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Computational discovery of gene modules and regulatory networks

Nature Biotechnology volume 21, pages 1337–1342 (2003)Cite this article

Abstract

We describe an algorithm for discovering regulatory networks of gene modules, GRAM (Genetic Regulatory Modules), that combines information from genome-wide location and expression data sets. A gene module is defined as a set of coexpressed genes to which the same set of transcription factors binds. Unlike previous approaches1,2,3,4,5 that relied primarily on functional information from expression data, the GRAM algorithm explicitly links genes to the factors that regulate them by incorporating DNA binding data, which provide direct physical evidence of regulatory interactions. We use the GRAM algorithm to describe a genome-wide regulatory network in Saccharomyces cerevisiae using binding information for 106 transcription factors profiled in rich medium conditions data* from over 500 expression experiments. We also present a genome-wide location analysis data set for regulators in yeast cells treated with rapamycin, and use the GRAM algorithm to provide biological insights into this regulatory network.

*Note: In the version of this article initially published online, the word "and" was omitted from the fourth sentence of the abstract, altering the meaning. The sentence should read: "We use the GRAM algorithm to describe a genome-wide regulatory network in Saccharomyces cerevisiae using binding information for 106 transcription factors profiled in rich medium conditions and data from over 500 expression experiments." This mistake has been corrected for the HTML and print versions of the article.

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Figure 1: Rich medium gene modules network.
Figure 2: The GRAM algorithm integrates genome-wide binding and expression data and improves on either data source alone.
Figure 3: Motif enrichment.
Figure 4: Rapamycin gene modules network.

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Acknowledgements

Z.B-J. is supported by the Program in Mathematics and Molecular Biology at Florida State University through the Burroughs Wellcome Fund Interfaces Program. G.G. is supported by a National Defense Engineering and Science graduate fellowship. This work was partially funded by a US National Institutes of Health grant.

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Author notes

  1. Ziv Bar-Joseph, Georg K Gerber and Tong Ihn Lee: These authors contributed equally to this work.

Authors and Affiliations

  1. MIT Computer Science and Artificial Intelligence Laboratory, 200 Technology Square, Cambridge, 02139, Massachusetts, USA

    Ziv Bar-Joseph, Georg K Gerber, Tommi S Jaakkola & David K Gifford

  2. Whitehead Institute for Biomedical Research, Nine Cambridge Center, Cambridge, 02142, Massachusetts, USA

    Tong Ihn Lee, Nicola J Rinaldi, Jane Y Yoo, François Robert, D Benjamin Gordon, Ernest Fraenkel & Richard A Young

  3. MIT Department of Biology, 31 Ames Street, Room 68-132, Cambridge, 02139, Massachusetts, USA

    Nicola J Rinaldi & Richard A Young

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  1. Ziv Bar-Joseph
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Bar-Joseph, Z., Gerber, G., Lee, T. et al. Computational discovery of gene modules and regulatory networks. Nat Biotechnol 21, 1337–1342 (2003). https://doi.org/10.1038/nbt890

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