Markov Model Variants for Appraisal of Coding Potential in Plant DNA

  • Michael E. Sparks
  • Volker Brendel
  • Karin S. Dorman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4463)

Abstract

Markov chain models are commonly used for content-based appraisal of coding potential in genomic DNA. The ability of these models to distinguish coding from non-coding sequences depends on the method of parameter estimation, the validity of the estimated parameters for the species of interest, and the extent to which oligomer usage characterizes coding potential. We assessed performances of Markov chain models in two model plant species, Arabidopsis and rice, comparing canonical fixed-order, χ2-interpolated, and top-down and bottom-up deleted interpolated Markov models. All methods achieved comparable identification accuracies, with differences usually within statistical error. Because classification performance is related to G+C composition, we also considered a strategy where training and test data are first partitioned by G+C content. All methods demonstrated considerable gains in accuracy under this approach, especially in rice. The methods studied were implemented in the C programming language and organized into a library, IMMpractical, distributed under the GNU LGPL.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Burge, C., Karlin, S.: Prediction of complete gene structures in human genomic DNA. Journal of Molecular Biology 268, 78–84 (1997)CrossRefGoogle Scholar
  2. 2.
    Majoros, W., et al.: GlimmerM, Exonomy and Unveil: three ab initio eukaryotic genefinders. Nucleic Acids Research 31, 3601–3604 (2003)CrossRefGoogle Scholar
  3. 3.
    Lukashin, A., Borodovsky, M.: GeneMark.HMM: new solutions for gene finding. Nucleic Acids Research 26, 1107–1115 (1998)CrossRefGoogle Scholar
  4. 4.
    Azad, R., Borodovsky, M.: Effects of choice of DNA sequence model structure on gene identification accuracy. Bioinformatics 20, 993–1005 (2004)CrossRefGoogle Scholar
  5. 5.
    Salzberg, S., et al.: Microbial gene identification using interpolated Markov models. Nucleic Acids Research 26, 544–548 (1998)CrossRefGoogle Scholar
  6. 6.
    Delcher, A., et al.: Improved microbial gene identification with GLIMMER. Nucleic Acids Research 27, 4636–4641 (1999)CrossRefGoogle Scholar
  7. 7.
    Potamianos, G., Jelinek, F.: A study of n-gram and decision tree letter language modeling methods. Speech Communication 24, 171–192 (1998)CrossRefGoogle Scholar
  8. 8.
  9. 9.
    TAIR: The Arabidopsis Information Resource, http://www.arabidopsis.org/
  10. 10.
    TIGR: The Institute for Genomic Research, http://www.tigr.org/
  11. 11.
    Zhang, M.: Computational prediction of eukaryotic protein-coding genes. Nature Reviews Genetics 3, 698–709 (2000)CrossRefGoogle Scholar
  12. 12.
    van Baren, M., Brent, M.: Iterative gene prediction and pseudogene removal improves genome annotation. Genome Research 16, 678–685 (2006)CrossRefGoogle Scholar
  13. 13.
  14. 14.
    Florea, L.: Bioinformatics of alternative splicing and its regulation. Briefings in Bioinformatics 7, 55–69 (2006)CrossRefGoogle Scholar
  15. 15.
    Altschul, S., et al.: Basic local alignment search tool. Journal of Molecular Biology 215, 403–410 (1990)Google Scholar
  16. 16.
    Borodovsky, M., McIninch, J.: GENMARK: Parallel gene recognition for both DNA strands. Computers in Chemistry 17, 123–133 (1993)CrossRefMATHGoogle Scholar
  17. 17.
    Salzberg, S., et al.: Interpolated Markov models for eukaryotic gene finding. Genomics 59, 24–31 (1999)CrossRefGoogle Scholar
  18. 18.
    Sparks, M., Brendel, V.: Incorporation of splice site probability models for non-canonical introns improves gene structure prediction in plants. Bioinformatics 21, iii20–iii30 (2005)Google Scholar
  19. 19.
    Baldi, P., et al.: Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16, 412–424 (2000)CrossRefGoogle Scholar
  20. 20.
    Sing, T., et al.: ROCR: visualizing classifier performance in R. Bioinformatics 21, 3940–3941 (2005)CrossRefGoogle Scholar
  21. 21.
    Guigó, R., Brent, M.: Recent advances in gene structure prediction. Current Opinion in Structural Biology 14, 264–272 (2004)CrossRefGoogle Scholar
  22. 22.
    Siepel, A., Haussler, D.: Computational identification of evolutionarily conserved exons. In: Proceedings of the 8th Annual International Conference on Research in Computational Biology, pp. 177–186 (2004)Google Scholar
  23. 23.
    Majoros, W., Pertea, M., Salzberg, S.: Efficient implementation of a generalized pair Hidden Markov model for comparative gene finding. Bioinformatics 21, 1782–1788 (2005)CrossRefGoogle Scholar
  24. 24.
    Chen, L., DeVries, A., Cheng, C.H.: Convergent evolution of antifreeze glycoproteins in Antarctic notothenioid fish and Arctic cod. Proceedings of the National Academy of Sciences USA 94, 3817–3822 (1997)CrossRefGoogle Scholar
  25. 25.
    Roelofs, W., et al.: Evolution of moth sex pheromones via ancestral genes. Proceedings of the National Academy of Sciences USA 99, 13621–13626 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Michael E. Sparks
    • 1
  • Volker Brendel
    • 1
    • 2
  • Karin S. Dorman
    • 1
    • 2
  1. 1.Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011USA
  2. 2.Department of Statistics, Iowa State University, Ames, IA 50011USA

Personalised recommendations