A Computational Model for RNA Multiple Structural Alignment

  • Eugene Davydov
  • Serafim Batzoglou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3109)


This paper addresses the problem of aligning multiple sequences of non-coding RNA genes. We approach this problem with the biologically motivated paradigm that scoring of ncRNA alignments should be based primarily on secondary structure rather than nucleotide conservation. We introduce a novel graph theoretic model (NLG) for analyzing algorithms based on this approach, prove that the RNA multiple alignment problem is NP-Complete in this model, and present a polynomial time algorithm that approximates the optimal structure of size S within a factor of O(log2 S).


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  1. 1.
    Bafna, V., Huson, D.: The conserved exon method for gene finding. In: Proceedings of the Fifth International Conference on Intelligent Systems for Molecular Biology, pp. 3–12 (2000)Google Scholar
  2. 2.
    Batzoglou, S., Pachter, L., Mesirov, J.P., Berger, B., Lander, E.: Human and mouse gene structure: Comparative analysis and application to exon prediction. Genome Research 10, 950–958 (2000)CrossRefGoogle Scholar
  3. 3.
    Bonizzoni, P., Vedova, G.D.: The complexity of multiple sequence alignment with SP-score that is a metric. Theoretical Computer Science 259, 63–79 (2001)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Cook, S.: The complexity of theorem-proving procedures. In: Proceedings of the Third ACM Symposium on Theory of Computing, pp. 151–158 (1971)Google Scholar
  5. 5.
    Eddy, S., Noncoding, R.N.A.: genes and the modern RNA world. Nature Review Genetics 2, 919–929 (2001)CrossRefGoogle Scholar
  6. 6.
    Eddy, S.R.: Computational genomics of noncoding RNA genes. Cell 109, 137–140 (2002)CrossRefGoogle Scholar
  7. 7.
    Floyd, R.W.: Algorithm 97: Shortest path. Comm. ACM 5(345) (1962)Google Scholar
  8. 8.
    Hardison, R.C., Oeltjen, J., Miller, W.: Long Human-Mouse Sequence Alignments Reveal Novel Regulatory Elements: A Reason to Sequence the Mouse Genome. Genome Research 7, 959–966 (1997)Google Scholar
  9. 9.
    : Pairwise RNA Structure Comparison with Stoachastic Context-Free Grammars. In: Pacific Symposium on Biocomputing, vol. 7, pp. 175–186.Google Scholar
  10. 10.
    Jareborg, N., Birney, E., Durbin, R.: Comparative analysis of noncoding regions of 77 orthologous mouse and human gene pairs. Genome Research 9, 815–824 (1999)CrossRefGoogle Scholar
  11. 11.
    Kasami, T.: An efficient recognition and syntax algorithm for context-free languages. Technical Report AF-CRL-65-758, Air Force Cambridge Research Laboratory, Bedford, MA (1965)Google Scholar
  12. 12.
    Lowe, T.M., Eddy, S.R.: tRNAscan-SE: a Program For Improved Detection of Transfer RNA genes in Genomic Sequence. Nucleic Acids Research 25, 955–964 (1997)CrossRefGoogle Scholar
  13. 13.
    Nussinov, R., Pieczenik, G., Griggs, J.R., Kleitman, D.: Algorithms for loop matching. SIAM Journal of Applied Mathematics 35, 68–82 (1978)MATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Pennacchio, L., Rubin, E.: Genomic strategies to identify mammalian regulatory sequences. Nature Reviews 2, 100–109 (2001)CrossRefGoogle Scholar
  15. 15.
    Rivas, E., Eddy, S.: A dynamic programming algorithm for RNA structure prediction including pseudoknots. Journal of Molecular Biology 285, 2053–2068 (1999)CrossRefGoogle Scholar
  16. 16.
    Rivas, E., Eddy, S.: Secondary structure alone is generally not statistically significant for the detection of noncoding RNAs. Bioinformatics 16, 573–583 (2000)CrossRefGoogle Scholar
  17. 17.
    Wang, L., Jiang, T.: On the complexity of multiple sequence alignment. Journal of Computational Biology 1, 337–348 (1994)CrossRefGoogle Scholar
  18. 18.
    Zuker, M., Stiegler, P.: Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Research 9, 133–148 (1981)CrossRefGoogle Scholar
  19. 19.
    Zuker, M.: Computer Prediction of RNA structure. Methods in Enzymology 180, 262–288 (1989)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Eugene Davydov
    • 1
  • Serafim Batzoglou
    • 1
  1. 1.Dept. of Computer ScienceStanford UniversityStanfordUSA

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