Predicting gene regulation by sigma factors in Bacillus subtilis from genome-wide data
Predicting gene regulation by sigma factors in Bacillus subtilis from genome-wide data
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DOI：
10.1093/bioinformatics/bth927
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发表时间：
2004-08-04
期刊：
影响因子：
5.8
通讯作者：
Miyano, S.
中科院分区：
文献类型：
--
作者：
de Hoon, M. J. L.;Makita, Y.;Miyano, S.
动机：Sigma因子在转录水平调控枯草芽孢杆菌中基因的表达。我们评估了折叠变化分析、贝叶斯网络、动态模型和基于协同调节的监督学习在预测基因表达数据中sigma因子的基因调控方面的准确性。为了提高预测精度，我们将序列信息与表达数据结合起来，通过添加它们的对数似然分数并使用逻辑回归模型。我们使用得到的分数函数通过sigma因子发现目前未知的基因调控。结果：基于协同调节的监督学习方法能最准确地从表达数据中预测sigma因子。我们发现逻辑回归模型有效地结合了表达数据和序列信息。在全基因组搜索中，发现了几个转录调控目前未知的基因的高度显著的逻辑回归得分。我们提供了相应的RNA聚合酶结合位点，以便对这些预测进行直接的实验验证。
Motivation: Sigma factors regulate the expression of genes in Bacillus subtilis at the transcriptional level. We assess the accuracy of a fold-change analysis, Bayesian networks, dynamic models and supervised learning based on coregulation in predicting gene regulation by sigma factors from gene expression data. To improve the prediction accuracy, we combine sequence information with expression data by adding their log-likelihood scores and by using a logistic regression model. We use the resulting score function to discover currently unknown gene regulations by sigma factors.Results: The coregulation-based supervised learning method gave the most accurate prediction of sigma factors from expression data. We found that the logistic regression model effectively combines expression data with sequence information. In a genome-wide search, highly significant logistic regression scores were found for several genes whose transcriptional regulation is currently unknown. We provide the corresponding RNA polymerase binding sites to enable a straightforward experimental verification of these predictions.