A mixed model approach to identify yeast transcriptional regulatory motifs via microarray experiments.
A genome-wide location analysis method has been introduced as a means to simultaneously study protein-DNA binding interactions for a large number of genes on a microarray platform. Identification of interactions between transcription factors (TF) and genes provide insight into the mechanisms that regulate a variety of cellular responses. Drawing proper inferences from the experimental data is key to finding statistically significant TF-gene binding interactions. We describe how the analysis and interpretation of genome-wide location data can be fit into a traditional statistical modeling framework that considers the data across all arrays and formulizes appropriate hypothesis tests. The approach is illustrated with data from a yeast transcription factor binding experiment that illustrates how identified TF-gene interactions can enhance initial exploration of transcriptional regulatory networks. Examples of five kinds of transcriptional regulatory structure are also demonstrated. Some stark differences with previously published results are explored.
Yu
Xiang
X
Bioinformatics Research Center, North Carolina State University, Merck & Co., Inc., RY80M-180C, P.O. Box 2000, Rahway, NJ 07065-0900, USA. xiang yu@merck.com
Chu
Tzu-Ming
TM
Gibson
Greg
G
Wolfinger
Russell D
RD