Abstract
Hidden Markov models have wide applications in pattern recognition. In genome sequence analysis, hidden Markov models (HMMs) have been applied to the identification of regions of the genome that contain regulatory information, i.e., binding sites. In higher eukaryotes, the regulatory information is organized into modular units called cis-regulatory modules. Each module contains multiple binding sites for a specific combination of several transcription factors. In this chapter, we gave a brief review of hidden Markov models, standard algorithms from HMM, and their applications to motif findings. We then introduce the application of HMM to a complex system in which an HMM is combined with Bayesian inference to identify transcription factor binding sites and cis-regulatory modules.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Crowley, E.M., Roeder, K., and Bina, M. (1997) A statistical model for locating regulatory regions in genomic DNA. J Mol Biol 268, 8–14.
Frith, M.C., Hansen, U., and Weng, Z. (2001) Detection of cis-element clusters in higher eukaryotic DNA. Bioinformatics 17, 878–889.
Bailey, T.L. and Noble, W.S. (2003) Searching for statistically significant regulatory modules. Bioinformatics 19, (Suppl. 2), ii16–ii25.
Rajewsky, N., Vergassola, M., Gaul, U., and Siggia, E.D. (2002) Computational detection of genomic cis-regulatory modules applied to body patterning in the early Drosophila embryo. BMC Bioinformatics 3, 30.
Sinha, S., van Nimwegen, E., and Siggia, E.D. (2003) A probabilistic method to detect regulatory modules. Bioinformatics 19, (Suppl. 1), i292–i301.
Wu, J. and Xie, J. (2008) Computation-Based Discovery of Cis-Regulatory Modules by Hidden Markov Model. J Comput Biol 15(3), 279–290.
Rabiner, L. and Juang, H. (1993) Fundamentals of Speech Recognition. Prentice Hall, USA.
Durbin, R., Eddy, S., Krogh, A., and Mitchison, G. (1998) Biological Sequence Analysis. Cambridge University Press, Cambridge, UK.
Baum, L.E. (1972) An equality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes. Inequalities 3, 1–8.
Yuh, C.H., Bolouri, H., and Davidson, E.H. (1998) Genomic cis-regulatory logic: Experimental and computational analysis of a sea urchin gene. Science 279, 1896–1902.
Wingender, E., Chen, X., Hehl, R., et al. (2000) TRANSFAC: an integrated system for gene expression regulation. Nucleic Acids Res 28, 316–319.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Humana Press, a part of Springer Science+Business Media, LLC
About this protocol
Cite this protocol
Wu, J., Xie, J. (2010). Hidden Markov Model and Its Applications in Motif Findings. In: Bang, H., Zhou, X., van Epps, H., Mazumdar, M. (eds) Statistical Methods in Molecular Biology. Methods in Molecular Biology, vol 620. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60761-580-4_13
Download citation
DOI: https://doi.org/10.1007/978-1-60761-580-4_13
Published:
Publisher Name: Humana Press, Totowa, NJ
Print ISBN: 978-1-60761-578-1
Online ISBN: 978-1-60761-580-4
eBook Packages: Springer Protocols