e20 Nucleic Acids Research, 2007, Vol. 35, No. 3 indicate positional clustering is less likely to improve predic- tions when Gibbs sampling identiﬁes a correct site in <20% of repetitions (Figure 3). Thus, it is possible the rather simp- listic linear model overestimates improvement in robustness beyond what is practically achievable. Moreover, when mul- tiple motifs exist in the input promoters, preliminary evidence suggests positional clustering will uniquely identify a single dominant motif (Supplementary Figure S5). With further reﬁnement, however, it may be possible to recover subordi- nate motifs, enabling identiﬁcation of cis-regulatory modules. In spite of these limitations, using positional clustering of repeated runs, researchers can successfully apply sampling algorithms in identiﬁcation of functional binding sites in datasets with a signiﬁcant proportion of noise. Computational prediction of TF binding in mammalian genomes poses just such a challenge due to increased decoy sequence in large upstream regions (24). Thus, having estab- lished increased robustness to DSs in yeast, we applied our approach to identify potentially unknown GABAA receptor subunit gene regulatory sequences that may participate in the response of the genome to seizure activity. We reasoned that GABAA receptor subunit genes either up-regulated or down-regulated in the animal model of epilepsy would share common binding motifs. Using positional clustering, we predicted 13 TF-binding sites upstream of GABAA recep- tor subunit genes (Table 1). Twelve of our predictions were veriﬁed by either comparison to known binding sites or experimental veriﬁcation using in vitro binding assays. Ini- tially positive experimental results highlight the ability of computational techniques to direct research into transcrip- tional regulation in mammalian models. As such, our approach may be applicable in the study of other protein complexes in the mammalian proteome. The reported predictions may enable pharmacologically important downstream research. For example the predicted sites can be used as a starting point for quantifying in vivo effect on downstream transcription; for identifying the TFs bound; and even for the more complex task of understanding the role of each site in determining the relative abundance of GABAA receptor isoforms. Research along these lines may dramatically improve our understanding of GABAA receptor regulation and its role in disease and development. Addition- ally, a more comprehensive evaluation of the remaining GABAA receptor subunit genes may reveal additional TF- binding sites that uncover the evolutionary signiﬁcance of g -a-b GABR clusters in the human genome. SUPPLEMENTARY DATA Supplementary Data are available at NAR Online. ACKNOWLEDGEMENT Charles DeLisi is partially supported by NIH grants A08 POGM66401A and J50 01-130021. Daniel S. Roberts is supported by NIH training grant 2T32 GM00854. Shelley J Russek is supported by NIH/NINDS Grant NS050393. Funding to pay the Open Access publication charges for this article was provided by the Boston University Bioinformatics Program. Conflict of interest statement. None declared. PAGE 8 OF 9 REFERENCES 1. Winderickx,J., de Winde,J.H., Crauwels,M., Hino,A., Hohmann,S., Van Dijck,P. and Thevelein,J.M. (1996) Regulation of genes encoding subunits of the trehalose synthase complex in Saccharomyces cerevisiae: novel variations of STRE-mediated transcription control? Mol. Gen. Genet., 252, 470–482. 2. Madhani,H.D. and Fink,G.R. (1997) Combinatorial control required for the specificity of yeast MAPK signaling. Science, 275, 1314–1317. 3. Niehrs,C. and Pollet,N. (1999) Synexpression groups in eukaryotes. Nature, 402, 483–487. 4. Lewin,B. (2004) Genes VIII. Pearson Prentice Hall, Upper Saddle River, NJ. 5. Stormo,G.D. (1990) Consensus patterns in DNA. Methods Enzymol., 183, 211–221. 6. 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. 7. Bussemaker,H.J., Li,H. and Siggia,E.D. (2000) From the Cover: building a dictionary for genomes: identification of presumptive regulatory sites by statistical analysis. Proc. Natl Acad. Sci. USA, 97, 10096–10100. 8. 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., 127–138. 9. Macisaac,K.D., Wang,T., Gordon,B.D., Gifford,D.K., Stormo,G.D. and Fraenkel,E. (2006) An improved map of conserved regulatory sites for Saccharomyces cerevisiae. BMC Bioinformatics, 7, 113. 10. Harbison,C.T., Gordon,D.B., Lee,T.I., Rinaldi,N.J., Macisaac,K.D., Danford,T.W., Hannett,N.M., Tagne,J.-B., Reynolds,D.B., Yoo,J. et al. (2004) Transcriptional regulatory code of a eukaryotic genome. Nature, 431, 99–104. 11. MacIsaac,K.D. and Fraenkel,E. (2006) Practical strategies for discovering regulatory DNA sequence motifs. PLoS Comput. Biol., 2, e36. 12. Kuo,M.H. and Allis,C.D. (1999) In vivo cross-linking and immunoprecipitation for studying dynamic Protein:DNA associations in a chromatin environment. Methods, 19, 425–433. 13. Iyer,V.R., Horak,C.E., Scafe,C.S., Botstein,D., Snyder,M. and Brown,P.O. (2001) Genomic binding sites of the yeast cell-cycle transcription factors SBF and MBF. Nature, 409, 533–538. 14. Lee,T.I., Rinaldi,N.J., Robert,F., Odom,D.T., Bar-Joseph,Z., Gerber,G.K., Hannett,N.M., Harbison,C.T., Thompson,C.M., Simon,I. et al. (2002) Transcriptional regulatory networks in Saccharomyces cerevisiae. Science, 298, 799–804. 15. Pellegrini,M., Marcotte,E.M., Thompson,M.J., Eisenberg,D. and Yeates,T.O. (1999) Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc. Natl Acad. Sci. USA, 96, 4285–4288. 16. Wu,J., Kasif,S. and DeLisi,C. (2003) Identification of functional links between genes using phylogenetic profiles. Bioinformatics, 19, 1524–1530. 17. Friberg,M., von Rohr,P. and Gonnet,G. (2005) Scoring functions for transcription factor binding site prediction. BMC Bioinformatics, 6, 84. 18. 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. 19. Stormo,G.D. (2000) DNA binding sites: representation and discovery. Bioinformatics, 16, 16–23. 20. Morozov,A.V., Havranek,J.J., Baker,D. and Siggia,E.D. (2005) Protein-DNA binding specificity predictions with structural models. Nucleic Acids Res., 33, 5781–5798. 21. Li,N. and Tompa,M. (2006) Analysis of computational approaches for motif discovery. Algorithms Mol. Biol., 1, 8. 22. Reddy,T.E., DeLisi,C. and Shakhnovich,B.E. (2005) Assessing transcription factor motif drift from noisy decoy sequences. Genome Inform. Ser. Workshop Genome Inform., 16, 59–67. 23. Tompa,M., Li,N., Bailey,T.L., Church,G.M., De Moor,B., Eskin,E., Favorov,A.V., Frith,M.C., Fu,Y., Kent,W.J. et al. (2005) Assessing computational tools for the discovery of transcription factor binding sites. Nat. Biotechnol., 23, 137–144. 24. Blackwood,E.M. and Kadonaga,J.T. (1998) Going the Distance: a current view of enhancer action. Science, 281, 60–63.