A simple hyper-geometric approach for discovering putative transcription factor binding sites

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2149, Issue , 2001, Pages 278-293

  Barash, Yoseph ^a   Bejerano, Gill ^a   Friedman, Nir ^a  

^a HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS; GENE EXPRESSION; GENES; MOLECULAR BIOLOGY; TRANSCRIPTION; TRANSCRIPTION FACTORS;

CO-REGULATION; COMPUTATIONAL PROCEDURES; GEOMETRIC APPROACHES; HYPERGEOMETRIC; MICROARRAY EXPRESSIONS; PROMOTER REGION; PUTATIVE TRANSCRIPTION FACTORS; REGULATORY MECHANISM;

BINDING SITES;

EID: 84959058940     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44696-6_22     Document Type: Conference Paper

References (27)

1. T.L. Bailey and Elkan C. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. In Proc. Int. Conf. Intell. Syst. Mol. Biol., volume 2, pages 28–36. 1994.
2. Y. Barash and N. Friedman. Context-specific Bayesian clustering for gene expression data. In Proc. Ann. Int. Conf. Comput. Mol. Biol., volume 5, pages 12–21. 2001.
3. Y. Benjamini and Y. Hochberg. Controlling the False Discovery Rate: a practical and powerful approach to multiple testing. J. Royal Statistical Society B, 57:289–300, 1995.
4. A. Brazma, I. Jonassen, J. Vilo, and E. Ukkonen. Predicting gene regulatory elements in silico on a genomic scale. Genome Res., 8:1202–15, 1998.
5. J. Buhler and M. Tompa. finding motifs using random projections. In Proc. Ann. Int. Conf. Comput. Mol. Biol., volume 5, pages 69–76. 2001.
6. H. J. Bussemaker, H. Li, and E. D. Siggia. building a dictionary for genomes: identification of presumptive regulatory sites by statistical analysis. PNAS, 97(18):10096–100, 2000.
7. H. J. Bussemaker, H. Li, and E. D. Siggia. Regulatory element detection using a probabilistic segmentation model. In Proc. Int. Conf. Intell. Syst. Mol. Biol., volume 8, pages 67–74. 2000.
8. S. Chu, J. DeRisi, M. Eisen, J. Mullholland, D. Botstein, P. Brown, and I. Herskowitz. The transcriptional program of sporulation in budding yeast. Science, 282:699–705, 1998.
9. J. DeRisi, V. Iyer, and P. Brown. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science, 282:699–705, 1997.
10. R. Durbin, S. Eddy, A. Krogh, and G. Mitchison. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, 1998.
11. R. Durrett. Probablity Theory and Examples. Wadsworth and Brooks, Cole, California, 1991.
12. M.B. Eisen, P.T. Spellman, P.O. Brown, and D. Botstein. Cluster analysis and display of genome-wide expression patterns. PNAS, 95:14863–14868, 1998.
13. D. J. Galas, M. Eggert, and M. S. Waterman. Rigorous pattern-recognition methods for dna sequences: analysis of promoter sequences from Escherichia coli. J. Mol. Biol., 186:117–28, 1985.
14. A. P. Gasch, P. T. Spellman, C. M. Kao, O. Carmel-Harel, M. B. Eisen, G. Storz, D. Botstein, and P. O. Brown. Genomic expression program in the response of yeast cells to environmental changes. Mol. Bio. Cell, 11:4241–4257, 2000.
15. T. R. Hughes, M. J. Marton, A. R. Jones, C. J. Roberts, R. Stoughton, C. D. Armour, H. A. Bennett, E. Coffey, H. Dai, Y. D. He, M. J. Kidd, A. M. King, M. R. Meyer, D. Slade, P. Y. Lum, S. B. Stepaniants, D. D. Shoemaker, D. Gachotte, K. Chakraburtty, J. Simon, M. Bard, and S. H. Friend. Functional discovery via a compendium of expression profiles. Cell, 102(1):109–26, 2000.
16. V. R. Iyer, C. E. Horak, C. S. Scafe, D. Botstein, M. Snyder, and P. O. Brown. Genomic binding sites of the yeast cell-cycle transcription factors sbf and mbf. Nature, 409:533–538, 2001.
17. L. J. Jensen and S. Knudsen. Automatic discovery of regulatory patterns in promoter regions based on whole cell expression data and functional annotation. Bioinformatics, 16:326–333, 2000.
18. C. E. Lawrence, S. F. Altschul, M. S. Boguski, J. S. Liu, R. F. Neuwald, and J. C. Wooton. Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment. Science, 262:208–214, 1993.
19. P.A. Pevzner and S.H. Sze. Combinatorial approaches to finding subtle signals in dna sequences. In Proc. Int. Conf. Intell. Syst. Mol. Biol., volume 8, pages 269–78. 2000.
20. F.P. Roth, P.W. Hughes, J.D. Estep, and G.M. Church. Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation. Nat. Biotechnol., 16:939–945, 1998.
21. S. Sinha and M. Tompa. A statistical method for finding transcription factor binding sites. In Proc. Int. Conf. Intell. Syst. Mol. Biol., volume 8, pages 344–54. 2000.
22. P. T. Spellman, G. Sherlock, M. Q. Zhang, V. R. Iyer, K. Anders, M. B. Eisen, P. O. Brown, D. Botstein, and B. Futcher. Comprehensive identification of cell cycle-regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell, 9(12):3273–97, 1998.
23. S. Tavazoie, J. D. Hughes, M. J. Campbell, R. J. Cho, and G. M. Church. Systematic determination of genetic network architecture. Nat Genet, 22(3):281–5, 1999. Comment in: Nat Genet 1999 Jul;22(3):213-5.
24. J. van Helden, B. Andre, and J. Collado-Vides. Extracting regulatory sites from the upstream region of yeast genes by computational analysis of oligonucleotide frequencies. J. Mol. Biol., 281(5):827–42, 1998.
25. J. van Helden, A. F. Rios, and J. Collado-Vides. discovering regulatory elements in noncoding sequences by analysis of spaced dyads. Nucl. Acids Res., 28(8):1808–18, 2000.
26. J. Vilo, A. Brazma, I. Jonassen, A. Robinson, and E. Ukkonen. Mining for putative regulatory elements in the yeast genome using gene expression data. In Proc. Int. Conf. Intell. Syst. Mol. Biol., volume 8, pages 384–94. 2000.
27. F. Wolfertstetter, K. Frech, G. Herrmann, and T. Werner. Identification of functional elements in unaligned nucleic acid sequences by a novel tuple search algorithm. Comput. Appl. Biosci., 12(1):71–80, 1996.