Identification of DNA regulatory motifs using Bayesian variable selection
Identification of DNA regulatory motifs using Bayesian variable selection
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DOI：
10.1093/bioinformatics/bth282
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发表时间：
2004-11-01
期刊：
影响因子：
5.8
通讯作者：
Liò, P
中科院分区：
文献类型：
--
作者：
Tadesse, MG;Vannucci, M;Liò, P
动机：了解决定基因表达调控的机制是一个重要而具有挑战性的问题。一种常见的方法是从一组共同调节的基因及其附近的非编码DNA序列中识别DNA结合位点。在这里，我们考虑一个回归模型，它将基因表达水平与核苷酸模式的序列匹配分数线性关联。我们使用贝叶斯模型和随机搜索技术来选择转录因子结合位点候选，作为其他研究人员使用的逐步回归方法的替代。结果：通过模拟数据，我们证明了贝叶斯变量选择方法比逐步回归方法的性能有所改善。然后，我们对酿酒酵母和庞贝裂殖酵母的实验结果进行了分析和讨论。我们确定了已知的与所考虑的实验条件有关的调控基序。我们选择的一些主题也与其他研究人员最近的发现一致。此外，我们的结果包括新的基序，这些基序构成了进一步评估的有希望的集合。
Motivation: Understanding the mechanisms that determine gene expression regulation is an important and challenging problem. A common approach consists of identifying DNA-binding sites from a collection of co-regulated genes and their nearby non-coding DNA sequences. Here, we consider a regression model that linearly relates gene expression levels to a sequence matching score of nucleotide patterns. We use Bayesian models and stochastic search techniques to select transcription factor binding site candidates, as an alternative to stepwise regression procedures used by other investigators.Results: We demonstrate through simulated data the improved performance of the Bayesian variable selection method compared to the stepwise procedure. We then analyze and discuss the results from experiments involving well-studied pathways of Saccharomyces cerevisiae and Schizosaccharomyces pombe. We identify regulatory motifs known to be related to the experimental conditions considered. Some of our selected motifs are also in agreement with recent findings by other researchers. In addition, our results include novel motifs that constitute promising sets for further assessment.