Identification of transcription factor binding sites with variable-order Bayesian networks

Bioinformatics

Volumn 21, Issue 11, 2005, Pages 2657-2666

  Ben Gal, I ^a   Shani, A ^a   Gohr, A ^b,c   Grau, J ^b,c   Arviv, S ^a   Shmilovici, A ^d   Posch, S ^c   Grosse, I ^b  

^a TEL AVIV UNIVERSITY   (Israel)

^b LEIBNIZ INSTITUTE OF PLANT GENETICS AND CROP PLANT RESEARCH IPK   (Germany)

^c MARTIN LUTHER UNIVERSITY HALLE WITTENBERG   (Germany)

^d BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

TRANSCRIPTION FACTOR;

AMINO ACID SEQUENCE; ARTICLE; BAYES THEOREM; BINDING SITE; CONTROLLED STUDY; ESCHERICHIA COLI; MATHEMATICAL COMPUTING; NONHUMAN; PRIORITY JOURNAL; PROBABILITY; SEQUENCE ANALYSIS; STATISTICAL ANALYSIS; STATISTICAL SIGNIFICANCE;

ESCHERICHIA COLI;

EID: 20744440509     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/bti410     Document Type: Article

References (51)

1. Baldi,P. and Brunak,S. (2001) Bioinformatics: The Machine Learning Approach, 2nd edn. The MIT Press, Cambridge, MA.
2. Barash,Y., Elidan,G., Friedman,N. and Kaplan,T. (2003) Modeling dependencies in protein-DNA binding sites. In Proceedings of the Seventh Annual International Conference on Computational Molecular Biology (RECOMB), NY, ACM.
3. Bejerano,G. and Yona,G. (2001) Variations on probabilistic suffix trees: statistical modeling and prediction of protein families. Bioinformatics, 17, 23-43.
4. Ben-Gal,I. and Shmilovici,A. (2001) Promoters recognition by variable-length Markov models. In Workshop on Artificial Intelligence and Heuristic Methods for Bioinformatics, San-Miniato, Italy.
5. Ben-Gal,I., Morag,G. and Shmilovici,A. (2003) CSPC: a monitoring procedure for state dependent processes. Technometrics, 45, 293-311.
6. Benos,P.V., Lapedes,A.S., Fields,D.S. and Stormo,G.D. (2001) SAMIE: statistical algorithm for modeling interaction energies. In PSB'01.
7. Bilu,Y., Linial,M., Slonim,N. and Tishby,N. (2002) Locating transcription factors binding sites using a variable memory Markov model. Leibintz Center TR 2002-57.
8. Blattner, F.R. and Schroeder, J.L. (1984) A computer package for DNA sequence analysis. Nucleic Acids Res., 12, 615-617.
9. Boutilier,C., Friedman,N., Goldszmidt,M. and Koller,D. (1996) Context-specific independence in Bayesian networks. In Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence, August 1-4, 1996, Reed College, Portland, Oregon, USA, pp. 115-123. Also appears at http://www.informatik.uni-trier.de/~ley/db/conf/uai/uai1996.html
10. Buhlmann,P. and Wyner,A.J. (1999) Variable length Markov chains. Ann. Statist., 27, 480-513.
11. Bulyk,M.L. et al. (2002) Nucleotides of transcription factor binding sites exert interdependent effects on the binding affinities of transcription factors. Nucleic Acids Res., 30, 1255-1261.
12. Cai,D. et al. (2000) Modeling splice sites with Bayes Networks. Bioinformatics, 16, 152-158.
13. Castelo,R. and Guigo,R. (2004) Splice site identification by idlBNs. Bioinformatics, 20, i69-i76.
14. Chow,C.K. and Liu,C.N. (1968) Approximating discrete probability distributions with dependence trees. IEEE Trans. Inform. Theory, 14, 462-467.
15. Chu,S. et al. (1998) The transcriptional program of sporulation in budding yeast. Science, 282, 699-705.
16. Djordjevic,M. et al. (2003) A biophysical approach to transcription factor binding site discovery. Genome Res. 13, 2381-2390.
17. Ewens,W.J. and Grant,G.R. (2001) Statistical Methods in Bioinformatics: An Introduction. Springer-Verlag, New York, Inc.
18. Fickett,J.W. and Hatzigeorgiou,A.G. (1997) Eukaryotic promoter recognition. Genome Res., 7, 861-878.
19. Friedman,N. and Goldszmidt,M. (1996) Learning Bayesian networks with local Structure. In Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence, August 1-4, 1996, Reed College, Portland, Oregon, USA, pp. 252-262. Also appears at http://www.informatik.uni-trier.de/~ley/db/conf/uai/uai1996.html
20. Friedman,N. et al. (2000) Using Bayesian networks to analyze expression data. J. Comput. Biol., 7, 601-620.
21. Hanisch,D. et al. (2002) Co-clustering of biological networks and gene expression data. Bioinformatics, 1, 1-10.
