A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription
A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription
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DOI：
10.1093/bioinformatics/btl154
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发表时间：
2006-07
期刊：
影响因子：
5.8
通讯作者：
G. Sanguinetti;M. Rattray;Neil D. Lawrence
中科院分区：
文献类型：
--
作者：
G. Sanguinetti;M. Rattray;Neil D. Lawrence
动机在尝试开发细胞过程模型时，定量估计转录因子和基因之间的调节关系是一个基本的垫脚石。然而，由于多种原因，这项任务很困难：转录因子的表达水平通常较低且嘈杂，并且许多转录因子受到转录后调节。因此，从转录因子的靶基因的表达水平推断转录因子的活性是有用的。结果我们引入了一种新颖的概率模型，当调节网络的结构已知时，可以从微阵列数据推断转录因子的活性。该模型基于回归，保留了允许全基因组研究的计算效率，但通过对每个基因独立采样回归系数而变得更加灵活。这使我们能够确定转录因子调节每个靶基因的强度，从而提供转录调节网络的定量描述。模型的概率性质还意味着我们可以将可信区间与我们对活动的估计相关联。我们在两个酵母数据集上演示了我们的模型。在这两种情况下，网络结构都是使用染色质免疫沉淀数据获得的。我们展示了我们的模型的预测如何与基础生物学相一致，并为酵母细胞的调控结构提供了新颖的定量见解。可用性 MATLAB 代码可从 http://umber.sbs.man.ac.uk/resources/puma 获取。
MOTIVATION Quantitative estimation of the regulatory relationship between transcription factors and genes is a fundamental stepping stone when trying to develop models of cellular processes. This task, however, is difficult for a number of reasons: transcription factors' expression levels are often low and noisy, and many transcription factors are post-transcriptionally regulated. It is therefore useful to infer the activity of the transcription factors from the expression levels of their target genes. RESULTS We introduce a novel probabilistic model to infer transcription factor activities from microarray data when the structure of the regulatory network is known. The model is based on regression, retaining the computational efficiency to allow genome-wide investigation, but is rendered more flexible by sampling regression coefficients independently for each gene. This allows us to determine the strength with which a transcription factor regulates each of its target genes, therefore providing a quantitative description of the transcriptional regulatory network. The probabilistic nature of the model also means that we can associate credibility intervals to our estimates of the activities. We demonstrate our model on two yeast datasets. In both cases the network structure was obtained using chromatin immunoprecipitation data. We show how predictions from our model are consistent with the underlying biology and offer novel quantitative insights into the regulatory structure of the yeast cell. AVAILABILITY MATLAB code is available from http://umber.sbs.man.ac.uk/resources/puma.