An optimal DNA segmentation based on the MDL principle

Proceedings of the 2003 IEEE Bioinformatics Conference, CSB 2003

Volumn , Issue , 2003, Pages 541-546

  Szpankowski, W ^a   Ren, W ^a   Szpankowski, L ^b  

^a Purdue University   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS; CHROMOSOMES; COMPUTATION THEORY; DNA; INFORMATION THEORY; STOCHASTIC SYSTEMS;

ASYMPTOTICALLY OPTIMAL; BIOLOGICAL INFORMATION; DISCRIMINANT FUNCTIONS; FALSE POSITIVE; HUMAN CHROMOSOMES; MINIMUM DESCRIPTION LENGTH PRINCIPLE; NON-CODING REGION; SEGMENTATION ALGORITHMS;

DNA SEQUENCES;

EID: 34748835079     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CSB.2003.1227402     Document Type: Conference Paper

References (22)

1. P. Baldi and S. Brunak, Bioinformatics: The Machine Learning Approach, MIT Press, Cambridge, Mass., 2000.
2. P. Bernaola-Galván, R. Roman-Roldán, J.L. Oliver, Compositional Segmentation and Long-Range Fractal Correlations in DNA Sequences, Phys. Review E, 53, 5181-5189, 1996.
3. P. Bernaola-Galván, P. Carpena, R. Roman-Roldán, J.L. Oliver, Decomposition of DNA Sequence Complexity, Phys. Rev. Letters, 83, 3336-3339, 1999.
4. P. Bernaola-Galván, I. Grosse, P. Carpena, J.L. Oliver, R. Roman-Roldán, H.E. Stanley, Finding Borders Between Coding and Non-coding DNA, Phys. Rev. Letters, 85, 1342-1345, 2000.
5. G.A. Churchill, Stochastic Models for heterogeneous DNA Sequences, Bull. Mathematical Biology, 51, 79-94, 1989.
6. P. Clote and R. Backofen, Computational Molecular Biology, John Wiley & Sons, Chichester, 2000.
7. T.M. Cover and J.A. Thomas, Elements of Information Theory, John Wiley&Sons, New York, 1991.
8. R. Durbin and S. Eddy and A. Krogh and G. Mitchison, Biological Sequence Analysis, Cambridge University Press, Cambridge, 1999.
9. M. Gutman, Asymptotically Optimal Classification for Multiple Tests with Empirically Observed Statistics, IEEE Trans. Information Theory, 35, 401-408, 1989.
10. W. Li, DNA Segmentation as a Model Selection Process, Proc. RECOMB, 204-210, 2001.
11. W. Li, New Stopping Criteria for Segmenting DNA Sequences, Phys. Rev. Letters, 86, 5815-5818, 2001.
12. N. Merhav, On the Minimum Description Length Principle for Sources with Piecewise Constant Parameters, IEEE Trans. Information Theory, 39, 1962-1967, 1993.
13. C. Nevill-Manning and I. Witten, Protein Is Incompressible, Proc. Data Compression Conference, 257-286, 1999.
14. P. Pevzner, Computational Molecular Biology: An Algorithmic Approach, MIT Press, Cambridge, 2000.
15. V. E. Ramensky, V. Markeev, M. Roytberg, V. Tumanyan, DNA Segmentation Through the Bayesian Approach, J. Computational Biology, 7, 215-231, 2000.
16. M. Régnier, and W. Szpankowski, On Pattern Frequency Occurrences in a Markovian Sequence, Algorithmica, 22, 631-649, 1998.
17. S. Sahinalp, E. Eichler, P. Goldberg, P. Berebrink, T. Friedetzky and F. Egrin, Statistical Identification of Uniformly Mutated Segments within Repeats, CPM 2002, LNCS 2373, 249-261, 20002.
18. S. Salzberg and D. Searls and S. Kasif, Computational Methods in Molecular Biology, Elsevier, Amsterdam, 1999.
19. G. Shamir and N. Merhav, Asymptotically Optimal Low-Complexity Sequential Lossless Coding for Piecewise- Stationary Memoryless Sources, IEEE Trans. Information Theory, 46, 2244-2467, 2000.
20. W. Szpankowski, Average Case Analysis of Algorithms on Sequences, John Wiley & Sons, New York, 2001.
21. I. Tabus, G. Korodi and J. Rissanen, DNA Sequence Compression Using the Normalized Maximum Likelihood Model for Discrete Regression, Proc. Data Compression Conference, 253-262, Snowbird 2003.
22. J. Ziv, On Classification with Empirically Observed Statistics and Universal Data Compression, IEEE Trans. Information Theory, 34, 278-286, 1988.