Integration of Known Transcription Factor Binding Site Information and Gene Expression Data to Advance from Co-expression to Co-regulation

Overview

Journal Genomics Proteomics Bioinformatics

Specialty Biology

Date 2007 Sep 26

PMID 17893074

Citations 12

Authors

Maarten Clements

Eugene P van Someren

Theo A Knijnenburg

Marcel J T Reinders

Affiliations

Soon will be listed here.

Abstract

The common approach to find co-regulated genes is to cluster genes based on gene expression. However, due to the limited information present in any dataset, genes in the same cluster might be co-expressed but not necessarily co-regulated. In this paper, we propose to integrate known transcription factor binding site information and gene expression data into a single clustering scheme. This scheme will find clusters of co-regulated genes that are not only expressed similarly under the measured conditions, but also share a regulatory structure that may explain their common regulation. We demonstrate the utility of this approach on a microarray dataset of yeast grown under different nutrient and oxygen limitations. Our integrated clustering method not only unravels many regulatory modules that are consistent with current biological knowledge, but also provides a more profound understanding of the underlying process. The added value of our approach, compared with the clustering solely based on gene expression, is its ability to uncover clusters of genes that are involved in more specific biological processes and are evidently regulated by a set of transcription factors.

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References

Heyer L, Kruglyak S, Yooseph S . Exploring expression data: identification and analysis of coexpressed genes. Genome Res. 1999; 9(11):1106-15. PMC: 310826. DOI: 10.1101/gr.9.11.1106. View

van Helden J, Andre B, Collado-Vides J . Extracting regulatory sites from the upstream region of yeast genes by computational analysis of oligonucleotide frequencies. J Mol Biol. 1998; 281(5):827-42. DOI: 10.1006/jmbi.1998.1947. View

Dhaeseleer P . How does gene expression clustering work?. Nat Biotechnol. 2005; 23(12):1499-501. DOI: 10.1038/nbt1205-1499. View

Zhang Z, Gu J, Gu X . How much expression divergence after yeast gene duplication could be explained by regulatory motif evolution?. Trends Genet. 2004; 20(9):403-7. DOI: 10.1016/j.tig.2004.07.006. View

Alon U, Barkai N, Notterman D, Gish K, Ybarra S, Mack D . Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci U S A. 1999; 96(12):6745-50. PMC: 21986. DOI: 10.1073/pnas.96.12.6745. View

Hughes T, Marton M, Jones A, Roberts C, STOUGHTON R, Armour C . Functional discovery via a compendium of expression profiles. Cell. 2000; 102(1):109-26. DOI: 10.1016/s0092-8674(00)00015-5. View

Rutherford J, Ojeda L, Balk J, Muhlenhoff U, Lill R, Winge D . Activation of the iron regulon by the yeast Aft1/Aft2 transcription factors depends on mitochondrial but not cytosolic iron-sulfur protein biogenesis. J Biol Chem. 2005; 280(11):10135-40. DOI: 10.1074/jbc.M413731200. View

Segal E, Yelensky R, Koller D . Genome-wide discovery of transcriptional modules from DNA sequence and gene expression. Bioinformatics. 2003; 19 Suppl 1:i273-82. DOI: 10.1093/bioinformatics/btg1038. View

Wingender E, Chen X, Hehl R, Karas H, Liebich I, Matys V . TRANSFAC: an integrated system for gene expression regulation. Nucleic Acids Res. 1999; 28(1):316-9. PMC: 102445. DOI: 10.1093/nar/28.1.316. View

10.

Gancedo J . Yeast carbon catabolite repression. Microbiol Mol Biol Rev. 1998; 62(2):334-61. PMC: 98918. DOI: 10.1128/MMBR.62.2.334-361.1998. View

11.

Hughes J, Estep P, Tavazoie S, Church G . Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae. J Mol Biol. 2000; 296(5):1205-14. DOI: 10.1006/jmbi.2000.3519. View

12.

Cherry J, Ball C, Weng S, Juvik G, Schmidt R, Adler C . Genetic and physical maps of Saccharomyces cerevisiae. Nature. 1997; 387(6632 Suppl):67-73. PMC: 3057085. View

13.

Tai S, Boer V, Daran-Lapujade P, Walsh M, de Winde J, Daran J . Two-dimensional transcriptome analysis in chemostat cultures. Combinatorial effects of oxygen availability and macronutrient limitation in Saccharomyces cerevisiae. J Biol Chem. 2004; 280(1):437-47. DOI: 10.1074/jbc.M410573200. View

14.

Hubbard T, Andrews D, Caccamo M, Cameron G, Chen Y, Clamp M . Ensembl 2005. Nucleic Acids Res. 2004; 33(Database issue):D447-53. PMC: 540092. DOI: 10.1093/nar/gki138. View

15.

Latchman D . Transcription factors as potential targets for therapeutic drugs. Curr Pharm Biotechnol. 2001; 1(1):57-61. DOI: 10.2174/1389201003379022. View

16.

Zhu J, Zhang M . SCPD: a promoter database of the yeast Saccharomyces cerevisiae. Bioinformatics. 1999; 15(7-8):607-11. DOI: 10.1093/bioinformatics/15.7.607. View

17.

Harbison C, Gordon D, Lee T, Rinaldi N, MacIsaac K, Danford T . Transcriptional regulatory code of a eukaryotic genome. Nature. 2004; 431(7004):99-104. PMC: 3006441. DOI: 10.1038/nature02800. View

18.

Robinson K, Lopes J . SURVEY AND SUMMARY: Saccharomyces cerevisiae basic helix-loop-helix proteins regulate diverse biological processes. Nucleic Acids Res. 2000; 28(7):1499-505. PMC: 102793. DOI: 10.1093/nar/28.7.1499. View

19.

Hertz G, Stormo G . Identifying DNA and protein patterns with statistically significant alignments of multiple sequences. Bioinformatics. 1999; 15(7-8):563-77. DOI: 10.1093/bioinformatics/15.7.563. View

20.

Tavazoie S, Hughes J, Campbell M, Cho R, Church G . Systematic determination of genetic network architecture. Nat Genet. 1999; 22(3):281-5. DOI: 10.1038/10343. View