A Two-Phase ANN Method for Genome-Wide Detection of Hormone Response Elements

  • Maria Stepanova
  • Feng Lin
  • Valerie C. -L. Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4774)

Abstract

Steroid hormone receptors compose a subgroup of regulatory proteins which tend to recognize partially symmetric response elements on DNA. Identification of the members of a gene regulatory machine conducted by steroid hormones could provide better understanding of nature and development of diseases. We present an approach based on a succession of neural networks, which can be used for highly specific detection of binding signals. It exploits the capability of a feed-forward neural network to model datasets with high confidence, while a recurrent network grants putative response elements with biologically meaningful structures. We have used a novel method to train such a two-phase artificial neural network with a set of experimentally validated response elements for steroid hormone receptors. We have demonstrated that sequence-based prediction followed by structure-based classification of putative binding sites allows to eliminate large amount of false positives. An implementation of the neural network with Field-Programmable Gate Array is also briefly described.

Keywords

Transcription Factor Binding Site Steroid Hormone Receptor Recurrent Neural Network Milk Protein Gene Hardware Acceleration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Larsen, P., Kronenberg, H., Melmed, S., Polonsky, K.: Williams textbook of endocrinology. Saunders, Philadelphia (2003)Google Scholar
  2. 2.
    Conneely, O.M.: Perspective: Female Steroid Hormone Action. Endocrinology 142(6), 2194–2199 (2001)CrossRefGoogle Scholar
  3. 3.
    Ko, Y.J., Balk, S.P.: Targeting Steroid Hormone Receptor Pathways in the Treatment of Hormone Dependent Cancers. Curr. Pharm. Biotechnol. 5(5), 459–470 (2004)CrossRefGoogle Scholar
  4. 4.
    Danielsen, M., Hinck, L., Ringold, G.M.: Two amino acids within the knuckle of the first zinc finger specify DNA response element activation by the glucocorticoid receptor. Cell 57(7), 1131–1138 (1989)CrossRefGoogle Scholar
  5. 5.
    Wasserman, W.W., Sandelin, A.: Applied bioinformatics for the identification of regulatory elements. Nat. Rev. Genet. 5(4), 276–287 (2004)CrossRefGoogle Scholar
  6. 6.
    Kel, A.E., Gossling, E., Reuter, I., et al.: MATCH: A tool for searching transcription factor binding sites in DNA sequences. Nucleic Acids Res. 31(13), 3576–3579 (2003)CrossRefGoogle Scholar
  7. 7.
    Sandelin, A., Alkema, W., Engstrom, P., et al.: JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res. 32(DB), D91–D94 (2004)CrossRefGoogle Scholar
  8. 8.
    Rahmann, S., Muller, T., Vingron, M.: On the power of profiles for transcription factor binding site detection. Stat. Appl. Genet. Mol. Biol. 2(1): Article 7 (2003)Google Scholar
  9. 9.
    Stepanova, M., Lin, F., Lin, V.: Establishing a Statistic Model for Recognition of Steroid Hormone Response Elements. Comput. Biol. Chem. 30(5), 339–347 (2006)MATHCrossRefGoogle Scholar
  10. 10.
    Bajic, V.B., Tan, S.L., Chong, A., et al.: Dragon ERE Finder version 2: A tool for accurate detection and analysis of estrogen response elements in vertebrate genomes. Nucleic Acids Res. 31(13), 3605–3607 (2003)CrossRefGoogle Scholar
  11. 11.
    Sandelin, A., Wasserman, W.W.: Prediction of nuclear hormone receptor response elements. Mol. Endocrinol. 19(3), 595–606 (2005)CrossRefGoogle Scholar
  12. 12.
    Favorov, A.V., Gelfand, M.S., Gerasimova, A.V., et al.: A Gibbs sampler for identification of symmetrically structured, spaced DNA motifs with improved estimation of the signal length. Bioinformatics 21(10), 2240–2245 (2005)CrossRefGoogle Scholar
  13. 13.
    Khorasanizadeh, S., Rastinejad, F.: Nuclear-receptor interactions on DNA-response elements. Trends Biochem. Sci. 26(6), 384–390 (2001)CrossRefGoogle Scholar
  14. 14.
