Topological comparison of methods for predicting transcriptional cooperativity in yeast

Aguilar, Daniel; Oliva Miguel, Baldomero

doi:http://dx.doi.org/10.1186/1471-2164-9-137

Topological comparison of methods for predicting transcriptional cooperativity in yeast

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Aguilar D, Oliva B. Topological comparison of methods for predicting transcriptional cooperativity in yeast. BMC Genomics. 2008;9:137. DOI: 10.1186/1471-2164-9-137

Abstract

Background: The cooperative interaction between transcription factors has a decisive role in the control of the fate of the eukaryotic cell. Computational approaches for characterizing cooperative transcription factors in yeast, however, are based on different rationales and provide a low overlap between their results. Because the wealth of information contained in protein interaction networks and regulatory networks has proven highly effective in elucidating functional relationships between proteins, we compared different sets of cooperative transcription factor pairs (predicted by four different computational methods) within the frame of those networks. Results: /nOur results show that the overlap between the sets of cooperative transcription factors predicted by the different methods is low yet significant. Cooperative transcription factors predicted by all methods are closer and more clustered in the protein interaction network than expected by chance. On the other hand, members of a cooperative transcription factor pair neither seemed to regulate each other nor shared similar regulatory inputs, although they do regulate similar groups of target genes. Conclusion: Despite the different definitions of transcriptional cooperativity and the different computational approaches used to characterize cooperativity between transcription factors, the analysis of their roles in the framework of the protein interaction network and the regulatory network indicates a common denominator for the predictions under study. The knowledge of the shared topological properties of cooperative transcription factor pairs in both networks can be useful not only for designing better prediction methods but also for better understanding the complexities of transcriptional control in eukaryotes.