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Advances in Bioinformatics
Volume 2008 (2008), Article ID 369830, 9 pages
http://dx.doi.org/10.1155/2008/369830
Research Article

Genomic Promoter Analysis Predicts Functional Transcription Factor Binding

1Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106, USA
2Department of Pathology & Laboratory Medicine and Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA 30322, USA

Received 31 January 2008; Revised 15 May 2008; Accepted 17 July 2008

Academic Editor: Ramana Davuluri

Copyright © 2008 J. Sunil Rao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Karanam and C. S. Moreno, “CONFAC: automated application of comparative genomic promoter analysis to DNA microarray datasets,” Nucleic Acids Research, vol. 32, web server issue, pp. W475–W484, 2004. View at Publisher · View at Google Scholar
  2. J. Flint, C. Tufarelli, J. Peden et al., “Comparative genome analysis delimits a chromosomal domain and identifies key regulatory elements in the a globin cluster,” Human Molecular Genetics, vol. 10, no. 4, pp. 371–382, 2001. View at Publisher · View at Google Scholar
  3. R. C. Hardison, J. Oeltjen, and W. Miller, “Long human mouse sequence alignments reveal novel regulatory elements: a reason to sequence the mouse genome,” Genome Research, vol. 7, no. 10, pp. 959–966, 1997. View at Google Scholar
  4. B. Lenhard, A. Sandelin, L. Mendoza, P. Engström, N. Jareborg, and W. W. Wasserman, “Identification of conserved regulatory elements by comparative genome analysis,” Journal of Biology, vol. 2, no. 2, article 13, pp. 131–141, 2003. View at Publisher · View at Google Scholar
  5. W. W. Wasserman, M. Palumbo, W. Thompson, J. W. Fickett, and C. E. Lawrence, “Human-mouse genome comparisons to locate regulatory sites,” Nature Genetics, vol. 26, no. 2, pp. 225–228, 2000. View at Publisher · View at Google Scholar
  6. J. W. Fickett and W. W. Wasserman, “Discovery and modeling of transcriptional regulatory regions,” Current Opinion in Biotechnology, vol. 11, no. 1, pp. 19–24, 2000. View at Publisher · View at Google Scholar
  7. W. W. Wasserman and J. W. Fickett, “Identification of regulatory regions which confer muscle-specific gene expression,” Journal of Molecular Biology, vol. 278, no. 1, pp. 167–181, 1998. View at Publisher · View at Google Scholar
  8. J. C. Oeltjen, T. M. Malley, D. M. Muzny, W. Miller, R. A. Gibbs, and J. W. Belmont, “Large-scale comparative sequence analysis of the human and murine Bruton's tyrosine kinase loci reveals conserved regulatory domains,” Genome Research, vol. 7, no. 4, pp. 315–329, 1997. View at Google Scholar
  9. G. G. Loots and I. Ovcharenko, “rVISTA 2.0: evolutionary analysis of transcription factor binding sites,” Nucleic Acids Research, vol. 32, web server issue, pp. W217–W221, 2004. View at Publisher · View at Google Scholar
  10. D. T. Odom, N. Zizlsperger, D. B. Gordon et al., “Control of pancreas and liver gene expression by HNF transcription factors,” Science, vol. 303, no. 5662, pp. 1378–1381, 2004. View at Publisher · View at Google Scholar
  11. A. E. Kel, E. Gößling, I. Reuter, E. Cheremushkin, O. V. Kel-Margoulis, and E. Wingender, “MATCHTM: a tool for searching transcription factor binding sites in DNA sequences,” Nucleic Acids Research, vol. 31, no. 13, pp. 3576–3579, 2003. View at Publisher · View at Google Scholar
  12. M. A. Beer and S. Tavazoie, “Predicting gene expression from sequence,” Cell, vol. 117, no. 2, pp. 185–198, 2004. View at Publisher · View at Google Scholar
  13. H. Ishwaran and J. S. Rao, “Detecting differentially expressed genes in microarrays using Bayesian model selection,” Journal of the American Statistical Association, vol. 98, no. 462, pp. 438–455, 2003. View at Publisher · View at Google Scholar
  14. A. D. Smith, P. Sumazin, D. Das, and M. Q. Zhang, “Mining ChIP-chip data for transcription factor and cofactor binding sites,” Bioinformatics, vol. 21, supplement 1, pp. i403–i412, 2005. View at Publisher · View at Google Scholar
