基于多变量基因表达数据的转录因子结合位点预测
Transcription factor binding site prediction with multivariate gene expression data
摘要：
多样本微阵列实验已经成为研究生物系统的标准实验方法。这类研究的一个常见目标是揭示基因之间的调控关系。在过去的几年中，回归模型已经提出了从头发现的顺式作用的调控序列，使用基因表达数据。然而，当应用于多样本实验时，现有的基于回归的方法分别对每个单独的样本进行建模。为了更好地捕捉多样本微阵列实验中的动态关系，我们提出了一种灵活的启动子序列和多变量表达数据的联合建模方法。在更高级的真核基因组中，表达调控通常涉及几个转录因子之间的组合相互作用。实验表明，转录因子结合位点之间的间隔可以显着影响其激活基因表达的强度。我们提出了一个自适应的模型构建程序来捕获这种间距依赖的顺式作用的调控模块。我们将我们的方法应用于酵母和拟南芥中的微阵列时程实验的分析。这些实验表现出非常不同的动态时间关系。对于这两个数据集，我们已经找到了所有已知的顺式作用的调控元件在相关的背景下，以及能够预测新的元素。
英文摘要：
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.
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