TimeXNet: identifying active gene sub-networks using time-course gene expression profiles.
TimeXNet: identifying active gene sub-networks using time-course gene expression profiles.
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DOI：
10.1186/1752-0509-8-s4-s2
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发表时间：
2014
影响因子：
--
通讯作者：
Nakai K
中科院分区：
文献类型：
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作者：
Patil A;Nakai K
时程基因表达谱经常用于提供对细胞状态随时间的变化的洞察，并推断所涉及的分子途径。当与大规模分子相互作用网络相结合时，这些数据可以提供有关细胞对刺激反应的动态信息。然而，目前很少有工具可用于从时程基因表达谱预测单个活性基因子网络。我们介绍了一个工具，TimeXNet，它确定了活跃的基因子网络的时间路径，在加权基因调控和蛋白质-蛋白质相互作用网络的背景下，使用时程基因表达谱。TimeXNet使用一种特殊形式的网络流优化方法来确定连接基因的最可能路径，这些基因在连续的时间间隔内表达发生了显著变化。TimeXNet已被广泛评估其预测小鼠先天免疫反应和酵母渗透应激反应中活性基因子网络内新型调节剂及其相关途径的能力。与其他类似的方法相比，TimeXNet从独立的实验数据集中识别出的新型调节剂多出了50%。它预测了更多已知路径中的路径，这些路径中有更长的重叠（最多7个连续边缘）。TimeXNet还被证明在分子相互作用网络中存在不同数量的噪声时具有鲁棒性。TimeXNet是一个可靠的工具，可用于通过识别不同生物系统中的时间依赖性活性基因子网络来研究细胞对刺激的反应。它明显优于其他类似工具。TimeXNet作为一个独立的应用程序在Java中实现，并支持Linux、MS Windows和Macintosh。TimeXNet的输出可以直接在Cytoscape中查看。TimeXNet免费提供给非商业用户。
Time-course gene expression profiles are frequently used to provide insight into the changes in cellular state over time and to infer the molecular pathways involved. When combined with large-scale molecular interaction networks, such data can provide information about the dynamics of cellular response to stimulus. However, few tools are currently available to predict a single active gene sub-network from time-course gene expression profiles. We introduce a tool, TimeXNet, which identifies active gene sub-networks with temporal paths using time-course gene expression profiles in the context of a weighted gene regulatory and protein-protein interaction network. TimeXNet uses a specialized form of the network flow optimization approach to identify the most probable paths connecting the genes with significant changes in expression at consecutive time intervals. TimeXNet has been extensively evaluated for its ability to predict novel regulators and their associated pathways within active gene sub-networks in the mouse innate immune response and the yeast osmotic stress response. Compared to other similar methods, TimeXNet identified up to 50% more novel regulators from independent experimental datasets. It predicted paths within a greater number of known pathways with longer overlaps (up to 7 consecutive edges) within these pathways. TimeXNet was also shown to be robust in the presence of varying amounts of noise in the molecular interaction network. TimeXNet is a reliable tool that can be used to study cellular response to stimuli through the identification of time-dependent active gene sub-networks in diverse biological systems. It is significantly better than other similar tools. TimeXNet is implemented in Java as a stand-alone application and supported on Linux, MS Windows and Macintosh. The output of TimeXNet can be directly viewed in Cytoscape. TimeXNet is freely available for non-commercial users.