A Simple Hyper-Geometric Approach for Discovering Putative Transcription Factor Binding Sites
WABI '01 Proceedings of the First International Workshop on Algorithms in Bioinformatics
CSBW '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference - Workshops
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Regulatory networks are usually presented as graph structures showing the (combinatorial) regulatory effect of transcription factors (TF’s) on modules of similarly expressed or otherwise related genes. However, from these networks it is not clear when and how TF’s are activated. The actual conditions or perturbations that trigger a change in the activity of TF’s should be a crucial part of the generated regulatory network. Here, we demonstrate the power to uncover TF activity by focusing on a small, homogeneous, yet well defined set of chemostat cultivation experiments, where the transcriptional response of yeast grown under four different nutrient limitations, both aerobically as well as anaerobically was measured. We define a condition transition as an instant change in yeast’s extracellular environment by comparing two cultivation conditions, where either the limited nutrient or the oxygen availability is different. Differential gene expression as a consequence of such a condition transition is represented in a tertiary matrix, where zero indicates no change in expression; 1 and -1 respectively indicate an increase and decrease in expression as a consequence of a condition transition. We uncover TF activity by assessing significant TF binding in the promotor region of genes that behave accordingly at a condition transition. The interrelatedness of the conditions in the combinatorial setup is exploited by performing specific hypergeometric tests that allow for the discovery of both individual and combined effects of the cultivation parameters on TF activity. Additionally, we create a weight-matrix indicating the involvement of each TF in each of the condition transitions by posing our problem as an orthogonal Procrustes problem. We show that the Procrustes analysis strengthens and broadens the uncovered relationships. The resulting regulatory network reveals nutrient-limitation-specific effects of oxygen presence on expression behavior and TF activity. Our analysis identifies many TF’s that seem to play a very specific regulatory role at the nutrient and oxygen availability transitions.