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Revealing modular organization in the yeast transcriptional network

Nature Genetics volume 31, pages 370–377 (2002)Cite this article

Abstract

Standard clustering methods can classify genes successfully when applied to relatively small data sets, but have limited use in the analysis of large-scale expression data, mainly owing to their assignment of a gene to a single cluster. Here we propose an alternative method for the global analysis of genome-wide expression data. Our approach assigns genes to context-dependent and potentially overlapping 'transcription modules', thus overcoming the main limitations of traditional clustering methods. We use our method to elucidate regulatory properties of cellular pathways and to characterize cis-regulatory elements. By applying our algorithm systematically to all of the available expression data on Saccharomyces cerevisiae, we identify a comprehensive set of overlapping transcriptional modules. Our results provide functional predictions for numerous genes, identify relations between modules and present a global view on the transcriptional network.

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Figure 1: The recurrence signature method.
Figure 2: The recurrence criteria.
Figure 3: Co-regulation of TCA cycle genes.
Figure 4: Functional consistency of transcription modules.
Figure 5: Experimental verification of the involvement of Sda1 and Ltv1 in rRNA processing.
Figure 6: Correlation between the regulatory contexts of different modules.
Figure 7: Comparison between the signature approach and clustering using in silico expression data (Methods).

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References

  1. Bittner, M., Meltzer, P. & Trent, J. Data analysis and integration: of steps and arrows. Nature Genet. 22, 213–215 (1999).

    Article  CAS  Google Scholar 

  2. Cheng, Y. & Church, G.M. Biclustering of expression data. Proc. Int. Conf. Intell. Syst. Mol. Biol. 8, 93–103 (2000).

    CAS  PubMed  Google Scholar 

  3. Getz, G., Levine, E. & Domany, E. Coupled two-way clustering analysis of gene microarray data. Proc. Natl Acad. Sci. USA 97, 12079–12084 (2000).

    Article  CAS  Google Scholar 

  4. Eisen, M.B., Spellman, P.T., Brown, P.O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 95, 14863–14868 (1998).

    Article  CAS  Google Scholar 

  5. Alon, U. et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl Acad. Sci. USA 96, 6745–6750 (1999).

    Article  CAS  Google Scholar 

  6. Tamayo, P. et al. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl Acad. Sci. USA 96, 2907–2912 (1999).

    Article  CAS  Google Scholar 

  7. Mewes, H.W. et al. MIPS: a database for genomes and protein sequences. Nucleic Acids Res. 30, 31–34 (2002).

    Article  CAS  Google Scholar 

  8. Costanzo, M.C. et al. The yeast proteome database (YPD) and Caenorhabditis elegans proteome database (WormPD): comprehensive resources for the organization and comparison of model organism protein information. Nucleic Acids Res. 28, 73–76 (2000).

    Article  CAS  Google Scholar 

  9. Costanzo, M.C. et al. YPD, PombePD & WormPD: model organism volumes of the BioKnowledge library, an integrated resource for protein information. Nucleic Acids Res. 29, 75–79 (2001).

    Article  CAS  Google Scholar 

  10. Zimmerman, Z.A. & Kellogg, D.R. The Sda1 protein is required for passage through start. Mol. Biol. Cell 12, 201–219 (2001).

    Article  CAS  Google Scholar 

  11. Buscemi, G., Saracino, F., Masnada, D. & Carbone, M.L. The Saccharomyces cerevisiae SDA1 gene is required for actin cytoskeleton organization and cell cycle progression. J. Cell Sci. 113, 1199–1211 (2000).

    CAS  PubMed  Google Scholar 

  12. Fikus, M.U. et al. The product of the DNA damage-inducible gene of Saccharomyces cerevisiae, DIN7, specifically functions in mitochondria. Genetics 154, 73–81 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Liu, Z. & Butow, R.A. A transcriptional switch in the expression of yeast tricarboxylic acid cycle genes in response to a reduction or loss of respiratory function. Mol. Cell. Biol. 19, 6720–6728 (1999).

    Article  CAS  Google Scholar 

  14. Bojunga, N. & Entian, K.D. Cat8p, the activator of gluconeogenic genes in Saccharomyces cerevisiae, regulates carbon source-dependent expression of NADP-dependent cytosolic isocitrate dehydrogenase (Idp2p) and lactate permease (Jen1p). Mol. Gen. Genet. 262, 869–875 (1999).

    Article  CAS  Google Scholar 

  15. Milkereit, P. et al. Maturation and intranuclear transport of pre-ribosomes requires Noc proteins. Cell 105, 499–509 (2001).

    Article  CAS  Google Scholar 

  16. Harnpicharnchai, P. et al. Composition and functional characterization of yeast 66S ribosome assembly intermediates. Mol. Cell 8, 505–515 (2001).

    Article  CAS  Google Scholar 

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Acknowledgements

We thank D.R. Kellogg for the sda2-1 strain; U. Alon, M. Dolev, E. Domany, A. Eldar, O. Gileadi, Y. Kafri, B.-Z. Shilo and S. Shnider for discussions and comments on the manuscript; G. Jona and O. Gileadi for experimental help. This work was supported by the US National Institutes of Health, the Israeli Science Ministry and the Benoziyo center. S.B. is a Koshland fellow. N.B. is the incumbent of the Soretta and Henry Shapiro career development chair.

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  1. Departments of Molecular Genetics and Physics of Complex Systems, Weizmann Institute of Science, Rehovot, 76100, Israel

    Jan Ihmels, Gilgi Friedlander, Sven Bergmann, Ofer Sarig, Yaniv Ziv & Naama Barkai

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  1. Jan Ihmels
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Correspondence to Naama Barkai.

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Ihmels, J., Friedlander, G., Bergmann, S. et al. Revealing modular organization in the yeast transcriptional network. Nat Genet 31, 370–377 (2002). https://doi.org/10.1038/ng941

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