Abstract
Expectation maximization and Gibbs’ sampling are two statistical approaches used to identify transcription factor binding sites and the motif that represents them. Both take as input unaligned sequences and search for a statistically significant alignment of putative binding sites. Expectation maximization is deterministic so that starting with the same initial parameters will always converge to the same solution, making it wise to start it multiple times from different initial parameters. Gibbs’ sampling is stochastic so that it may arrive at different solutions from the same initial parameters. In both cases multiple runs are advised because comparisons of the solutions after each run can indicate whether a global, optimum solution is likely to have been achieved.
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
Pribnow, D. (1975) Nucleotide sequence of an RNA polymerase binding site at an early T7 promoter. Proc Natl Acad Sci USA 72, 784–788.
Rosenberg, M., and Court, D. (1979) Regulatory sequences involved in the promotion and termination of RNA transcription. Annu Rev Genet 13, 319–353.
Galas, D.J., Eggert, M., and Waterman, M.S. (1985) Rigorous pattern-recognition methods for DNA sequences. Analysis of promoter sequences from Escherichia coli. J Mol Biol 186, 117–128.
Pavesi, G., Mauri, G., and Pesole, G. (2001) An algorithm for finding signals of unknown length in DNA sequences. Bioinformatics 17(Suppl. 1), S207–S214.
Marschall, T., and Rahmann, S. (2009) Efficient exact motif discovery. Bioinformatics 25, i356–i364.
Stormo, G.D. (2000) DNA binding sites: representation and discovery. Bioinformatics 16, 16–23.
Stormo, G.D., Schneider, T.D., Gold, L., and Ehrenfeucht, A. (1982) Use of the ‘Perceptron’ algorithm to distinguish translational initiation sites in E. coli. Nucleic Acids Res 10, 2997–3011.
Staden, R. (1984) Computer methods to locate signals in nucleic acid sequences. Nucleic Acids Res 12, 505–519.
Stormo, G.D., and Hartzell, G.W., 3rd. (1989) Identifying protein-binding sites from unaligned DNA fragments. Proc Natl Acad Sci USA 86, 1183–1187.
Das, M.K., and Dai, H.K. (2007) A survey of DNA motif finding algorithms. BMC Bioinformatics 8(Suppl. 7), S21.
GuhaThakurta, D. (2006) Computational identification of transcriptional regulatory elements in DNA sequence. Nucleic Acids Res 34, 3585–3598.
Lawrence, C.E., and Reilly, A.A. (1990) An expectation maximization (EM) algorithm for the identification and characterization of common sites in unaligned biopolymer sequences. Proteins 7, 41–51.
Lawrence, C.E., Altschul, S.F., Boguski, M.S., Liu, J.S., Neuwald, A.F., and Wootton, J.C. (1993) Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment. Science 262, 208–214.
Dempster, A.P., Laird, N.M., and Rubin, D.B. (1977). Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc. Ser B (Methodol) 39, 1–38.
Little, R.J.A., and Rubin, D.B. (2002). Statistical analysis with missing data, 2nd edn. Wiley, New York, NY.
Narlikar, L., Gordân, R., Ohler, U., and Hartemink, A.J. (2006) Informative priors based on transcription factor structural class improve de novo motif discovery. Bioinformatics 22, e384–e392.
Bailey, T.L., and Elkan, C. (1995) The value of prior knowledge in discovering motifs with MEME. Proc Int Conf Intell Syst Mol Biol 3, 21–29.
Bailey, T.L., and Elkan, C. (1994) Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proc Int Conf Intell Syst Mol Biol 2, 28–36.
Bailey, T.L., and Elkan, C.P. (1995) Unsupervised learning of multiple motifs in biopolymers using expectation maximization. Mach Learn 21, 51–80.
Bailey, T.L. (2002) Discovering novel sequence motifs with MEME. Curr Protoc Bioinformatics Chapter 2, Unit 2.4.
Liu, J.S., Neuwald, A.F., and Lawrence, C.E. (1995) Bayesian models for multiple local sequence alignment and Gibbs sampling strategies. J Am Stat Assoc 90, 1156–1170.
Roth, F.P., Hughes, J.D., Estep, P.W., and Church, G.M. (1998) Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation. Nat Biotechnol 16, 939–945.
Liu, X., Brutlag, D.L., and Liu, J.S. (2001) BioProspector: discovering conserved DNA motifs in upstream regulatory regions of co-expressed genes. Pac Symp Biocomput 2001, 127–138.
Benos, P.V., Bulyk, M.L., and Stormo, G.D. (2002) Additivity in protein-DNA interactions: how good an approximation is it? Nucleic Acids Res 30, 4442–4451.
Djordjevic, M., Sengupta, A.M., and Shraiman, B.I. (2003) A biophysical approach to transcription factor binding site discovery. Genome Res 13, 2381–2390.
Zhao, Y., Granas, D., and Stormo, G.D. (2009) Inferring binding energies from selected binding sites. PLoS Comp Bio, 5, e1000590.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media, LLC
About this protocol
Cite this protocol
Stormo, G.D. (2010). Motif Discovery Using Expectation Maximization and Gibbs’ Sampling. In: Ladunga, I. (eds) Computational Biology of Transcription Factor Binding. Methods in Molecular Biology, vol 674. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60761-854-6_6
Download citation
DOI: https://doi.org/10.1007/978-1-60761-854-6_6
Published:
Publisher Name: Humana Press, Totowa, NJ
Print ISBN: 978-1-60761-853-9
Online ISBN: 978-1-60761-854-6
eBook Packages: Springer Protocols