caa a22 4500 378926055 CHVBK 20180305123617.0 cr unu---uuuuu 161128e20040929xx s 000 0 eng 10.2202/1544-6115.1045 doi (NATIONALLICENCE)gruyter-10.2202/1544-6115.1045 A Mixed Model Approach to Identify Yeast Transcriptional Regulatory Motifs via Microarray Experiments [Elektronische Daten] [Xiang Yu, Tzu-Ming Chu, Greg Gibson, Russell D Wolfinger] 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. ©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston Microarrays nationallicence Computational Biology/Bioinformatics nationallicence Yu Xiang Bioinformatics Research Center, North Carolina State University aut Chu Tzu-Ming Department of Genomics, SAS Institute Inc aut Gibson Greg Bioinformatics Research Center, North Carolina State University; Department of Genetics, North Carolina State University aut Wolfinger Russell D. Department of Genomics, SAS Institute Inc aut Statistical Applications in Genetics and Molecular Biology De Gruyter 3/1(2004-09-29), 1-20 3:1<1 2004 3 sagmb https://doi.org/10.2202/1544-6115.1045 text/html Onlinezugriff via DOI 1 research article jats NATIONALLICENCE 856 40 https://doi.org/10.2202/1544-6115.1045 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Yu Xiang Bioinformatics Research Center, North Carolina State University aut NATIONALLICENCE 700 1- Chu Tzu-Ming Department of Genomics, SAS Institute Inc aut NATIONALLICENCE 700 1- Gibson Greg Bioinformatics Research Center, North Carolina State University; Department of Genetics, North Carolina State University aut NATIONALLICENCE 700 1- Wolfinger Russell D. Department of Genomics, SAS Institute Inc aut NATIONALLICENCE 773 0- Statistical Applications in Genetics and Molecular Biology De Gruyter 3/1(2004-09-29), 1-20 3:1<1 2004 3 sagmb CC0 http://creativecommons.org/publicdomain/zero/1.0 nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-gruyter