Eukaryotic transcriptional regulation networks are extremely complex. Usually, multiple transcription factors (TFs) bind to the promoter region of a gene and cooperate to control gene expression precisely. Identifying cooperative TFs remains a major challenge in modern biological research. Various types of data, including genomic sequences, expression profiles, ChIP-chip data and protein-protein interactions, have been used to identify mechanisms of cooperative transcriptional regulation. However, because of the noise inherent in these data and the fact that each data source only provides partial information about regulation, combining multiple types of data to improve their ability to infer cooperative TFs is advantageous 1, 2, 3.

In our previous work, we successfully integrated ChIP-chip data 4 and expression profiles with individual TF knockout strains 5 to unravel potential relations between TFs and their target genes 6. This combination of two independent and complementary sources of data improved the accuracy of our prediction. Here, we have extended the work to identify cooperativity between TFs in Saccharomyces cerevisiae. We achieved high prediction performance by identifying the most statistically significant overlap of target genes regulated by two TFs in ChIP-chip data and TF knockout data. In addition, we attempted to find the appropriate point to which extent the threshold should be relaxed by looking at the increasing number of cooperative TFs identified within different threshold ranges. Finally, identified TF pairs were ranked using Fisher's combined probability test 7 by combining two independent P-values calculated from the ChIP-chip and knockout data (METHODS, Supplementary information, Data S1).

Log in or create a free account to read this content

Gain free access to this article, as well as selected content from this journal and more on nature.com

Access through your institution

or

Sign in or create an account
Continue with Google
Continue with ORCiD