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

Yu Xiang, Chu Tzu-Ming, Gibson Greg and Wolfinger Russell D
Additional contact information
Yu Xiang: Bioinformatics Research Center, North Carolina State University
Chu Tzu-Ming: Department of Genomics, SAS Institute Inc
Gibson Greg: Bioinformatics Research Center, North Carolina State University; Department of Genetics, North Carolina State University
Wolfinger Russell D: Department of Genomics, SAS Institute Inc

Statistical Applications in Genetics and Molecular Biology, 2004, vol. 3, issue 1, 22

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.

Date: 2004
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2202/1544-6115.1045 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:3:y:2004:i:1:n:22

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.2202/1544-6115.1045

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().