A Gibbs sampling method to detect overrepresented motifs in the upstream regions of coexpressed genes
A Gibbs sampling method to detect overrepresented motifs in the upstream regions of coexpressed genes
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DOI：
10.1089/10665270252935566
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发表时间：
2002-01-01
影响因子：
1.7
通讯作者：
Moreau, Y
中科院分区：
文献类型：
--
作者：
Thijs, G;Marchal, K;Moreau, Y
微阵列实验可以揭示有关转录调控的重要信息。在我们的研究中，我们在共表达基因的上游区域寻找潜在的启动子调控元件。在这里，我们提出了用于基序发现的原始吉布斯采样算法的两个修改（Lawrence等人，1993年）。首先，我们介绍了使用概率分布来估计序列中基序的拷贝数。其次，我们描述了一个高阶背景模型的应用，我们在Thijs等人（2001年）中讨论的合并的技术方面。我们的实现被称为Motif Sampler。我们成功地验证了我们的算法在几个数据集。首先，我们展示了三组含有已知基序的上游序列的结果：1）植物中的G盒光响应元件，2）酿酒酵母中参与甲硫氨酸响应的元件，3）细菌中的FNR O-2响应元件。我们使用这些数据集来解释我们的算法的性能上的参数的影响。第二，我们显示结果的上游序列从四个集群的共表达基因在拟南芥的创伤微阵列实验中确定。在我们的植物顺式作用调控元件数据库（PlantCARE）中，有几个基序可以与来自植物防御途径的调控元件相匹配。其他一些强基序在PlantCARE中没有相应的基序，但有希望用于进一步分析。
Microarray experiments can reveal important information about transcriptional regulation. In our case, we look for potential promoter regulatory elements in the upstream region of coexpressed genes. Here we present two modifications of the original Gibbs sampling algorithm for motif finding (Lawrence et al., 1993). First, we introduce the use of a probability distribution to estimate the number of copies of the motif in a sequence. Second, we describe the technical aspects of the incorporation of a higher-order background model whose application we discussed in Thijs et al. (2001). Our implementation is referred to as the Motif Sampler. We successfully validate our algorithm on several data sets. First, we show results for three sets of upstream sequences containing known motifs: 1) the G-box light-response element in plants, 2) elements involved in methionine response in Saccharomyces cerevisiae, and 3) the FNR O-2-responsive element in bacteria. We use these data sets to explain the influence of the parameters on the performance of our algorithm. Second, we show results for upstream sequences from four clusters of coexpressed genes identified in a microarray experiment on wounding in Arabidopsis thaliana. Several motifs could be matched to regulatory elements from plant defence pathways in our database of plant cis-acting regulatory elements (PlantCARE). Some other strong motifs do not have corresponding motifs in PlantCARE but are promising candidates for further analysis.