    Hagan, M., Demuth, H., Beale, M.: Neural Network Design. PWS Publishing company, Boston (1996)Google Scholar
  15. 15.
    Evans, R.M.: The steroid and thyroid hormone receptor superfamily. Science 240(4854), 889–895 (1988)CrossRefGoogle Scholar
  16. 16.
    Nelson, C.C., Hendy, S.C., Shukin, R.J., et al.: Determinants of DNA sequence specificity of the androgen, progesterone, and glucocorticoid receptors: evidence for differential steroid receptor response elements. Mol. Endocrinol. 13(12), 2090–2107 (1999)CrossRefGoogle Scholar
  17. 17.
    Hawkins, J., Boden, M.: The Applicability of Recurrent Neural Networks for Biological Sequence Analysis. IEEE ACM T. Comput. BI. 2(3), 243–253 (2005)Google Scholar
  18. 18.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York (2001)MATHGoogle Scholar
  19. 19.
    Thackray, V.G., Lieberman, B.A., Nordeen, S.K.: Differential gene induction by glucocorticoid and progesterone receptors. J. Steroid Biochem. Mol. Biol. 66(4), 171–178 (1998)CrossRefGoogle Scholar
  20. 20.
    Lieberman, B.A., Bona, B.J., Edwards, D.P., Nordeen, S.K.: The constitution of a progesterone response element. Mol. Endocrinol. 7(4), 515–527 (1993)CrossRefGoogle Scholar
  21. 21.
    Stepanova, M., Lin, F., Lin, V.C.: A Hopfield Neural Classifier and Its FPGA Implementation for Identification of Symmetrically Structured DNA Motifs. J. VLSI Sig. Proc. Syst. (in press)Google Scholar
  22. 22.
    Jantzen, K., Fritton, H.P., Igo-Kemenes, T., et al.: Partial overlapping of binding sequences for steroid hormone receptors and DNaseI hypersensitive sites in the rabbit uteroglobin gene region. Nucleic Acids Res. 15(11), 4535–4552 (1987)CrossRefGoogle Scholar
  23. 23.
    von der Ahe, D., Renoir, J.M., Buchou, T., et al.: Receptors for glucocorticosteroid and progesterone recognize distinct features of a DNA regulatory element. Proc. Natl. Acad. Sci. USA. 83(9), 2817–2821 (1986)CrossRefGoogle Scholar
  24. 24.
    Lamian, V., Gonzalez, B.Y., Michel, F.J., Simmen, R.C.: Non-consensus progesterone response elements mediate the progesterone-regulated endometrial expression of the uteroferrin gene. J. Steroid Biochem. Mol. Biol. 46(4), 439–450 (1993)CrossRefGoogle Scholar
  25. 25.
    Drouin, J., Trifiro, M.A., Plante, R.K., et al.: Glucocorticoid receptor binding to a specific DNA sequence is required for hormone-dependent repression of pro-opiomelanocortin gene transcription. Mol. Cell Biol. 9(12), 5305–5314 (1989)Google Scholar
  26. 26.
    Ma, T., Copland, J.A., Brasier, A.R., Thompson, E.A.: A novel glucocorticoid receptor binding element within the murine c-myc promoter. Mol. Endocrinol. 14(9), 1377–1386 (2000)CrossRefGoogle Scholar
  27. 27.
    Moens, U., Subramaniam, N., Johansen, B., et al.: A steroid hormone response unit in the late leader of the noncoding control region of the human polyomavirus BK confers enhanced host cell permissivity. J. Virol. 68(4), 2398–2408 (1994)Google Scholar
  28. 28.
    Welte, T., Philipp, S., Cairns, C., et al.: Glucocorticoid receptor binding sites in the promoter region of milk protein genes. J. Steroid Biochem. Mol. Biol. 47(1-6), 75–81 (1993)CrossRefGoogle Scholar
  29. 29.
    Kolla, V., Robertson, N.M., Litwack, G.: Identification of a mineralocorticoid/glucocorticoid response element in the human Na/K ATPase alpha1 gene promoter. Biochem. Biophys. Res. Commun. 266(1), 5–14 (1999)CrossRefGoogle Scholar
  30. 30.
    Verrijdt, G., Schauwaers, K., Haelens, A., et al.: Functional interplay between two response elements with distinct binding characteristics dictates androgen specificity of the mouse sex-limited protein enhancer. J. Biol. Chem. 277(38), 35191–35201 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Maria Stepanova
    • 1
  • Feng Lin
    • 2
  • Valerie C. -L. Lin
    • 3
  1. 1.Bioinformatics Research Centre 
  2. 2.School of Computer Engineering 
  3. 3.School of Biological Sciences, Nanyang Technological University, 637551Singapore

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