  15. H. Ishwaran and J. S. Rao, “Spike and slab variable selection: frequentist and Bayesian strategies,” Annals of Statistics, vol. 33, no. 2, pp. 730–773, 2005. View at Publisher · View at Google Scholar
  16. X. Xie, J. Lu, E. J. Kulbokas et al., “Systematic discovery of regulatory motifs in human promoters and 3' UTRs by comparison of several mammals,” Nature, vol. 434, no. 7031, pp. 338–345, 2005. View at Publisher · View at Google Scholar
  17. C. T. Harbison, D. B. Gordon, T. I. Lee et al., “Transcriptional regulatory code of a eukaryotic genome,” Nature, vol. 431, no. 7004, pp. 99–104, 2004. View at Publisher · View at Google Scholar
  18. S. J. Ho Sui, D. L. Fulton, D. J. Arenillas, A. T. Kwon, and W. W. Wasserman, “oPOSSUM: integrated tools for analysis of regulatory motif over-representation,” Nucleic Acids Research, vol. 35, web server issue, pp. W245–W252, 2007. View at Publisher · View at Google Scholar
  19. S. J. Ho Sui, J. R. Mortimer, D. J. Arenillas et al., “oPOSSUM: identification of over-represented transcription factor binding sites in co-expressed genes,” Nucleic Acids Research, vol. 33, no. 10, pp. 3154–3164, 2005. View at Publisher · View at Google Scholar
  20. D. C. King, J. Taylor, L. Elnitski, F. Chiaromonte, W. Miller, and R. C. Hardison, “Evaluation of regulatory potential and conservation scores for detecting cis-regulatory modules in aligned mammalian genome sequences,” Genome Research, vol. 15, no. 8, pp. 1051–1060, 2005. View at Publisher · View at Google Scholar
  21. B. D. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, Cambridge, UK, 1996.
  22. P. Sudarsanam, Y. Pilpel, and G. M. Church, “Genome-wide co-occurrence of promoter elements reveals a cis-regulatory cassette of rRNA transcription motifs in Saccharomyces cerevisiae,” Genome Research, vol. 12, no. 11, pp. 1723–1731, 2002. View at Publisher · View at Google Scholar
  23. Y. Pilpel, P. Sudarsanam, and G. M. Church, “Identifying regulatory networks by combinatorial analysis of promoter elements,” Nature Genetics, vol. 29, no. 2, pp. 153–159, 2001. View at Publisher · View at Google Scholar
  24. F. Long, H. Liu, C. Hahn, P. Sumazin, M. Q. Zhang, and A. Zilberstein, “Genome-wide prediction and analysis of function-specific transcription factor binding sites,” In Silico Biology, vol. 4, no. 4, pp. 395–410, 2004. View at Google Scholar
  25. J. Locker, D. Ghosh, P.-V. Luc, and J. Zheng, “Definition and prediction of the full range of transcription factor binding sites—the hepatocyte nuclear factor 1 dimeric site,” Nucleic Acids Research, vol. 30, no. 17, pp. 3809–3817, 2002. View at Publisher · View at Google Scholar
  26. M. Blanchette, A. R. Bataille, X. Chen et al., “Genome-wide computational prediction of transcriptional regulatory modules reveals new insights into human gene expression,” Genome Research, vol. 16, no. 5, pp. 656–668, 2006. View at Publisher · View at Google Scholar
  27. E. Segal, Y. Fondufe-Mittendorf, L. Chen et al., “A genomic code for nucleosome positioning,” Nature, vol. 442, no. 7104, pp. 772–778, 2006. View at Publisher · View at Google Scholar
  28. H. Ishwaran, J. S. Rao, and U. B. Kogalur, “BAMARRYTM: Java software for Bayesian analysis of variance for microarray data,” BMC bioinformatics, vol. 7, article 59, pp. 1–21, 2006. View at Publisher · View at Google Scholar
  29. H. Ishwaran and J. S. Rao, “Spike and slab gene selection for multigroup microarray data,” Journal of the American Statistical Association, vol. 100, no. 471, pp. 764–780, 2005. View at Publisher · View at Google Scholar
  30. J. Li and L. Wong, “Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns,” Bioinformatics, vol. 18, no. 5, pp. 725–734, 2002. View at Publisher · View at Google Scholar
  31. J. S. Rao and H. Ishwaran, “Multigroup classification by marginal effect isolation,” Tech. Rep., Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA, 2006. View at Google Scholar
  32. W. M. Patefield, “Algorithm AS 159: an efficient method of generating r×c tables with given row and column totals,” Applied Statistics, vol. 30, no. 1, pp. 91–97, 1981. View at Publisher · View at Google Scholar