Inferring transcriptional logic from multiple dynamic experiments.
Inferring transcriptional logic from multiple dynamic experiments.
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DOI：
10.1093/bioinformatics/btx407
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发表时间：
2017-11-01
期刊：
影响因子：
--
通讯作者：
Finkenstädt B
中科院分区：
文献类型：
--
作者：
Minas G;Jenkins DJ;Rand DA;Finkenstädt B
在多种实验条件下，更多动态基因表达数据的可用性提供了新的信息，使识别基因的转录调节因子和潜在逻辑结构的关键目标成为可能。我们提出了一种新的方法来推断转录调控，使用一个简单的，但生物学上可解释的模型，找到一组候选基因及其相关转录因子（TFs）调节感兴趣基因的转录过程的逻辑。我们的动态模型将靶基因的mRNA转录率与tf的激活状态联系起来，假设这些相互作用在多个实验中和随着时间的推移是一致的。采用一种跨维马尔可夫链蒙特卡罗（MCMC）算法对不同父母组合下的调节逻辑进行有效采样，并根据后验概率对估计模型进行排序。我们通过模拟示例演示并比较了我们的方法与其他方法，并将其应用于从多种生物胁迫下获得的微阵列时间序列数据中选择拟南芥靶基因的转录调控研究。我们表明，我们的方法能够检测在多个实验条件下一致的复杂调控相互作用。程序使用MATLAB和Statistics Toolbox Release 2016b编写，The MathWorks, Inc., Natick, Massachusetts, usa，可在GitHub https://github.com/giorgosminas/TRS和http://www2.warwick.ac.uk/fac/sci/systemsbiology/research/software上获得。补充数据可在生物信息学网站获得。
The availability of more data of dynamic gene expression under multiple experimental conditions provides new information that makes the key goal of identifying not only the transcriptional regulators of a gene but also the underlying logical structure attainable. We propose a novel method for inferring transcriptional regulation using a simple, yet biologically interpretable, model to find the logic by which a set of candidate genes and their associated transcription factors (TFs) regulate the transcriptional process of a gene of interest. Our dynamic model links the mRNA transcription rate of the target gene to the activation states of the TFs assuming that these interactions are consistent across multiple experiments and over time. A trans-dimensional Markov Chain Monte Carlo (MCMC) algorithm is used to efficiently sample the regulatory logic under different combinations of parents and rank the estimated models by their posterior probabilities. We demonstrate and compare our methodology with other methods using simulation examples and apply it to a study of transcriptional regulation of selected target genes of Arabidopsis Thaliana from microarray time series data obtained under multiple biotic stresses. We show that our method is able to detect complex regulatory interactions that are consistent under multiple experimental conditions. Programs are written in MATLAB and Statistics Toolbox Release 2016b, The MathWorks, Inc., Natick, Massachusetts, United States and are available on GitHub https://github.com/giorgosminas/TRS and at http://www2.warwick.ac.uk/fac/sci/systemsbiology/research/software. Supplementary data are available at Bioinformatics online.
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DOI：
10.1038/nrg3788
发表时间：
2014-11
期刊：
Nature reviews. Genetics
影响因子：
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作者：
通讯作者：
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DOI：
10.1073/pnas.0913357107
发表时间：
2010-04-06
影响因子：
11.1
作者：
Marbach, Daniel;Prill, Robert J.;Stolovitzky, Gustavo
通讯作者：
Stolovitzky, Gustavo
影响因子：
3.7
作者：
Madar A;Greenfield A;Vanden-Eijnden E;Bonneau R
通讯作者：
Bonneau R
影响因子：
5.8
作者：
Jenkins, Dafyd J.;Finkenstaedt, Baerbel;Rand, David A.
通讯作者：
Rand, David A.
WIGWAMS：识别在多种生物条件下共同调节的基因模块。
DOI：
10.1093/bioinformatics/btt728
发表时间：
2014-04-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Polanski K;Rhodes J;Hill C;Zhang P;Jenkins DJ;Kiddle SJ;Jironkin A;Beynon J;Buchanan-Wollaston V;Ott S;Denby KJ
通讯作者：
Denby KJ