Identifying proteins controlling key disease signaling pathways.
Identifying proteins controlling key disease signaling pathways.
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DOI：
10.1093/bioinformatics/btt241
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发表时间：
2013-07-01
期刊：
影响因子：
--
通讯作者：
Bar-Joseph Z
中科院分区：
文献类型：
--
作者：
Gitter A;Bar-Joseph Z
动机：多种类型的研究，包括全基因组关联研究和 RNA 干扰筛选，都致力于将基因与疾病联系起来。尽管这些方法取得了一些成功，但遗传变异通常只存在于一小部分人群中，而且屏幕噪音很大，不同实验室的实验之间的重叠度较低。两者都没有提供机械模型来解释已识别的基因如何影响感兴趣的疾病或这些基因调节途径的动态。这种机制模型可用于准确预测击倒通路成员的下游效应，并允许全面探索靶向基因对或高阶组合的效应。结果：我们开发了方法来模拟疾病进展中涉及的信号传导和动态调节网络的激活。我们的模型 SDREM 集成了静态和时间序列数据，以将蛋白质及其在这些网络中调节的途径联系起来。 SDREM 使用有关蛋白质参与疾病的可能性的先验信息（例如来自筛选的信息）来提高预测信号通路的质量。我们使用我们的算法来研究人类对 H1N1 流感感染的免疫反应。由此产生的网络正确地识别了这种疾病的许多已知途径和转录调节因子。此外，它们可以准确预测 RNA 干扰效应，并可用于推断遗传相互作用，大大优于针对此任务建议的其他方法。将我们的方法应用于致病性更强的 H5N1 流感，使我们能够确定这种感染的几个毒株特异性目标。可用性：SDREM 可从 http://sb.cs.cmu.edu/sdrem 获取 联系方式：zivbj@cs.cmu.edu 补充信息：补充数据可在生物信息学在线获取。
Motivation: Several types of studies, including genome-wide association studies and RNA interference screens, strive to link genes to diseases. Although these approaches have had some success, genetic variants are often only present in a small subset of the population, and screens are noisy with low overlap between experiments in different labs. Neither provides a mechanistic model explaining how identified genes impact the disease of interest or the dynamics of the pathways those genes regulate. Such mechanistic models could be used to accurately predict downstream effects of knocking down pathway members and allow comprehensive exploration of the effects of targeting pairs or higher-order combinations of genes. Results: We developed methods to model the activation of signaling and dynamic regulatory networks involved in disease progression. Our model, SDREM, integrates static and time series data to link proteins and the pathways they regulate in these networks. SDREM uses prior information about proteins’ likelihood of involvement in a disease (e.g. from screens) to improve the quality of the predicted signaling pathways. We used our algorithms to study the human immune response to H1N1 influenza infection. The resulting networks correctly identified many of the known pathways and transcriptional regulators of this disease. Furthermore, they accurately predict RNA interference effects and can be used to infer genetic interactions, greatly improving over other methods suggested for this task. Applying our method to the more pathogenic H5N1 influenza allowed us to identify several strain-specific targets of this infection. Availability: SDREM is available from http://sb.cs.cmu.edu/sdrem Contact: zivbj@cs.cmu.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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影响因子：
--
作者：
Collins, Sean R.;Roguev, Assen;Krogan, Nevan J.
通讯作者：
Krogan, Nevan J.
影响因子：
5.8
作者：
Bornigen, Daniela;Tranchevent, Leon-Charles;Moreau, Yves
通讯作者：
Moreau, Yves
影响因子：
15.8
作者：
Desai, Keyur H.;Tan, Chuen Seng;Storey, John D.
通讯作者：
Storey, John D.
影响因子：
7.3
作者：
Huang SS;Fraenkel E
通讯作者：
Fraenkel E
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y