A Mixed Model Approach to Identify Yeast Transcriptional Regulatory Motifs via Microarray Experiments

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, 3 1: 1-22.


Author Yu, Xiang
Chu, Tzu-Ming
Gibson, Greg
Wolfinger, Russell D.
Title A Mixed Model Approach to Identify Yeast Transcriptional Regulatory Motifs via Microarray Experiments
Journal name Statistical Applications in Genetics and Molecular Biology   Check publisher's open access policy
ISSN 1544-6115
Publication date 2004
DOI 10.2202/1544-6115.1045
Volume 3
Issue 1
Start page 1
End page 22
Total pages 22
Place of publication Berkeley, CA
Publisher Berkeley Electronic Press
Language eng
Subject 0104 Statistics
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.
Keyword Bioinformatics
Molecular biology - Statistical methods
Bioinformatics
Additional Notes 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.

 
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Created: Thu, 05 Mar 2009, 10:33:23 EST by Ms Karen Naughton on behalf of School of Biological Sciences