A higher-order background model improves the detection of promoter regulatory elements by Gibbs sampling
A higher-order background model improves the detection of promoter regulatory elements by Gibbs sampling
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DOI：
10.1093/bioinformatics/17.12.1113
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发表时间：
2001-12-01
期刊：
影响因子：
5.8
通讯作者：
Moreau, Y
中科院分区：
文献类型：
--
作者：
Thijs, G;Lescot, M;Moreau, Y
动机：转录组分析可以检测在不同生物环境下共表达的基因并将其聚集在一起。在共调控基因共享顺式作用调控元件的假设下，研究控制这些基因转录的上游序列是很重要的。为了提高Gibbs采样算法对噪声数据集的稳健性，我们提出了一种基于高阶背景模型的基序发现算法的扩展。结果：使用模拟数据和具有良好调控元素的真实生物数据集来测试不同背景模型对基序检测算法性能的影响。我们表明，高阶模型的使用大大提高了我们的基序发现算法在存在噪声数据的情况下的性能。对于拟南芥，基于一组精心选择的基因间序列构建了一个可靠的背景模型。
Motivation: Transcriptome analysis allows detection and clustering of genes that are coexpressed under various biological circumstances. Under the assumption that coregulated genes share cis-acting regulatory elements, it is important to investigate the upstream sequences controlling the transcription of these genes. To improve the robustness of the Gibbs sampling algorithm to noisy data sets we propose an extension of this algorithm for motif finding with a higher-order background model.Results: Simulated data and real biological data sets with well-described regulatory elements are used to test the influence of the different background models on the performance of the motif detection algorithm. We show that the use of a higher-order model considerably enhances the performance of our motif finding algorithm in the presence of noisy data. For Arabidopsis thaliana, a reliable background model based on a set of carefully selected intergenic sequences was constructed.