22. Heckerman,D., Geiger,D. and Chickering,D.M. (1995) Learning Bayesian networks. The combination of knowledge and statistical data. Machine Learning, 20, 197-243.
23. Hughes,J.D. et al. (2000) Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae. J. Mol. Biol., 296, 1205-1214.
24. Kel,A.E. et al. (2003) MATCH: a tool for searching transcription factor binding sites in DNA sequences. Nucleic Acids Res., 31, 3576-3579.
25. Kel-Margoulis,O.V. et al. (2003) Composition-sensitive analysis of the human genome for regulatory signals. In Silico Biol., 3, 13.
26. Kullback,S. (1959) Information Theory and Statistics. Wiley, New York (reprinted in 1978 by Peter Smith, MA).
27. Liu,J.S. et al. (1995) Bayesian models for multiple local sequence alignment and Gibbs sampling strategies. J. Am. Stat. Assoc., 90, 1156-1170.
28. Liu,X. et al. (2001) BioProspector: discovering conserved DNA motifs in upstream regulatory regions of co-expressed genes. Pac. Symp. Biocomput., 6, 127-138.
29. Mount,D.W. (2001) Bioinformatics, Sequence and Genome Analysis. Cold Spring Harbor Laboratory Press, pp. 357-365.
30. Neuwald,A.F., et al. (1995) Gibbs motif sampling: detection of bacterial outer membrane protein repeats. Protein Sci., 4, 1618-1632.
31. Ohler,U. and Niemann,H. (2001) Identification and analysis of eukaryotic promoters: recent computational approaches. Trends Genet., 17, 56-60.
32. Ohler,U. et al. (1999) Interpolated Markov chains for eukaryotic promoter recognition. Bioinformatics, 15, 362-369.
33. Orlov,Y.L. and Potapov, V.N. (2000) Determining Markov Model of Genetical Texts by Stochastic Complexity Estimation. BGRS, Novosibirsk, pp. 71-73.
34. Orlov, Y.L. et al. (2002) Construction of stochastic context trees for genetic texts. In Silico Biol., 2, 233-247.
35. Pickert,L. et al. (1998) Transcription regulatory region analysis using signal detection and fuzzy clustering. Bioinformatics, 14, 244-251.
36. Pearl,J. (1988) Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, California.
37. Rissanen,J. (1983) A universal data compression system. IEEE Trans. Inform. Theory, 29, 656-664.
38. Ron,D. et al. (1996) The power of amnesia: learning probabilistic automata with variable memory length. Machine Learning, 25, 117-149.
39. Salgado,H. et al. (2000) RegulonDB (version 3.0): transcriptional regulation and operon organization in Escherichia coli K-12. Nucleic Acids Res., 28, 65-67.
40. Salzberg,S.L. (1997) A method for identifying splice sites and translational start sites in eukaryotic mRNA. CABIOS, 13, 365-376.
41. Salzberg,S.L. et al. (1998) Microbial gene identification using interpolated Markov models. Nucleic Acids Res., 26, 544-548.
42. Salzberg,S.L. et al. (1999) Interpolated Markov models for eukaryotic gene finding. Genomics, 59, 24-31.
43. Spellman,P.T. et al. (1998) Comprehensive identification of cell cycle-regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell, 9, 3273-3297.
44. Stormo,G.D. and Fields,D.S. (1998) Specificity, free energy and information content in protein - DNA interactions. Trends Biochem. Sci., 23, 109-113.
45. Thijs,G. et al. (2001) A higher-order background model improves the detection of promoter regulatory elements by Gibbs sampling. Bioinformatics, 17, 1113-1122.
46. Vert,J.P. (2001) Adaptive context trees and text clustering. IEEE Trans. Inform. Theory, 47, 1884-1901.
47. Weinberger,M. et al. (1995) A universal finite memory source. IEEE Trans. Inform. Theory, 41, 643-652.
48. Wingender,E. et al. (2000) TRANSFAC: an integrated system for gene expression regulation. Nucleic Acids Res., 28, 316-319.
49. Wingender,E. (2001) The TRANSFAC system on gene expression regulation. Nucleic Acids Res., 29, 281-283.
50. Zhang,M.Q. and Marr,T.G. (1993) A weight array method for splicing signal analysis. Comput. Appl. Biosci., 9, 499-509.
51. Zhao, X. et al. (2004) Finding Short DNA Motifs Using Permuted Markov Models. Proceedings of the 8th Annual International Conference on Computational Molecular Biology, 68-75, March 27-31, 2004, San Diego, California, USA, ACM.