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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido


IMPACT FACTOR 2018: 0.536
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Online
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1544-6115
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Volume 3, Issue 1

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Volume 1 (2002)

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

Xiang Yu / Tzu-Ming Chu / Greg Gibson
  • Bioinformatics Research Center, North Carolina State University; Department of Genetics, North Carolina State University
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Russell D Wolfinger
Published Online: 2004-09-29 | DOI: https://doi.org/10.2202/1544-6115.1045

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.

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Published Online: 2004-09-29


Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 3, Issue 1, Pages 1–20, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1045.

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