The 4M (mixed memory markov model) algorithm for finding genes in prokaryotic genomes

IEEE Transactions on Automatic Control

Volumn 53, Issue SPECIAL ISSUE, 2008, Pages 26-37

  Vidyasagar, Mathukumalli ^a   Mande, Sharmila S ^a   Reddy, Ch V Siva Kumar ^a   Rao, V V Raja ^a  

^a TATA CONSULTANCY SERVICES   (India)

Author keywords

Algorithm; Gene prediction; K L divergence; Markov model; Prokaryotes

Indexed keywords

CODING REGION; GENE PREDICTION; GENOME ANNOTATION; K-L DIVERGENCE; M-ALGORITHMS; MARKOV CHAIN; MARKOV MODEL; MATRIX; MEMORY LENGTH; MULTI-STEP; NON-CODING REGION; OPEN READING FRAME; PROKARYOTIC GENOMES; RANK CONDITION; SAMPLE PATH; STATISTICAL SIGNIFICANCE; MIXED MEMORY MARKOV MODEL;

ALGORITHMS; FORECASTING; GENES; MARKOV PROCESSES; MICROORGANISMS; GENE ENCODING; INTERPOLATION;

MATHEMATICAL MODELS; MARKOV PROCESSES;

EID: 39849101337     PISSN: 00189286     EISSN: None     Source Type: Journal    
DOI: 10.1109/TAC.2007.911360     Document Type: Article

References (21)

1. P. Baldi and S. Brünak, Bioinformatics: A Machine Learning Approach. Cambridge, MA: MIT Press, 2001.
2. C. Burge and S. Karlin, "Prediction of complete gene structures in human genomic DNA," J. Molec. Biol., vol.268, pp. 78-94, 1997.
3. C. Burge and S. Karlin, "Finding genes in genomic DNA," Curr. Opin. Struct. Biol., vol.8, pp. 346-354, 1998.
4. A. L. Delcher, D. Harmon, S. Kasif, O. White, and S. L. Salzberg, "Improved microbial gene identification with GLIMMER," Nucleic Acids Res., vol. 27, no. 23, pp. 4636-4641, 1999.
5. W. J. Ewens and G. R. Grant, Statistical Methods in Bioinformatics, 2nd ed. New York: Springer-Verlag, 2006.
6. R. M. Gray, Entropy and Information Theory. New York: Springer-Verlag, 1990.
7. D. Gusfield, Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology. Cambridge, U.K.: Cambridge Univ. Press, 1997.
8. F. Jelinek, Statistical Methods for Speech Recognition. Cambridge, MA: MIT Press, 1997.
9. B.-H. Juang and L. R. Rabiner, "A probabilistic distance measure for hidden Markov models," AT&T Tech. J., vol. 64, no. 2, pp. 391-408, Feb. 1985.
10. A. Krogh, I. S. Mian, and D. Haussler, "A hidden Markov model that finds genes in E. coli DNA," Nucleic Acids Res., vol. 22, no. 22, pp. 4768-4778, 1994.
11. F. Liese and L. Vajda, "On divergences and informations in statistics and information theory," IEEE Trans. Inf. Theory, vol. 52, no. 10, pp. 4394-4412, Oct. 2006.
12. A. V. Lukashin and M. Borodovsky, "GeneMark.hmm: New solutions for gene finding," Nucleic Acids Res., vol. 26, no. 4, pp. 11-7, 1998.
13. W. H. Majoros and S. L. Salzberg, "An empirical analysis of training protocols for probabilistic gene finders," BMC Bioinformat., vol., p. 206, 2004.
14. K. Marton and P. C. Shields, "The positive-divergence and blowing up properties," Israel J. Math, vol.86, pp. 331-348, 1994.
15. L. W. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition," Proc. IEEE, vol. 77, no. 2, pp. 257-285, Feb. 1989.
16. Z. Rached, F. Alalaji, and L. L. Campbell, "The Kullback-Leibler divergence rate between Markov sources," IEEE Trans. Inf. Theory, vol. 50, no. 5, pp. 917-921, May 2004.
17. S. L. Salzberg, A. L. Delcher, S. Kasif, and O. White, "Microbial gene identification using interpolated Markov models," Nucleic Acids Res., vol. 26, no. 2, pp. 544-548, 1998.
18. S. Tiwari, S. Ramachandran, A. Bhattacharya, S. bhattacharya, and R. Ramaswamy, "Prediction of probable genes by Fourier analysis of gene sequences," Computat. Appl. Biosci., vol. 13, no. 3, pp. 263-270, Jun. 1997.
19. J. C. Venter et al., "The sequence of the human genome," Science, vol.291, pp. 1304-1351, 2001.
20. M. Vidyasagar, "A realization theory for hidden Markov models: The partial realization problem," in Proc. Symp. Math. Theory Netw. Syst., Kyoto, Japan, Jul. 2006, pp. 2145-2150.
21. M. Vidyasagar, "Bounds on the Kullback-Leibler divergence rate between hidden Markov models," in Proc. IEEE Conf. Decision Control, Dec. 12-17, 2007.