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Published online 2 January 2004

Nucleic Acids Research, 2004, Vol. 32, No. 1 179-188
© 2004 Oxford University Press

Computational inference of transcriptional regulatory networks from expression profiling and transcription factor binding site identification

Peter M. Haverty1, Ulla Hansen1,2 and Zhiping Weng*,1,3

1 Bioinformatics Program, 2 Department of Biology and 3 Biomedical Engineering Department, Boston University, 44 Cummington Street, Boston, MA 02215, USA

*To whom correspondence should be addressed. Tel: +1 617 353 3509; Fax: +1 617 353 6766; Email: zhiping{at}bu.edu

We have developed a computational method for transcriptional regulatory network inference, CARRIE (Computational Ascertainment of Regu latory Relationships Inferred from Expression), which combines microarray and promoter sequence analysis. CARRIE uses sources of data to identify the transcription factors (TFs) that regulate gene expression changes in response to a stimulus and generates testable hypotheses about the regulatory network connecting these TFs to the genes they regulate. The promoter analysis component of CARRIE, ROVER (Relative OVER-abundance of cis-elements), is highly accurate at detecting the TFs that regulate the response to a stimulus. ROVER also predicts which genes are regulated by each of these TFs. CARRIE uses these transcriptional interactions to infer a regulatory network. To demonstrate our method, we applied CARRIE to six sets of publicly available DNA microarray experiments on Saccharomyces cerevisiae. The predicted networks were validated with comparisons to literature sources, experimental TF binding data, and gene ontology biological process information.


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