Transcription factor binding site prediction with multivariate gene expression data. (English) Zbl 1137.62083

Zhang, Nancy R.; Wildermuth, Mary C.; Speed, Terence P.

Ann. Appl. Stat. 2, No. 1, 332-365 (2008).

Summary: Multi-sample microarray experiments have become a standard experimental method for studying biological systems. A frequent goal in such studies is to unravel the regulatory relationships between genes. During the last few years, regression models have been proposed for the de novo discovery of \(cis\)-acting regulatory sequences using gene expression data. However, when applied to multi-sample experiments, existing regression based methods model each individual sample separately. To better capture the dynamic relationships in multi-sample microarray experiments, we propose a flexible method for the joint modeling of promoter sequence and multivariate expression data.
In higher order eukaryotic genomes expression regulation usually involves combinatorial interaction between several transcription factors. Experiments have shown that spacing between transcription factor binding sites can significantly affect their strength in activating gene expression. We propose an adaptive model building procedure to capture such spacing dependent \(cis\)-acting regulatory modules.
We apply our methods to the analysis of microarray time-course experiments in yeast and in Arabidopsis. These experiments exhibit very different dynamic temporal relationships. For both data sets, we have found all of the well-known cis-acting regulatory elements in the related context, as well as being able to predict novel elements.

MSC:

62P10	Applications of statistics to biology and medical sciences; meta analysis
92C40	Biochemistry, molecular biology
62H99	Multivariate analysis
65C60	Computational problems in statistics (MSC2010)

Keywords:

linear models; transcription regulation; DNA motifs; gene expression

Software:

impute

References:

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