Learning module networks from genome-wide location and expression data

We develop a systematic algorithm for discovering network of regulatory modules, which identifies regulatory modules and their regulation program by integrating genome-wide location and expression data. Unlike previous approaches [Eisen, M.B., Spellman, P.T., Brown, P.O. and Botstein, D. (1998) Proc. Natl. Acad. Sci. USA 95, 14863-14868; Tavazoie, S., Hughes, J.D., Campbell, M.J., Cho, R.J. and Church, G.M. (1999) Nat. Genet. 22, 281-285; Ihmels, J., Friedlander, G., Bergmann, S., Sarig, O., Ziv, Y. and Barkai, N. (2002) Nat. Genet. 31, 370-377; Segal, E., Shapira, M., Regev, A., Pe'er, D., Botstein, D., Koller, D. and Friedman, N. (2003) Nat. Genet. 34, 166-176] that relied primarily on gene expression data, our algorithm regards the regulator binding data as prior knowledge that provide direct evidence of physical regulatory interactions. We applied the method to a Saccharomyces cerevisiae genome-wide location data [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., Zeitlinger, J., Jennings, E.G., Murray, H.L. Gordon, D.B., Ren, B., Wyrick, J.J., Tagne, J.B., Volkert, T.L., Fraenkel, E., Gifford, D.K. and Young, R.A. (2002) Science 298, 799-804] for 106 DNA-binding transcription factors and 250 gene expression experiments under the conditions from the cell cycle to responses to various stress conditions. The results show that our method is able to identify functionally coherent modules and their proper regulators. Supplementary materials are available at http://compbio.sibnet.org/projects/module-network/.