A common task in bioinformatics is the comparison of biological sequences to probabilistic models in order to evaluate their similarity. Completion of genomes of most of the organisms lead to profitable comparative analyses, providing insights into non-coding regions as well as into protein coding regions of DNA. In the present work we propose a method for finding similar sequence in a database of upstream sequences of DNA. For testing purpose, we have extracted upstream sequences of different mammals of citrate synthase and actin genes and also that of cab gene in different plants. The promoter sequences are extracted from NCBI database. Motifs/ TFBS of the upstream sequences are extracted using the software tool ‘TF search’. Then probabilistic models are obtained for motif sequences by HMM method. Query motif sequence can be compared with all the motif sequences in the data base and based on maximum likelihood procedure, degree of similarity between query and all the motif sequences is obtained.

1. Baum L. E., Ted petrie, George Soules, and Normal Weiss. 1970. A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Ann. Math. Statist.; 41:pp164—171.
2. Brutlag.D. 2002. Multiple sequence alignment and Motifs. Bioinformatics methods and Techniques. Stanford University, Stanford center for Professional development.
3. D.A. Benson, I. Karsch-Mizrachi, D.J. Lipman, J. Ostell, and D.L.Wheeler, 2006. GenBank. Nucleic Acids Research, vol. 34, pp. D16--D20
4. Denis F Wolf, Gaurav S Sukhatme, Dieter Fox, Wolfram Burgard, 2005. Autonomous Terrain Mapping and Classification Using Hidden Markov Models. Proceedings of the 2005 IEEE International Conference on Robotics and Automation Volume: 2005, Issue: April, Publisher: Ieee, pp 2026—2031.
5. E. Lawrence and A. Reilly, 1990. An expectation maximization algorithm for the identification and characterization of common sites in unaligned biopolymer sequences. Proteins, vol. 7, pp. 41—51.
6. Eddy SR. 1998. Profile hidden Markov models. Bioinformatics.; 14:755
7. Georgina Mirceva and Danco Davcev, 2009. HMM based approach for classifying protein structures. International Journal of Bio- Science and Bio- Technology Vol. 1, No. 1, December.
8. J. Baker, 1975. The DRAGON system-an overview, IEEE Trans. Acoustics Speech and Signal Processing, vol. ASSP-23, pp. 24-9.
9. Krogh, M. Brown, I.S. Mian, K. Sjolander, and D. Haussler, 1994. Hidden Markov Models in Computational Biology: Applications to Protein Modeling. J. Molecular Biology, vol. 235, pp. 1501-1531.
10. Meera A, Lalitha Rangarajan, Savithri Bhat:, 2009. Computational Approach Towards Finding Evolutionary Distance And Gene order Using Promoter Sequences Of Central Metabolic Pathway” Inter disciplinary sciences-computational life sciences DOI: 0.1007/s12539-009-0017-3 [Spriger link]
11. Rabiner, L.R. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77:257-286.
12. S. Altschul, T.L. Madden, A.A. Schaffer, J. Zheng, Z. Zhang, M.Miller, and D.L. Lipman, 1997. Gapped BLAST and PSI-BLAST: A New Generation of Protein Database Search Programs, Nucleic Acid Research, vol. 25, pp. 3389—3402
13. S.F. Altschul, W. Gish, W. Miller, E.W. Myers, and D.J. Lipman, 1990. Basic Local Alignment Search Tool. J. Molecular Biology, vol. 215,pp. 403--410.
14. Wasserman WW, Sandelin A, 2004. Applied bioinformatics for the identification of regulatory elements. Nat. Rev Genet. 5, 276–286.
15. W.R. Pearson and D.J. Lipman, 1988. Improved Tools for Biological Sequence Comparison, Proc. Nat’l Academy of Science, vol. 85,pp. 2444-2448.
16. L. E. Baum, J. A. Eagon, 1967. An inequality with application to statistical estimation for probabilistic functions of Markov processes and to a model of ecology, Bull American Society, Vol, 73, pp. 360-363.

Database Hidden Markov Model Promoter sequence pattern matching Transcription factors (TFs) Transcription factor binding sites (TFBS) Similarity measure Promoter sequence pattern matching Transcription factors (TFs) Transcription factor binding sites (TFBS) Similarity measure