Identifying combinatorial regulation of transcription factors and binding motifs

Mamoru Kato¹^,2, Naoya Hata², Nilanjana Banerjee²^,3, Bruce Futcher⁴ and Michael Q Zhang²

¹Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokane-dai, Minato-ku, Tokyo 108-8639, Japan

²Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA

³George Mason University, School of Computational Sciences, 10900 University Boulevard, Manassas, VA 20110, USA

⁴Department of Molecular Genetics and Microbiology, University of Stony Brook, Stony Brook, NY 11794, USA

author email corresponding author email

Genome Biology 2004, 5:R56doi:10.1186/gb-2004-5-8-r56


Published:	28 July 2004

Subject areas: Molecular biology, Bioinformatics, Genome studies, Cell biology, Model organisms

Abstract

Background

Combinatorial interaction of transcription factors (TFs) is important for gene regulation. Although various genomic datasets are relevant to this issue, each dataset provides relatively weak evidence on its own. Developing methods that can integrate different sequence, expression and localization data have become important.

Results

Here we use a novel method that integrates chromatin immunoprecipitation (ChIP) data with microarray expression data and with combinatorial TF-motif analysis. We systematically identify combinations of transcription factors and of motifs. The various combinations of TFs involved multiple binding mechanisms. We reconstruct a new combinatorial regulatory map of the yeast cell cycle in which cell-cycle regulation can be drawn as a chain of extended TF modules. We find that the pairwise combination of a TF for an early cell-cycle phase and a TF for a later phase is often used to control gene expression at intermediate times. Thus the number of distinct times of gene expression is greater than the number of transcription factors. We also see that some TF modules control branch points (cell-cycle entry and exit), and in the presence of appropriate signals they can allow progress along alternative pathways.

Conclusions

Combining different data sources can increase statistical power as demonstrated by detecting TF interactions and composite TF-binding motifs. The original picture of a chain of simple cell-cycle regulators can be extended to a chain of composite regulatory modules: different modules may share a common TF component in the same pathway or a TF component cross-talking to other pathways.