Abstract

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.

Erratum

A previously published version of this paper had an error in the normalization model. The mistake was omitting the channel random effect DA, which led to several genes exhibiting significant differences due plainly to dye bias. The version now posted is corrected; the previous version is here for archival purposes.

Submitted: January 30, 2004 · Accepted: June 14, 2004 · Published: September 29, 2004

Recommended Citation

Yu, Xiang; Chu, Tzu-Ming; Gibson, Greg; and Wolfinger, Russell D. (2004) "A Mixed Model Approach to Identify Yeast Transcriptional Regulatory Motifs via Microarray Experiments," Statistical Applications in Genetics and Molecular Biology: Vol. 3 : Iss. 1, Article 22.
Available at: http://www.bepress.com/sagmb/vol3/iss1/art22

Related Files

sagmb1045.pdf (2525 kB)
Originally published version.