Mining of cis-Regulatory Motifs Associated with Tissue-Specific Alternative Splicing

  • Jihye Kim
  • Sihui Zhao
  • Brian E. Howard
  • Steffen Heber
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5542)


Alternative splicing (AS) is an important post-transcriptional mechanism that can increase protein diversity and affect mRNA stability and translation efficiency. Many studies targeting the regulation of alternative splicing have focused on individual motifs; however, little is known about how such motifs work in concert. In this paper, we use distribution-based quantitative association rule mining to find combinatorial cis-regulatory motifs and to investigate the effect of motif pairs. We also show that motifs that occur in motif pairs typically occur in clusters.


Alternative splicing cis-regulatory motifs association rule mining quantitative association rule mining 


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  1. 1.
    Brudno, M., et al.: Computational analysis of candidate intron regulatory elements for tissue-specific alternative pre-mRNA splicing. Nucleic Acids Res. 9(11), 2338–2348 (2001)CrossRefGoogle Scholar
  2. 2.
    Faustino, N.A., Cooper, T.A.: Pre-mRNA splicing and human disease. Genes Dev. 17(4), 419–437 (2003)CrossRefPubMedGoogle Scholar
  3. 3.
    Garcia-Blanco, M.A., et al.: Alternative splicing in disease and therapy. Nat. Biotechnol. 22(5), 535–546 (2004)CrossRefPubMedGoogle Scholar
  4. 4.
    Ladd, A.N., Cooper, T.A.: Finding signals that regulate alternative splicing in the post-genomic era. Genome Biol. 3(11), reviews0008 (2002)CrossRefGoogle Scholar
  5. 5.
    Yeo, G., et al.: Variation in alternative splicing across human tissues. Genome Biol. 5(10), R74 (2004)CrossRefGoogle Scholar
  6. 6.
    Burge, C.B., et al.: Splicing of precursors to mRNAs by the spliceosomes. In: Gesteland, R.F., Cech, T., Atkins, J.F. (eds.) The RNA World, 2nd edn., pp. 525–560. Cold Spring Harbor Laboratory Press, Plainview (1999)Google Scholar
  7. 7.
    Akerman, M., et al.: Alternative splicing regulation at tandem 3’ splice sites. Nucleic Acids Res. 34(1), 23–31 (2006)CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Yun, L., Harold, R.G.: Evidence for the regulation of alternative splicing via complementary DNA sequence repeats. Bioinformatics 21(8), 1358–1364 (2005)CrossRefGoogle Scholar
  9. 9.
    Fairbrother, W.G., et al.: Predictive identification of exonic splicing enhancers in human genes. Science 297, 1007–1013 (2002)CrossRefPubMedGoogle Scholar
  10. 10.
    Zhang, X.H., et al.: Dichotomous splicing signals in exon flanks. Genome Res. 15(6), 768–779 (2005)CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Famulok, M., Szostak, J.W.: Selection of Functional RNA and DNA Molecules from Randomized Sequences. In: Eckstein, F., Lilley, D.M.J. (eds.) Nucleic Acids and Molecular Biology, vol. 7, p. 271. Springer, Heidelberg (1993)CrossRefGoogle Scholar
  12. 12.
    Stamm, S., et al.: An alternative-exon database and its statistical analysis. DNA Cell Biol. 19(12), 739–756 (2000)CrossRefPubMedGoogle Scholar
  13. 13.
    Friedman, B.A., et al.: Ab initio identification of functionally interacting pairs of cis-regulatory elements. Genome Res. 18, 1643–1651 (2008)CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Pan, Q., et al.: Revealing global regulatory features of mammalian alternative splicing using a quantitative microarray platform. Mol. Cell. 16(6), 929–941 (2004)CrossRefPubMedGoogle Scholar
  15. 15.
    Shai, O., et al.: Inferring global levels of alternative splicing isoforms using a generative model of microarray data. Bioinformatics 22(5), 606–613 (2006)CrossRefPubMedGoogle Scholar
  16. 16.
    Benson, D.A., et al.: GenBank. Nucleic Acids Res. 1(34), D16–D20 (2006)CrossRefGoogle Scholar
  17. 17.
    Rice, P., et al.: EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet. 16(6), 276–277 (2000)CrossRefPubMedGoogle Scholar
  18. 18.
    Kent, W.J.: BLAT–the BLAST-like alignment tool. Genome Res. 12(4), 656–664 (2002)CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Agrawal, R.S.: Fast Algorithms for Mining Association Rules. In: Proc. of the 20th Int’l Conference on Very Large Databases (1994)Google Scholar
  20. 20.
    Park, J.S., et al.: An effective Hash-Based Algorithm for Mining Association Rules. In: Proc. of the ACM SIGMOD Int’l Conference on Management of Data (1995)Google Scholar
  21. 21.
    Cheung, D.: A Fast Distributed Algorithm for Mining Association Rules. In: Proc, 4th Int’l Conference Parallel and Distributed Information Systems. IEEE Computer Soc. Press, Los Alamitos (1996)Google Scholar
  22. 22.
    Agrawal, R., et al.: Parallel Mining of Association Rules. IEEE Transactions on Knowledge and Data Engineering 8(6) (1996)Google Scholar
  23. 23.
    Han, E.H.K., Kumar, V.: Scalable parallel data mining for association rules. In: ACM SIGMOD Conference Management of Data (1997)Google Scholar
  24. 24.
    Zaki, M.J.: Parallel Data Mining for Association Rules on Shared-Memory Multi-Processors. In: Proc. Supercomputing 1996. IEEE Computer Soc. Press, Los Alamitos (1996)Google Scholar
  25. 25.
    Fukuda, Takeshi, et al.: Mining optimized association rules for numeric attributes. In: Proceedings of the 15th ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems, Montreal, Quebec, Canada, pp. 182–191 (1996)Google Scholar
  26. 26.
    Aumann, Y., Lindell, Y.: A statistical theory for quantitative association rules. In: KDD 1999, pp. 261–270 (1999)Google Scholar
  27. 27.
    Brin, S., et al.: Mining optimized gain rules for numeric attributes. In: Proceedings of the 5th ACM SIGKDD International conference on Knowledge Discovery and Data Mining, pp. 324–333 (2003)Google Scholar
  28. 28.
    Voelker, R.B., Berglund, J.: A comprehensive computational characterization of conserved mammalian intronic sequence reveals conserved motifs associated with constitutive and alternative splicing. Genome Res. 17, 1023–1103 (2007)CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Grabowski, P.J., et al.: Exon silencing by UAGG motifs in response to neuronal excitation. PLoS Biol. 5(2), e3 (2007)Google Scholar
  30. 30.
    Zaki, M., et al.: An Efficient Algorithm for Closed Itemset Mining. In: 2nd SIAM International Conference on Data Mining (2000)Google Scholar
  31. 31.
    Hodges, D., et al.: The role of evolutionarily conserved sequences in alternative splicing at the 3’ end of Drosophila melanogaster Myosin heavy chain RNA. Genetics 151, 263–276 (1999)PubMedPubMedCentralGoogle Scholar
  32. 32.
    Siepel, A., et al.: Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 15(8), 1034–1050 (2005)CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Stamm, S., et al.: An alternative exon database (AEDB) and its statistical analysis. DNA and Cell Biol. 19, 739–756 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jihye Kim
    • 1
  • Sihui Zhao
    • 1
  • Brian E. Howard
    • 1
  • Steffen Heber
    • 1
  1. 1.Bioinformatics Research CenterNorth Carolina State UniversityRaleighU.S.A.

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