A network-based method to assess the statistical significance of mild co-regulation effects.
A network-based method to assess the statistical significance of mild co-regulation effects.
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DOI：
10.1371/journal.pone.0073413
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Zweig KA
中科院分区：
文献类型：
--
作者：
Horvát EÁ;Zhang JD;Uhlmann S;Sahin Ö;Zweig KA
高通量、多路复用技术的最新发展启动了系统地研究生物网络中两种类型的组分之间的相互作用的项目，例如转录因子和启动子序列，或microRNA（miRNAs）和mRNA。在网络生物学方面，这种筛选方法主要试图阐明两种不同类型的生物成分之间的关系，这些生物成分可以表示为二分图中节点之间的边。然而，通常不仅需要确定不同类型的节点之间的调节关系，而且还需要理解相同类型的节点的连接模式。特别有趣的是相同类型的两个节点的共同出现，即，它们的共同邻居的数量，目前的高通量筛选分析未能解决。共同出现给出了两种生物成分以相同方式受到影响的情况的数量。在这里，我们提出了SICORE，一种新的基于网络的方法来检测具有统计意义的同现节点对。我们首先证明了所提出的方法在人工数据集上的稳定性：当随机添加和删除观测时，即使在大规模实验中噪声超过预期水平，我们也能获得可靠的结果。随后，我们说明了基于蛋白质组学筛选数据集的分析的方法的可行性，以揭示EGFR驱动的细胞周期信号系统中的人类microRNA靶向蛋白质的调控模式。由于统计学上显著的共同出现可能表明功能协同作用和潜在的渠道化机制，因此在药物靶点识别和治疗开发中有希望，我们提供了一个平台独立的实现SICORE与图形用户界面作为一种新的工具，在阿森纳的高通量筛选分析。
Recent development of high-throughput, multiplexing technology has initiated projects that systematically investigate interactions between two types of components in biological networks, for instance transcription factors and promoter sequences, or microRNAs (miRNAs) and mRNAs. In terms of network biology, such screening approaches primarily attempt to elucidate relations between biological components of two distinct types, which can be represented as edges between nodes in a bipartite graph. However, it is often desirable not only to determine regulatory relationships between nodes of different types, but also to understand the connection patterns of nodes of the same type. Especially interesting is the co-occurrence of two nodes of the same type, i.e., the number of their common neighbours, which current high-throughput screening analysis fails to address. The co-occurrence gives the number of circumstances under which both of the biological components are influenced in the same way. Here we present SICORE, a novel network-based method to detect pairs of nodes with a statistically significant co-occurrence. We first show the stability of the proposed method on artificial data sets: when randomly adding and deleting observations we obtain reliable results even with noise exceeding the expected level in large-scale experiments. Subsequently, we illustrate the viability of the method based on the analysis of a proteomic screening data set to reveal regulatory patterns of human microRNAs targeting proteins in the EGFR-driven cell cycle signalling system. Since statistically significant co-occurrence may indicate functional synergy and the mechanisms underlying canalization, and thus hold promise in drug target identification and therapeutic development, we provide a platform-independent implementation of SICORE with a graphical user interface as a novel tool in the arsenal of high-throughput screening analysis.
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影响因子：
64.8
作者：
Baek, Daehyun;Villen, Judit;Shin, Chanseok;Camargo, Fernando D.;Gygi, Steven P.;Bartel, David P.
通讯作者：
Bartel, David P.
影响因子：
56.9
作者：
Milo, R;Shen-Orr, S;Alon, U
通讯作者：
Alon, U
影响因子：
4.4
作者：
FREEMAN, LC
通讯作者：
FREEMAN, LC
影响因子：
30.8
作者：
Krek, A;Grun, D;Rajewsky, N
通讯作者：
Rajewsky, N
影响因子：
2.4
作者：
Leicht, EA;Holme, P;Newman, MEJ
通讯作者：
Newman, MEJ