Inferring regulatory networks by combining perturbation screens and steady state gene expression profiles.
Inferring regulatory networks by combining perturbation screens and steady state gene expression profiles.
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通过结合扰动筛选和稳态基因表达曲线来推断调节网络。
DOI：
10.1371/journal.pone.0082393
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Michailidis G
中科院分区：
文献类型：
--
作者：
Shojaie A;Jauhiainen A;Kallitsis M;Michailidis G
重建转录调控网络是功能基因组学研究的重要内容。从通过敲除或RNA干扰基因的实验中获得的数据包含了解决这个重建问题的有用信息。然而，这样的数据可能在大小上受到限制和/或获取起来是昂贵的。另一方面，稳态生物体的观测数据（例如，野生型）更容易获得，但它们的信息内容不足以完成手头的任务。我们开发了一种计算方法，以适当地利用这两个数据源来估计监管网络。所提出的方法是基于一个三步算法来估计潜在的有向但循环网络，作为输入扰动屏幕和稳态基因表达数据。在第一步中，该算法确定与扰动数据相一致的基因的因果排序，通过结合穷举搜索方法与快速启发式算法，该方法又将Monte Carlo技术与快速搜索算法相结合。在第二步中，对于每个获得的因果排序，使用基于惩罚似然的方法估计监管网络，而在第三步中，从得分最高的网络构建共识网络。大量的计算实验表明，该算法在重建底层网络方面表现良好，明显优于仅依赖于单个数据源的竞争方法。此外，它是建立该算法产生一致的估计的监管网络。
Reconstructing transcriptional regulatory networks is an important task in functional genomics. Data obtained from experiments that perturb genes by knockouts or RNA interference contain useful information for addressing this reconstruction problem. However, such data can be limited in size and/or are expensive to acquire. On the other hand, observational data of the organism in steady state (e.g., wild-type) are more readily available, but their informational content is inadequate for the task at hand. We develop a computational approach to appropriately utilize both data sources for estimating a regulatory network. The proposed approach is based on a three-step algorithm to estimate the underlying directed but cyclic network, that uses as input both perturbation screens and steady state gene expression data. In the first step, the algorithm determines causal orderings of the genes that are consistent with the perturbation data, by combining an exhaustive search method with a fast heuristic that in turn couples a Monte Carlo technique with a fast search algorithm. In the second step, for each obtained causal ordering, a regulatory network is estimated using a penalized likelihood based method, while in the third step a consensus network is constructed from the highest scored ones. Extensive computational experiments show that the algorithm performs well in reconstructing the underlying network and clearly outperforms competing approaches that rely only on a single data source. Further, it is established that the algorithm produces a consistent estimate of the regulatory network.
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影响因子：
3
作者：
Opgen-Rhein R;Strimmer K
通讯作者：
Strimmer K
影响因子：
5.8
作者：
Markowetz, F;Bloch, J;Spang, R
通讯作者：
Spang, R
影响因子：
5.8
作者：
Perrin, Bruno-Edouard;Ralaivola, Liva;d'Alche-Buc, Florence
通讯作者：
d'Alche-Buc, Florence
影响因子：
7.5
作者：
Friedman, N;Koller, D
通讯作者：
Koller, D
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