. Comp. Biol. (to appear)
Fraenkel, Y., Mandel, Y., Friedberg, D., Margalit, H.: Identification of common motifs in unaligned dna sequences: application to Escherichia coli Lrp regulon. Bioinformatics 11, 379–387 (1995)
Gelfand, M., Koonin, E., Mironov, A.: Prediction of transcription regulatory sites in archaea by a comparative genomic approach. Nucl. Acids Res. 28, 695–705 (2000)
van Helden, J., Andre, B., Vides, J.C.: Extracting regulatory sites from the upstream region of yeast genes by computational analysis of oligonucleotide frequencies. Journal of Molecular Biology 281(5), 827–842 (1998)
Hertz, G.Z., Stormo, G.D.: Identification of consensus patterns in unaligned dna and protein sequences: a large-deviation statistical basis for penalizing gaps. International Conference on Bioinformatics and Genome Research 3, 201–216 (1995)
Lawrence, C., Altschul, S., Boguski, M., Liu, J., Neuwald, A., Wootton, J.: Detecting subtule sequence signals: a Gibbs sampling strategy for multiple alignment. Science 262, 208–214 (1993)
Lawrence, C., Reilly, A.: An expectation maximization (em) algorithm for the identification and characterization of common sites in unaligned biopolymer sequences. Proteins: Structure, Function and Genetics 7, 41–51 (1990)
Leung, H., Chin, F.: Finding Exact Optimal Motif in Matrix Representation by Partitioning. In: European Conference on Computational Biology ECCB (2005) (to appear)
Liang, S.: cWINNOWER Algorithm for Finding Fuzzy DNA Motifs. Computer Society Bioinformatics Conference 2, 260–265 (2003)
Marsan, L., Sagot, M.F.: Algorithms for extracting structured motifs using a suffix tree with an application to promoter and regulatory site consensus identification. Jour. Comp. Biol. 7(3-4), 345–362 (2000)
Pesole, G., Prunella, N., Liuni, S., Attimonelli, M., Saccone, C.: Wordup: an efficient algorithm for discovering statistically significant patterns in dna sequences. Nucl. Acids. Res. 20(11), 2871–2875 (1992)
Pevzner, P., Sze, S.H.: Combinatorial approaches to finding subtle signals in dna sequences. In: International Conference on Intelligent Systems for Molecular Biology vol. 8, pp. 269–278 (2000)
Sagot, M.F.: Spelling approximate repeated or common motifs using a suffix tree. In: Lucchesi, C.L., Moura, A.V. (eds.) LATIN 1998. LNCS, vol. 1380, pp. 111–127. Springer, Heidelberg (1998)
Sinha, S.: Discriminative motifs. Jour. Comp. Biol. 10, 599–616 (2003)
Zhu, J., Zhang, M.: SCPD: a promoter database of the yeast Saccha-romyces cerevisiae. Bioinformatics 15, 563–577 (1999), http://cgsigma.cshl.org/jian/
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Leung, H.C.M., Chin, F.Y.L. (2005). Generalized Planted (l,d)-Motif Problem with Negative Set. In: Casadio, R., Myers, G. (eds) Algorithms in Bioinformatics. WABI 2005. Lecture Notes in Computer Science(), vol 3692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11557067_22
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DOI: https://doi.org/10.1007/11557067_22
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